HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) All Versions of this Article: M200054-MCP200v1 1/11/904    most recent Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Glossary Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Bock, J. R. Articles by Gough, D. A. Search for Related Content PubMed PubMed Citation Articles by Bock, J. R. Articles by Gough, D. A. Social Bookmarking                      What's this?

Molecular & Cellular Proteomics 1:904-910, 2002.
© 2002 by The American Society for Biochemistry and Molecular Biology, Inc.

---

Research

A New Method to Estimate Ligand-Receptor Energetics^*

Joel R. Bock and David A. Gough

From the Department of Bioengineering, University of California San Diego, La Jolla, California 92093-0412

In the discovery of new drugs, lead identification and optimization have assumed critical importance given the number of drug targets generated from genetic, genomics, and proteomic technologies. High-throughput experimental screening assays have been complemented recently by "virtual screening" approaches to identify and filter potential ligands when the characteristics of a target receptor structure of interest are known. Virtual screening mandates a reliable procedure for automatic ranking of structurally distinct ligands in compound library databases. Computing a rank score requires the accurate prediction of binding affinities between these ligands and the target. Many current scoring strategies require information about the target three-dimensional structure. In this study, a new method to estimate the free binding energy between a ligand and receptor is proposed. We extend a central idea previously reported (Bock, J. R., and Gough, D. A. (2001) Predicting protein-protein interactions from primary structure. Bioinformatics 17, 455–460; Bock, J. R., and Gough, D. A. (2002) Whole-proteome interaction mining. Bioinformatics, in press) that uses simple descriptors to represent biomolecules as input examples to train a support vector machine (Smola, A. J., and Schölkopf, B. (1998) A Tutorial on Support Vector Regression, NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK) and the application of the trained system to previously unseen pairs, estimating their propensity for interaction. Here we seek to learn the function that maps features of a receptor-ligand pair onto their equilibrium free binding energy. These features do not comprise any direct information about the three-dimensional structures of ligand or target. In cross-validation experiments, it is demonstrated that objective measurements of prediction error rate and rank-ordering statistics are competitive with those of several other investigations, most of which depend on three-dimensional structural data. The size of the sample (n = 2,671) indicates that this approach is robust and may have widespread applicability beyond restricted families of receptor types. It is concluded that newly sequenced proteins, or those for which three-dimensional crystal structures are not easily obtained, can be rapidly analyzed for their binding potential against a library of ligands using this methodology.

CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?

This article has been cited by other articles:

				J. C. Tong, G. L. Zhang, T. W. Tan, J. T. August, V. Brusic, and S. Ranganathan Prediction of HLA-DQ3.2{beta} Ligands: evidence of multiple registers in class II binding peptides Bioinformatics, May 15, 2006; 22(10): 1232 - 1238. [Abstract] [Full Text] [PDF]