Abstract
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, Neuro-COLT 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.
Highlights
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
Databases of compound libraries are searched, and scoring or discrimination functions are used to select the “best” candidate compounds for biological activity analysis [32]
The scoring of ligands in virtual screening is often associated with computational docking simulations that mate receptor and cognate small molecule ligand in three-dimensional space
Summary
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. 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. High-throughput experimental screening assays [30] have been complemented recently by computational (“virtual screening”) approaches to identify and filter potential ligands when the characteristics of the target receptor structure of interest are known [3, 35]. To provide broad generalization in “chemical diversity” space, computing this score requires the accurate prediction of binding affinities of many structurally distinct ligands [31]. In order of computational complexity, these are: 1) knowledge-based scoring functions, 2) partitioning the binding energy into biophysical energy terms, and 3)
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