Download Combining docking and molecular dynamic simulations in drug design

Combining Docking and Molecular
Dynamic Simulations in
Drug Design
Hernán Alonso,1 Andrey A. Bliznyuk,2 Jill E. Gready1
1
Computational Proteomics Group, John Curtin School of Medical Research,
The Australian National University, Canberra ACT 0200, Australia
2
ANU Supercomputer Facility, The Australian National University,
Canberra ACT 0200, Australia
Published online 6 June 2006 in Wiley InterScience (www.interscience.wiley.com).
DOI 10.1002/med.20067
!
Abstract: A rational approach is needed to maximize the chances of finding new drugs, and to
exploit the opportunities of potential new drug targets emerging from genomic and proteomic
initiatives, and from the large libraries of small compounds now readily available through
combinatorial chemistry. Despite a shaky early history, computer-aided drug design techniques can
now be effective in reducing costs and speeding up drug discovery. This happy outcome results
from development of more accurate and reliable algorithms, use of more thoughtfully planned
strategies to apply them, and greatly increased computer power to allow studies with the necessary
reliability to be performed. Our review focuses on applications and protocols, with the main
emphasis on critical analysis of recent studies where docking calculations and molecular dynamics
(MD) simulations were combined to dock small molecules into protein receptors. We highlight
successes to demonstrate what is possible now, but also point out drawbacks and future directions.
The review is structured to lead the reader from the simpler to more compute-intensive methods.
Thus, while inexpensive and fast docking algorithms can be used to scan large compound libraries
and reduce their size, more accurate but expensive MD simulations can be applied when a few
selected ligand candidates remain. MD simulations can be used: during the preparation of the
protein receptor before docking, to optimize its structure and account for protein flexibility; for the
refinement of docked complexes, to include solvent effects and account for induced fit; to calculate
binding free energies, to provide an accurate ranking of the potential ligands; and in the latest
developments, during the docking process itself to find the binding site and correctly dock the
ligand a priori. ß 2006 Wiley Periodicals, Inc. Med Res Rev, 26, No. 5, 531–568, 2006
Key words: docking; molecular dynamics; drug design; binding free energies; protein flexibility;
ligand conformations; protein-ligand interactions; ligand-binding site; scoring function; virtual
screening; rotamer library; trajectory
Correspondence to: Jill E. Gready, Computational Proteomics Group, John Curtin School of Medical Research,The Australian
National University, P.O. Box 334,Canberra ACT 2601, Australia. E-mail: [email protected]
Medicinal Research Reviews, Vol. 26, No. 5, 531^568, 2006
ß 2006 Wiley Periodicals, Inc.
532
*
ALONSO, BLIZNYUK, AND GREADY
1. INTRODUCTION
The development of new drugs is undoubtedly one of the most challenging tasks of today’s science.
Driven by the combined efforts of the pharmaceutical industry, biotech companies, regulatory
authorities, academic researchers, and other private and public sectors, the development of new drugs
is a very complex and demanding interdisciplinary process. This enterprise has produced not only a
general improvement in health from the discovery and manufacture of new and more effective drugs,
but has contributed to the advance of science itself, impelling the development of complex and more
accurate tools and techniques for the discovery and improvement of new active compounds, and the
understanding of their targets.
After the completion of the human genome project, it was expected that a large number of new
drug targets would be found expeditiously. However, the 30,000 or so genes encoded within the
human genome did not turn out to offer a direct source for drug development, as it is not them, but the
proteins they encode, that are the usual targets of drugs. This much larger proteome is far more
complex than the collection of genes, as proteins may undergo post-translational modifications,
associations with other molecules and prosthetic groups, and formation of multimeric complexes.1
Moreover, most of these proteins have unknown or poorly characterized functions, and their connection with diseases is usually complex and difficult to define. It soon became evident that the blind
expression, purification, and in vitro assay of hundreds if not thousands of proteins against libraries of
hundred of thousands if not millions of compounds does not constitute a rational approach.
The approaches and methodologies used in drug design have changed over time, exploiting and
driving new technological advances to solve the varied bottlenecks found along the way. While until
the 90s, the major issues were lead discovery and chemical synthesis of drug-like molecules, the
emergence of combinatorial chemistry,2 gene technology, and high-throughput tests3,4 shifted the
focus, with poor absorption, distribution, metabolism, and excretion (ADME) properties of the new
drugs capturing more attention.5 Today, the field of drug development may seem more fertile than
ever before, with vast amounts of information from genomic and proteomic studies facilitating the
finding of new targets, the usage of rational combinatorial chemistry for the production of libraries of
compounds, the generation of genetically modified animal models for the development and testing
of new drugs, and the possibility of using ultra-high-throughput test techniques for the screening of
large libraries. However, despite all these advances, the revolutionary era of drug design has not
arrived yet.6–8
There is no unique solution to a drug design problem. The appropriate experimental techniques
or computational methods to use will depend on the characteristics of the system itself and the
information available. In the present review, we cover the case where both the structure of the protein
receptor and the binding site are known. While it is possible to develop drugs without such
information, the methods involved are quite different and are described elsewhere.9–12
A variety of computational approaches can be applied at different stages of the drug-design
process: in an early stage, these focus on reducing the number of possible ligands, while at the end,
during lead-optimization stages, the emphasis is on decreasing experimental costs and reducing
times. Although this is simple to articulate, it has been tried many times with only a few fruitful
examples.13–20 The lack of success has led to a re-examination of the underlying principles. For
example, recent publications have shown that some of the hypotheses used during the enrichment
steps may need to be refined.21,22 While some drug developers opted for alternative experimental
solutions,23,24 others focused their attention on the improvement of computational protocols. These
enhancements include, among others: incorporation of protein flexibility in the docking process,
extensive exploration of the ligand conformation within the binding site, refinement and stability
evaluation of the final complexes, and estimation of the binding free energies. Not surprisingly,
molecular dynamics (MD) simulations have played a dominant role in these attempts to improve
docking procedures; they are the focus of the present review.
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
533
Our emphasis is on protocols and approaches rather than on the theory behind the methods, as our
intention is to provide the reader with a practical overview of the potential of combining docking and
MD simulations for the rational design of novel drugs. The first section, ‘‘Rational Drug Design,’’
presents a brief introduction to application of computational techniques in the drug-design process.
The ‘‘Protein Flexibility’’section examines various ways of including flexibility of the target receptor
in the docking using both approximate and MD approaches. The ‘‘Refinement of Docked
Complexes’’ section looks into the applications of MD for the optimization and validation of the
final complexes. The ‘‘Free Energy Calculations’’ section briefly describes widely used approaches
for the evaluation of accurate binding energies. This is followed by ‘‘MD Simulations at Different
Docking Stages’’ which reviews some published examples in which MD simulations have been used
at several steps of the docking procedure. Finally, ‘‘Docking with MD Simulations’’ discusses how
the docking of a small molecule into its protein target can be carried out using MD simulations
exclusively. Terms which may be unfamiliar to the reader are marked in the text by * and defined in
the Glossary before the References.
2. RATIONAL DRUG DESIGN
When the structure of the target protein is known, the drug discovery process usually follows a wellestablished procedure shown schematically in Figure 1. Virtual screening* techniques are applied
early during the docking protocol to reduce the size of large compound libraries.10,11 Initially,
libraries are ‘‘pre-filtered’’ using a series of simple physicochemical descriptors to eliminate
compounds not expected to be suitable drugs. Pharmacophore analysis, neural nets, similarity
analysis, scaffold analysis, Lipinski’s rule of five25,26, and garbage filters are used to sort out
molecules according to their ADME* properties, among others.27–30 This procedure, which reduces
the size of the library to a group of molecules more likely to bind the target receptor, is known as
enrichment*. It is necessary to stress that the selection criteria used during the enrichment steps need
to be carefully chosen, as application of too stringent filters may lead to early exclusion of potential
leads.21,22 Similarly, drug-likeness of potential leads may be less important at the early stages than
ease of the molecule to experimental validation with in vitro assays and X-ray crystallography.
Similar compounds can be further grouped together and arranged in smaller assemblies to assist
the screening process. The use of several small libraries is not only a more cost-effective
approach, but can usually provide a broader chemical diversity than a single large library. Once an
optimum library has been produced, molecules are docked to the target receptor to reduce further the
number of candidates. This initial screening makes use of fast, but not very accurate, ranking
functions* to evaluate the relative stability of the docked complexes. The selected candidates, usually
a few hundred, are subject to further docking experiments using more sophisticated scoring
functions*.
A. Docking
Docking* techniques, designed to find the correct conformation of a ligand and its receptor, have now
been used for decades (for recent reviews and comparisons see References 31–36). The process of
binding a small molecule to its protein target is not simple; several entropic and enthalpic factors
influence the interactions between them. The mobility of both ligand and receptor, the effect of the
protein environment on the charge distribution over the ligand,37 and their interactions with the
surrounding water molecules, further complicate the quantitative description of the process. The idea
behind this technique is to generate a comprehensive set of conformations of the receptor complex,
and then to rank them according to their stability. The most popular docking programs include
DOCK,38,39 AutoDock,40 FlexX,41 GOLD,42 and GLIDE,43,44 among others.
534
*
ALONSO, BLIZNYUK, AND GREADY
Figure 1. Schematic representation of the protocol commonly followed during a drug-design process, when the structure of the
protein target is known or can be modeled. Steps within square brackets are not always performed, and those shaded in gray may
incorporate MD simulations.
B. MD Simulations
Molecular dynamics simulations* are one of the most versatile and widely applied computational
techniques for the study of biological macromolecules.45–47 They are very valuable for
understanding the dynamic behavior of proteins at different timescales, from fast internal motions
to slow conformational changes or even protein folding processes.48 It is also possible to study the
effect of explicit solvent molecules on protein structure and stability to obtain time-averaged
properties of the biomolecular system, such as density, conductivity, and dipolar moment, as well as
different thermodynamic parameters, including interactions energies and entropies. MD is useful not
only for rationalizing experimentally measured properties at the molecular level, but it is well known
that most structures determined by X-ray or NMR methods have been refined using MD methods.
Therefore, the interplay between computational and experimental techniques in the area of MD
simulations is longstanding, with the theoretical methods assisting in understanding and analyzing
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
535
experimental data. These, in turn, are vital for the validation and improvement of computational
techniques and protocols.
Although the first protein MD simulation (bovine pancreatic trypsin inhibitor; 58 residues and
450 atoms) was done in vacuo and for only 8.8 psec*,49 enormous increases in computer power
nowadays permit simulations of systems comprising 104 –106 atoms50,51 and simulation times in the
order of nsec* to msec*.52 Simulations of more realistic systems, including explicit water molecules,
counterions*, and even a complete membrane-like environment are possible, and new properties can
now be studied as they evolve in real time. This progress in system representation has been
accompanied by methodological improvements: better force fields,53,54 improved treatment of longrange electrostatic interactions and system boundary conditions, and better algorithms used to control
temperature and pressure. However, despite all these advances, the set up of an MD simulation can be
far from trivial. Parameters used to describe proteins and their interactions are normally found within
modern force fields, but adequate descriptors for non-standard molecules, such as ligands, might be
missing. In such cases, the determination and fitting of new parameters is usually straightforward, but
may be a time-consuming process if it needs to be done for many ligands, limiting the general
applicability of the method. Commonly used programs for MD simulations of biomolecules include
Amber,55 CHARMM,56 GROMOS,57 and NAMD,58 among others.
C. Combined Docking and MD Simulations
Fast and inexpensive docking protocols can be combined with accurate but more costly MD
techniques to predict more reliable protein–ligand complexes. The strength of this combination lies
in their complementary strengths and weaknesses. One the one hand, docking techniques are used to
explore the vast conformational space* of ligands in a short time, allowing the scrutiny of large
libraries of drug-like compounds at a reasonable cost. The major drawbacks are the lack, or poor
flexibility of the protein, which is not permitted to adjust its conformation upon ligand binding, and
the absence of a unique and widely applicable scoring function, necessary to generate a reliable
ranking of the final complexes. On the other hand, MD simulations can treat both ligand and protein in
a flexible way, allowing for an induced fit of the receptor-binding site around the newly introduced
ligand. In addition, the effect of explicit water molecules can be studied directly, and very accurate
binding free energies can be obtained. However, the main problems with MD simulations are that
they are time-consuming and that the system can get trapped in local minima*. Therefore, the
combination of the two techniques in a protocol where docking is used for the fast screening of large
libraries and MD simulations are then applied to explore conformations of the protein receptor,
optimize the structures of the final complexes, and calculate accurate energies, is a logical approach
to improving the drug-design process.
3. PROTEIN FLEXIBILITY
It is now accepted that the old idea of the ‘‘key and lock’’ interaction of a ligand and its protein
receptor is not an accurate description of most biological complexes. The ligand–protein interactions
resemble more a ‘‘hand and glove’’ association, where both parts are flexible and adjust to
complement each other—induced fit*. They can modify their shape and mould their complementarity so as to increase favorable contacts and reduce adverse interactions, maximizing the total bindingfree energy.59 It has been found that active-site regions of enzymes appear to present areas of both low
and high conformational stability.60 Mobile loops that close over the ligand upon binding are
included within the flexible parts, while catalytic residues, for example, are usually structurally
stable. This dual character of the active-site environment appears important for optimum binding.
