The study of protein self-interactions (SIPs) can not only reveal the function of proteins at the molecular level, but is also crucial to understand activities such as growth, development, differentiation, and apoptosis, providing an important theoretical basis for exploring the mechanism of major diseases. With the rapid advances in biotechnology, a large number of SIPs have been discovered. However, due to the long period and high cost inherent to biological experiments, the gap between the identification of SIPs and the accumulation of data is growing. Therefore, fast and accurate computational methods are needed to effectively predict SIPs. In this study, we designed a new method, NLPEI, for predicting SIPs based on natural language understanding theory and evolutionary information. Specifically, we first understand the protein sequence as natural language and use natural language processing algorithms to extract its features. Then, we use the Position-Specific Scoring Matrix (PSSM) to represent the evolutionary information of the protein and extract its features through the Stacked Auto-Encoder (SAE) algorithm of deep learning. Finally, we fuse the natural language features of proteins with evolutionary features and make accurate predictions by Extreme Learning Machine (ELM) classifier. In the SIPs gold standard data sets of human and yeast, NLPEI achieved 94.19% and 91.29% prediction accuracy. Compared with different classifier models, different feature models, and other existing methods, NLPEI obtained the best results. These experimental results indicated that NLPEI is an effective tool for predicting SIPs and can provide reliable candidates for biological experiments.

蛋白质自相互作用（SIPs）的研究不仅可以揭示蛋白质在分子水平上的功能，而且对于理解诸如生长，发育，分化和凋亡等活动也至关重要，为探索该机制提供了重要的理论依据。主要疾病。随着生物技术的飞速发展，已经发现了大量的SIP。然而，由于生物学实验固有的长期性和高成本，SIP的识别和数据积累之间的差距越来越大。因此，需要快速而准确的计算方法来有效地预测SIP。在这项研究中，我们设计了一种基于自然语言理解理论和进化信息的SIP预测新方法NLPEI。特别，我们首先将蛋白质序列理解为自然语言，然后使用自然语言处理算法提取其特征。然后，我们使用特定位置评分矩阵（PSSM）表示蛋白质的进化信息，并通过深度学习的堆叠自动编码器（SAE）算法提取其特征。最后，我们将蛋白质的自然语言特征与进化特征融合在一起，并通过极限学习机（ELM）分类器做出准确的预测。在人类和酵母菌的SIP金标准数据集中，NLPEI达到了94.19％和91.29％的预测准确度。与不同的分类器模型，不同的特征模型和其他现有方法相比，NLPEI获得了最佳结果。