Protein–protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state‐of‐the‐art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state‐of‐art methods.

中文翻译：

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使用结构模型的集合来预测由蛋白质-蛋白质相互作用的突变引起的结合亲和力的变化。

发生在细胞内外的所有分子方面的蛋白质-蛋白质相互作用 (PPI)。然而，通过实验确定 PPI 的结构和亲和力既昂贵又耗时。因此，作为实验方法的补充，计算工具的开发是基础。在这里，我们提出了一个计算套件：MODPIN，用于建模和预测 PPI 结合亲和力的变化。在这种方法中，我们使用同源建模来推导 PPI 的结构，并使用最先进的评分函数对其进行评分。我们不是通过生成单个结构模型而是基于多个模板生成具有不同构象的结构模型集合来探索 PPI 的构象空间。我们应用该方法来预测 PPI 大型数据集的突变和拼接变体时自由能的变化，以统计量化预测的质量和准确性。例如，我们使用 MODPIN 研究突变对大肠菌素核酸内切酶 9 和大肠菌素核酸内切酶 2 免疫蛋白相互作用的影响。大肠杆菌。最后，我们将我们的结果与其他最先进的方法进行了比较。