A Machine Learning Approach to Predicting Protein-ligand Binding Affinity with Applications to Molecular Docking

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2010 Mar 19

PMID 20236947

Citations 255

Authors

Pedro J Ballester

John B O Mitchell

Affiliations

Soon will be listed here.

Abstract

Motivation: Accurately predicting the binding affinities of large sets of diverse protein-ligand complexes is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for analysing the outputs of molecular docking, which in turn is an important technique for drug discovery, chemical biology and structural biology. Each scoring function assumes a predetermined theory-inspired functional form for the relationship between the variables that characterize the complex, which also include parameters fitted to experimental or simulation data and its predicted binding affinity. The inherent problem of this rigid approach is that it leads to poor predictivity for those complexes that do not conform to the modelling assumptions. Moreover, resampling strategies, such as cross-validation or bootstrapping, are still not systematically used to guard against the overfitting of calibration data in parameter estimation for scoring functions.

Results: We propose a novel scoring function (RF-Score) that circumvents the need for problematic modelling assumptions via non-parametric machine learning. In particular, Random Forest was used to implicitly capture binding effects that are hard to model explicitly. RF-Score is compared with the state of the art on the demanding PDBbind benchmark. Results show that RF-Score is a very competitive scoring function. Importantly, RF-Score's performance was shown to improve dramatically with training set size and hence the future availability of more high-quality structural and interaction data is expected to lead to improved versions of RF-Score.

Contact: pedro.ballester@ebi.ac.uk; jbom@st-andrews.ac.uk

Supplementary Information: Supplementary data are available at Bioinformatics online.

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References

Mooij W, Verdonk M . General and targeted statistical potentials for protein-ligand interactions. Proteins. 2005; 61(2):272-87. DOI: 10.1002/prot.20588. View

Friesner R, Murphy R, Repasky M, Frye L, Greenwood J, Halgren T . Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem. 2006; 49(21):6177-96. DOI: 10.1021/jm051256o. View

Friesner R, Banks J, Murphy R, Halgren T, Klicic J, Mainz D . Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem. 2004; 47(7):1739-49. DOI: 10.1021/jm0306430. View

Ferrara P, Gohlke H, Price D, Klebe G, Brooks 3rd C . Assessing scoring functions for protein-ligand interactions. J Med Chem. 2004; 47(12):3032-47. DOI: 10.1021/jm030489h. View

Rucker C, Rucker G, Meringer M . y-Randomization and its variants in QSPR/QSAR. J Chem Inf Model. 2007; 47(6):2345-57. DOI: 10.1021/ci700157b. View

Berman H, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H . The Protein Data Bank. Nucleic Acids Res. 1999; 28(1):235-42. PMC: 102472. DOI: 10.1093/nar/28.1.235. View

Eldridge M, MURRAY C, Auton T, Paolini G, Mee R . Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des. 1997; 11(5):425-45. DOI: 10.1023/a:1007996124545. View

Pan Y, Huang N, Cho S, MacKerell Jr A . Consideration of molecular weight during compound selection in virtual target-based database screening. J Chem Inf Comput Sci. 2003; 43(1):267-72. DOI: 10.1021/ci020055f. View

Cheng T, Li X, Li Y, Liu Z, Wang R . Comparative assessment of scoring functions on a diverse test set. J Chem Inf Model. 2009; 49(4):1079-93. DOI: 10.1021/ci9000053. View

10.

Favia A, Nobeli I, Glaser F, Thornton J . Molecular docking for substrate identification: the short-chain dehydrogenases/reductases. J Mol Biol. 2007; 375(3):855-74. DOI: 10.1016/j.jmb.2007.10.065. View

11.

Jones G, Willett P, Glen R . Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol. 1995; 245(1):43-53. DOI: 10.1016/s0022-2836(95)80037-9. View

12.

Wang R, Lai L, Wang S . Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des. 2002; 16(1):11-26. DOI: 10.1023/a:1016357811882. View

13.

Muegge I . PMF scoring revisited. J Med Chem. 2006; 49(20):5895-902. DOI: 10.1021/jm050038s. View

14.

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(6):2114-25. DOI: 10.1021/ci049733j. View

15.

Wang R, Fang X, Lu Y, Yang C, Wang S . The PDBbind database: methodologies and updates. J Med Chem. 2005; 48(12):4111-9. DOI: 10.1021/jm048957q. View

16.

Moitessier N, Englebienne P, Lee D, Lawandi J, Corbeil C . Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br J Pharmacol. 2007; 153 Suppl 1:S7-26. PMC: 2268060. DOI: 10.1038/sj.bjp.0707515. View

17.

Bohm H . The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J Comput Aided Mol Des. 1994; 8(3):243-56. DOI: 10.1007/BF00126743. View

18.

Jones G, Willett P, Glen R, Leach A, Taylor R . Development and validation of a genetic algorithm for flexible docking. J Mol Biol. 1997; 267(3):727-48. DOI: 10.1006/jmbi.1996.0897. View

19.

Seifert M . Targeted scoring functions for virtual screening. Drug Discov Today. 2009; 14(11-12):562-9. DOI: 10.1016/j.drudis.2009.03.013. View

20.

Muegge I, MARTIN Y . A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem. 1999; 42(5):791-804. DOI: 10.1021/jm980536j. View