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FIS-PNN: a hybrid computational method for protein-protein interactions prediction using the secondary structure information

机译：FIS-PNN：使用二级结构信息进行蛋白质-蛋白质相互作用预测的混合计算方法

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摘要

The study of protein-protein interactions (PPI) is an active area of research in biology because it mediates most of the biological functions in any organism. This work is inspired by the fact that proteins with similar secondary structures mostly share very similar three-dimensional structures, and consequently, very similar functions. As a result, they must interact with each other. In this study we used our approach, namely FIS-PNN, to predict the interacting proteins in yeast from the information of their secondary structures using hybrid machine learning algorithms. Two main stages of our approach are similarity score computation, and classification. The first stage is further divided into three steps: (1) Multiple-sequence alignment, (2) Secondary structure prediction, and (3) Similarity measurement. In the classification stage, several independent first order Sugeno Fuzzy Inference Systems and probabilistic neural networks are generated to model the behavior of similarity scores of all possible proteins pairs. The final results show that the multiple classifiers have significantly improved the performance of the single classifier. Our method, namely FIS-PNN, successfully predicts PPI with 96% of accuracy, a level that is significantly greater than all other sequence-based prediction methods.
机译：蛋白质间相互作用（PPI）的研究是生物学研究的活跃领域，因为它介导了任何生物体中的大多数生物学功能。这项工作受到以下事实的启发：具有相似二级结构的蛋白质通常共享非常相似的三维结构，因此功能也非常相似。结果，它们必须彼此交互。在这项研究中，我们使用混合机器学习算法从FIS-PNN的二级结构信息预测酵母中的相互作用蛋白，即FIS-PNN。我们方法的两个主要阶段是相似性分数计算和分类。第一阶段进一步分为三个步骤：（1）多序列比对；（2）二级结构预测；（3）相似性测量。在分类阶段，将生成几个独立的一阶Sugeno模糊推理系统和概率神经网络，以对所有可能的蛋白质对的相似性评分行为进行建模。最终结果表明，多个分类器显着提高了单个分类器的性能。我们的方法，即FIS-PNN，能够以96％的准确度成功预测PPI，该水平明显高于所有其他基于序列的预测方法。

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