A scoring function for docking ligands to low‐resolution protein structures

Eckart Bindewald; Jeffrey Skolnick

doi:10.1002/jcc.20175

journal article Jan 13, 2005

A scoring function for docking ligands to low‐resolution protein structures

Eckart Bindewald Jeffrey Skolnick

Journal of Computational Chemistry Vol. 26 No. 4 pp. 374-383 · Wiley

View at Publisher Save 10.1002/jcc.20175

Abstract

AbstractWe present a docking method that uses a scoring function for protein–ligand docking that is designed to maximize the docking success rate for low‐resolution protein structures. We find that the resulting scoring function parameters are very different depending on whether they were optimized for high‐ or low‐resolution protein structures. We show that this docking method can be successfully applied to predict the ligand‐binding site of low‐resolution structures. For a set of 25 protein–ligand complexes, in 76% of the cases, more than 50% of ligand‐contacting residues are correctly predicted (using receptor crystal structures where the binding site is unspecified). Using decoys of the receptor structures having a 4 Å RMSD from the native structure, for the same set of complexes, in 72% of the cases, we obtain at least one correctly predicted ligand‐contacting residue. Furthermore, using an 81‐protein–ligand set described by Jain, in 76 (93.8%) cases, the algorithm correctly predicts more than 50% of the ligand‐contacting residues when native protein structures are used. Using 3 Å RMSD from native decoys, in all but two cases (97.5%), the algorithm predicts at least one ligand‐binding residue correctly. Finally, compared to the previously published Dolores method, for 298 protein–ligand pairs, the number of cases in which at least half of the specific contacts are correctly predicted is more than four times greater. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 374–383, 2005

Topics

No keywords indexed for this article. Browse by subject →

References

25

[1]

Perola E. J Med Chem (2000) 10.1021/jm990408a

[2]

10.1016/s0969-2126(02)00799-2

[3]

Rational discovery of novel nuclear hormone receptor antagonists

Matthieu Schapira, Bruce M. Raaka, Herbert H. Samuels et al.

Proceedings of the National Academy of Sciences 10.1073/pnas.97.3.1008

[4]

Principles of docking: An overview of search algorithms and a guide to scoring functions

Inbal Halperin, Buyong Ma, Haim Wolfson et al.

Proteins: Structure, Function, and Bioinformatics 10.1002/prot.10115

[5]

Lessons in Molecular Recognition: The Effects of Ligand and Protein Flexibility on Molecular Docking Accuracy

Jon A. Erickson, Mehran Jalaie, Daniel H. Robertson et al.

Journal of Medicinal Chemistry 10.1021/jm030209y

[6]

De Novo Prediction of Three-dimensional Structures for Major Protein Families

Richard Bonneau, Charlie E.M Strauss, Carol A Rohl et al.

Journal of Molecular Biology 10.1016/s0022-2836(02)00698-8

[7]

Automated structure prediction of weakly homologous proteins on a genomic scale

Yang Zhang, Jeffrey Skolnick

Proceedings of the National Academy of Sciences 10.1073/pnas.0305695101

[8]

10.1021/jm0300173

[9]

10.1006/jmbi.2001.4551

[10]

Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen

Gareth Jones, Peter Willett, Robert C Glen et al.

Journal of Molecular Biology 1997 10.1006/jmbi.1996.0897

[11]

Docking of protein models

Andrei Tovchigrechko, Christopher A. Wells, Ilya A. Vakser

Protein Science 10.1110/ps.4730102

[12]

Docking of small ligands to low‐resolution and theoretically predicted receptor structures

Marek Wojciechowski, Jeffrey Skolnick

Journal of Computational Chemistry 10.1002/jcc.1165

[13]

10.1016/s1093-3263(02)00164-x

[14]

10.1002/jcc.540100804

[15]

10.1021/jm000467k

[16]

10.1002/(sici)1096-987x(19971115)18:14<1812::aid-jcc10>3.0.co;2-h

[17]

DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases

Todd J.A. Ewing, Shingo Makino, A. Geoffrey Skillman et al.

Journal of Computer-Aided Molecular Design 10.1023/a:1011115820450

[18]

10.1007/bf00124464

[19]

10.1002/jcc.10306

[20]

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs

S. Altschul

Nucleic Acids Research 10.1093/nar/25.17.3389

[21]

10.1021/jm020406h

[22]

Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation 1 1Edited by J. Thornton

J. Michael Word, Simon C. Lovell, Jane S. Richardson et al.

Journal of Molecular Biology 10.1006/jmbi.1998.2401

[23]

A new test set for validating predictions of protein–ligand interaction

J. Willem M. Nissink, Chris Murray, Mike Hartshorn et al.

Proteins: Structure, Function, and Bioinformatics 10.1002/prot.10232

[24]

Holm L. Nucleic Acid Res (1994)

[25]

A Fast Flexible Docking Method using an Incremental Construction Algorithm

Matthias Rarey, Bernd Kramer, Thomas Lengauer et al.

Journal of Molecular Biology 10.1006/jmbi.1996.0477

Cited By

24

eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands

Michal Brylinski, Wei P. Feinstein · 2013

Journal of Computer-Aided Molecular...

FINDSITE: a combined evolution/structure-based approach to protein function prediction

J Skolnick, M. Brylinski · 2009

Briefings in Bioinformatics

Metrics

24

Citations

25

References

Details

Published: Jan 13, 2005
Vol/Issue: 26(4)
Pages: 374-383
License: View

Authors

Georgia Institute of Technology

Cite This Article

Eckart Bindewald, Jeffrey Skolnick (2005). A scoring function for docking ligands to low‐resolution protein structures. Journal of Computational Chemistry, 26(4), 374-383. https://doi.org/10.1002/jcc.20175

A scoring function for docking ligands to low‐resolution protein structures

You May Also Like