Download Ligand-protein docking and rational drug design

Ligand-protein docking and rational drug design
Terry P Lybrand
University of Washington, Seattle, USA
Over the past year there have been some interesting and significant advances
in computer-based ligand-protein docking techniques and related rational
drug-design tools, including flexible ligand docking and better estimation of
binding free energies and solvation energies. As a result, the successful use of
computational tools to help generate interesting new guide (lead) compounds
for targeted receptors is becoming more commonplace.
Current Opinion in Structural Biology 1995, 5:224-228
Introduction
Computational tools
Ligand-binding interactions (i.e. formation of a complex between two molecules) are central to numerous
biological processes such as signal transduction, physiological regulation, gene transcription, and enzymatic
reactions. Ligand-binding interactions encompass both
macromolecular complexes (e.g. protein-protein and
protein-DNA) and complexes of small molecules with
macromolecules. As many proteins regulate key biological functions via interactions with small molecules,
these receptor proteins are often prime targets for therapeutic agents. A detailed understanding of interactions
between small molecules and proteins may therefore
form the basis for a rational drug-design strategy. Rational drug design is attractive as a drug development
paradigm for two reasons: it offers some hope for reduction of the enormous costs and time required in
traditional random screening protocols for drug discovery, and may facilitate the development of more selective
therapeutic agents with fewer undesirable side effects.
Interactive molecular graphics
Numerous computational tools have evolved to investigate ligand-receptor complexes and to develop new
ligands [5]. Interactive molecular graphics methods have
long been used to analyze receptor crystal structures and
to manually dock candidate ligands in the binding site
[6]. The interactive graphics approach is extremely labor intensive, primarily qualitative rather than quantitative in nature, and subject to personal biases. On the
other hand, it takes advantage of the knowledge and intuition possessed by experienced structural biologists in
a way that no strictly computational techniques have yet
been able to do. As a result, interactive molecular graphics methods are used extensively and remain the principal
tool for ligand design in many cases.
Developments in molecular biology over the past 15
years make it possible to obtain experimentally useful quantities of numerous receptor proteins of potential therapeutic importance. Technical advances in
X-ray crystallography, multidimensional NMtL spectroscopy, and other structural characterization methods
have made it easier to obtain high-resolution structural data for many important ligand-protein complexes.
Still, there is at present no structural information for the
vast majority of therapeutically interesting receptor proteins. In favorable cases, computational procedures such
as homology model building may be used to generate
approximate three-dimensional models for receptor proteins [1,2]. Development of various molecular modeling
tools and ready availability of high-performance computing resources make possible detailed computational
analyses and design projects for ligand-receptor complexes, provided suitable structural models are available
for the receptors [3,4]. Over the past year, there have
been some significant advances and improvements in
computational tools used for ligand-protein docking
and rational drug-design applications, and these developments are the focus of this review.
224
Binding energy calculations
Graphics model-building methods are often combined
with energy calculations based on potential energy functions (see also this issue, Halgren, pp 205-210 and Sippl,
pp 229-235 for a general discussion of current issues
related to potential energy functions). Energy calculations used routinely in rational drug-design applications include energy minimization, molecular dynamics, Poisson-Boltzmann electrostatics, and free energy
perturbation methods [7",8]. In principle, these methods should provide quite detailed and definitive information for rational drug-design projects, and there
are many recent examples where these methods have
been used to good effect [9-13,14°,15]. Inadequacies
in potential energy functions and conformational sampling, however, restrict the power of these methods in
modeling ligand-receptor complexes. These methods
are also computationally quite expensive, which limits
their practical utility in many cases.
In some recent ligand-binding studies, attempts have
been made to overcome certain limitations relating to
potential functions and computational expense through
the use of empirical free energy functions or free energy
estimations [16,17]. These approaches often estimate the
free energy of ligand-receptor interactions as a function
© Current Biology Ltd ISSN 0959-440X
Ligand-protein docking and rational drug design Lybrand 225
of hydrophobic contact surface area, number of hydrogen bonds, buried polar surface area, and similar terms.
As a result, these methods tend to be much less computationaUy demanding than methods involving potential energy functions (free energy interactions may be analyzed
using static crystal structures, for example), and, unlike
free energy perturbation methods, they are not dependent on traditional potential energy functions or molecular dynamics configurational sampling. These empirical
free energy function approaches may offer some promise
for improved ligand-receptor binding free energy estimates, though they too are subject to inaccuracies in
calibration of the functions.
