Department of Biochemistry, Sanger Building, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK.
Centro de Pesquisas René Rachou, Fundação Oswaldo Cruz, Avenida Augusto de Lima 1715, Belo Horizonte, 30190-002, Brazil.
Sci Rep. 2016 Jul 7;6:29575. doi: 10.1038/srep29575.
The ability to predict how a mutation affects ligand binding is an essential step in understanding, anticipating and improving the design of new treatments for drug resistance, and in understanding genetic diseases. Here we present mCSM-lig, a structure-guided computational approach for quantifying the effects of single-point missense mutations on affinities of small molecules for proteins. mCSM-lig uses graph-based signatures to represent the wild-type environment of mutations, and small-molecule chemical features and changes in protein stability as evidence to train a predictive model using a representative set of protein-ligand complexes from the Platinum database. We show our method provides a very good correlation with experimental data (up to ρ = 0.67) and is effective in predicting a range of chemotherapeutic, antiviral and antibiotic resistance mutations, providing useful insights for genotypic screening and to guide drug development. mCSM-lig also provides insights into understanding Mendelian disease mutations and as a tool for guiding protein design. mCSM-lig is freely available as a web server at http://structure.bioc.cam.ac.uk/mcsm_lig.
预测突变如何影响配体结合的能力是理解、预测和改进耐药性新治疗方法设计以及理解遗传疾病的关键步骤。在这里，我们提出了 mCSM-lig，这是一种基于结构的计算方法，用于量化单点错义突变对小分子与蛋白质亲和力的影响。mCSM-lig 使用基于图的特征来表示突变的野生型环境，以及小分子化学特征和蛋白质稳定性变化作为证据，使用来自 Platinum 数据库的一组代表性蛋白质-配体复合物来训练预测模型。我们表明，我们的方法与实验数据具有很好的相关性（高达 ρ=0.67），并且能够有效地预测一系列化疗、抗病毒和抗生素耐药性突变，为基因筛选提供有用的见解并指导药物开发。mCSM-lig 还深入了解孟德尔疾病突变，并作为指导蛋白质设计的工具。mCSM-lig 可在 http://structure.bioc.cam.ac.uk/mcsm_lig 作为网络服务器免费获得。
