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首页> 外文期刊>Journal of Molecular Biology Research >Combining Network Topological Characteristics With Sequence and Structure Based Features for Predicting Protein Stability Changes Upon Single Amino Acid Mutation
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Combining Network Topological Characteristics With Sequence and Structure Based Features for Predicting Protein Stability Changes Upon Single Amino Acid Mutation

机译：结合网络拓扑特征与基于序列和结构的特征，预测单个氨基酸突变后蛋白质稳定性的变化

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It has been shown that the stability of protein structure could be significantly changed by single amino acid substitution. Accurate prediction of protein stability changes caused by single amino acid substitutions is valuable for understanding the relationship between protein structures and functions as well as designing new proteins. Currently, various computational methods have been developed to study the effect of single amino acid mutation on protein stability. In this study, by combining network topological characteristics extracted from Protein Structure Network (PSN) with other physicochemical features obtained from protein sequence or structure, a Support Vector Machine (SVM) model was developed to distinguish the stabilizing mutants from the destabilizing mutants. 20-fold cross-validation was implemented for performance evaluation. An accuracy of 0.88 and a Matthews Correlation Coefficient (MCC) of 0.71 were obtained for the dataset with 1925 variants. Our method is superior to the existing machine learning approaches evaluated under the same datasets. It suggests that such a combining strategy should be valuable in predicting protein stability changes upon amino acid mutation. In our study, the topological parameters are informative for prediction upon substitutions. Moreover, it is indicated that the Protein Structure Network (PSN) could be effectively used for representing the three-dimensional structure of protein and such network parameters are associated with the changes of protein function and structure.
机译：已经显示蛋白质结构的稳定性可以通过单个氨基酸取代而显着改变。准确预测由单个氨基酸取代引起的蛋白质稳定性变化，对于了解蛋白质结构与功能之间的关系以及设计新蛋白质非常有价值。当前，已经开发了各种计算方法来研究单个氨基酸突变对蛋白质稳定性的影响。在这项研究中，通过结合从蛋白质结构网络（PSN）提取的网络拓扑特征与从蛋白质序列或结构获得的其他物理化学特征，开发了一种支持向量机（SVM）模型以区分稳定突变体和不稳定突变体。实施了20倍交叉验证以进行绩效评估。对于具有1925个变体的数据集，其准确性为0.88，Matthews相关系数（MCC）为0.71。我们的方法优于在相同数据集下评估的现有机器学习方法。这表明这种组合策略在预测氨基酸突变后蛋白质稳定性的变化中应该是有价值的。在我们的研究中，拓扑参数可为替换预测提供参考。而且，表明蛋白质结构网络（PSN）可以有效地用于表示蛋白质的三维结构，并且这种网络参数与蛋白质功能和结构的变化有关。

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