A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance

A thorough evaluation of some of the most advanced docking and scoring methods currently available is described, and guidelines for the choice of an appropriate protocol for docking and virtual screening are defined. The generation of a large and highly curated test set of pharmaceutically relevant protein–ligand complexes with known binding affinities is described, and three highly regarded docking programs (Glide, GOLD, and ICM) are evaluated on the same set with respect to their ability to reproduce crystallographic binding orientations. Glide correctly identified the crystallographic pose within 2.0 Å in 61% of the cases, versus 48% for GOLD and 45% for ICM. In general Glide appears to perform most consistently with respect to diversity of binding sites and ligand flexibility, while the performance of ICM and GOLD is more binding site–dependent and it is significantly poorer when binding is predominantly driven by hydrophobic interactions. The results also show that energy minimization and reranking of the top N poses can be an effective means to overcome some of the limitations of a given docking function. The same docking programs are evaluated in conjunction with three different scoring functions for their ability to discriminate actives from inactives in virtual screening. The evaluation, performed on three different systems (HIV‐1 protease, IMPDH, and p38 MAP kinase), confirms that the relative performance of different docking and scoring methods is to some extent binding site–dependent. GlideScore appears to be an effective scoring function for database screening, with consistent performance across several types of binding sites, while ChemScore appears to be most useful in sterically demanding sites since it is more forgiving of repulsive interactions. Energy minimization of docked poses can significantly improve the enrichments in systems with sterically demanding binding sites. Overall Glide appears to be a safe general choice for docking, while the choice of the best scoring tool remains to a larger extent system‐dependent and should be evaluated on a case‐by‐case basis. Proteins 2004. © 2004 Wiley‐Liss, Inc.

[1]  C L Brooks,et al.  Ligand-protein database: linking protein-ligand complex structures to binding data. , 2001, Journal of medicinal chemistry.

[2]  Matthias Rarey,et al.  Small Molecule Docking and Scoring , 2001 .

[3]  Thomas A. Halgren,et al.  Merck molecular force field. II. MMFF94 van der Waals and electrostatic parameters for intermolecular. interactions , 1996, J. Comput. Chem..

[4]  Gennady Verkhivker,et al.  Deciphering common failures in molecular docking of ligand-protein complexes , 2000, J. Comput. Aided Mol. Des..

[5]  A. Ortiz,et al.  Evaluation of docking functions for protein-ligand docking. , 2001, Journal of medicinal chemistry.

[6]  D. Rognan,et al.  Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. , 2000, Journal of medicinal chemistry.

[7]  Thomas A. Halgren,et al.  Merck molecular force field. III. Molecular geometries and vibrational frequencies for MMFF94 , 1996, J. Comput. Chem..

[8]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

[9]  Jonathan W. Essex,et al.  A review of protein-small molecule docking methods , 2002, J. Comput. Aided Mol. Des..

[10]  Robin Taylor,et al.  A new test set for validating predictions of protein–ligand interaction , 2002, Proteins.

[11]  Thomas Lengauer,et al.  Evaluation of the FLEXX incremental construction algorithm for protein–ligand docking , 1999, Proteins.

[12]  Walters Wp,et al.  Guiding molecules towards drug-likeness. , 2002 .

[13]  Didier Rognan,et al.  ConsDock: A new program for the consensus analysis of protein–ligand interactions , 2002, Proteins.

[14]  W. L. Jorgensen,et al.  Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids , 1996 .

[15]  Todd J. A. Ewing,et al.  DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases , 2001, J. Comput. Aided Mol. Des..

[16]  Neera Borkakoti,et al.  Discovery of novel low molecular weight inhibitors of IMPDH via virtual needle screening. , 2003, Bioorganic & medicinal chemistry letters.

[17]  M Stahl,et al.  Development of filter functions for protein-ligand docking. , 1998, Journal of molecular graphics & modelling.

[18]  Richard D. Taylor,et al.  Improved protein–ligand docking using GOLD , 2003, Proteins.

[19]  Ruth Nussinov,et al.  Principles of docking: An overview of search algorithms and a guide to scoring functions , 2002, Proteins.

[20]  H. Scheraga,et al.  Energy parameters in polypeptides. 10. Improved geometrical parameters and nonbonded interactions for use in the ECEPP/3 algorithm, with application to proline-containing peptides , 1994 .

[21]  Ruben Abagyan,et al.  Derivation of sensitive discrimination potential for virtual ligand screening , 1999, RECOMB.

[22]  Thomas A. Halgren.  Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 , 1996, J. Comput. Chem..

[23]  M. Murcko,et al.  Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. , 1999, Journal of medicinal chemistry.

[24]  G. V. Paolini,et al.  Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes , 1997, J. Comput. Aided Mol. Des..

[25]  Martin Stahl,et al.  Binding site characteristics in structure-based virtual screening: evaluation of current docking tools , 2003, Journal of molecular modeling.

[26]  Bohdan Waszkowycz,et al.  Structure-based approaches to drug design and virtual screening. , 2002, Current opinion in drug discovery & development.

[27]  J. Irwin,et al.  Lead discovery using molecular docking. , 2002, Current opinion in chemical biology.

[28]  M Rarey,et al.  Detailed analysis of scoring functions for virtual screening. , 2001, Journal of medicinal chemistry.

[29]  R Abagyan,et al.  Flexible protein–ligand docking by global energy optimization in internal coordinates , 1997, Proteins.

[30]  Gisbert Schneider,et al.  Virtual screening and fast automated docking methods. , 2002, Drug discovery today.

[31]  M. Murcko,et al.  Guiding molecules towards drug-likeness. , 2002, Current opinion in drug discovery & development.

[32]  T. Halgren.  Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 , 1996, J. Comput. Chem..

[33]  Christopher W. Murray,et al.  Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model , 1998, J. Comput. Aided Mol. Des..

[34]  R. Clark,et al.  Consensus scoring for ligand/protein interactions. , 2002, Journal of molecular graphics & modelling.

[35]  P Willett,et al.  Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.

[36]  W. Patrick Walters,et al.  Filtering databases and chemical libraries , 2004, Molecular Diversity.

[37]  Jeremy L Jenkins,et al.  Virtual screening to enrich hit lists from high‐throughput screening: A case study on small‐molecule inhibitors of angiogenin , 2002, Proteins.

[38]  D. Fabbro,et al.  Discovery of a potent and selective protein kinase CK2 inhibitor by high-throughput docking. , 2003, Journal of medicinal chemistry.

[39]  R Abagyan,et al.  High-throughput docking for lead generation. , 2001, Current opinion in chemical biology.

[40]  J. Gasteiger,et al.  Automatic generation of 3D-atomic coordinates for organic molecules , 1990 .