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Molecular Modeling: A Magical Tool for Drug Design and Discovery
*
2
Mehul M Patel , Laxman J.Patel
1
2
Ramanbhai Patel College of Pharmacy, CHARUSAT, S.K.Patel College of Pharmacy Education & Research
*[email protected], +91-9879247492
The aim of this review is to give an outline of studies in the field of medicinal chemistry in which Molecular Modeling has helped in the discovery process of new drugs .
STRATEGIES
INTRODUCTION
OBJECTIVES
The development of new drugs with potential therapeutic applications is one of the most
complex and difficult process in the pharmaceutical industry. Millions of dollars and manhours are devoted to the discovery of new therapeutical agents. There is an ever growing
effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical
arena, computer-aided or in silico design is being utilized to expedite and facilitate hit
identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3-D spatial arrangement of
chemical features essential for biological activity), structure-based drug design (drugtarget docking), and quantitative structure-activity and quantitative structure-property relationships.
Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by
two or more constituent molecules with known structures. An important type of molecular
docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design1
A binding interaction between a small molecule ligand and an enzyme protein may result
in activation or inhibition of the enzyme. Docking is most commonly used in the field of
drug design as most drugs are small organic molecules, and docking may be applied to:
1. Hit identification: Docking combined with a scoring function can be used to quickly
screen large databases of potential drugs in silico to identify molecules that are likely to
bind to protein target of interest.
2. Lead optimization – docking can be used to predict in where and in which relative orientation a ligand binds to a protein. This information may in turn be used to design more
potent and selective analogs.
Currently, two major modeling strategies are used for the conception of new drugs. They
are:
i) Direct drug design: In the direct approach, the three-dimensional features of the
known receptor site are determined from X-ray crystallography to design a lead molecule.
In direct design, the receptor site geometry is known; the problem is to find a molecule
that satisfies some geometric constraints and is also a good chemical match. After finding
good candidates according these criteria, a docking step with energy minimization can be
used to predict binding strength.
ii) Indirect drug design: The indirect dr ug design approach involves compar ative
analysis of structural features of known active and inactive molecules that are complementary with a hypothetical receptor site. If the site geometry is not known, as is often the
case, the designer must base the design on other ligand molecules that bind well to the
Table-2: Types of scoring functions and softwares
Force field based
Empirical
Simulation
D-Score
G-Score
GOLD
AutoDock
DOCK
LUDI
F-Score
ChemScore
SCORE
Fresno
PMF
DrugScore
SMoG
EXAMPLE
Figure 3. Comparison of Traditional & Computer Aided Drug Development Process
METHODOLOGIES
Figure 1. Drug Development Process
Estimates of time and cost of currently bringing a new drug to market vary, but 7–12 years
and $ 1.2 billion are often cited . Furthermore, five out of 40,000 compounds tested in animals reach human testing and only one of five compounds reaching clinical studies is approved. This represents an enormous investment in terms of time, money and human and
other resources. It includes chemical synthesis, purchase, curation, and biological screening of hundreds of thousands of compounds to identify hits followed by their optimization
to generate leads which requiring further synthesis. In addition, predictability of animal
studies in terms of both efficacy and toxicity is frequently suboptimal. Therefore, new approaches are needed to facilitate, expedite and streamline drug discovery and development, save time, money and resources, and as per pharma mantra “fail fast, fail early”. It is
estimated that computer modeling and simulations account for ~ 10% of pharmaceutical
R&D expenditure and that they will rise to 20% by 20162-4.
Structure (target)-based drug design represents docking i.e. ligand binding to its receptor,
target protein. Docking is used to identify and optimize drug candidates by examining
and modeling molecular interactions between ligands and target macromolecules.
Structure (target)-based design requires structural information for the receptor which can
be obtained from X-ray crystallography, NMR or homology modeling. The latter being
another computational technique used to predict unknown protein structure from a sequence similarity to known protein structure (s). In the process of docking, multiple ligand conformations and orientations are generated and the most appropriate ones are selected. Scoring functions are applied to evaluate tightness of interaction i.e. estimate
binding free energy. General observation is that consensus (combination of different scoring algorithms) scoring yields better results than individual scoring . Validations may be
performed with known active and inactive ligands, comparisons to crystallographic data
and prediction of rank-ordering and binding affinities5.
The success of a docking program depends on two components: the search algorithm and
the scoring function
Table 1. Softwares used for flexible ligand docking methods6
Searching
Algorithm
Description
Examples
It uses Stochastic method for generation of conformations Ligand Fit
Monte Carlo
Table 1. Softwares
and uses
used
Metropolis
for flexible
criterion
ligand
for docking
selection of
methods
conformations.
