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通过计算机诱变鉴定抗原-抗体相互作用界面的关键残基

Identification of Strategic Residues at the Interface of Antigen-Antibody Interactions by In Silico Mutagenesis.

机构信息

State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361002, Fujian, People's Republic of China.

School of Public Health, Xiamen University, Xiamen, 361005, Fujian, People's Republic of China.

出版信息

Interdiscip Sci. 2018 Jun;10(2):438-448. doi: 10.1007/s12539-017-0242-7. Epub 2017 May 30.

DOI:10.1007/s12539-017-0242-7
PMID:28560699
Abstract

Structural information pertaining to antigen-antibody interactions is fundamental in immunology, and benefits structure-based vaccine design. Modeling of antigen-antibody immune complexes from co-crystal structures or molecular docking simulations provides an extensive profile of the epitope at the interface; however, the key amino acids involved in the interaction must be further clarified, often through the use of experimental mutagenesis and subsequent binding assays. Here, we describe an in silico mutagenesis method to identify key sites at antigen-antibody interfaces, using significant increase in pH-dependency energy among saturated point mutations. Through a comprehensive analysis of the crystal structures of three antigen-antibody immune complexes, we show that a cutoff value of 1 kcal/mol of increased interaction energy provides good congruency with the experimental non-binding mutations conducted in vitro. This in silico mutagenesis strategy, in association with energy calculations, may provide an efficient tool for antibody-antigen interface analyses, epitope optimization, and/or conformation prediction in structure-based vaccine design.

摘要

与抗原-抗体相互作用相关的结构信息在免疫学中是基础的,并且有利于基于结构的疫苗设计。从共晶结构或分子对接模拟中对抗原-抗体免疫复合物进行建模,可以提供在界面处的表位的广泛概况;然而,必须进一步阐明参与相互作用的关键氨基酸,通常通过使用实验诱变和随后的结合测定来进行。在这里,我们描述了一种通过增加饱和点突变中 pH 依赖性能量来识别抗原-抗体界面关键部位的计算突变方法。通过对三个抗原-抗体免疫复合物的晶体结构的全面分析,我们表明,增加 1 kcal/mol 的相互作用能量的截止值与在体外进行的非结合实验突变具有很好的一致性。这种与能量计算相关的计算突变策略可能为抗体-抗原界面分析、表位优化和/或结构基疫苗设计中的构象预测提供一种有效的工具。

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