计算机科学
对接(动物)
参数化(大气建模)
正确性
Python(编程语言)
软件
寻找对接的构象空间
杠杆(统计)
小分子
互操作性
构象集合
理论计算机科学
化学
分子动力学
化学信息学
大分子对接
分子生物物理学
核酸
分子模型
生物系统
蛋白质数据库
源代码
蛋白质-配体对接
高分子
计算科学
分子
元动力学
计算化学
蛋白质数据库
自动化
作者
Diogo Santos‐Martins,Yiran He,Jérôme Eberhardt,Parnika Sharma,Niccolò Bruciaferri,Matthew Holcomb,Manuel A. Llanos,Althea Hansel-Harris,Allison Barkdull,Andreas F. Tillack,Giulia Bianco,May‐Linn Paulsen,Joani Mato,Ishan Taneja,Stefano Forli
标识
DOI:10.1021/acs.jcim.5c02271
摘要
Molecule parametrization is an essential requirement to guarantee the accuracy of docking calculations. Parametrization includes a proper perception of chemical properties such as bonds, formal charges and protonation states. This includes large biological macromolecules, such as proteins and nucleic acids, and small molecules, such as ligands and cofactors. The structures of proteins and nucleic acids are challenging due to omission of several atoms from the structural model, and from the lack of connectivity and bond order information in the PDB and mmCIF file formats. For small molecules, the very large chemical diversity poses challenges for both validating correctness and providing accurate parameters. These challenges affect various modeling approaches like molecular docking and molecular dynamics. Moreover, several specialized methods (particularly in molecular docking) leverage specific chemical properties to add custom potentials, pseudoatoms, or manipulate atomic connectivity. To address these challenges, we developed Meeko, a molecular parametrization Python package that leverages the widely used RDKit cheminformatics library for a chemically accurate description of the molecular representation. Small molecules are modeled as single RDKit molecules, and biological macromolecules as multiple RDKit molecules, one for each residue. Meeko is highly customizable and designed to be easily scriptable for high-throughput processing, replacing MGLTools for receptor and ligand preparation.
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