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Journal of Computational Biology
Deterministic Pharmacophore Detection via Multiple Flexible Alignment of Drug-Like Molecules
To cite this article:
Dina Schneidman-Duhovny, Oranit Dror, Yuval Inbar, Ruth Nussinov, Haim J. Wolfson.
Journal of Computational Biology.
September 2008,
15(7): 737-754.
doi:10.1089/cmb.2007.0130.
Dina Schneidman-Duhovny School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel. Oranit Dror School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel. Yuval Inbar School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel. Ruth Nussinov Sackler Institute of Molecular Medicine, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. Basic Research Program, SAIC-Frederick, Inc., Center for Cancer Research Nanobiology Program NCI-Frederick, Frederick, Maryland. Haim J. Wolfson School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel. We present a novel highly efficient method for the detection of a pharmacophore from a set of drug-like ligands that interact with a target receptor. A pharmacophore is a spatial arrangement of physico-chemical features in a ligand that is essential for the interaction with a specific receptor. In the absence of a known three-dimensional (3D) receptor structure, a pharmacophore can be identified from a multiple structural alignment of ligand molecules. The key advantages of the presented algorithm are: (a) its ability to multiply align flexible ligands in a deterministic manner, (b) its ability to focus on subsets of the input ligands, which may share a large common substructure, resulting in the detection of both outlier molecules and alternative binding modes, and (c) its computational efficiency, which allows to detect pharmacophores shared by a large number of molecules on a standard PC. The algorithm was extensively tested on a dataset of almost 80 ligands acting on 12 different receptors. The results, which were achieved using a set of standard default parameters, were consistent with reference pharmacophores that were derived from the bound ligand-receptor complexes. The pharmacophores detected by the algorithm are expected to be a key component in the discovery of new leads by screening large databases of drug-like molecules. A user-friendly web interface is available at http://bioinfo3d.cs.tau.ac.il/pharma. Supplementary material can be found at http://bioinfo3d.cs.tau.ac.il/pharma/reduction/. 
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