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.
Highlights
When a high-resolution structure of a ta rget protein is known, computational structure-based drug design is an efficient and effective methodology for the identification and fur ther optimization of hit compounds in order to generate lead co mpounds
Kinase (Erk2)/N-benzyl-4-[4-(3-chlorophenyl)-1H-pyrazol-3-yl]-1H-pyrole-2-carboxamide (33A) complex, and c -Jun N-terminal ki nase 3 (JNK3)/N-(3,4-dichlorophenyl)-4-hydroxy-1-methyl-2,2-dioxo1,2-dihydro-2lamda~6~-thieno[3,2-c][1,2]thiazine-3-carboxamide c omplex obtained from the Protein Data Bank (PDB) [31]
The chosen compound was Z1208-8, ranked 8th in Table 5, and its inhibitory activity was measured against ATP hydrolysis by Jun N-terminal kinase 3 (JNK3)
Summary
When a high-resolution structure of a ta rget protein is known, computational structure-based drug design is an efficient and effective methodology for the identification and fur ther optimization of hit compounds in order to generate lead co mpounds. The general assumption underlying these methods is that compounds with similar geometries will interact in a similar manner with the ta rget protein and t herefore, w ill show sim ilar or improved i nhibitory activity This assumption is based on the lock-and-key model for p rotein-ligand interactions [27] and most of the methods are based in making changes in the n ative moiety of t he ligand scaffolds and their geometries. In the JNK 3 s ystems, a co mpound fro m the 10 top ranking cand idates w as chem ically synthesized and IC50 measurements showed inhibitory activity against ATP hydrolysis These results suggest that our method can be useful in the identification and generation of lead compounds as drug candidates
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