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Molecules 2010, 15(6), 4382-4400; doi:10.3390/molecules15064382

Lead Generation and Optimization Based on Protein-Ligand Complementarity

1
Centre for Computational Biology, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada
2
Advanced Medical Research Laboratories, Mitsubishi Tanabe Pharma Corporation, 1000, Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa 227-0033, Japan
3
Medicinal Chemistry Research Laboratories, Mitsubishi Tanabe Pharma Corporation, 1000, Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa 227-0033, Japan
4
Department of Biochemistry, University of Toronto, Toronto, Canada
5
Department of Molecular Genetics, University of Toronto, Toronto, Canada
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 24 May 2010 / Accepted: 7 June 2010 / Published: 17 June 2010
(This article belongs to the Special Issue Structure-Based Drug Design)
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Abstract

This work proposes a computational procedure for structure-based lead generation and optimization, which relies on the complementarity of the protein-ligand interactions. This procedure takes as input the known structure of a protein-ligand complex. Retaining the positions of the ligand heavy atoms in the protein binding site it designs structurally similar compounds considering all possible combinations of atomic species (N, C, O, CH3, NH,etc). Compounds are ranked based on a score which incorporates energetic contributions evaluated using molecular mechanics force fields. This procedure was used to design new inhibitor molecules for three serine/threonine protein kinases (p38 MAP kinase, p42 MAP kinase (ERK2), and c-Jun N-terminal kinase 3 (JNK3)). For each enzyme, the calculations produce a set of potential inhibitors whose scores are in agreement with IC50 data and Ki values. Furthermore, the native ligands for each protein target, scored within the five top-ranking compounds predicted by our method, one of the top-ranking compounds predicted to inhibit JNK3 was synthesized and his inhibitory activity confirmed against ATP hydrolysis. Our computational procedure is therefore deemed to be a useful tool for generating chemically diverse molecules active against known target proteins.
Keywords: lock-and-key problem; computational structure-based drug design; lead generation; lead optimization lock-and-key problem; computational structure-based drug design; lead generation; lead optimization
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Ogata, K.; Isomura, T.; Kawata, S.; Yamashita, H.; Kubodera, H.; Wodak, S.J. Lead Generation and Optimization Based on Protein-Ligand Complementarity. Molecules 2010, 15, 4382-4400.

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