Deterministic pharmacophore detection via multiple flexible alignment of drug-like molecules

  • Authors:
  • Yuval Inbar;Dina Schneidman-Duhovny;Oranit Dror;Ruth Nussinov;Haim J. Wolfson

  • Affiliations:
  • School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel;School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel;School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel;Sackler Inst. of Molecular Medicine, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel and SAIC-Frederick, Inc., Center for Cancer Research Nanobiology Program, NCI-Frederick, Fre ...;School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel

  • Venue:
  • RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
  • Year:
  • 2007

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Abstract

We present a novel highly efficient method for the detection of a pharmacophore from a set of ligands/drugs that interact with a target receptor. A pharmacophore is a spatial arrangement of physicochemical features in a ligand that is responsible for the interaction with a specific receptor. In the absence of a known 3D receptor structure, a pharmacophore can be identified from a multiple structural alignment of the 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 standard default parameter set, 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 drug-like molecule databases.