†Information and Communication Technologies Department, Computer Science Faculty, University of A Coruna, Campus de Elviña s/n, 15071 A Coruña, Spain.
‡REQUIMTE/Departamento de Química e Bioquímica, Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre s/n, 4169-007 Porto, Portugal.
J Chem Inf Model. 2015 May 26;55(5):1077-86. doi: 10.1021/ci500760m. Epub 2015 Apr 17.
Due to the importance of hot-spots (HS) detection and the efficiency of computational methodologies, several HS detecting approaches have been developed. The current paper presents new models to predict HS for protein-protein and protein-nucleic acid interactions with better statistics compared with the ones currently reported in literature. These models are based on solvent accessible surface area (SASA) and genetic conservation features subjected to simple Bayes networks (protein-protein systems) and a more complex multi-objective genetic algorithm-support vector machine algorithms (protein-nucleic acid systems). The best models for these interactions have been implemented in two free Web tools.
由于热点（HS）检测的重要性和计算方法的效率，已经开发了几种 HS 检测方法。本文提出了新的模型来预测蛋白质-蛋白质和蛋白质-核酸相互作用的热点，与目前文献中报道的模型相比，这些模型具有更好的统计学特性。这些模型基于溶剂可及表面积（SASA）和遗传保守特征，并受到简单贝叶斯网络（蛋白质-蛋白质系统）和更复杂的多目标遗传算法-支持向量机算法（蛋白质-核酸系统）的约束。这些相互作用的最佳模型已经在两个免费的 Web 工具中实现。
