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Development and validation of a genetic algorithm for flexible docking - ScienceDirect

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Volume 267, Issue 3, 4 April 1997, Pages 727-748 Regular article Development and validation of a genetic algorithm for flexible docking1 Author links open overlay panel Gareth Jones 1 , Peter Willett 1, Robert C Glen 2 2, Andrew R Leach 3, Robin Taylor 4 Show more Add to Mendeley Share Cite https://doi.org/10.1006/jmbi.1996.0897 Get rights and content Abstract Prediction of small molecule binding modes to macromolecules of known three-dimensional structure is a problem of paramount importance in rational drug design (the “docking” problem). We report the development and validation of the program GOLD (Genetic Optimisation for Ligand Docking). GOLD is an automated ligand docking program that uses a genetic algorithm to explore the full range of ligand conformational flexibility with partial flexibility of the protein, and satisfies the fundamental requirement that the ligand must displace loosely bound water on binding. Numerous enhancements and modifications have been applied to the original technique resulting in a substantial increase in the reliability and the applicability of the algorithm. The advanced algorithm has been tested on a dataset of 100 complexes extracted from the Brookhaven Protein DataBank. When used to dock the ligand back into the binding site, GOLD achieved a 71% success rate in identifying the experimental binding mode. Introduction Protein binding sites exhibit highly selective recognition of small organic molecules, in that evolution has equipped them with a complex three-dimensional “lock” into which only specific “keys” will fit. This has been exploited by medicinal chemists in the design of molecules selectively to augment or retard biochemical pathways and so exhibit a clinical effect. X-ray crystallography has revealed the structure of a significant number of these binding sites. It would be advantageous in attempting the computer-aided design of therapeutic molecules to be able to predict and to explain the binding mode of novel chemical entities (the “docking” problem) when the active site geometry is known. Any solution to the docking problem requires both a powerful search technique to explore the conformation space available to the protein and the ligand and a good understanding of the process of molecular recognition to devise scoring functions that can reliably predict binding modes. Furthermore, since many putative dockings will require evaluation before elucidating the binding mode, any scoring function must be rapid in operation. There are currently many different approaches to solving the docking problem Blaney and Dixon 1993, Jones and Willett 1995. Early approaches to ligand docking consider both protein and ligand to be rigid, as typified by the DOCK program (Kuntz et al., 1982). Since the bioactive conformation of a bound ligand rarely corresponds to the isolated ligand X-ray structure (Nicklaus et al., 1995), recent techniques have dealt with the issue of conformational flexibility. Deterministic approaches include the FLOG system of Miller et al. (1994) and FLEXX of Rarey et al. (1996). The latter algorithm is very efficient and has been verified on 19 protein-ligand complexes. Alternative, stochastic sampling techniques include genetic algorithms Jones et al 1995a, Judson et al 1994, Oshiro et al 1995, simulated annealing (Goodsell & Olsen, 1990) and evolutionary programming (Gehlhaar et al., 1995). Inspection of the X-ray crystallographic structures of proteins with associated high-affinity ligands reveals that the ligands appear to conform closely to the shape of the binding cavity, maximising the hydrophobic contribution to binding, and to interact at a number of hydrogen bonding sites. The optimal binding mode may thus involve the ligand forming hydrogen bonds at key hydrogen-bonding sites, accompanied by hydrophobic surface area burial. The most significant contributions to apolar surface area burial are likely to be dispersive interactions between protein and ligand atoms together with an entropic contribution from the displacement of ordered water from the active site into the solvent. Sufficiently accurate simulation of many of these interactions may be enough to predict the binding mode of the majority of high-affinity ligands. We have reported the use of a genetic algorithm, hereinafter a GA Davis 1991, Goldberg 1989, Holland 1992 to perform protein docking (Jones et al., 1995a), where an evolutionary strategy is employed to explore the conformational variability of a flexible ligand while simultaneously sampling available binding modes into a partially flexible protein active site. The GA provides a search paradigm that enables the rapid identification of good, though not necessarily optimal, solutions to combinatorial optimisation problems. Of particular interest is the use of GAs in performing conformational analysis of both small molecules Jones et al 1995b, Brodmeier and Pretsch 1994, Clark et al 1994 and macromolecules Dandekar and Argos 1996, Sun 1993. Here, we describe a docking program called GOLD (Genetic Optimisation for Ligand Docking) that is based on the algorithm described by (Jones et al., 1995a). GOLD performs automated docking with full acyclic ligand flexibility, partial cyclic ligand flexibility and partial protein flexibility in the neighbourhood of the protein active site. In order to search the space of available binding modes efficiently, hydrogen bond motifs have been directly encoded into the GA. A simple scoring function was used to rank generated binding modes. This