Improved calculation of ligand-binding energies may
also be possible through a combined use of PoissonBoltzmann electrostatics calculations with systematic
conformational search, as has been reported recently for
several systems [18°,19]. This approach ensures that all
significant conformational states are sampled via a systematic variation of all important rotatable bonds in the
molecules, and utilizes Poisson-Boltzmann electrostatics
calculations (which have proven to be quite reliable for
energetic calculations in many examples) to compute
relative energies. This approach is quite computationally expensive, however, and offers little advantage over
free energy perturbation methods in that regard. Furthermore, this approach has been tested for only a few
systems thus far, so it is not clear whether it will constitute a general strategy for calculating the free energy of
formation of ligand-receptor complexes.
Docking programs
Many recent modeling programs entail some type of
docking strategy for ligand discovery and design. Some
programs consider only structural or steric aspects of
ligand-receptor interactions, whereas others also include
chemical complementarity [20°]. These algorithms attempt to match or fit a candidate ligand to a target
binding site. They may be used to locate plausible
binding pockets in a protein for a specific ligand, but
more often in rational drug-design projects are used to
screen a database of small molecules to identify those
that best fit a target receptor site. This approach has
yielded a number of documented successes in lead compound discovery [21°°,22], but there are still a number of
weaknesses with general docking methods. For example,
some recent crystal structures reveal that successful lead
compounds identified by docking searches bind rather
differently to the target receptor than predicted by the
docking programs [23]. All docking strategies incorporate some type of scoring scheme to rank the quality
of the ligand-receptor site complexes they discover. At
present, results using these scoring schemes do not correlate well with experimental relative binding affinities.
For example, the best-binding molecule from a series
of compounds (as determined by experimental measurements) will frequently not receive the top score in
a docking search, though it is often among the top
candidates. Much work currently focuses on the ira-
provement of scoring schemes, but it seems likely that
other factors, such as the rigid molecule approximation
or lack of structural refinement for docked complexes,
may contribute substantially to the problem, and thus the
scoring scheme itself (which is usually some sort of simplified potential energy function) may not be seriously
flawed.
Several issues have been addressed recently in order to
improve docking algorithms. Some docking programs
now incorporate an energy minimization procedure
to facilitate the identification of complexes with good
geometries and plausible ligand-receptor interactions,
with some apparent improvement of results [24]. Most
docking algorithms at present utilize rigid ligand and
receptor-site geometries; in the best of cases this is a serious oversimplification, but inclusion of flexibility via
a minimization or molecular dynamics method or via
a systematic conformational search procedure has been
computationally unfeasible for most problems.
Recently, new docking programs that include at least
limited conformational flexibility have started to appear. A number of methods now consider conformational flexibility for the ligands. In some cases, ligand
flexibility is incorporated via inclusion of multiple conformers of flexible ligands in databases [25°,26]. During
the docking search, each unique conformer is considered
as a separate molecule. Although effective, this approach
requires that all relevant conformations for all molecules
of interest be identified and archived in the data base
before docking searches.
Some approaches utilize Monte Carlo and/or multiple copy simultaneous search techniques to enhance
orientational sampling and incorporate ligand flexibility [27,28°,29]. Multiple copy simultaneous search involves placement of numerous copies of the ligand in
the binding pocket, each with a different initial orientation and position. Then, an energy minimization
or molecular dynamics calculation can be used to optimize the interactions of each copy of the ligand with
the receptor simultaneously. During the energy calculations, the nmltiple copies of ligand do not interact
with each other, and thus produce no adverse effects
in the calculation. As each copy of the ligand is positioned differently at the beginning of the calculations,
this method should provide a much more thorough exploration of all possible binding modes than could be
obtained by conventional energy-calculation methods.
Monte Carlo and multiple copy simultaneous search
techniques exact much greater computational requirements than simple rigid molecule docking methods, but
can be more effective for large, flexible ligands such as
peptides [28°,29].
Several recent approaches also include at least limited
flexibility for the receptor site as well as for the ligand. In one recent study, limited receptor-site flexibility was included via systematic variation of amino acid
side chains over a tabulated set of allowable conformations, with standard systematic conformational search
226
Theory and simulation
used to introduce ligand flexibility [30"]. In another
method, systematic conformational search is used to
account for ligand flexibility, with energy minimization of candidate docked complexes to permit receptor site flexibility [31°,32",33]. To offset the enormous
increase in computational effort required by this strategy, trial ligand-binding orientations are prescreened to
select complexes that maximize hydrogen-bonding contacts before the energy minimization calculation is initiated. This approach has yielded some impressive results
in several tests for receptor sites with significant polar
character [31°,32"]. As pointed out recently by Blaney
and Dixon [20°], however, this approach will probably
be less effective for docking predominantly non-polar ligands and receptor sites, as prescreening based on
hydrogen-bonding contacts or other polar interactions
tends to de-emphasize steric and hydrophobic complementarity too much.