536
*
ALONSO, BLIZNYUK, AND GREADY
A. Receptor Conformation
The three dimensional (3-D) structure of both ligand and protein are necessary for the application of
docking techniques. While the manifold of conformational structures of small molecules may be
relatively easy to predict, the lowest energy conformation obtained may not correspond to that of the
bound ligand. The structures of proteins present a bigger challenge. Although experimental
techniques involving X-ray and NMR analysis are now routine, inherent difficulties in the preparation
of samples and data collection and interpretation mean we are still far from a complete automated and
high-throughout process.61 Many proteins targeted for drug design do not have an experimentally
determined structure and, therefore, docking studies cannot be performed directly. In some cases,
computational techniques can be used to predict the 3-D structure of a protein provided the structure
of a closely related protein homolog is known. Homology modeling* or sequence threading
techniques may be used to generate models of protein structures62–64 which, although not as good as
experimentally determined structures, can be used as docking targets.65–70
Several studies have highlighted the importance of the conformation of a protein receptor for
docking analysis.68,71,72 McGovern and Shoichet68 analyzed the influence of the receptor
conformation on the final outcome of a docking screening of 95,000 small molecules. Three
different conformations of 10 different target enzymes including a holo* (ligand-bound), an apo*
(unligated), and a homology-modeled structure were used. The level of enrichment attained during
the screening process was greatly affected by the quality of the protein structures, decreasing from the
holo to the apo to the modeled structures as the conformation of the receptor is less prepared to
accommodate the ligand. Despite this general trend, interesting exceptions were observed. In a few
cases, the conformation of the holo protein was such that only molecules structurally similar to that
present in the original crystal-structure determination were recognized as potential ligands, missing
all other molecules that exhibited a different binding mode. In the case of the apo structures, their
conformations may be inadequate to accommodate a ligand, because of wrongly positioned residues
or the presence of loops blocking access to the binding site. Modeled molecules, even those modeled
with a template of high sequence identity, can have badly placed side chains or missing loops or
residues, hindering the docking process. In the latter case, it has been reported that the use of multiple
homology models constructed from different crystal structures could provide a better representation
of the protein receptor and improve the docking.69
The biased selection of ligands as a result of using ligand-bound protein structures during a
docking process was clearly shown in a series of cross-docking analyses performed by Murray et al.72
Three different cases were studied; thrombin, thermolysin, and influenza virus neuraminidase. A
series of crystal structures for each protein complexed with several ligands was used as docking
targets. As expected, the best results were obtained when a given ligand was docked onto its own
original structure, while poor placements were found when docking was done on the crystal
conformation of a different complex. Most failures were because of movements of side chains,
displacement of particular portions of the protein backbone, and mobility of metal atoms found
within the active site. It was observed that the movements of side chains were usually linked to those
of the backbone Ca atoms and, as a result, it was concluded necessary to consider more than sidechain flexibility to avoid mis-docking of ligands.
In summary, it is of great importance to carefully prepare the structure of the protein target before
the docking process. While structures of ligand-bound protein may provide the highest enrichments,
the final results might be biased towards particular types of ligands. An example illustrating this effect
for three trypsin inhibitor complexes is shown in Figure 2. On the other hand, while this could be
avoided by using the structure of the unbound receptor, the conformation of the apo protein may be
inadequate for accommodating the ligand (e.g., ‘‘closed’’ conformation of a loop). A desirable alternative is to treat the receptor as a flexible molecule, and to allow conformational changes during the
docking process. Methods to allow this are reviewed in the next sections and summarized in Figure 3.
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
537
Figure 2. Several distinct binding modes for different ligands to a single protein receptor are possible. This superposition of
three different complexes of the enzyme trypsin with the bis-phenylamidine inhibitor (1AZ8, blue), BX5633 (1MTV, green), and
1-(2-amidinophenyl)-3-(phenoxyphenyl)urea (1BJV, orange) shows that the ligands can adopt quite different orientations with the
side chains of the protein presenting different conformations depending on which ligand is bound.
B. Approximate Methods
When receptor flexibility is included during the docking process, the risks associated with inadequate
conformation of the protein target are reduced.95–97 Although originally restricted to the docking of
rigid ligands into rigid receptors, recent advances in docking algorithms have allowed incorporation
of ligand flexibility and, to less extent, protein mobility, during the docking procedure. Most modern
Soft Docking ligand "penetrates" protein
Single Protein
Conformation
Protein
Flexibility
Molecular
Dynamics
Side Chain
Flexibility
Before docking: alternative receptor conformations
During docking: dynamic exploration
After docking: final optimization of contacts
Average Grid: single docking grid
Multiple Protein
Conformations
United description of the protein: rigid framework, flexible regions
Individual conformations: docking into several conformers
Figure 3. Different approaches that can be used during docking studies to incorporate protein flexibility, at least partially.
538
*
ALONSO, BLIZNYUK, AND GREADY
algorithms account for ligand flexibility; this can be addressed by systematic methods (i.e.,
incremental search), stochastic methods (i.e., Monte Carlo simulation*), and deterministic search
(i.e., MD simulation).31 Programs that incorporate protein receptor flexibility, at least partially, began
to appear more recently.42,73–75 The size and complexity of proteins makes it difficult to fully account
for their mobility during a docking process and, therefore, its treatment is usually restricted to
selected residues.
1. Soft docking
The simpler approaches deal with protein flexibility in an indirect way. Despite treating the receptor
as a rigid object, the repulsive terms of the Lennard-Jones potential can be attenuated by generating a
‘‘soft’’ interaction. Thus, the ligand is allowed to ‘‘penetrate’’ the protein surface to some extent and
to account for small and localized changes that would take place in a flexible environment.76–78
Although this approach does not increase computational costs, the changes in protein conformation
that can be accounted for are minimal. Ferrari et al.79 performed a comparison between a ‘‘soft’’
docking and a multiple-structure docking approach (see below) for virtual screening*. They
concluded that while a ‘‘soft’’ scoring function performs better than a ‘‘hard’’ scoring function when a
single configuration of the receptor is used, use of the ‘‘hard’’ function is recommended when
multiple conformations of the receptor are considered. Overall, docking against multiple conformations of the receptor led to qualitatively different and better results than ‘‘soft’’ docking against a
single structure of the protein studied.
2. Sidechain flexibility
In a different and more comprehensive approach, the mobility of some residues, particularly those
within an enzyme active site, can be treated explicitly either during the docking process or after the
ligand has been approximately placed.80,81 A set of rotamer libraries* can be used to explore the
conformational space of selected side chains.82–84
Leach84 was among the first to introduce receptor flexibility using rotamer libraries. One of the
major limitations of this early approach was use of discrete pre-determined conformations of both the
ligand and side chains. Schnecke and Kuhn80 presented a new docking algorithm, SLIDE, which
incorporates side-chain mobility. A rigid anchor fragment of the ligand is initially positioned,
followed by addition of the remaining fragments according to their database conformation. Clashes
between the ligand and the receptor are finally resolved by rotations of single bonds of non-anchor
regions of the ligand and protein side chains. The main drawback of this program is the restricted
flexibility of the ligand and the post-docking treatment of side-chain flexibility. Kallblad and Dean83
incorporated side-chain flexibility within the binding site by pre-generating an ensemble of protein
conformers using a rotamer library. A limited number of representative structures from this random
ensemble was selected and used as targets for rigid docking. They found that while the synthetic
inhibitor of human collagenase-1, RS-104966, could not be properly docked into the crystal
structure, some members of the ensemble could accommodate the molecule. In a similar approach,
Frimurer et al.82 generated an ensemble of tyrosine phosphatase B1 structures using a rotamer library
for three selected active-site residues, and then performed docking of the flexible ligand with the
program FlexX.41 They obtained improved binding conformations and energies compared with the
case where only a single conformation of the enzyme was used.
Although consideration of side-chain flexibility increases the computational cost of the docking
process, it allows localized protein movement and results in improved fit of the ligand. As only the
side chains of selected residues are allowed to move, important changes in the protein backbone, such
as those involved in loop movements, are not considered.
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
539
3. Combined protein grid
Several alternative structures of the protein receptor can be combined into a single representation
of the ensemble to account for bigger conformational changes that may be critical for the
binding process. The averaging can be done over atom coordinates, to generate a final average
structure, or over the grid representation of all receptor conformations, to produce an average
docking grid*.
These grids,85 or pre-calculated two-body potentials, are usually focused around the binding site
and are used during the docking process to determine the interaction energy of different
conformations of the ligand and the active site, in a fast and computationally inexpensive way.
Different grids produced from several conformations of the receptor can be combined into a single
global grid using a simple average-weighted scheme, or a differential weighting scheme which favors
the contribution of some conformations over others.
Knegtel et al.86 were among the first to use multiple protein structures to account for protein
flexibility during docking analysis. In their original study, they evaluated two different ways of
combining several experimentally determined structures into an average representation; an ‘‘energyweighted average’’ composition, based on a weighted grid potential for each atom, and a ‘‘geometryweighted average,’’ based on the positional variation of each atom. For five different systems
analyzed, these proved to offer a better representation of the receptor than single structures. The
ensemble-based grids minimized the effect of steric clashes between particular conformations of
receptor and ligand, allowing the establishment of more favorable interactions. It was concluded that
ensemble-based grids presented a good filter for ligand database searches, as they offer a relatively
inexpensive approach for considering receptor conformational variability during the docking
process.
Goodsell and co-workers87 compared four different combination protocols to group 21 crystal
structures of HIV-1 protease complexed with peptidomimetic inhibitors. All 21 crystal structures
were combined into a docking grid to incorporate protein flexibility and water structure variations in a
single representation of the target. Of the four combination protocols, neither the grid of mean values
(too stringent), nor that of minimum energy (too permissive), led to an adequate representation of the
ensemble. On the other hand, the two weighted-average grids constructed using the energy-weighted
and the clamped techniques resulted in good docked conformations with accurate free energies.
Broughton88 arrived at similar conclusions in his studies of dihydrofolate reductase and cyclooxygenase-2. These studies also found that although the preparation of average grids is a timeconsuming process, the overall time of the docking procedure is noticeably improved, as the
smoother surface of the average grids requires less scanning by the docking algorithm than the usual
crystal-derived grids.
In summary, docking methods that have combined multiple protein structures, whether from
NMR, X-ray complexes, or MD simulations, into a single grid representation of the target molecule
have provided better results than grids from single structures. The choice of combination procedure,
however, has an even greater impact on the final outcome, with weighted-averaged protocols
providing better results than simple average combination of structures.
4. United description of the receptor
The docking program FlexE74 implements a different solution to the protein flexibility problem.
Instead of combining different conformations of the protein receptor into a single docking grid, a
united protein description of the target is created. The alternative conformations are superimposed
and a rigid average structure is constructed from the most conserved structural features. For the
variable regions, different conformations are explicitly considered and retained as an ensemble,
which can be combinatorially explored during the docking process to generate novel protein
540
*
ALONSO, BLIZNYUK, AND GREADY
structures. The ligand structure is incrementally built within the active site (as in FlexX),41 and after
placement of each new fragment, all possible interactions of the partially built ligand with the
alternative protein conformations are evaluated. Those protein conformations that best accommodate
the partially grown ligand are retained for further cycles of growth and optimization. The program
was evaluated using 10 different proteins, with 7–16 different crystal structures each. The docking of
60 different ligands produced conformations within 2.0 Å of the crystal position in 83% of the cases.
However, the scoring function was not able to rank correctly the best complexes; these had to be
identified by comparison with the crystal structures. Although the final results proved to be similar to
those obtained by sequential docking to each protein structure, use of a united description of the
ensemble reduced the docking times significantly.
In a similar approach, Wei et al.89 used a modified version of the program DOCK39 to incorporate
receptor flexibility during the docking process. In this case, the interactions between a given
configuration of the ligand and different flexible parts of the receptor were calculated independently.
Then, those flexible regions that presented the best interaction energies with the ligand were
recombined into a final representation of the protein receptor. One of the most significant
observations of this study was the importance of the receptor conformational energy for the final
ranking. When protein flexibility is taken into account during the docking process, not only is the
interaction energy between ligand and receptor important, but the internal energy of the protein also
provides a major contribution. When this energy was ignored, many known ligands ranked poorly
because of the presence of ‘‘decoys’’* that could complement better some high-energy conformations of the receptor.
Clearly, the inclusion of protein flexibility during the docking procedure using a united
description of the protein with rigid conserved regions and alternative flexible parts can improve the
screening process and produce new ‘‘hits’’* in a shorter time than simple sequential docking against
each protein structure. To obtain an adequate ranking of the final complexes, the internal energy of the
receptor must be taken into account, as high-energy conformations of the protein may lead to
unrealistic low-energy positioning of a ligand.
C. MD Simulations for Receptor Flexibility
Proteins in solution are mobile molecules. They do not exist in a single conformation, but in a
manifold of different conformational states separated by low-and higher-energy barriers. An example
of such mobility is shown for two ternary complexes of dihydrofolate reductase in Figure 4. The
distribution and stability of each conformational state will depend on the physicochemical properties
of the environment and the protein itself (e.g., free or ligand-bound).90 Moreover, not all these
conformations will be equally able to bind productively with a given ligand. Some will be more likely
to accommodate the ligand molecule within the binding site without having to undergo large changes,
while others will be less likely, or even incapable, of accommodating the ligand due, for example, to
loop conformations that block the access to the binding site.91 The presence of the ligand itself is
expected to affect the structure of the binding site and the dynamic equilibrium between different
conformational states of the protein.92 During a binding event, the protein conformer most likely to
accommodate the ligand will be depleted from solution to form a ligand-bound complex, and other
conformers will then adjust to fill the vacated conformational space, driving the binding process
forward.90 Therefore, an ensemble of receptor conformations and not a single structure is expected to
provide a better representation of the system. Docking against several structures of the protein
increases the chances of finding a receptor in the right conformational state to accommodate a
particular ligand. However, it also reduces the selectivity of the docking process, as a wider variety of
ligands will be able to fit in this more relaxed representation of the protein. It is important, therefore,
to use accurate scoring functions during the final screening process to maximize selection of the most
active ligands.
