Improved protein–ligand docking using GOLD

The Chemscore function was implemented as a scoring function for the protein–ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, “Goldscore‐CS” and “Chemscore‐GS,” in terms of docking accuracy, prediction of binding affinities, and speed. In the “Goldscore‐CS” protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the “Chemscore‐GS” protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a “clean” set of 224 protein–ligand complexes, and for two subsets of this set, one for which the ligands are “drug‐like,” the other for which they are “fragment‐like.” For “drug‐like” and “fragment‐like” ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. “Goldscore‐CS” gives success rates of up to 81% (top‐ranked GOLD solution within 2.0 Å of the experimental binding mode) for the “clean list,” but at the cost of long search times. For most virtual screening applications, “Chemscore‐GS” seems optimal; search settings that give docking speeds of around 0.25–1.3 min/compound have success rates of about 78% for “drug‐like” compounds and 85% for “fragment‐like” compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1–2 min/compound, the Goldscore function predicts binding energies with a standard deviation of ∼10.5 kJ/mol. Proteins 2003;52:609–623. © 2003 Wiley‐Liss, Inc.

[1]  Hans-Joachim Böhm,et al.  The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure , 1994, J. Comput. Aided Mol. Des..

[2]  R. Glen,et al.  Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. , 1995, Journal of molecular biology.

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

[4]  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..

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

[6]  Irwin D. Kuntz,et al.  Automated flexible ligand docking method and its application for database search , 1997 .

[7]  D. E. Clark,et al.  Flexible docking using tabu search and an empirical estimate of binding affinity , 1998, Proteins.

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

[9]  Ajay,et al.  The SHAPES strategy: an NMR-based approach for lead generation in drug discovery. , 1999, Chemistry & biology.

[10]  P. Hajduk,et al.  NMR-based screening in drug discovery , 1999, Quarterly Reviews of Biophysics.

[11]  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.

[12]  T Lengauer,et al.  Two-stage method for protein-ligand docking. , 1999, Journal of medicinal chemistry.

[13]  C. Lipinski.  Drug-like properties and the causes of poor solubility and poor permeability. , 2000, Journal of pharmacological and toxicological methods.

[14]  D. Kostrewa,et al.  Novel inhibitors of DNA gyrase: 3D structure based biased needle screening, hit validation by biophysical methods, and 3D guided optimization. A promising alternative to random screening. , 2000, Journal of medicinal chemistry.

[15]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[16]  Tim D. J. Perkins,et al.  New Approach to Molecular Docking and Its Application to Virtual Screening of Chemical Databases , 2000, J. Chem. Inf. Comput. Sci..

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

[18]  Vicki L. Nienaber,et al.  Discovering novel ligands for macromolecules using X-ray crystallographic screening , 2000, Nature Biotechnology.

[19]  G. Klebe,et al.  Knowledge-based scoring function to predict protein-ligand interactions. , 2000, Journal of molecular biology.

[20]  F. Jørgensen,et al.  A new concept for multidimensional selection of ligand conformations (MultiSelect) and multidimensional scoring (MultiScore) of protein-ligand binding affinities. , 2001, Journal of medicinal chemistry.

[21]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.

[22]  Paul D Lyne,et al.  Structure-based virtual screening: an overview. , 2002, Drug discovery today.

[23]  H. Jhoti,et al.  Structure-based screening of low-affinity compounds. , 2002, Drug discovery today.

[24]  Marcel L. Verdonk,et al.  Sensitivity of molecular docking to induced fit effects in influenza virus neuraminidase , 2002, J. Comput. Aided Mol. Des..

[25]  Harren Jhoti,et al.  High-throughput crystallography for lead discovery in drug design , 2002, Nature Reviews Drug Discovery.

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

[27]  Stephen R. Johnson,et al.  Molecular properties that influence the oral bioavailability of drug candidates. , 2002, Journal of medicinal chemistry.

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

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

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