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高级建模解决了先导优化中违背直觉的决策。

Advanced Modeling Reconciles Counterintuitive Decisions in Lead Optimization.

机构信息

Argentine Institute of Mathematics, National Research Council, CONICET, Buenos Aires 1083, Argentina; AF Innovation, GmbH, a pharmaceutical consultancy, Avenida del Libertador 1092, Buenos Aires 1112, Argentina.

Institute for Biophysical Dynamics, The Computation Institute and Departments of Computer Science and Mathematics, University of Chicago, Chicago, IL 60637, USA.

出版信息

Trends Biotechnol. 2017 Jun;35(6):490-497. doi: 10.1016/j.tibtech.2016.12.003. Epub 2017 Jan 7.

Abstract

Lead optimization (LO) is essential to fulfill the efficacy and safety requirements of drug-based targeted therapy. The ease with which water may be locally removed from around the target protein crucially influences LO decisions. However, inferred binding sites often defy intuition and the resulting LO decisions are often counterintuitive, with nonpolar groups in the drug placed next to polar groups in the target. We first introduce biophysical advances to reconcile these apparent mismatches. We incorporate three-body energy terms that account for the net stabilization of preformed target structures upon removal of interfacial water concurrent with drug binding. These unexplored drug-induced environmental changes enhancing the target electrostatics are validated against drug-target affinity data, yielding superior computational accuracy required to improve drug design.

摘要

先导优化 (LO) 对于满足基于药物的靶向治疗的疗效和安全性要求至关重要。水从靶蛋白周围局部去除的容易程度极大地影响 LO 决策。然而,推断出的结合位点常常违背直觉,并且由此产生的 LO 决策往往是违反直觉的,药物中的非极性基团被放置在靶标中的极性基团旁边。我们首先介绍了生物物理方面的进展,以调和这些明显的不匹配。我们结合了三体能量项,这些项考虑了在药物结合的同时去除界面水时预先形成的靶标结构的净稳定化。这些未被探索的药物诱导的环境变化增强了靶标静电特性,经药物-靶标亲和力数据验证,产生了提高药物设计所需的更高计算准确性。

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