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
541
Figure 4. The flexibility of proteins is clearly shown in these two superimposed ternary complexes of the enzyme dihydrofolate
reductase with folate and NADPþ (1RA2 and 1RX2). The flexible loop has been shown to adopt different conformations during the
catalytic process; here it is seen ‘‘frozen’’ in the open (orange) and closed (green) states in two different crystal forms of the same
complex.
1. Generation of multiple protein conformations
Multiple structures of the protein receptor could be obtained from experimental studies, such as NMR
and X-ray analysis, or generated using computational tools. Philippopoulos and Lim93 suggested that
the best source of protein conformations is NMR studies. A set of 15 NMR structures of E. coli
ribonuclease HI was shown to explore a bigger conformational space than that of a conventional
1.7 nsec MD simulation of the system. Although both NMR and MD sampled similar conformations,
NMR conformers covered a larger space with increased side-chain and protein-backbone mobility.
As a caveat on these conclusions, it should be noted that this study employed conventional MD singletrajectory* simulations, which are known to be inadequate for exploring large conformational spaces.
Multiple-trajectory and replica-exchange MD methods94–96 introduced more recently for protein
systems, and other modified MD simulations designed to improve the conformational sampling of the
system (see ‘‘Docking with MD Simulations’’ section), would likely produce better results. Also, MD
simulations provide an easy practical alternative to explore the conformational space of the protein
receptor in the many cases where multiple experimental conformations are not available. Several
different studies have shown that MD simulations are generally in good agreement with experimental
results in reproducing the general protein structure and dynamic processes occurring on the psectimescale.97–99 In a different approach, Thorpe and co-workers100 used graph and constraint theories
to identify possible movements of a protein structure. Although several protein conformations could
be easily generated, their relative energies were not computed and, therefore, it was not possible to
select the most stable conformations for docking.
When preparing MD simulations for exploration of the conformational space of the protein
receptor and generation of a proper ensemble of conformations, it should be remembered that the
542
*
ALONSO, BLIZNYUK, AND GREADY
dynamic behavior of the free and ligand-bound forms of the protein might be very different. Kua
et al.101 studied the binding specificity of acetylcholinesterase (AChE) using a combined MD/
docking approach. A series of ligands was docked to several ‘‘snapshots’’* obtained from two
different MD simulations, one for the apo-AChE and another for the acetylcholine–AChE complex.
Although it was found that acetylcholine was correctly docked to more than 95% of the ‘‘snapshots’’
of both simulations, the energies of the complexes obtained from the ligand-bound trajectory were
0.7 kcal/mol more stable. The increased stability resulted from the induced fit observed during the
simulation of the complex. However, as noted in a previous section, receptor conformations obtained
from the simulation of a ligand-bound protein may be biased to accommodate particular types of
ligands with particular binding modes. If the objective of a docking search is to find novel inhibitors
with new binding modes, simulations of the apo protein may provide a more suitable variety of
conformations which are not tuned to interact with a particular ligand and, thus, may offer a more
versatile target for the docking protocol.
Once a set of adequate structures has been obtained, it is necessary to determine how to use this
ensemble of conformations to account for protein mobility during the docking process. We
introduced two alternatives before; combination of the structures into a single docking grid or the
generation of a united description of the protein with conserved and mobile regions. While these two
alternatives are the most convenient ones for virtual screening of large libraries, there is a third
approach that involves docking the ligand to every single conformation. This last alternative has been
applied particularly in cases where several protein conformations have been obtained from MD
simulations. Some literature examples are reviewed below, and more technical details for several
studies are compiled in Table 1.
2. Docking into several individual protein conformations
Docking the ligand against each protein structure in the ensemble constitutes the most
comprehensive, although expensive, approach. While this strategy is not a realistic option for the
virtual screening of a large library, it is a valid approach for difficult docking problems where even
minor conformational changes of the receptor are expected to have a major influence on the binding
process.
Carlson et al.102 developed ‘‘dynamic’’ pharmacophore models of HIV-1 integrase using several
snapshots from an MD simulation. Hundreds of probe molecules were energy minimized within the
binding site of several snapshots. The probe molecules mapped the most favorable positions for
certain functional groups within the receptor. Binding sites conserved during the MD simulation were
combined into a ‘‘dynamic’’ pharmacophore model. While the composite model was able to
accommodate known inhibitors, a model from a single crystal structure failed to do so. Even the use
of just two crystal-structure models produced improved results over single-structure models.103
McCammon and co-workers104 introduced the so-called relaxed-complex scheme, which takes
into account the possibility that a ligand may bind to only a few conformations of the receptor. A long
MD simulation of the apo receptor is first conducted to sample extensively its conformational space,
followed by the rapid docking of mini-libraries of candidate inhibitors against a large ensemble of
snapshots. In their original work, the FK506 binding protein, FKBP, was studied. Two different
compounds, trimethoxyphenyl pipecolinic acid and 4-hydroxy(1-hydroxy) benzanilide, were
sequentially docked to 200 snapshots. Although the final ternary complex was in good agreement
with the experimental structure, the AutoDock40 scoring function did not properly discriminate
between different conformations of the ligands. In a second article, the Molecular Mechanics/PoissonBoltzmann Surface Area (MM/PBSA) approach was employed to re-score the docking results and
the best complex was found accurately105 (see ‘‘Free Energy Calculations’’ section).
The advantage of performing MD simulations of the protein receptor prior to the docking
analysis has been clearly shown in another application of the relaxed-complex scheme. Schames
Table 1. Summary of Docking Studies That Made Use of MD Simulations for the Generation of Alternative Protein Receptor Conformations
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
543
544
*
ALONSO, BLIZNYUK, AND GREADY
et al.106 discovered a novel binding trench in HIV-1 integrase by docking the 5CITEP inhibitor to
snapshots of a 2 nsec trajectory. The docking procedure revealed the existence of two different
binding modes of the ligand, one of which made use of a new open space adjacent to the binding site.
A series of ‘‘butterfly compounds,’’ with the ability to bind simultaneously to both the binding site
and the trench, were designed and shown to dock into the open state of the protein receptor. The
discovery of this new binding trench would not have been possible without the initial MD simulations
of the receptor.
Although computationally expensive, docking against individual protein structures has proven
to be effective not only in finding the correct docking pose* within a flexible receptor (both in
evaluative and predictive contexts), but has been found useful also for discovering alternative binding
modes otherwise not apparent from the rigid picture of proteins extracted from crystal structures. This
method can have important applications in lead optimization and refinement, despite not being useful
for the virtual screening of large libraries. Inclusion of protein flexibility does not necessarily lead to
improvements in the final docking results. Increased capacity of the receptor to accommodate several
ligand conformations may lead to the generation of very similar complexes not distinguishable by
modern scoring functions. Therefore, the validity of the final predictions should be assessed
experimentally.
3. Value of MD simulations before the docking process
In summary, the application of MD before the docking process offers a suitable approach to explore
the conformational space of the protein receptor. This information can then be transferred to the
docking protocol in several ways. The simplest and most computationally expensive approaches
use docking against individual snapshots of the receptor to generate a collection of docked complexes
of different stabilities. To decrease computing times and make possible the virtual screening of
compound libraries, the group of snapshots can be combined into a single representation of the
ensemble. This combination could involve docking grids, where the grid representation of several
protein structures is joined into a single average-weighted docking grid. Alternatively, a united
description of the protein could be constructed, in which relatively stable regions of the protein are
averaged into a single rigid framework and the flexible parts are treated as an ensemble of alternative
positions, which can recombine during the docking process. Regardless of the specific approach used
to deal with multiple protein structures, it is clear that the consideration of different possible receptor
conformations can increase the accuracy of the docking process and opens new opportunities for the
discovery of novel potential drugs.
4. REFINEMENT OF DOCKED COMPLEXES
The most practical and convenient approach to address the docking problem seems to be a two-step
protocol. Fast and less accurate algorithms are first used to scan large databases of molecules and
reduce their size to a reasonable number of hits. This step is then followed by application of more
accurate and time-consuming methods which can refine the conformation of the complexes and
produce accurate free energies.107,108
Molecular dynamics simulations present an attractive alternative for structural refinement of the
final docked complexes. They incorporate flexibility of both ligand and receptor, improving
interactions and enhancing complementarity between them, and thus accounting for induced fit.
Moreover, the evolution of the complexes over the simulation timecourse is an indication of their
stability and reliability; incorrectly docked structures are likely to produce unstable trajectories,
leading to the disruption of the complex, while realistic complexes will show stable behavior. In
addition, the ability to incorporate explicit solvent molecules and their interactions in the simulations
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
545
of the docked systems is very important for understanding the role of water and its effect on the
stability of the ligand–protein complexes. Some literature examples in which simulations were used
to optimize docked structures are reviewed below, and more technical details for several studies are
compiled in Table 2.
Park et al.109 studied the differential inhibition of two cyclin-dependent kinases (CDKs), CDK2,
and CDK4, by three different selective inhibitors. The final MD simulations of the docked complexes
provided molecular insight into the preferential binding of the inhibitors to CDK4. It was seen that the
presence of the inhibitors reduced the mobility of a disordered loop in the case of CDK4, but did not
seriously affect CDK2. Not only protein mobility but also the effect of explicit water molecules was
analyzed. Tighter binding within CDK4 was reflected in a smaller number of water molecules
diffusing into the active site compared with the CDK2 complexes. In the latter case, weaker
hydrogen bonding with active-site residues and greater exposure to bulk solvent resulted in less
stable complexes. Therefore, MD simulations of the final docked structures in an aqueous
environment helped to rationalize at the molecular level, the differential inhibition observed
experimentally.
Another study that made use of MD simulations to analyze the relative stability of different
docked complexes was published by Cavalli et al.110 MD simulations of the final docked complexes
of propidium within human acetylcholinesterase (HuAChE) were found to be useful not only for
relaxing the protein receptor and accounting for the induced-fit effects, but also for discriminating
among conformations of different stability. The dynamically most stable structures were in good
agreement with the two possible binding modes found experimentally, while other intermediate
configurations produced unstable trajectories. The authors highlight the importance of their
combined approach: docking calculations to provide reliable starting structures and MD simulations
to incorporate protein flexibility and analyze complex stability.
Karplus and co-workers111 analyzed the binding of D-glucose onto the surface of insulin. Several
possible binding sites were found after docking, and MD simulations were used to study their kinetic
stabilities. It was found that the best-ranked docked conformers produced stable trajectories, and
although the glucose molecule actively explored different conformations, it never left the binding
pocket. The low number and unstable character of hydrogen bonds between glucose and insulin were
in agreement with the experimental low binding free energy. On the other hand, MD simulations of
complexes where the glucose was bound elsewhere on the surface appeared less stable, providing
information otherwise unobtainable on the relevance and stability of these different binding modes.
Rasteli et al.112 performed a database screening to find novel inhibitors of aldose reductase, and
then used MD to optimize the structures of selected candidates. One interesting outcome of the study
was that sulfonamide derivatives, one of the chemical families predicted to have good binding, were
experimentally inactive. Seeking a molecular explanation for this finding, the authors performed MD
simulations on different complexes. They found that during the trajectory, a water molecule entered
the active site and hydrogen bonded to a key residue, weakening the interaction with the sulfonamide
inhibitor and, thus, reducing its potential activity. This type of effect could not be predicted by
docking analysis, again highlighting the value of MD simulations in accounting for solvent effects.
In our work on bacterial R67 dihydrofolate reductase (DHFR)113, we used MD simulations to
test the relative stability of several different binding modes of the ligands dihydrofolate and NADPH
(see Fig. 5). After docking analysis, it was found that several complexes presented stable MD
trajectories and protein–ligand interactions in good agreement with experimental data, despite
having different global conformations of the ligand. We concluded that more than one possible ligand
conformation was stable within the spacious and symmetric active-site pore, which is provided by the
unusual tetrameric structure of the enzyme. While the reacting rings adopt a stacked conformation
close to the center of the active-site cavity, where there is little if any water access, the long charged
tails of the ligands extend towards opposite directions adopting multiple conformations in a solventrich environment.
Table 2. Summary of Docking Studies That Made Use of MD Simulations for the Optimization of Final Docked Structures
546
*
ALONSO, BLIZNYUK, AND GREADY
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
547
Figure 5. Evolutionduring MDsimulations ofthe distancebetweenthereactingrings ofthe ligands dihydrofolateand NADPHinfour
different ternary complexes of R67 dihydrofolate reductase. It may be seen that while the ligands remain within reacting distance for
Complexes 2 and 4, they separate from each other in Complexes1and 4.This differential behavioralong the MD trajectories allowed
properly docked complexes to be distinguished from incorrect unstable complexes.
These represent some selected examples of work where MD simulations have been applied after
docking analysis to optimize the final structures, analyze the stability of different complexes, and
account for solvent effects. Other studies include the work by Cannizzaro et al.114 on the origins of the
enantioselectivity of an antibody catalyzed Diels-Alder reaction, Garcia-Nieto et al.115 on the
interaction modes of nimesulide and prostaglandin-endoperoxide synthase-2, and Hammer et al.116
who used MD simulations to optimize the manually docked structures of several glucocorticoids
within a model of the glucocorticoid receptor.
5. FREE ENERGY CALCULATIONS
For a docking process to be successful, it is necessary that both the right conformation of the ligand–
receptor complex is predicted, and that the ranking of final structures is correct. The procedure needs
to be able to differentiate among similar conformations of the same system, as well as to predict the
relative stability of different complexes.