An alternative strategy to include ligand flexibility involves docking of molecular fragments independently to
the receptor site, with subsequent (re)connection of the
fragments to form the full ligand. This strategy has been
used for a number of years in many different contexts. It
has the additional advantage that it can be extended to
become a de novo rational ligand design procedure; functional groups and other assorted molecular fragnmnts are
placed at logical positions in the receptor site and appropriate connector fragments are chosen to link all pieces
together in a contiguous ligand. The CAVEAT [34] and
H O O K [35] programs are typical recent examples of this
pseudo-docking/ligand design strategy.
Another serious weakness in current scoring schemes for
most ligand-docking programs is the complete neglect of
different solvation (desolvation) energies for various ligands. Within a closely related series ofligand molecules,
the intrinsic ligand-receptor interactions may be nearly
indistinguishable, although the solvation energies vary
more significantly. In such cases, the ease of desolvation
for the various ligand molecules may play the major role
in governing relative ligand-binding affinity. A variety of
methods are available to estimate solvation energies for
small molecules. In principle, it is possible to calculate
relative solvation energies for a series of ligands using
the free energy perturbation techniques discussed above.
These calculations are extremely computationally intensive, however, and in some cases may not yield completely rehable results for ligands with subtle solvation
differences [19]. In recent years, many ab initio and semiempirical quantum mechanical methods have been modified to permit solvation energy calculations by the use
of continuum solvent models, that is, the solvent is represented as a continuous, homogeneous medium rather
than as numerous explicit solvent molecules [36]. These
quantum mechanical approaches are also computationally expensive when many ligands are to be considered,
and in some cases solvation energy estimations are based
on reaction field methods, thus limiting their applicability to polar molecules (non-polar solute molecules, i.e.
molecules without permanent dipole moments, do not
induce dipoles in the solvent medium, and thus do not
give rise to a reaction field that interacts with the solute).
Much work has also focused in recent years on the
development o f empirical functions based on accessible surface area and/or molecular volume to estimate solvation energies. Encouraging results have been
obtained recently with continuum methods that use
Poisson-Boltzmann calculations to deal with electrostatic effects, and molecular surface or volume terms to
account for non-polar contributions to solvation energy
[37",38]. These methods are much more efficient computationally than methods that include explicit solvent
molecules or quantum mechanical reaction field techniques, and appear to provide quite accurate and reliable
results for many classes of small molecules. The computational efficiency and reliability of these new methods
may make them suitable for incorporation in general ligand docking scoring schemes.
In addition to these specific hmitations in docking scoring schemes and algorithms, there are other areas where
important improvements have been made over the past
year. Nearly all docking algorithms rely at least in part
on some type o f surface matching operation to position
ligands in binding sites, and to evaluate the quality
of the steric fit. Accurate representation of molecular
geometries and shapes can sometimes require a large
number of surface points, which in turn increases the
computational requirements for docking algorithms significantly. More efficient molecular surface representations based on limited numbers of surface points have
been refined [39]. Improved algorithms to identify good
surface matches have also been reported recently [40,41].
These enhancements in surface representation and surface matching can be coupled with other docking algorithm enhancements discussed above to yield better
performance and reliability.
Conclusions
Significant advances have been made in ligand-docking
methods and other rational drug-design tools over the
past year or so. Advances in computer hardware capabilities must also be considered. It is now possible to
obtain affordable desktop workstations whose processor
power rivals that of many supercomputers. As a result,
individual researchers can now perform detailed energy
calculations or ligand-docking sinmlations with complex
surface representations and full flexibihty, tasks that were
practical a few years ago only with extensive access to
large supercomputers. It is surely not a coincidence that
the number of 'successful' rational drug (or, more accurately, lead compound) design efforts reported in the
literature and at conferences seems to have increased noticeably over the past year. Although there is still nmch
room for improvement in methods and software, it appears that computer-aided rational drug design is poised
to make far more significant contributions to drug development than has been possible in the past.
Ligand-protein dockingand rational drug design Lybrand 227
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TP Lybrand, University of Washington, Molecular Bioengineering Program, BF-10, Seattle, WA 98195, USA.
E-mail: [email protected]