Literature DB >> 11858637

Consensus scoring for ligand/protein interactions.

Robert D Clark1, Alexander Strizhev, Joseph M Leonard, James F Blake, James B Matthew.   

Abstract

Several different functions have been put forward for evaluating the energetics of ligand binding to proteins. Those employed in the DOCK, GOLD and FlexX docking programs have been especially widely used, particularly in connection with virtual high-throughput screening (vHTS) projects. Until recently, such evaluation functions were usually considered only in conjunction with the docking programs that relied on them. In such studies, the evaluation function in question actually fills two distinct roles: it serves as the objective function being optimized (fitness function), but is also the scoring function used to compare the candidate docking configurations generated by the program. We have used descriptions available in the open literature to create free-standing scoring functions based on those used in DOCK and GOLD, and have implemented the more recently formulated PMF [J. Med. Chem. 42 (1999) 791] scoring function as well. The performance of these functions was examined individually for each of several data sets for which both crystal structures and affinities are available, as was the performance of the FlexX scoring function. Various ways of combining individual scores into a consensus score (CScore) were also considered. The individual and consensus scores were also used to try to pick out configurations most similar to those found in crystal structures from among a set of candidate configurations produced by FlexX docking runs. We find that the reliability and interpretability of results can be improved by combining results from all four functions into a CScore.

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Year:  2002        PMID: 11858637     DOI: 10.1016/s1093-3263(01)00125-5

Source DB:  PubMed          Journal:  J Mol Graph Model        ISSN: 1093-3263            Impact factor:   2.518


  69 in total

1.  Inhibition and substrate recognition--a computational approach applied to HIV protease.

Authors:  H M Vinkers; M R de Jonge; E D Daeyaert; J Heeres; L M H Koymans; J H van Lenthe; P J Lewi; H Timmerman; P A J Janssen
Journal:  J Comput Aided Mol Des       Date:  2003-09       Impact factor: 3.686

2.  Binding of alpha-hydroxy-beta-amino acid inhibitors to methionine aminopeptidase. The performance of two types of scoring functions.

Authors:  Anne Techau Jørgensen; Morten Dahl Sørensen; Fredrik Björkling; Tommy Liljefors
Journal:  J Comput Aided Mol Des       Date:  2003 May-Jun       Impact factor: 3.686

3.  CoMFA and docking study of novel estrogen receptor subtype selective ligands.

Authors:  Peter Wolohan; David E Reichert
Journal:  J Comput Aided Mol Des       Date:  2003 May-Jun       Impact factor: 3.686

4.  Rational design and characterization of a Rac GTPase-specific small molecule inhibitor.

Authors:  Yuan Gao; J Bradley Dickerson; Fukun Guo; Jie Zheng; Yi Zheng
Journal:  Proc Natl Acad Sci U S A       Date:  2004-05-05       Impact factor: 11.205

5.  Identifying the binding mode of a molecular scaffold.

Authors:  Doron Chema; Doron Eren; Avner Yayon; Amiram Goldblum; Andrea Zaliani
Journal:  J Comput Aided Mol Des       Date:  2004-01       Impact factor: 3.686

6.  Are predefined decoy sets of ligand poses able to quantify scoring function accuracy?

Authors:  Oliver Korb; Tim Ten Brink; Fredrick Robin Devadoss Victor Paul Raj; Matthias Keil; Thomas E Exner
Journal:  J Comput Aided Mol Des       Date:  2012-01-10       Impact factor: 3.686

7.  Multiple ligand-binding modes in bacterial R67 dihydrofolate reductase.

Authors:  Hernán Alonso; Malcolm B Gillies; Peter L Cummins; Andrey A Bliznyuk; Jill E Gready
Journal:  J Comput Aided Mol Des       Date:  2005-03       Impact factor: 3.686

8.  Protein protein interaction inhibition (2P2I) combining high throughput and virtual screening: Application to the HIV-1 Nef protein.

Authors:  Stéphane Betzi; Audrey Restouin; Sandrine Opi; Stefan T Arold; Isabelle Parrot; Françoise Guerlesquin; Xavier Morelli; Yves Collette
Journal:  Proc Natl Acad Sci U S A       Date:  2007-11-27       Impact factor: 11.205

9.  Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening.

Authors:  Jui-Hua Hsieh; Xiang S Wang; Denise Teotico; Alexander Golbraikh; Alexander Tropsha
Journal:  J Comput Aided Mol Des       Date:  2008-03-13       Impact factor: 3.686

Review 10.  Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection--what can we learn from earlier mistakes?

Authors:  Johannes Kirchmair; Patrick Markt; Simona Distinto; Gerhard Wolber; Thierry Langer
Journal:  J Comput Aided Mol Des       Date:  2008-01-15       Impact factor: 3.686

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