There are several different scoring functions for this purpose (for recent comparisons of scoring
functions see117–119). As most contain empirically fitted parameters, their performance on any
particular problem will depend on the set of structures used for the calibration. So far, no scoring
function has proven to be reliable for every docking case tested. The main constraint on their
improvement rests with the need for speed; when ranking hundreds, if not thousands, of complexes a
compromise in accuracy must be made. Knowledge-based functions used in the ranking of molecular
interactions may not be general and accurate enough, because of the limited number of interactions
that can be inferred from crystal structures and the inadequate description of repulsive forces. MMbased functions, on the other hand, inherit all common problems of molecular mechanics parameters,
and recent calculations have shown that they may result in large electrostatic errors.120–122 Several
pilot studies on the use of semi-empirical quantum mechanical methods for a more accurate
description of the interactions of proteins with small ligands have been recently published.123–125
548
*
ALONSO, BLIZNYUK, AND GREADY
Taking account of these factors, the type of scoring functions currently implemented in docking
programs cannot be expected to distinguish energetically between close conformations of the same
molecule, or even to rank properly a group of ligands of similar activity. Although the combination of
several scoring functions into a consensus score has been shown to provide better results,126–129 this
merely produces a ranking of complexes without offering final energies. While knowledge of the
relative stability of different complexes may be an adequate result for an initial screening protocol,
estimates of the absolute binding free energy may be necessary in later stages of docking or during
lead refinement, when only few selected ligands remain. If stringent rankings or accurate energies are
needed, different MD-based calculations can be carried out on the final complexes to estimate the
binding free energy.84,130–138
Thermodynamic integration (TI) and free energy perturbation (FEP) are among the most
rigorous methods currently available for the calculation of free energies. Despite providing very
accurate free energies, they are not widely applied as they are computationally expensive.136,139,140
The main limitation of these approaches is the exhaustive conformational sampling required to
obtained a proper averaged ensemble, and their slow convergence. Inefficiencies in configurational
sampling because of the appearance/disappearance of atoms (explained in more detail below) restrict
their use to small transformations, and limit analysis to a few closely related compounds.
Recently developed approaches that provide relatively good energy values at a moderate cost
include MD-based methods such as the linear interaction energy (LIE) method,130,141–144 and the socalled MM-PBSA method.145 As for previous sections, we review below some literature examples in
which simulations were used to calculate binding free energies and provide more technical details in
Table 3.
A. Free Energy Perturbation
Free energy perturbation methods can be applied to predict the relative binding strength of different
complexes. The difference in binding free energy between two given ligands L and L 0 and the receptor
R is calculated using the following thermodynamic cycle:
LþR
Gwmut #
L0 þ R
Gbind ðLÞ
!
!
Gbind ðL0 Þ
LR
# Gpmut
L0 R
Instead of calculating the individual binding energies (DGbind) to determine the relative bindingfree energy (DDGbind), the energies of the non-physical transformations L ! L0 ðGwmut Þ in
solution, and LR ! L0 RðGpmut Þ when bound to the protein, are estimated instead using
bind ¼ Gbind ðL0 Þ Gbind ðLÞ ¼ Gpmut Gwmut
To effect this, the states L and L 0 are linearly combined using a coupling parameter l, and an MD
simulation is used to slowly transform one ligand (L, l ¼ 0) into the other (L 0 , l ¼ 1) in both the free
and receptor-bound forms. This type of alchemic transformation can be used to determine relative
free energies, as the free energy is a state function which can be calculated by any reversible path
between the initial and final states.
Park and Lee146 combined homology modeling, docking, and free-energy calculations to
optimize the activities of histone deacetylase inhibitors. A series of 12 hydroxamate inhibitors on
three different scaffolds were automatically docked onto the protein. The best complexes were
energy minimized and submitted to MD simulations for FEP calculations. The final relative energies
of the 12 complexes were in good agreement with experimental results. As the chemical
Table 3. Summary of Docking Studies That Made Use of MD Simulations for the Calculation of Accurate Binding Free Energies
(Continued )
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
549
FEP, free energy perturbation.
LIE, linear interaction energy method.
c
MM-PBSA, molecular mechanics/Poisson^Boltzmann surface area method.
b
*
a
Table 3. (Continued )
550
ALONSO, BLIZNYUK, AND GREADY
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
551
modifications of the scaffolds directed towards creating stronger enzyme–ligand interactions usually
resulted in stabilization of the free ligand in solution, it was inferred that modifications needed to be
carefully planned to produce a net increase in the inhibitor potency.
In a similar approach, the same authors used the FEP method to study the selectivity of different
cyclooxygenase-2 inhibitors.147 In this case, two different cyclooxygenases, COX-2 and COX-1,
were used as protein receptor targets for the binding of 10 structurally different inhibitors. MD
simulations were used to perform a single mutation of the receptor COX-2 into a close model of COX1 by changing a valine residue into an isoleucine. A wide range of structurally different inhibitors
could be studied as it was the receptor, and not the ligands, that was involved in the non-physical
transformation. The final results were in good agreement with experimentally determined IC50 values
and offered a structural explanation for the selectivity of known COX inhibitors for one of the two
isozymes.
Luzhkov et al.148 analyzed the binding of three tetraalkylammonium ions to the KcsA potassium
channel. The predicted binding free energies were in good agreement with experimental data; they
suggested that the preferred binding of tetraethylammonium over the other two inhibitors originates
from the van der Waals interactions and the steric response of the binding site, with only very small
electrostatic contributions.
One of the most important limitations in free energy calculations is the sampling of the
conformational space.149 Exploration of the appropriate conformations is not guaranteed simply by
longer simulations. To avoid convergence problems and inadequate sampling during the simulations,
only transformations between similar molecules are feasible, constraining the type of ligands that can
be compared. This, together with the computational cost of such approaches, has prevented the wide
application of FEP for determining binding free energies, despite its accuracy.
B. Linear Interaction Energy Method
Aqvist et al.130 introduced the LIE semi-empirical MD approach for the estimation of binding free
energies.137,150 This method assumes that the binding free energy can be extracted from simulations
of the free and bound state of the ligand. The energy is divided into electrostatic and van der Waals
components, and the final binding energy is calculated as
elec
vdw
elec
vdw
þ Vbound
þ
Vfree
Vfree
Gbind ¼ Vbound
elec
vdw
elec
vdw
where Vbound
represents the averaged change in electrostatic energy and Vbound
Vfree
Vfree
the averaged change in van der Waals energy in going from an aqueous solution to a protein
environment. , , and are empirically determined constants. Two different MD simulations, one
for the ligand bound to the protein and another for the free ligand in water, are used to calculate the
energies. During the early applications of the LIE approach, only two coefficients, and , were
considered. Although , the electrostatic coefficient, appeared to have a constant value of 0.5 for
several protein systems, as predicted by the linear response approximation,130 the van der Waals
coefficient, , seemed to adopt various values depending on the characteristics of the protein
receptor.141,142,151,152 Kollman and co-workers144 suggested that the value of depended on the
hydrophobicity of the binding site, and that it could be predicted by calculating the weighted
desolvation non-polar ratio (WDNR) of the system. Jorgensen’s group extended the method to
calculate both the hydration and binding free energy, adding a new term to account for the solvent
accessible surface and scaling it by a new empirical coefficient.134,153,154 It was later found, however,
that the non-polar component , although considered zero in many cases,130,143 could adopt different
values155 and account for the variability earlier assigned to . In a recent study, Aqvist and coworkers156 performed a systematic analysis of several ligands in complex with P450cam. Using fixed
552
*
ALONSO, BLIZNYUK, AND GREADY
values for and , while optimizing , not only provided the best absolute binding free energies
for the ligands but also showed that the coefficients of the LIE method are independent of the
force field used and that only might need to be optimized to account for the hydrophobicity of the
active site.
Gutierrez-de-Teran et al.157 used a two-step approach to analyze the binding modes of different
agonists on human A1 adenosine receptor (hA1AR). The natural agonist adenosine and three
synthetic derivatives were docked onto a theoretical model of hA1AR. As two different binding
modes were found for the ligands, binding free energies were calculated using the LIE method. The
final energies permitted the selection of one preferred binding mode, which was favored by better
interactions between the ligands and the protein. These results suggested that there is a single
preferred binding mode for adenosine and its derivatives within hA1AR.
Osterberg et al.158 studied the binding of several sertindole analogs, which are strong blockers of
the hERG Kþ channel. The different blockers were docked against a homology model of the open
channel. A few highly populated clusters, representing different binding modes, were obtained for
most ligands. As the scoring function of the docking program was not able to discriminate between
the good and weak binders, representative conformations from the best clusters were submitted to
MD simulations. Not only protein flexibility and solvent effects were studied, but also the binding
free energies were estimated using the LIE approach. The final relative LIE energies were in excellent
agreement with the experimental values, thus validating the model used.
Luzhkov et al.159 studied the binding of the tetraethylammonium ion (TEA) to the KcsA
potassium channel. The inhibitor was docked automatically to the crystal structure of the channel;
two major binding regions near the intracellular and extracellular entrances were found, in agreement
with experiment. The final complexes were grouped and the most stable ones were selected for further
analysis by LIE. It was found that binding of TEA depends on the number of Kþ ions within the
channel, and that four tyrosine residues at the entrance of the pore form a hydrophobic cage that
stabilizes the binding of the inhibitor.
The binding free energies obtained in all these cases were in very good agreement with
experimental results and the LIE approach seems to be a good alternative to the more expensive
FEP calculations. The two main shortcomings of the method are the need for two different MD
simulations, one of the complex structure and another for the free ligand in water, and the use
of empirically derived constants which may need to be modified for each particular system. These
requirements restrict the broad application of the LIE method in docking/scoring procedures.
C. Molecular Mechanics/Poisson–Boltzmann Surface Area Method
The MM/PBSA method132,160 was introduced by Srinivasan et al.161 It combines molecular
mechanics (MM) and continuum solvent approaches to estimate binding energies. An initial MD
simulation in explicit solvent provides a thermally average ensemble of structures. Several snapshots
are then processed, removing all water and counterion molecules, and used to calculate the total
binding free energy of the system with the equation
complex ½G
protein þ G
ligand Gbind ¼ G
of the complex, protein, and ligand, are calculated according to the
where the average free energy G
following equations:
solvation T S
¼E
MM þ G
G
MM ¼ E
int þ E
elec þ E
vdw
E
solvation ¼ G
polar þ G
nonpolar
G
MM is the average MM energy in the gas phase, calculated for each desolvated snapshot with the
E
solvation , the solvation free
same MM potential used during the simulation but with no cut-offs. G
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
553
polar using a Poisson–Boltzmann
energy, is calculated in two parts, the electrostatic component G
approach, and a non-polar part using the solvent-accessible surface area (SASA) model.162 The
entropy ðT SÞ is the most difficult term to evaluate; it can be estimated by quasi-harmonic analysis163–
165
of the trajectory or using normal mode analysis.161,164,165 The entropy change can be omitted if
only the relative binding energies of a series of structurally similar compounds is required, but if the
absolute energy is important, or if the compounds are notably different, then its contribution to the
final free energy cannot be ignored. A recent study by Kuhn et al.166 suggests that the MM-PBSA
function could be used as a post-docking filter during the virtual screening of compounds, as their use
of a single relaxed structure provided better results than usual averaging over MD simulation
snapshots. However, as the simulation conditions used in this work were not optimal, improved
calculations could lead to significantly different conclusions.
Although only a single MD simulation of the complex is commonly used to determine the
conformational free energy,145 as the structures for both the free ligand and ligand-free protein
molecules are extracted from the simulation for the protein–ligand complex, this approach might not
be the best. A recent study by Pearlman167 showed that using a single simulation to generate all
structures for a series of complexes of p38 MAP kinase and 16 different ligands provides final results
that are significantly worse than those from separate simulations, and that savings achieved in
computing time are minimal and do not justify the simplification.
Application of the MM-PBSA approach has produced reasonable binding energies for several
systems,161,168–170 but not for others.167 Evaluation of the MM-PBSA method using a series of p38
MAP kinase complexes resulted in very poor results compared with other approaches, and at an
appreciably larger computational cost.167
In a very interesting work, von Langen et al.171 studied the selectivity of the human
glucocorticoid receptor (hGR) both experimentally and theoretically. The experimental relative
binding affinity of five steroids with similar carbon skeletons showed that the natural ligand cortisol
presents the highest affinity (100%) followed by progesterone (22%), aldosterone (20%),
testosterone (1.5%), and estradiol (0.2%). To rationalize the observed selectivity at the molecular
level, several different theoretical studies were done. A homology model of the hGR ligand-binding
domain was constructed and used as a target to dock the five different steroids. The ranking of the final
complexes provided by FlexX41 was not in agreement with the experimental affinities. All five
complexes were submitted to MD simulations to further study their characteristics and stabilities.
During the 4 nsec trajectories, it was seen that the complexes of cortisol and aldosterone were the
most stable, while those of the other steroids showed an increased mobility of the protein and a
collapse or an expansion of the active site. The binding free energy for the different complexes was
calculated using the MM/PBSA method. Although the approach could properly discriminate
compounds with strong affinity from those with weak binding, it could not correctly rank low-affinity
ligands. Further docking of the ligands to an average structure from the MD simulations showed
better results than the initial docking to an energy-minimized homology model structure,
highlighting the importance of a proper conformation of the protein receptor for docking studies.
Altogether, utilization of these computational techniques allowed the authors to understand the
selectivity of hGR for cortisol in molecular detail. Although similar steroids may fit within the active
site, the interactions they establish with the surrounding protein environment may not be adequate to
generate a stable and active receptor conformation.
