Hu Jing, Li Jiarui, Chen Nansheng, Zhang Xiaolong
School of Computer Science and Technology, Wuhan University of Science and Technology, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, China.
Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada.
Methods. 2016 Nov 1;110:73-80. doi: 10.1016/j.ymeth.2016.06.020. Epub 2016 Jun 21.
The hot regions of protein-protein interactions refer to the active area which formed by those most important residues to protein combination process. With the research development on protein interactions, lots of predicted hot regions can be discovered efficiently by intelligent computing methods, while performing biology experiments to verify each every prediction is hardly to be done due to the time-cost and the complexity of the experiment. This study based on the research of hot spot residue conservations, the proposed method is used to verify authenticity of predicted hot regions that using machine learning algorithm combined with protein's biological features and sequence conservation, though multiple sequence alignment, module substitute matrix and sequence similarity to create conservation scoring algorithm, and then using threshold module to verify the conservation tendency of hot regions in evolution. This research work gives an effective method to verify predicted hot regions in protein-protein interactions, which also provides a useful way to deeply investigate the functional activities of protein hot regions.
蛋白质-蛋白质相互作用的热点区域是指在蛋白质结合过程中由那些最重要的残基形成的活性区域。随着蛋白质相互作用研究的发展,通过智能计算方法可以高效地发现许多预测的热点区域,然而由于实验的时间成本和复杂性,对每一个预测进行生物学实验验证却很难做到。本研究基于对热点残基保守性的研究,提出的方法用于验证预测热点区域的真实性,该方法使用机器学习算法结合蛋白质的生物学特征和序列保守性,通过多序列比对、模块替代矩阵和序列相似性创建保守性评分算法,然后使用阈值模块验证热点区域在进化中的保守趋势。这项研究工作给出了一种验证蛋白质-蛋白质相互作用中预测热点区域的有效方法,也为深入研究蛋白质热点区域的功能活性提供了一条有用的途径。