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DPP-IV 抑制剂的活性和选择性悬崖:从 SAR 研究中汲取的经验教训及其在虚拟筛选中的应用。

Activity and selectivity cliffs for DPP-IV inhibitors: Lessons we can learn from SAR studies and their application to virtual screening.

机构信息

Research Group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Campus de Sescelades, Tarragona, Spain.

EURECAT, TECNIO, CEICS, Avinguda Universitat 1, Reus, Spain.

出版信息

Med Res Rev. 2018 Sep;38(6):1874-1915. doi: 10.1002/med.21499. Epub 2018 Apr 16.

DOI:10.1002/med.21499
PMID:29660786
Abstract

The inhibition of dipeptidyl peptidase-IV (DPP-IV) has emerged over the last decade as one of the most effective treatments for type 2 diabetes mellitus, and consequently (a) 11 DPP-IV inhibitors have been on the market since 2006 (three in 2015), and (b) 74 noncovalent complexes involving human DPP-IV and drug-like inhibitors are available at the Protein Data Bank (PDB). The present review aims to (a) explain the most important activity cliffs for DPP-IV noncovalent inhibition according to the binding site structure of DPP-IV, (b) explain the most important selectivity cliffs for DPP-IV noncovalent inhibition in comparison with other related enzymes (i.e., DPP8 and DPP9), and (c) use the information deriving from this activity/selectivity cliff analysis to suggest how virtual screening protocols might be improved to favor the early identification of potent and selective DPP-IV inhibitors in molecular databases (because they have not succeeded in identifying selective DPP-IV inhibitors with IC ≤ 100 nM). All these goals are achieved with the help of available homology models for DPP8 and DPP9 and an analysis of the structure-activity studies used to develop the noncovalent inhibitors that form part of some of the complexes with human DPP-IV available at the PDB.

摘要

过去十年中,二肽基肽酶-4(DPP-4)抑制剂已成为治疗 2 型糖尿病的最有效方法之一,因此:(a) 自 2006 年以来已有 11 种 DPP-4 抑制剂上市(2015 年有 3 种);(b) 蛋白质数据库(PDB)中已有 74 个涉及人 DPP-4 和类药抑制剂的非共价复合物。本综述旨在:(a) 根据 DPP-4 的结合位点结构,解释 DPP-4 非共价抑制的最重要活性位;(b) 与其他相关酶(即 DPP8 和 DPP9)相比,解释 DPP-4 非共价抑制的最重要选择性位;(c) 利用来自这些活性/选择性位分析的信息,提出如何改进虚拟筛选方案,以有利于在分子数据库中早期识别有效且选择性的 DPP-4 抑制剂(因为它们未能识别出 IC≤100nM 的选择性 DPP-4 抑制剂)。借助 DPP8 和 DPP9 的可用同源模型以及对用于开发与 PDB 中某些与人类 DPP-4 形成复合物的非共价抑制剂的结构-活性研究的分析,实现了所有这些目标。

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