Jemimah Sherlyn, Gromiha M Michael
Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, India.
Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, India; School of Computing, Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Kanagawa, 226-8503, Yokohama, Japan.
Comput Biol Med. 2020 Aug;123:103829. doi: 10.1016/j.compbiomed.2020.103829. Epub 2020 Jul 18.
Mutation of amino acid residues at protein-protein interfaces alters the binding affinity of protein-protein complexes and may lead to diseases. In this study, we have systematically analysed the relationship between the changes in binding affinity upon amino acid substitutions and the effect of mutations as disease-causing or neutral. We observed that a large proportion of disease-causing mutations decrease the binding affinity in all the considered datasets such as (i) experimentally known binding affinity and disease causing mutations, (ii) experimentally known binding affinity and predicted effects of mutations, and (iii) experimentally known disease causing mutations and predicted binding affinity. However, this relationship depends on the disease class, and the statistics indicate that factors other than binding affinity are also influencing the disease development. Further, structural analysis of protein-protein complexes revealed that disease-causing mutations are mainly attributed with the disruption of non-covalent interactions. In certain cancers, several mutations increase the binding affinity and they may have been selected to enhance cell survival and growth. Further, incorporating the effects of mutations on binding affinity in protein-protein interaction network studies may enable researchers to deduce the mechanisms of specific diseases and also help to identify novel drug targets.
蛋白质 - 蛋白质界面处氨基酸残基的突变会改变蛋白质 - 蛋白质复合物的结合亲和力,并可能导致疾病。在本研究中,我们系统地分析了氨基酸替换后结合亲和力的变化与作为致病或中性突变的影响之间的关系。我们观察到,在所有考虑的数据集中,很大一部分致病突变会降低结合亲和力,例如:(i)实验已知的结合亲和力和致病突变,(ii)实验已知的结合亲和力和预测的突变效应,以及(iii)实验已知的致病突变和预测的结合亲和力。然而,这种关系取决于疾病类别,并且统计数据表明,除结合亲和力外的其他因素也在影响疾病发展。此外,蛋白质 - 蛋白质复合物的结构分析表明,致病突变主要归因于非共价相互作用的破坏。在某些癌症中,一些突变会增加结合亲和力,并且它们可能是为了增强细胞存活和生长而被选择的。此外,在蛋白质 - 蛋白质相互作用网络研究中纳入突变对结合亲和力的影响,可能使研究人员能够推断特定疾病的机制,并有助于识别新的药物靶点。