Wang Hongwei, Liu Fang, Dong Tiange, Du Likai, Zhang Dongju, Gao Jun
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P. R. China.
Institute of Theoretical Chemistry, Shandong University, Jinan 250100, P. R. China.
ACS Omega. 2018 Apr 11;3(4):4094-4104. doi: 10.1021/acsomega.8b00336. eCollection 2018 Apr 30.
The charge-transfer coupling is an important component in tight-binding methods. Because of the highly complex chemical structure of biomolecules, the anisotropic feature of charge-transfer couplings in realistic proteins cannot be ignored. In this work, we have performed the first large-scale quantitative assessment of charge-transfer preference by calculating the charge-transfer couplings in all 20 × 20 possible amino acid side-chain combinations, which are extracted from available high-quality structures of thousands of protein complexes. The charge-transfer database quantitatively shows distinct features of charge-transfer couplings among millions of amino acid side-chain combinations. The overall distribution of charge-transfer couplings reveals that only one average or representative structure cannot be regarded as the typical charge-transfer preference in realistic proteins. This work provides us an alternative route to comprehensively understand the charge-transfer couplings for the overall distribution of realistic proteins in the foreseen big data scenario.
电荷转移耦合是紧束缚方法中的一个重要组成部分。由于生物分子的化学结构高度复杂,实际蛋白质中电荷转移耦合的各向异性特征不容忽视。在这项工作中,我们通过计算从数千个蛋白质复合物的可用高质量结构中提取的所有20×20种可能的氨基酸侧链组合中的电荷转移耦合,首次对电荷转移偏好进行了大规模定量评估。电荷转移数据库定量显示了数百万种氨基酸侧链组合中电荷转移耦合的明显特征。电荷转移耦合的总体分布表明,仅一个平均或代表性结构不能被视为实际蛋白质中的典型电荷转移偏好。这项工作为我们提供了一条替代途径,以便在可预见的大数据场景中全面理解实际蛋白质总体分布中的电荷转移耦合。