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多尺度模拟能否揭示金属酶的功能,从而提高基于知识的药物发现?

Can multiscale simulations unravel the function of metallo-enzymes to improve knowledge-based drug discovery?

机构信息

Institute for Research in Biomedicine (IRB), Università della Svizzera Italiana (USI), CH-6500, Bellinzona, Switzerland.

Department of Chemistry & Biochemistry, University of California, San Diego, La Jolla, CA 92093-0340, USA.

出版信息

Future Med Chem. 2019 Apr;11(7):771-791. doi: 10.4155/fmc-2018-0495. Epub 2019 Apr 2.

DOI:10.4155/fmc-2018-0495
PMID:30938544
Abstract

Metallo-enzymes are a large class of biomolecules promoting specialized chemical reactions. Quantum-classical quantum mechanics/molecular mechanics molecular dynamics, describing the metal site at quantum mechanics level, while accounting for the rest of system at molecular mechanics level, has an accessible time-scale limited by its computational cost. Hence, it must be integrated with classical molecular dynamics and enhanced sampling simulations to disentangle the functions of metallo-enzymes. In this review, we provide an overview of these computational methods and their capabilities. In particular, we will focus on some systems such as CYP19A1 a Fe-dependent enzyme involved in estrogen biosynthesis, and on Mg-dependent DNA/RNA processing enzymes/ribozymes and the spliceosome, a protein-directed ribozyme. This information may guide the discovery of drug-like molecules and genetic manipulation tools.

摘要

金属酶是一大类生物分子,能促进特定的化学反应。量子-经典量子力学/分子力学分子动力学,在量子力学水平上描述金属位点,同时在分子力学水平上考虑系统的其余部分,具有可访问的时间尺度,受到其计算成本的限制。因此,它必须与经典分子动力学和增强采样模拟相结合,以分离金属酶的功能。在这篇综述中,我们提供了这些计算方法及其功能的概述。特别是,我们将集中讨论一些系统,如 CYP19A1 一种参与雌激素生物合成的铁依赖性酶,以及 Mg 依赖性 DNA/RNA 加工酶/核酶和剪接体,一种蛋白质导向的核酶。这些信息可以指导类似药物分子和遗传操作工具的发现。

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