Zhang Ning, Chen Yuting, Lu Haoyu, Zhao Feiyang, Alvarez Roberto Vera, Goncearenco Alexander, Panchenko Anna R, Li Minghui
Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China.
National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.
iScience. 2020 Mar 27;23(3):100939. doi: 10.1016/j.isci.2020.100939. Epub 2020 Feb 27.
Missense mutations may affect proteostasis by destabilizing or over-stabilizing protein complexes and changing the pathway flux. Predicting the effects of stabilizing mutations on protein-protein interactions is notoriously difficult because existing experimental sets are skewed toward mutations reducing protein-protein binding affinity and many computational methods fail to correctly evaluate their effects. To address this issue, we developed a method MutaBind2, which estimates the impacts of single as well as multiple mutations on protein-protein interactions. MutaBind2 employs only seven features, and the most important of them describe interactions of proteins with the solvent, evolutionary conservation of the site, and thermodynamic stability of the complex and each monomer. This approach shows a distinct improvement especially in evaluating the effects of mutations increasing binding affinity. MutaBind2 can be used for finding disease driver mutations, designing stable protein complexes, and discovering new protein-protein interaction inhibitors.
错义突变可能通过破坏或过度稳定蛋白质复合物以及改变途径通量来影响蛋白质稳态。预测稳定突变对蛋白质-蛋白质相互作用的影响非常困难，因为现有的实验数据集偏向于降低蛋白质-蛋白质结合亲和力的突变，而且许多计算方法无法正确评估其影响。为了解决这个问题，我们开发了一种方法MutaBind2，它可以估计单突变和多突变对蛋白质-蛋白质相互作用的影响。MutaBind2仅使用七个特征，其中最重要的特征描述了蛋白质与溶剂的相互作用、位点的进化保守性以及复合物和每个单体的热力学稳定性。这种方法在评估增加结合亲和力的突变影响方面有显著改进。MutaBind2可用于寻找疾病驱动突变、设计稳定的蛋白质复合物以及发现新的蛋白质-蛋白质相互作用抑制剂。
