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A novel approach to local similarity of protein binding sites substantially improves computational drug design results
Author(s) -
Ramensky Vasily,
Sobol Alexandr,
Zaitseva Natalia,
Rubinov Anatoly,
Zosimov Victor
Publication year - 2007
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.21487
Subject(s) - similarity (geometry) , computational biology , drug , computer science , artificial intelligence , pharmacology , medicine , biology , image (mathematics)
We present a novel notion of binding site local similarity based on the analysis of complete protein environments of ligand fragments. Comparison of a query protein binding site (target) against the 3D structure of another protein (analog) in complex with a ligand enables ligand fragments from the analog complex to be transferred to positions in the target site, so that the complete protein environments of the fragment and its image are similar. The revealed environments are similarity regions and the fragments transferred to the target site are considered as binding patterns. The set of such binding patterns derived from a database of analog complexes forms a cloud‐like structure (fragment cloud), which is a powerful tool for computational drug design. It has been shown on independent test sets that the combined use of a traditional energy‐based score together with the cloud‐based score responsible for the quality of embedding of a ligand into the fragment cloud improves the self‐docking and screening results dramatically. The usage of a fragment cloud as a source of positioned molecular fragments fitting the binding protein environment has been validated by reproduction of experimental ligand optimization results. Proteins 2007. © 2007 Wiley‐Liss, Inc.