Kollman and co-workers135 presented a combined approach that implements docking, MD
simulations, and MM-PBSA, and used it to predict the binding mode of the inhibitor efavirenz to
HIV-1 reverse transcriptase. Initially, they evaluated the capacity of combined MD simulations and
MM-PBSA to reproduce binding free energies of 12 crystal structures of HIV-1 RT complexed with
different TIBO-like inhibitors. They found that both relative and absolute free energies were
correctly predicted with an error of 1.0 kcal/mol. For the docking of efavirenz, five different
binding modes were submitted to MD simulation and further processed using the MM-PBSA
554
*
ALONSO, BLIZNYUK, AND GREADY
approach. The most stable binding mode was clearly identified, with a binding free energy of
13.2 kcal/mol in good agreement with the experimental value of 11.6 kcal/mol. The final
structure was found to be in very good agreement with a crystal structure of the complex, not
initially available to the authors. They concluded that molecular docking combined with MD
simulations followed by MM-PBSA analysis presented a reasonable approach for modeling protein
complexes a priori.
Others studies that employed MM-PBSA calculations include the analysis of cathepsin Dinhibitors by Huo et al.131 and the study of avidin ligands by Kuhn and Kollman.133 In the latter case, it
was found that free energy components for solute entropy were quite variable depending on the
snapshots analyzed, and the authors concluded that more accurate methods to predict entropic
changes may be required.
The MM-PBSA method has been shown to produce accurate free energies at a moderate
computational cost. Its main advantages are the lack of adjustable parameters and the option of using
a single MD simulation for the complete system to determine all energy values. Nevertheless, this
approach does have drawbacks, including the difficulties of predicting the entropic component of the
free energy and the fact that the changes in internal energy of the ligand and receptor upon complex
formation are neglected, which would produce significant errors in flexible systems where there is an
important induced-fit effect.
D. Value of MD Simulations After Docking
In the previous two sections, we have shown the advantages of applying MD simulations to the final
complexes of a docking study. Such simulations can have a dual use; they can refine the final
structures and also be used to predict accurate binding free energies.
In terms of structure optimization, MD simulations allow flexibility for both the ligand and
protein receptor, facilitating the relaxation of the complete system and accounting for induced-fit
effects. The effect of solvent molecules can also be treated explicitly; with the incorporation of water
molecules in the simulated system, important stabilizing/destabilizing effects and water-mediated
interactions can be observed. Furthermore, the time-dependent evolution of the system during the
simulation provides a dynamic picture of the complex and helps to discriminate the correctly docked
conformations from the unstable ones.
With respect to free energy calculations, we have pointed out that scoring functions implemented
within docking programs are not sufficiently accurate to identify, in every case, the most stable
conformation of a given ligand or drug with the highest binding affinity among a set of compounds.
Although library-screening processes require fast and inexpensive scoring functions, more accurate
and expensive calculations can be employed in the last stages of a docking process, when only a few
possible candidates are left, or during lead optimization. MD-based methods are among the most
accurate current techniques available for the calculation of free energies. FEP and the more recent
LIE and MM-PBSA approaches have been used successfully to predict both relative and
absolute binding free energies of many different complexes with errors of chemical accuracy, that
is, 1–2 kcal/mol.
6. MD SIMULATIONS AT DIFFERENT DOCKING STAGES
So far we have described various studies in which authors used MD simulations at different stages of
the docking process so as to improve the final results. Here we summarize two different works in
which MD simulations were used both before and after the actual docking to account for protein–
receptor flexibility, to optimize the final complexes and to obtain accurate free energies. Technical
details of these studies are given in Table 4.
MM-PBSA, molecular mechanics/Poisson^Boltzmann surface area method.
a
Table 4. Summary of Docking Studies That Made Use of MD Simulations During Most Stages of the Docking Process
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
555
556
*
ALONSO, BLIZNYUK, AND GREADY
Brigo et al.172 employed a combination of MD simulations, docking, and energy calculations to
understand the molecular mechanism of resistance of the mutant T66I/M154I HIV-1 integrase to
the inhibitor L-731,988. MD simulations were initially used to produce several alternative
conformations of the protein receptor, followed by automatic docking of the inhibitor. MD
simulations in explicit solvent were carried out to study stability of the final docked complex and
the effect of the inhibitor on the dynamic behavior of both the mutant and wild type integrase.
They found that while the inhibitor did not have an effect on the mobility of the mutant enzyme, it
caused an important constraint on the catalytic loop region of the wild-type protein. It was
concluded that the mutations conferred drug resistance by disrupting a key hydrogen bond between
the ligand and the enzyme, thus preventing the inhibitor from restricting the mobility of the
catalytic loop.
Gao et al.173 studied the binding of the curare derivatives d-tubocurarine and metocurine to the
acetylcholine-binding protein (AChBP). An initial MD simulation of the receptor was used to
generate different conformations against which the ligands were docked. The final complexes were
further refined by MD simulations, and their energies determined by the MM-PBSA approach.
Despite their structural similarity, fundamentally different binding orientations were found for both
compounds. Mutagenesis analysis confirmed the predicted differential binding, validating the
predictions of the computational approach.
7. DOCKING WITH MD SIMULATIONS
Although the physics predicts that a properly set up MD simulation of a solvated protein and its
unbound ligand would eventually lead to the formation of the most stable protein–ligand complex, no
current simulation protocol can deal with the long time span required for a binding process to occur
for such a large and complex system. In addition to time restrictions, the inherent tendency of an MDsimulated system to get trapped in local minima (sampling problem) makes the use of ordinary MD
simulations as docking techniques infeasible.
To improve the exploration of the free energy landscape and reproduce a possible binding event
within feasible computation times, it is necessary to increase the sampling power of conventional
MD simulations.47,174 The two basic approaches developed comprise flattening of the energy
surface, which allows the system to overcome large energy barriers, and the simulation of several
copies of the system. Multiple-copy simultaneous search94 and locally enhanced sampling95 are
examples of the latter approach. Alternatively, in replica exchange MD (REMD),96 several noninteracting replicas of the same system are simulated at different temperatures. At specified
intervals, replicas can exchange temperature, thereby overcoming energy barriers when simulated at
higher temperatures. Thermal heating of selected components of the system can also be used to
selectively enhance the sampling of certain regions, while the barriers separating local minima can
be lowered using the local elevation approach175 or the conformational flooding technique.176 These
approaches, although faster than conventional MD simulations, are still much slower than typical
docking techniques and could not be applied to more than a few examples. We review published
studies below.
A. Modified Potential Energy Surface
Nakajima et al.177 studied the docking of a short proline-rich peptide to an Src homology 3 (SH3)
domain by performing a multicanonical MD simulation*. In this protocol,178 increased sampling is
achieved by artificially flattening the potential energy surface, allowing an extensive exploration of
the conformational space. Five predominant binding modes of the peptide were identified, three in the
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
557
same orientation as that of the crystal structure and two in the opposite orientation. Although this
finding agrees with previous experimental results showing that proline-rich peptides can assume two
different orientations when bound to SH3 domains, the single conformation observed in the crystal
structure may indicate that there is a preferred one for this particular peptide. During the simulation
only the ligand and selected side chains were allowed to move, and no water molecules were
considered because of computational time constraints.
A different way of smoothing the potential energy was used by Pak and Wang.179 The Tsallis
scheme180,181 was employed to transform the non-bonding interaction potential and generate a
flattened alternative potential. They carried out in vacuo MD simulations for docking studies of two
different systems, streptavidin/biotin and protein kinase C/phorbol-13-acetate. Despite the lack of
solvent representation and the fact that only selected residues and the ligands were allowed to move
during the simulations, very good agreement with X-ray structures was found in both cases.
Interestingly, it was also found that receptor flexibility increases the efficiency of the docking
compared with a rigid treatment of the protein, probably because of lower energy barriers when the
ligand is allowed to explore a flexible binding site.
B. Localized Thermal Heating of the Ligand
Mangoni et al.182 performed the docking of a small flexible ligand, phosphocholine, onto a flexible
receptor, immunoglobulin McPC603, in explicit water solvent. They separated the center of mass
motion of the ligand from the internal motions and simulated it at a higher temperature.
Consequently, the sampling speed of the ligand was enhanced while the receptor, solvent, and
internal motions of the ligand were treated at room temperature. To obtain sensible results, the
interactions of the ligand with the solvent had to be scaled by half, as water molecules shielded the
interactions between ligand and receptor. After a very lengthy simulation, the final complex obtained
was in good agreement with the crystal structure.
C. Metadynamics
Parrinello and co-workers183 employed a new MD method, metadynamics*184, to find the correct
conformation of ligands inside flexible receptors in aqueous solution. A metadynamics run is a
standard MD simulation that implements harmonic restraints on certain collective variables (e.g., the
distance from the ligand to the binding site), which are explored along a time scale. A potential term,
constructed using a sum of Gaussians, prevents the system from re-visiting configurations, so that the
system is forced to move around the conformational space. One of the novelties of this approach is
that the free energy surface explored during the simulations can be reconstructed from the added
Gaussians, and the docking energy can be determined. Four different systems were analyzed, and
although the correct geometry was found and the experimental binding energy was predicted within
1 kcal/mol in all cases, most of the calculations started from the crystal configuration, with the ligand
already bound within the active site. In only one case, b-trypsin with a small and almost rigid ligand
(benzamidine), was simulation of the ligand entering the enzyme presented. Therefore, despite its
demonstrated ability to reproduce binding energies and provide a free energy surface as a function of
the collective variables, the utility of the method as a predictive docking tool to find the correct
binding mode of a free mobile ligand entering its protein receptor remains to be properly tested.
D. MD and Harmonic Dynamics
Most MD simulations used for docking purposes limit the mobility of the receptor to the binding-site
region, neglecting possible global changes of the protein. Tatsumi et al.185 used harmonic dynamics*
to account for global motions of the receptor during an MD simulation. The free protein receptor was
558
*
ALONSO, BLIZNYUK, AND GREADY
first submitted to an MD simulation and its characteristic motions were extracted using principal
component analysis (PCA). These collective motions were then incorporated into the MD docking
protocol used for studying inhibitor interactions. Although they were not able to reproduce the crystal
structure binding conformation of HIV-1 protease with the inhibitor MVT101, they showed that
harmonic dynamics are adequate for reproducing global receptor changes while MD accounts for the
local flexibility.
Zacharias186 performed PCA of the motions of the immunosuppressant FK506-binding protein
(FKBP) during an MD simulation and extracted its ‘‘soft’’ flexible modes. This information was then
used within their own docking program, PCRELAX, which apart from minimizing the position and
orientation of the ligand molecule allows the protein receptor to relax in the direction of the precalculated principal components of motion. To avoid large protein deformations during the docking
process, which would result in expensive re-calculation of the receptor energy, a simple penalty
function was implemented to limit deformations. It was found that docking of the ligand FK506 to the
average conformation of the MD simulation failed to identify the experimentally observed binding
mode, but when the flexible modes were included during the docking procedure, the final lowest
energy conformation was in good agreement with the X-ray structure. The main drawbacks of this
protocol are the lack of ligand flexibility and the fact that very mobile parts of the receptor, such as
side-chain fluctuations, are not accurately represented by the ‘‘soft’’ modes.
E. Value of MD Simulations During the Docking Procedure
All these techniques, although extremely time consuming when compared with conventional
docking algorithms, allow for the explicit consideration of both protein and ligand flexibility during
docking. Their advantage over other docking protocols that incorporate partial protein flexibility,
such as side-chain mobility or the use of multiple protein–receptor structures, is that the mobility of
the complete receptor can be taken into account explicitly. Moreover, both the induced fit of the
protein around the ligand and the docking process are carried out simultaneously. This, together with
the possibility of incorporating water molecules to account for their effect on the docking process,
makes this kind of approach very promising. It is especially useful when subtle motions and
rearrangement of the receptor are expected to be important, and where the role of water molecules
needs to be addressed. It can be used to analyze the effect of small changes on the ligand or the
binding-site environment, facilitating optimization of lead molecules or rationalizing the effect of a
given mutation. However, because of current computing time constraints, MD simulations for
docking purposes is limited to a single or few complexes.
8. CONCLUDING REMARKS
The importance of protein flexibility and solvent effects for the accurate modeling of ligand–protein
receptor complexes is now well known. As most docking algorithms do not account for receptor
flexibility or the effect of explicit water molecules, MD simulations can be used to complement and
improve a docking protocol. Given the higher computational cost of MD simulations compared with
docking algorithms, their use is usually restricted to certain steps of the drug-design process.
In the initial stages, MD simulations can be used to explore the conformational space of the
protein receptor. An ensemble of structures is expected to provide a more realistic representation of
the behavior of the protein in solution. For the virtual screening of large libraries, multiple protein
structures can be combined into a single docking grid or a united representation of the protein. For
more accuracy, docking against several independent conformations will provide more comprehensive results.
Protein flexibility and solvent effects can be evaluated after the docking process. Once a few
potential ligands have been identified, their complexes can be submitted to MD simulations for
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
559
further optimization and analysis. This will account for induced fit effects and explicit solvent
interactions, and will also test the stability of the complex over time. Once the final optimized
structures have been generated, MD simulations can be used for the calculation of accurate bindingfree energies. These are expected to provide a much better ranking than the simple algorithms used
during the docking process.
Despite offering, in principle, the most realistic representation of the binding process, the use of
MD simulations during the docking process itself is a very expensive approach. So far it has been
applied to just a few complexes, more with the objective of testing the methods, that is, in reproducing
the known binding conformation rather than for predicting the binding mode of a particular ligand. In
most examples, only simple representations of the system have been used, with the protein mobility
restricted to a few selected residues in the binding site and with the complete system immersed in a
spherical solvation shell. However, increasing computer power and improved algorithms will in
future allow use of more realistic representations of protein systems with improved boundary
conditions, and the application of this promising technique to the docking of flexible ligands
into mobile protein receptors in aqueous environments. Until then, thoughtful combination of
docking, MD simulations, and experiments offers the best alternative within the context of drug
design.