comprised a term for hydrogen bonding (which took account of the fundamental requirement that water must be displaced from both donor and acceptor before a bond is formed); a pairwise dispersion potential that was able to describe a significant contribution to the hydrophobic energy of binding; and a molecular mechanics term for the internal energy of the ligand. The original algorithm has now been substantially enhanced, as detailed in Materials and Methods. The resulting algorithm has been tested on a number of complex ligands and the result of docking NADPH into dihydrofolate reductase (DHFR) is reported here as an example of the power of this technique. In order to probe the strengths and weaknesses of GOLD in a more rigorous manner, 100 protein-ligand complexes were selected from the Protein Data Bank (PDB: Bernstein et al., 1977). These complexes were selected on the basis of pharmacological interest and whether or not the ligands involved were “drug like”. The result was a varied and demanding test set of complexes. We report here the results obtained by using GOLD to predict the binding modes for these test complexes and compare these predictions against the crystallographically observed binding modes. Access through your organization

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Access through your organization Section snippets Results The GA described in Materials and Methods required as input the approximate size and location of the active site, together with coordinates of the protein and a ligand conformation. As GOLD used a cavity detection procedure to further define the active site, the size and location input by the user was not critical. Although the determination of the active site is not currently automated, there are techniques available that are capable of predicting the location of the active site with Discussion Here, we have described the development of GOLD, a GA for flexible ligand docking. The effectiveness of the approach has been illustrated by the docking of NADPH to DHFR. The method has been verified by testing the program on a set of 100 complexes selected from the PDB. During this process GOLD achieved a 71% success rate in reproducing the experimentally observed binding mode. While this was a very encouraging result, an analysis of the results was performed in order to determine common Genetic algorithms A GA is a computer program that mimics the process of evolution by manipulating a collection of data structures called chromosomes. Each of these structures encodes a possible solution (i.e. a possible ligand orientation within the protein binding site) to the docking problem and may be assigned a fitness score based on the relative merit of that solution. A steady-state operator-based GA was used to explore conformation space and ligand binding modes (Davis, 1991). This GA is illustrated in Acknowledgements We thank J.C. Cole and J.P.M. Lommerse for useful discussions, the Biotechnology and Biological Sciences Research Council, the Cambridge Crystallographic Data Centre, the Department of Trade and Industry, Glaxo Wellcome Ltd. and the Medical Research Council for financial support and Tripos Inc. for the provision of software. References (87) F.C Bernstein et al. The Protein Data Banka computer-based archival file for macromolecular structures J. Mol. Biol. (1977) J.T Bolin et al. Crystal structures of Escherichia coli and Lactobacillus casei dihydrofolate reductase refined at 1.7 Å resolution J. Biol. Chem. (1982) A.T Brünger et al. 2.9 Å Resolution Structure of an anti-dinitrophenyl-spin-label monoclonal antibody fab fragment with bound hapten J. Mol. Biol. (1991) C.C.H Chen et al. Structure of a phosphonate inhibited β-lactamase. An analog of the tetrahedral transition state/intermediate of β-lactam hydrolysis J. Mol. Biol. (1993) T Dandekar et al. Identifying the tertiary fold of small proteins with different topologies from sequence and secondary structure using the genetic algorithm and extended criteria specific for strand regions J. Mol. Biol. (1996) J.S Delaney Finding and filling protein cavities using cellular logic operations J. Mol. Graph. (1992) K Diederichs et al. The refined structure of the complex between adenylate kynase from beef-heart mitochondrial matrix and its substrate AMP at 1.85 Å resolution J. Mol. Biol. (1991) J Eads et al. Escherichia coli-derived rat intestinal fatty acid binding protein with bound myristate at 1.5 Å resolution and I-FABPArg106 → Gln with bound oleate at 1.74 Å resolution J. Biol. Chem. (1993) F Fraternali et al. An efficient mean solvation force model for use in molecular dynamics simulations of proteins in aqueous solution J. Mol. Biol. (1996) D.K Gehlhaar et al. Molecular recognition of the inhibitor AG-1343 by HIV-1 proteaseconformationally flexible docking by evolutionary programming Chem. Biol. (1995) View more references Cited by (6440) Mercury 4.0: From visualization to analysis, design and prediction 2020, Journal of Applied Crystallography AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings 2021, Journal of Chemical Information and Modeling Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments 2013, Journal of Computer Aided Molecular Design Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes 2006, Journal of Medicinal Chemistry Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy 2004, Journal of Medicinal Chemistry Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening 2004, Journal of Medicinal Chemistry View all citing articles on Scopus 1 Edited by F. E. Cohen 2 Present address: R. C. Glen, Tripos Inc., 1699 South Hanley Road, St Louis, MO 63144, USA View full text Copyright © 1997 Academic Press. All rights reserved.