Li Minghui, Simonetti Franco L, Goncearenco Alexander, Panchenko Anna R
National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.
Institute Leloir Foundation, Buenos Aires, C1405BWE, Argentina.
Nucleic Acids Res. 2016 Jul 8;44(W1):W494-501. doi: 10.1093/nar/gkw374. Epub 2016 May 5.
Proteins engage in highly selective interactions with their macromolecular partners. Sequence variants that alter protein binding affinity may cause significant perturbations or complete abolishment of function, potentially leading to diseases. There exists a persistent need to develop a mechanistic understanding of impacts of variants on proteins. To address this need we introduce a new computational method MutaBind to evaluate the effects of sequence variants and disease mutations on protein interactions and calculate the quantitative changes in binding affinity. The MutaBind method uses molecular mechanics force fields, statistical potentials and fast side-chain optimization algorithms. The MutaBind server maps mutations on a structural protein complex, calculates the associated changes in binding affinity, determines the deleterious effect of a mutation, estimates the confidence of this prediction and produces a mutant structural model for download. MutaBind can be applied to a large number of problems, including determination of potential driver mutations in cancer and other diseases, elucidation of the effects of sequence variants on protein fitness in evolution and protein design. MutaBind is available at http://www.ncbi.nlm.nih.gov/projects/mutabind/.
蛋白质与其大分子伴侣进行高度选择性的相互作用。改变蛋白质结合亲和力的序列变异可能会导致功能的显著扰动或完全丧失,从而可能引发疾病。一直以来,人们迫切需要从机制上理解变异对蛋白质的影响。为满足这一需求,我们引入了一种新的计算方法MutaBind,用于评估序列变异和疾病突变对蛋白质相互作用的影响,并计算结合亲和力的定量变化。MutaBind方法使用分子力学力场、统计势和快速侧链优化算法。MutaBind服务器将突变映射到结构蛋白复合物上,计算结合亲和力的相关变化,确定突变的有害效应,估计该预测的可信度,并生成可供下载的突变体结构模型。MutaBind可应用于大量问题,包括确定癌症和其他疾病中的潜在驱动突变、阐明序列变异对进化中蛋白质适应性的影响以及蛋白质设计。可通过http://www.ncbi.nlm.nih.gov/projects/mutabind/获取MutaBind。