ACKNOWLEDGMENTS
We thank Dr. W.L.F. Armarego for helpful comments on the manuscript.
9. GLOSSARY
ADME: Absorption, distribution, metabolism and excretion; required pharmacokinetic properties of
viable drug compounds.
apo protein: Protein with no ligands bound.
Counterions: Ions added to a given simulation system to neutralize its total charge.
Conformational space: Group of conformations accessible to a flexible system under certain
temperature and pressure conditions.
Decoys: False positive hits obtained from docking calculations; incorrectly predicted ligand
geometries or selection of non-binding molecules over true ligands.
Docking: Computational procedure used to explore the possible binding modes of a ligand to its
receptor.
Docking grid: Schematic representation of the target structure where pre-calculated interactions
between the protein and different atom types are mapped onto a three dimensional grid; used to
rapidly estimate the interaction energy between different conformations of the ligand and the
protein receptor.
Enrichment: The percentage or fractional increase in the number of true ligands found within a
library of compounds after application of a particular selection procedure.
Harmonic dynamics: Collective motions, displacements that represent global conformational
changes of the system, as opposed to local fluctuations.
Hits: Ligands retrieved from a search of a library of compounds.
holo protein: Protein complex with bound ligand.
Homology model: Computer-generated three-dimensional model of a protein (target) based on the
known experimental structure of a homologous protein (template).
Induced fit: Conformational adjustment of a receptor to maximize its binding free energy with a
particular ligand, and vice versa.
560
*
ALONSO, BLIZNYUK, AND GREADY
Local minimum: Conformation of the system with the lowest energy within a particular region,
but which does not constitute the absolute energy minimum of the complete conformational
space.
Metadynamics: MD simulation where the system is forced to explore the conformational space
using harmonic restraints on certain collective variables (e.g., the distance from the ligand to the
active site) and restrictive potentials that prevent the system from re-visiting configurations.
Molecular dynamics simulation: Computational approach in which Newton’s equations of motions
are solved for an atomistic representation of a molecular system to obtain information about its
time-dependent properties.
Monte Carlo simulation: Statistical simulation method used to investigate the dynamic properties of
a system by performing a random sampling of the configurational space, as opposed to the
deterministic approach of MD simulations.
Multicanonical MD simulation: Constant temperature MD on an artificially flattened potential
energy surface; the probability of jumping over energy barriers is enhanced and very good
sampling efficiencies are achieved.
lsec: Micro (106) seconds.
nsec: Nano (109) seconds.
Pose: A given conformation of the ligand within the binding site; usually predicted by docking
methods.
Principal component analysis: Mathematical procedure used to identify patterns in data; the
method aims to reduce a large set of variables to a smaller set (the principal components) that
maintains most of the information in the larger set.
psec: Pico (1012) seconds.
Ranking: Procedure used to order a set of docked ligand–receptor complexes, or variable
conformations of such complexes, according to their stability.
Rotamer library: Collection of amino acid side-chain conformations and their associated relative
stability.
Scoring function: Empirically or theoretically derived function used to predict the interaction energy
between two given molecules (e.g., a ligand and its receptor).
Snapshot: A static view (one conformation) of a system at a particular instant during an MD
simulation.
Trajectory: Conformational path followed by a given system over the time period of an MD
simulation.
Virtual screening: Selection of compounds that satisfy specified characteristics, using computational tools.
REFERENCES
1. Persidis A. Proteomics. Nat Biotechnol 1998;16:393–394.
2. Acc Chem Res 1996;29: Several articles in issue 3.
3. Fernandes PB. Technological advances in high-throughput screening. Curr Opin Chem Biol 1998;2:
597–603.
4. Hertzberg RP, Pope AJ. High-throughput screening: New technology for the 21st century. Curr Opin Chem
Biol 2000;4:445–451.
5. Clark DE. Computational prediction of ADMET properties: Recent developments and future challenges.
Annu Rep Comput Chem 2005;1:133–151.
6. Brown D, Superti-Furga G. Rediscovering the sweet spot in drug discovery. Drug Discov Today 2003;8:
1067–1077.
7. Drews J. Stategic trends in the drug industry. Drug Discov Today 2003;8:411–420.
8. Lahana R. How many leads from HTS? Drug Discov Today 1999;4:447–448.
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
561
9. Jain AN. Virtual screening in lead discovery and optimization. Curr Opin Drug Discov Devel 2004;7:
396–403.
10. Oprea TI, Matter H. Integrating virtual screening in lead discovery. Curr Opin Chem Biol 2004;8:
349–358.
11. Shoichet BK. Virtual screening of chemical libraries. Nature 2004;432:862–865.
12. Stahura FL, Bajorath J. New methodologies for ligand-based virtual screening. Curr Pharm Des
2005;11:1189–1202.
13. Boehm HJ, Boehringer M, Bur D, Gmuender H, Huber W, Klaus W, Kostrewa D, Kuehne H, Luebbers T,
Meunier-Keller N, Mueller F. Novel inhibitors of DNA gyrase: 3D structure based biased needle screening,
hit validation by biophysical methods, and 3D guided optimization. A promising alternative to random
screening. J Med Chem 2000;43:2664–2674.
14. Bohm HJ, Banner DW, Weber L. Combinatorial docking and combinatorial chemistry: Design of potent
non-peptide thrombin inhibitors. J Comput Aided Mol Des 1999;13:51–56.
15. Bressi JC, Verlinde CL, Aronov AM, Shaw ML, Shin SS, Nguyen LN, Suresh S, Buckner FS, van Voorhis
WC, Kuntz ID, Hol WG, Gelb MH. Adenosine analogues as selective inhibitors of glyceraldehyde-3phosphate dehydrogenase of Trypanosomatidae via structure-based drug design. J Med Chem 2001;44:
2080–2093.
16. Burkhard P, Hommel U, Sanner M, Walkinshaw MD. The discovery of steroids and other novel FKBP
inhibitors using a molecular docking program. J Mol Biol 1999;287:853–858.
17. Filikov AV, Mohan V, Vickers TA, Griffey RH, Cook PD, Abagyan RA, James TL. Identification of ligands
for RNA targets via structure-based virtual screening: HIV-1 TAR. J Comput Aided Mol Des 2000;14:
593–610.
18. Iwata Y, Arisawa M, Hamada R, Kita Y, Mizutani MY, Tomioka N, Itai A, Miyamoto S. Discovery of novel
aldose reductase inhibitors using a protein structure-based approach: 3D-database search followed by
design and synthesis. J Med Chem 2001;44:1718–1728.
19. Schneider G, Clement-Chomienne O, Hilfiger L, Schneider P, Kirsch S, Bohm HJ, Neidhart W. Virtual
screening for bioactive molecules by evolutionary de novo design. Angew Chem Int Ed Engl 2000;39:
4130–4133.
20. Schneider G, Lee ML, Stahl M, Schneider P. De novo design of molecular architectures by evolutionary
assembly of drug-derived building blocks. J Comput Aided Mol Des 2000;14:487–494.
21. Kubinyi H. Drug research: Myths, hype and reality. Nat Rev Drug Discov 2003;2:665–668.
22. Martin YC. A bioavailability score. J Med Chem 2005;48:3164–3170.
23. Blundell TL, Jhoti H, Abell C. High-throughput crystallography for lead discovery in drug design. Nat Rev
Drug Discov 2002;1:45–54.
24. Congreve M, Murray CW, Blundell TL. Structural biology and drug discovery. Drug Discov Today
2005;10:895–907.
25. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to
estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliver Rev
1997;23:3–25.
26. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to
estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliver Rev
2001;46:3–26.
27. Muegge I. Selection criteria for drug-like compounds. Med Res Rev 2003;23:302–321.
28. Muegge I, Heald SL, Brittelli D. Simple selection criteria for drug-like chemical matter. J Med Chem
2001;44:1841–1846.
29. Congreve M, Carr R, Murray C, Jhoti H. A ‘rule of three’ for fragment-based lead discovery? Drug Discov
Today 2003;8:876–877.
30. Hann MM, Oprea TI. Pursuing the leadlikeness concept in pharmaceutical research. Curr Opin Chem Biol
2004;8:255–263.
31. Brooijmans N, Kuntz ID. Molecular recognition and docking algorithms. Annu Rev Biophys Biomol
Struct 2003;32:335–373.
32. Cummings MD, Des Jarlais RL, Gibbs AC, Mohan V, Jaeger EP. Comparison of automated docking
programs as virtual screening tools. J Med Chem 2005;48:962–976.
33. Halperin I, Ma B, Wolfson H, Nussinov R. Principles of docking: An overview of search algorithms and a
guide to scoring functions. Proteins 2002;47:409–443.
34. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery:
Methods and applications. Nat Rev Drug Discov 2004;3:935–949.
35. Shoichet BK, McGovern SL, Wei B, Irwin JJ. Lead discovery using molecular docking. Curr Opin Chem
Biol 2002;6:439–446.
562
*
ALONSO, BLIZNYUK, AND GREADY
36. Taylor RD, Jewsbury PJ, Essex JW. A review of protein-small molecule docking methods. J Comput Aided
Mol Des 2002;16:151–166.
37. Cho AE, Guallar V, Berne BJ, Friesner R. Importance of accurate charges in molecular docking: Quantum
mechanical/molecular mechanical (QM/MM) approach. J Comput Chem 2005;26:915–931.
38. Ewing TJA, Kuntz ID. Critical evaluation of search algorithms for automated molecular docking and
database screening. J Comput Chem 1997;18:1175–1189.
39. Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE. A geometric approach to macromolecule-ligand
interactions. J Mol Biol 1982;161:269–288.
40. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ. Automated docking using
a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 1998;19:
1639–1662.
41. Rarey M, Kramer B, Lengauer T, Klebe G. A fast flexible docking method using an incremental
construction algorithm. J Mol Biol 1996;261:470–489.
42. Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for
flexible docking. J Mol Biol 1997;267:727–748.
43. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M,
Perry JK, Shaw DE, Francis P, Shenkin PS. Glide: A new approach for rapid, accurate docking and scoring.
1. Method and assessment of docking accuracy. J Med Chem 2004;47:1739–1749.
44. Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL. Glide: A new approach
for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 2004;
47:1750–1759.
45. Hansson T, Oostenbrink C, van Gunsteren W. Molecular dynamics simulations. Curr Opin Struct Biol
2002;12:190–196.
46. Karplus M, McCammon JA. Molecular dynamics simulations of biomolecules. Nat Struct Biol 2002;9:
646–652.
47. Norberg J, Nilsson L. Advances in biomolecular simulations: Methodology and recent applications.
Quart Rev Biophys 2003;36:257–306.
48. Snow CD, Sorin EJ, Rhee YM, Pande VS. How well can simulation predict protein folding kinetics and
thermodynamics? Annu Rev Biophys Biomol Struct 2005;34:43–69.
49. McCammon JA, Gelin BR, Karplus M. Dynamics of folded proteins. Nature 1977;267:585–590.
50. de Groot BL, Grubmuller H. Water permeation across biological membranes: Mechanism and dynamics of
aquaporin-1 and GlpF. Science 2001;294:2353–2357.
51. Marelius J, Kolmodin K, Feierberg I, Aqvist J. Q: A molecular dynamics program for free energy
calculations and empirical valence bond simulations in biomolecular systems. J Mol Graph Model 1998;
16:213–225, 261.
52. Duan Y, Kollman PA. Pathways to a protein folding intermediate observed in a 1-microsecond simulation
in aqueous solution. Science 1998;282:740–744.
53. Mackerell AD Jr. Empirical force fields for biological macromolecules: Overview and issues. J Comput
Chem 2004;25:1584–1604.
54. Ponder JW, Case DA. Force fields for protein simulations. Adv Protein Chem 2003;66:27–85.
55. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM, Spellmeyer DC, Fox T, Caldwell
JW, Kollman PA. A second generation force field for the simulation of proteins, nucleic acids, and organic
molecules. J Am Chem Soc 1996;118:2309–2309.
56. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M. CHARMM—A program
for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 1983;4:187–217.
57. Scott WRP, Hunenberger PH, Tironi IG, Mark AE, Billeter SR, Fennen J, Torda AE, Huber T, Kruger P, van
Gunsteren WF. The GROMOS biomolecular simulation program package. J Phys Chem A 1999;103:
3596–3607.
58. Nelson MT, Humphrey W, Gursoy A, Dalke A, Kale LV, Skeel RD, Schulten K. NAMD: A parallel, object
oriented molecular dynamics program. Int J Supercomput Applic 1996;10:251–268.
59. Verkhivker GM, Bouzida D, Gehlhaar DK, Rejto PA, Freer ST, Rose PW. Complexity and simplicity of
ligand-macromolecule interactions: The energy landscape perspective. Curr Opin Struct Biol 2002;12:
197–203.
60. Luque I, Freire E. Structural stability of binding sites: Consequences for binding affinity and allosteric
effects. Proteins 2000;4:63–71.
61. Liu HL, Hsu JP. Recent developments in structural proteomics for protein structure determination.
Proteomics 2005;5:2056–2068.
62. Moult J. A decade of CASP: Progress, bottlenecks and prognosis in protein structure prediction. Curr Opin
Struct Biol 2005;15:285–289.
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
563
63. Schwede T, Kopp J, Guex N, Peitsch MC. SWISS-MODEL: An automated protein homology-modeling
server. Nucleic Acids Res 2003;31:3381–3385.