(MC)
Simulated
Annealing
(SA)
Genetic
Algorithm
(GA)
Tabu search
Incremental
construction
Matching
methods
Simulation
methods
high temperatures are used to induce Random thermal motions to discover the local search space. Further the system
is taking to a minimum energy conformation by decreasing
temperature. Normally SA will be used in combination with
MC.
This method is based on Darwin principles of evolution.
‘chromosome’ encoding model parameters (like torsion angles) is varied stochastically and through crossover, mutation, migration. The fittest one stays alive in the population.
MC-DOCK,
ICM-DOCK,
AutoDock
It keeps a record of previous conformations (tabu). Generated conformation in each step will be retained if it is not tabu
or if it scores better than that in tabu.
Where ligand is broken down into rigid fragments around rotatable bonds and generated fragments will be docked in all
possible ways. Finally it assembles the pieces to regenerate
ligand
PRO_LEADS
GOLD,
Glide,
DARWIN,
AutoDock
Flex-X,
DOCK,
HOOK,
LUDI,
Hammerhead
Based on clique detection technique from graph theory. Lig- FLOG,
and atoms matched to the complimentary atoms in the recep- DOCK
tor
It uses Molecular dynamics to generate conformations
DOCK
Docking, combined with other computational techniques and experimental data, also
could be involved in analyzing drug metabolism to obtain some useful information from
the cytochrome P450 system. Here example of successful applications of docking are presented.
DNA gyrase is a bacterial enzyme that introduces negative supercoils into bacterial DNA
and unwinds of DNA, thus being studied as antibacterial target. HTS failed to find novel
inhibitors of DNA gyrase. Boehm et. al. used de novo design for this enzyme and successfully obtained several new inhibitors. Firstly, 3D complex structures of DNA gyrase with
known inhibitors, ciprofloxacin and novobiocin, were carefully analyzed to get a common
binding pattern, in which both inhibitors donate one hydrogen bond to Asp73 and accept
one hydrogen bond from a conserved water molecule. In addition, some lipophilic fragments should be included in the molecule to have lipophilic interaction with the receptor.
Based on this information, LUDI and CATALYST were employed to search the Available
Chemicals Directory (ACD) and a part of the Roche compound inventory (RIC), respectively, and collected about 600 compounds. Close analogues of these compounds were also considered, thus in total 3000 compounds were further tested using biased screening.
Consequently 150 hits were selected and clustered into 14 classes of which 7 classes were
proven to be the true and novel inhibitors. Subsequent hit optimization relied strongly on
the knowledge of 3D structures of the binding site and eventually generated a series of
highly potent DNA gyrase inhibitors7.
CONCLUSION
Molecular modeling, an inexpensive, safe and easy to use tool, helps in investigating, interpreting, explaining and identification of molecular properties using three-dimensional
structures. Molecular docking tries to predict the structure of the intermolecular complex
formed between two or more constituent molecules. The techniques are used in the fields
of computational chemistry, computational biology and materials science for studying molecular systems ranging from small chemical systems to large biological molecules
REFERENCES
1. Erickson, J., Neidhart, DJ. VanDrie, J., Kempf, DJ., Wang, XC., Norbec, DW., Plattner JJ., Rittenhouse JW., Turon
M., Wideburg N., Science, 1990, 249, 527-533.
2. Shankar R, Frapaise X, Brown B. lean, drug development in R&D. Drug Discovery Development. 2006:57–60.
3. Van de Waterbeemd H, Gifford E. ADMET in silico modeling: towards prediction paradise? Nature Review Drug
Discovery, 2003;2(3):192–204.
4. Oprea TI, Matter H. Integrating virtual screening in lead discovery. Curr Opin Chem Biol. 2004;8(4):349–358
5. I.M. Kapetanovic1 Computer Aided drug discovery and developement (CADDD): in silico-chemico-biological approach, Chem Biol Interact. 2008 January 30; 171(2): 165–176.
6. Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Review Drug Discovery, 2004, 3, 935-949
7. Xuan-Yu Meng,Hong-Xing Zhang* Mihaly Mezei, and Meng Cui,* Molecular Docking: A powerful approach for
structure-based drug discovery , Current Computer Aided Drug Discovery, 2011 June 1; 7(2): 146–157.
ACKNOWLEGEMENT
Authors are thankful to Ramanbhai Patel College of Pharmacy & Charotar University of Science & Technology
(CHARUSAT) to provide support and facility.
Figure 2. Steps Involved in Drug Discovery
This Poster has been presented at RAPCOPINC-2014 organized by Ramanbhai patel College of Pharmacy, CHARUSAT Campus, Changa.