64. Tramontano A, Morea V. Assessment of homology-based predictions in CASP5. Proteins 2003;53:
352–368.
65. Al-Lazikani B, Jung J, Xiang Z, Honig B. Protein structure prediction. Curr Opin Chem Biol 2001;5:51–56.
66. Enyedy IJ, Ling Y, Nacro K, Tomita Y, Wu X, Cao Y, Guo R, Li B, Zhu X, Huang Y, Long YQ, Roller PP,
Yang D, Wang S. Discovery of small-molecule inhibitors of Bcl-2 through structure-based computer
screening. J Med Chem 2001;44:4313–4324.
67. Honma T, Hayashi K, Aoyama T, Hashimoto N, Machida T, Fukasawa K, Iwama T, Ikeura C, Ikuta M,
Suzuki-Takahashi I, Iwasawa Y, Hayama T, Nishimura S, Morishima H. Structure-based generation of a
new class of potent CDK4 inhibitors: New de novo design strategy and library design. J Med Chem
2001;44:4615–4627.
68. McGovern SL, Shoichet BK. Information decay in molecular docking screens against holo, apo, and
modeled conformations of enzymes. J Med Chem 2003;46:2895–2907.
69. Schafferhans A, Klebe G. Docking ligands onto binding site representations derived from proteins built by
homology modelling. J Mol Biol 2001;307:407–427.
70. Zuccotto F, Zvelebil M, Brun R, Chowdhury SF, Di Lucrezia R, Leal I, Maes L, Ruiz-Perez LM, Gonzalez
Pacanowska D, Gilbert IH. Novel inhibitors of Trypanosoma cruzi dihydrofolate reductase. Eur J Med
Chem 2001;36:395–405.
71. Erickson JA, Jalaie M, Robertson DH, Lewis RA, Vieth M. Lessons in molecular recognition: The effects
of ligand and protein flexibility on molecular docking accuracy. J Med Chem 2004;47:45–55.
72. Murray CW, Baxter CA, Frenkel AD. The sensitivity of the results of molecular docking to induced fit
effects: Application to thrombin, thermolysin and neuraminidase. J Comput Aided Mol Des 1999;13:547–
562.
73. Apostolakis J, Pluckthun A, Caflisch A. Docking small ligands in flexible binding sites. J Comput Chem
1998;19:21–37.
74. Claussen H, Buning C, Rarey M, Lengauer T. FlexE: Efficient molecular docking considering protein
structure variations. J Mol Biol 2001;308:377–395.
75. Trosset JY, Scheraga HA. PRODOCK: Software package for protein modeling and docking. J Comput
Chem 1999;20:412–427.
76. Fernandez-Recio J, Totrov M, Abagyan R. Soft protein-protein docking in internal coordinates. Protein Sci
2002;11:280–291.
77. Vieth M, Hirst JD, Kolinski A, Brooks CL. Assessing energy functions for flexible docking. J Comput
Chem 1998;19:1612–1622.
78. Wu G, Robertson DH, Brooks CL III, Vieth M. Detailed analysis of grid-based molecular docking: A case
study of CDOCKER-a CHARMM-based MD docking algorithm. J Comput Chem 2003;24:1549–1562.
79. Ferrari AM, Wei BQ, Costantino L, Shoichet BK. Soft docking and multiple receptor conformations in
virtual screening. J Med Chem 2004;47:5076–5084.
80. Schnecke V, Kuhn LA. Virtual screening with solvation and ligand-induced complementarity. Perspect
Drug Discov 2000;20:171–190.
81. Schnecke V, Swanson CA, Getzoff ED, Tainer JA, Kuhn LA. Screening a peptidyl database for potential
ligands to proteins with side-chain flexibility. Proteins 1998;33:74–87.
82. Frimurer TM, Peters GH, Iversen LF, Andersen HS, Moller NP, Olsen OH. Ligand-induced
conformational changes: Improved predictions of ligand binding conformations and affinities. Biophys
J 2003;84:2273–2281.
83. Kallblad P, Dean PM. Efficient conformational sampling of local side-chain flexibility. J Mol Biol 2003;
326:1651–1665.
84. Leach AR. Ligand docking to proteins with discrete side-chain flexibility. J Mol Biol 1994;235:345–356.
85. Goodford PJ. A computational procedure for determining energetically favorable binding sites on
biologically important macromolecules. J Med Chem 1985;28:849–857.
86. Knegtel RM, Kuntz ID, Oshiro CM. Molecular docking to ensembles of protein structures. J Mol Biol
1997;266:424–440.
87. Osterberg F, Morris GM, Sanner MF, Olson AJ, Goodsell DS. Automated docking to multiple target
structures: Incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins
2002;46:34–40.
88. Broughton HB. A method for including protein flexibility in protein-ligand docking: Improving tools for
database mining and virtual screening. J Mol Graph Model 2000;18:247–257, 302–244
89. Wei BQ, Weaver LH, Ferrari AM, Matthews BW, Shoichet BK. Testing a flexible-receptor docking
algorithm in a model binding site. J Mol Biol 2004;337:1161–1182.
564
*
ALONSO, BLIZNYUK, AND GREADY
90. Kumar S, Ma B, Tsai CJ, Sinha N, Nussinov R. Folding and binding cascades: Dynamic landscapes and
population shifts. Protein Sci 2000;9:10–19.
91. Ma B, Kumar S, Tsai CJ, Nussinov R. Folding funnels and binding mechanisms. Protein Eng 1999;12:713–
720.
92. Ma B, Shatsky M, Wolfson HJ, Nussinov R. Multiple diverse ligands binding at a single protein site: a
matter of pre-existing populations. Protein Sci 2002;11:184–197.
93. Philippopoulos M, Lim C. Exploring the dynamic information content of a protein NMR structure:
Comparison of a molecular dynamics simulation with the NMR and X-ray structures of Escherichia coli
ribonuclease HI. Proteins 1999;36:87–110.
94. Miranker A, Karplus M. Functionality maps of binding sites: A multiple copy simultaneous search
method. Proteins 1991;11:29–34.
95. Czerminski R, Elber R. Computational studies of ligand diffusion in globins: I. Leghemoglobin. Proteins
1991;10:70–80.
96. Sugita Y, Okamoto Y. Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett
1999;314:141–151.
97. Berndt KD, Guntert P, Wuthrich K. Conformational sampling by NMR solution structures calculated with
the program DIANA evaluated by comparison with long-time molecular dynamics calculations in explicit
water. Proteins 1996;24:304–313.
98. Brunne RM, Berndt KD, Guntert P, Wuthrich K, van Gunsteren WF. Structure and internal dynamics of the
bovine pancreatic trypsin inhibitor in aqueous solution from long-time molecular dynamics simulations.
Proteins 1995;23:49–62.
99. Dvorsky R, Hornak V, Sevcik J, Tyrrell GP, Caves LSD, Verma CS. Dynamics of RNAse Sa: A simulation
perspective complementary to NMR/X-ray. J Phys Chem B 2002;106:6038–6048.
100. Lei M, Zavodszky MI, Kuhn LA, Thorpe MF. Sampling protein conformations and pathways. J Comput
Chem 2004;25:1133–1148.
101. Kua J, Zhang Y, McCammon JA. Studying enzyme binding specificity in acetylcholinesterase using a
combined molecular dynamics and multiple docking approach. J Am Chem Soc 2002;124:8260–8267.
102. Carlson HA, Masukawa KM, Rubins K, Bushman FD, Jorgensen WL, Lins RD, Briggs JM, McCammon
JA. Developing a dynamic pharmacophore model for HIV-1 integrase. J Med Chem 2000;43:2100–
2114.
103. Carlson HA, Masukawa KM, McCammon JA. Method for including the dynamic fluctuations of a protein
in computer-aided drug design. J Phys Chem A 1999;103:10213–10219.
104. Lin JH, Perryman AL, Schames JR, McCammon JA. Computational drug design accommodating receptor
flexibility: The relaxed complex scheme. J Am Chem Soc 2002;124:5632–5633.
105. Lin JH, Perryman AL, Schames JR, McCammon JA. The relaxed complex method: Accommodating
receptor flexibility for drug design with an improved scoring scheme. Biopolymers 2003;68:47–62.
106. Schames JR, Henchman RH, Siegel JS, Sotriffer CA, Ni H, McCammon JA. Discovery of a novel binding
trench in HIV integrase. J Med Chem 2004;47:1879–1881.
107. Hoffmann D, Kramer B, Washio T, Steinmetzer T, Rarey M, Lengauer T. Two-stage method for proteinligand docking. J Med Chem 1999;42:4422–4433.
108. Wang J, Kollman PA, Kuntz ID. Flexible ligand docking: A multistep strategy approach. Proteins 1999;
36:1–19.
109. Park H, Yeom MS, Lee S. Loop flexibility and solvent dynamics as determinants for the selective inhibition
of cyclin-dependent kinase 4: Comparative molecular dynamics simulation studies of CDK2 and CDK4.
Chembiochem 2004;5:1662–1672.
110. Cavalli A, Bottegoni G, Raco C, de Vivo M, Recanatini M. A computational study of the binding of
propidium to the peripheral anionic site of human acetylcholinesterase. J Med Chem 2004;47:3991–3999.
111. Zoete V, Meuwly M, Karplus M. Investigation of glucose binding sites on insulin. Proteins 2004;55:
568–581.
112. Rastelli G, Ferrari AM, Costantino L, Gamberini MC. Discovery of new inhibitors of aldose reductase
from molecular docking and database screening. Bioorg Med Chem 2002;10:1437–1450.
113. Alonso H, Gillies MB, Cummins PL, Bliznyuk AA, Gready JE. Multiple ligand-binding modes in bacterial
R67 dihydrofolate reductase. J Comput Aided Mol Des 2005;19:165–187.
114. Cannizzaro CE, Ashley JA, Janda KD, Houk KN. Experimental determination of the absolute
enantioselectivity of an antibody-catalyzed Diels-Alder reaction and theoretical explorations of the
origins of stereoselectivity. J Am Chem Soc 2003;125:2489–2506.
115. Garcia-Nieto R, Perez C, Gago F. Automated docking and molecular dynamics simulations of nimesulide
in the cyclooxygenase active site of human prostaglandin-endoperoxide synthase-2 (COX-2). J Comput
Aided Mol Des 2000;14:147–160.
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
565
116. Hammer S, Spika I, Sippl W, Jessen G, Kleuser B, Holtje HD, Schafer-Korting M. Glucocorticoid receptor
interactions with glucocorticoids: Evaluation by molecular modeling and functional analysis of
glucocorticoid receptor mutants. Steroids 2003;68:329–339.
117. Ferrara P, Gohlke H, Price DJ, Klebe G, Brooks CL III. Assessing scoring functions for protein-ligand
interactions. J Med Chem 2004;47:3032–3047.
118. Wang R, Lu Y, Fang X, Wang S. An extensive test of 14 scoring functions using the PDBbind refined set of
800 protein-ligand complexes. J Chem Inf Comput Sci 2004;44:2114–2125.
119. Wang R, Lu Y, Wang S. Comparative evaluation of 11 scoring functions for molecular docking. J Med
Chem 2003;46:2287–2303.
120. Bliznyuk AA, Rendell AP. Electronic effects in biomolecular simulations: Investigation of the KcsA
potassium ion channel. J Phys Chem B 2004;108:13866–13873.
121. Bliznyuk AA, Rendell AP, Allen TW, Chung SH. The potassium ion channel: Comparison of linear scaling
semiempirical and molecular mechanics representations of the electrostatic potential. J Phys Chem B
2001;105:12674–12679.
122. Zuegg J, Bliznyuk AA, Gready JE. Comparison of electrostatic potential around proteins calculated
from Amber and AM1 charges: Application to mutants of prion protein. Mol Phys 2003;101:2437–
2450.
123. Raha K, Merz KM. Large-scale validation of a quantum mechanics based scoring function: Predicting the
binding affinity and the binding mode of a diverse set of protein-ligand complexes. J Med Chem
2005;48:4558–4575.
124. Vasilyev V, Bliznyuk A. Application of semiempirical quantum chemical methods as a scoring function in
docking. Theor Chem Acc 2004;112:313–317.
125. Villar R, Gil MJ, Garcia JI, Martinez-Merino V. Are AM1 ligand-protein binding enthalpies good enough
for use in the rational design of new drugs? J Comput Chem 2005;26:1347–1358.
126. Bissantz C, Folkers G, Rognan D. Protein-based virtual screening of chemical databases. 1. Evaluation of
different docking/scoring combinations. J Med Chem 2000;43:4759–4767.
127. Charifson PS, Corkery JJ, Murcko MA, Walters WP. Consensus scoring: A method for obtaining improved
hit rates from docking databases of three-dimensional structures into proteins. J Med Chem 1999;
42:5100–5109.
128. Clark RD, Strizhev A, Leonard JM, Blake JF, Matthew JB. Consensus scoring for ligand/protein
interactions. J Mol Graph Model 2002;20:281–295.
129. Perez C, Ortiz AR. Evaluation of docking functions for protein-ligand docking. J Med Chem 2001;44:
3768–3785.
130. Aqvist J, Medina C, Samuelsson JE. A new method for predicting binding affinity in computer-aided drug
design. Protein Eng 1994;7:385–391.
131. Huo S, Wang J, Cieplak P, Kollman PA, Kuntz ID. Molecular dynamics and free energy analyses of
cathepsin D-inhibitor interactions: Insight into structure-based ligand design. J Med Chem 2002;45:
1412–1419.
132. Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O,
Cieplak P, Srinivasan J, Case DA, Cheatham TE III. Calculating structures and free energies of complex
molecules: Combining molecular mechanics and continuum models. Acc Chem Res 2000;33:889–
897.
133. Kuhn B, Kollman PA. Binding of a diverse set of ligands to avidin and streptavidin: An accurate
quantitative prediction of their relative affinities by a combination of molecular mechanics and continuum
solvent models. J Med Chem 2000;43:3786–3791.
134. Smith RH Jr, Jorgensen WL, Tirado-Rives J, Lamb ML, Janssen PA, Michejda CJ, Kroeger Smith MB.
Prediction of binding affinities for TIBO inhibitors of HIV-1 reverse transcriptase using Monte Carlo
simulations in a linear response method. J Med Chem 1998;41:5272–5286.
135. Wang J, Morin P, Wang W, Kollman PA. Use of MM-PBSA in reproducing the binding free energies to
HIV-1 RT of TIBO derivatives and predicting the binding mode to HIV-1 RT of efavirenz by docking and
MM-PBSA. J Am Chem Soc 2001;123:5221–5230.
136. Straatsma TP, McCammon JA. Computational alchemy. Annu Rev Phys Chem 1992;43:407–435.
137. Brandsdal BO, Osterberg F, Almlof M, Feierberg I, Luzhkov VB, Aqvist J. Free energy calculations and
ligand binding. Adv Protein Chem 2003;66:123–158.
138. Wang W, Donini O, Reyes CM, Kollman PA. Biomolecular simulations: Recent developments in force
fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein-nucleic acid
noncovalent interactions. Annu Rev Biophys Biomol Struct 2001;30:211–243.
139. Beveridge DL, DiCapua FM. Free energy via molecular simulation: Applications to chemical and
biomolecular systems. Annu Rev Biophys Biophys Chem 1989;18:431–492.
566
*
ALONSO, BLIZNYUK, AND GREADY
140. Gready JE, Cummins PL. Mechanism-based substrates and inhibitors of dihydrofolate reductase.
In: Reddy MR, Erion MD, editors. Free energy calculations in rational drug design. New York: Kluwer
Academic/Plenum; 2001. p 343–364.
141. Aqvist J. Calculation of absolute binding free energies for charged ligands and effects of long-range
electrostatic interactions. J Comput Chem 1996;17:1587–1597.
142. Hansson T, Aqvist J. Estimation of binding free energies for HIV proteinase inhibitors by molecular
dynamics simulations. Protein Eng 1995;8:1137–1144.
143. Hansson T, Marelius J, Aqvist J. Ligand binding affinity prediction by linear interaction energy methods.
J Comput Aided Mol Des 1998;12:27–35.
144. Wang W, Wang J, Kollman PA. What determines the van der Waals coefficient beta in the LIE (linear
interaction energy) method to estimate binding free energies using molecular dynamics simulations?
Proteins 1999;34:395–402.
145. Swanson JM, Henchman RH, McCammon JA. Revisiting free energy calculations: A theoretical
connection to MM/PBSA and direct calculation of the association free energy. Biophys J 2004;86:
67–74.
146. Park H, Lee S. Homology modeling, force field design, and free energy simulation studies to optimize the
activities of histone deacetylase inhibitors. J Comput Aided Mol Des 2004;18:375–388.
147. Park H, Lee S. Free energy perturbation approach to the critical assessment of selective cyclooxygenase-2
inhibitors. J Comput Aided Mol Des 2005;19:17–31.
148. Luzhkov VB, Osterberg F, Aqvist J. Structure-activity relationship for extracellular block of Kþ channels
by tetraalkylammonium ions. FEBS Lett 2003;554:159–164.
149. Bash PA, Singh UC, Brown FK, Langridge R, Kollman PA. Calculation of the relative change in binding
free energy of a protein-inhibitor complex. Science 1987;235:574–576.
150. Aqvist J, Luzhkov VB, Brandsdal BO. Ligand binding affinities from MD simulations. Acc Chem Res
2002;35:358–365.
151. Paulsen MD, Ornstein RL. Binding free energy calculations for P450cam-substrate complexes. Protein
Eng 1996;9:567–571.
152. Wang J, Dixon R, Kollman PA. Ranking ligand binding affinities with avidin: A molecular dynamics-based
interaction energy study. Proteins 1999;34:69–81.
153. Jones-Hertzog DK, Jorgensen WL. Binding affinities for sulfonamide inhibitors with human thrombin
using Monte Carlo simulations with a linear response method. J Med Chem 1997;40:1539–1549.
154. Carlson HA, Jorgensen WL. An extended linear-response method for determining free-energies of
hydration. J Phys Chem 1995;99:10667–10673.
155. Aqvist J, Marelius J. The linear interaction energy method for predicting ligand binding free energies.
Comb Chem High Throughput Screen 2001;4:613–626.
156. Almlof M, Brandsdal BO, Aqvist J. Binding affinity prediction with different force fields: Examination of
the linear interaction energy method. J Comput Chem 2004;25:1242–1254.
157. Gutierrez-de-Teran H, Pastor M, Centeno NB, Aqvist J, Sanz F. Comparative analysis of putative agonistbinding modes in the human A1 adenosine receptor. Chembiochem 2004;5:841–849.
158. Osterberg F, Aqvist J. Exploring blocker binding to a homology model of the open hERG Kþ channel
using docking and molecular dynamics methods. FEBS Lett 2005;579:2939–2944.
159. Luzhkov VB, Aqvist J. Mechanisms of tetraethylammonium ion block in the KcsA potassium channel.
FEBS Lett 2001;495:191–196.
160. Massova I, Kollman PA. Combined molecular mechanical and continuum solvent approach (MM-PBSA/
GBSA) to predict ligand binding. Perspect Drug Discov 2000;18:113–135.
161. Srinivasan J, Cheatham TE, Cieplak P, Kollman PA, Case DA. Continuum solvent studies of the stability of
DNA, RNA, and phosphoramidate-DNA helices. J Am Chem Soc 1998;120:9401–9409.
162. Sitkoff D, Sharp KA, Honig B. Accurate calculation of hydration free-energies using macroscopic solvent
models. J Phys Chem 1994;98:1978–1988.
163. Brooks BR, Janezic D, Karplus M. Harmonic-analysis of large systems. 1. Methodology. J Comput Chem
1995;16:1522–1542.
164. Janezic D, Brooks BR. Harmonic-analysis of large systems. 2. Comparison of different protein models.
J Comput Chem 1995;16:1543–1553.
165. Janezic D, Venable RM, Brooks BR. Harmonic-analysis of large systems. 3. Comparison with moleculardynamics. J Comput Chem 1995;16:1554–1566.
166. Kuhn B, Gerber P, Schulz-Gasch T, Stahl M. Validation and use of the MM-PBSA approach for drug
discovery. J Med Chem 2005;48:4040–4048.
167. Pearlman DA. Evaluating the molecular mechanics Poisson–Boltzmann surface area free energy method
using a congeneric series of ligands to p38 MAP kinase. J Med Chem 2005;48:7796–7807.
DOCKING AND MD SIMULATIONS IN DRUG DESIGN
*
567
168. Chong LT, Duan Y, Wang L, Massova I, Kollman PA. Molecular dynamics and free-energy calculations
applied to affinity maturation in antibody 48G7. Proc Natl Acad Sci USA 1999;96:14330–14335.
169. Massova I, Kollman PA. Computational alanine scanning to probe protein-protein interactions: A novel
approach to evaluate binding free energies. J Am Chem Soc 1999;121:8133–8143.
170. Reyes CM, Kollman PA. Investigating the binding specificity of U1A-RNA by computational
mutagenesis. J Mol Biol 2000;295:1–6.
171. von Langen J, Fritzemeier KH, Diekmann S, Hillisch A. Molecular basis of the interaction specificity
between the human glucocorticoid receptor and its endogenous steroid ligand cortisol. Chembiochem
2005;6:1110–1118.
172. Brigo A, Lee KW, Fogolari F, Mustata GI, Briggs JM. Comparative molecular dynamics simulations of
HIV-1 integrase and the T66I/M154I mutant: Binding modes and drug resistance to a diketo acid inhibitor.
Proteins 2005;59:723–741.
173. Gao F, Bern N, Little A, Wang HL, Hansen SB, Talley TT, Taylor P, Sine SM. Curariform antagonists bind
in different orientations to acetylcholine-binding protein. J Biol Chem 2003;278:23020–23026.
174. Tai K. Conformational sampling for the impatient. Biophys Chem 2004;107:213–220.
175. Huber T, Torda AE, van Gunsteren WF. Local elevation: A method for improving the searching properties
of molecular dynamics simulation. J Comput Aided Mol Des 1994;8:695–708.
176. Schulze BG, Grubmuller H, Evanseck JD. Functional significance of hierarchical tiers in carbonmonoxy
myoglobin: Conformational substates and transitions studied by conformational flooding simulations.
J Am Chem Soc 2000;122:8700–8711.
177. Nakajima N, Higo J, Kidera A, Nakamura H. Flexible docking of a ligand peptide to a receptor protein by
multicanonical molecular dynamics simulation. Chem Phys Lett 1997;278:297–301.
178. Nakajima N, Nakamura H, Kidera A. Multicanonical ensemble generated by molecular dynamics
simulation for enhanced conformational sampling of peptides. J Phys Chem B 1997;101:817–824.
179. Pak YS, Wang SM. Application of a molecular dynamics simulation method with a generalized effective
potential to the flexible molecular docking problems. J Phys Chem B 2000;104:354–359.
180. Curado EMF, Tsallis C. Generalized statistical-mechanics—Connection with thermodynamics. J Phys
A-Math Gen 1991;24:L69–L72.
181. Tsallis C. Possible generalization of Boltzmann-Gibbs statistics. J Stat Phys 1988;52:479–487.
182. Mangoni M, Roccatano D, Di Nola A. Docking of flexible ligands to flexible receptors in solution by
molecular dynamics simulation. Proteins 1999;35:153–162.
183. Gervasio FL, Laio A, Parrinello M. Flexible docking in solution using metadynamics. J Am Chem Soc
2005;127:2600–2607.
184. Laio A, Parrinello M. Escaping free-energy minima. Proc Natl Acad Sci USA 2002;99:12562–12566.
185. Tatsumi R, Fukunishi Y, Nakamura H. A hybrid method of molecular dynamics and harmonic dynamics
for docking of flexible ligand to flexible receptor. J Comput Chem 2004;25:1995–2005.
186. Zacharias M. Rapid protein-ligand docking using soft modes from molecular dynamics simulations to
account for protein deformability: Binding of FK506 to FKBP. Proteins 2004;54:759–767.
187. Kua J, Zhang Y, Eslami AC, Butler JR, McCammon JA. Studying the roles of W86, E202, and Y337 in
binding of acetylcholine to acetylcholinesterase using a combined molecular dynamics and multiple
docking approach. Protein Sci 2003;12:2675–2684.
188. Ponder JW. TINKER, software tools for molecular design. version 3.6; 1998.
189. Sali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 1993;
234:779–815.
190. Majeux N, Scarsi M, Apostolakis J, Ehrhardt C, Caflisch A. Exhaustive docking of molecular fragments
with electrostatic solvation. Proteins 1999;37:88–105.
191. Majeux N, Scarsi M, Caflisch A. Efficient electrostatic solvation model for protein-fragment docking.
Proteins 2001;42:256–268.
192. Lindahl E, Hess B, van der Spoel D. GROMACS 3.0: A package for molecular simulation and trajectory
analysis. J Mol Model 2001;7:306–317.
193. INSIGHT II. San Diego, CA: Biosym/MSI; 1998.
194. Kick EK, Roe DC, Skillman AG, Liu G, Ewing TJ, Sun Y, Kuntz ID, Ellman JA. Structure-based design
and combinatorial chemistry yield low nanomolar inhibitors of cathepsin D. Chem Biol 1997;4: 297–
307.
195. Roe DC. Application and development tools for structure-base drug design. San Francisco, University of
California; 1995.
196. Kale L, Skeel R, Bhandarkar M, Brunner R, Gursoy A, Krawetz N, Phillips J, Shinozaki A, Varadarajan K,
Schulten K. NAMD2: Greater scalability for parallel molecular dynamics. J Comput Phys 1999;151: 283–
312.
568
*
ALONSO, BLIZNYUK, AND GREADY
Hernán Alonso graduated from the Universidad Nacional de Córdoba, Argentina, in 2002 with a degree similar
to a master in chemistry. He is currently a third year PhD student at the John Curtin School of Medical Research,
The Australian National University. He is interested in the study of enzymes and their reaction mechanisms using
computational techniques such as docking, molecular dynamics simulations, QM/MM energy calculations,
among others.
Andrey Bliznyuk graduated from the Novosibirsk State University in 1984 and received his doctorate degree in
physical chemistry in 1990. After post-doctoral appointments at the University of Sydney and The Australian
National University, he joined the Supercomputer Facility at the Australian National University in 1997. His
research interests include computational software and methods development and its applications to enzyme
reactions and drug design.
Jill Gready graduated from the University of Sydney with BSc (Hons) (chemistry 1973) and PhD (theoretical
chemistry 1978) degrees. After post-doctoral fellowships in the Physical Chemistry Laboratory, Oxford
University, she returned to the Biochemistry Department of the University of Sydney in 1981 and set up a
research group in computer-aided drug design and biomolecular simulation. In 1995, she relocated to the John
Curtin School of Medical Research, The Australian National University where her group broadly researches
problems in protein structure and function combining computational, bioinformatic and experimental
approaches, including drug design, enzyme reactions, and protein evolution. Much of this work employs MD
and QM/MM þ MD simulations run on the APAC supercomputer facility located at the ANU.