Jia Li-Na, Yan Xin, You Zhu-Hong, Zhou Xi, Li Li-Ping, Wang Lei, Song Ke-Jian
College of Information Science and Engineering, Zaozhuang University, Zaozhuang, China.
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.
Evol Bioinform Online. 2020 Dec 26;16:1176934320984171. doi: 10.1177/1176934320984171. eCollection 2020.
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）的研究不仅可以在分子水平上揭示蛋白质的功能，对于理解诸如生长、发育、分化和凋亡等活动也至关重要，为探索重大疾病的发病机制提供了重要的理论基础。随着生物技术的飞速发展，大量的SIPs已被发现。然而，由于生物实验固有的周期长和成本高的问题，SIPs的识别与数据积累之间的差距越来越大。因此，需要快速准确的计算方法来有效预测SIPs。在本研究中，我们基于自然语言理解理论和进化信息设计了一种预测SIPs的新方法NLPEI。具体来说，我们首先将蛋白质序列理解为自然语言，并使用自然语言处理算法提取其特征。然后，我们使用位置特异性得分矩阵（PSSM）来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，并通过极限学习机（ELM）分类器进行准确预测。在人类和酵母的SIPs金标准数据集中，NLPEI的预测准确率分别达到了94.19%和91.29%。与不同的分类器模型、不同的特征模型以及其他现有方法相比，NLPEI取得了最佳结果。这些实验结果表明，NLPEI是预测SIPs的有效工具，可以为生物实验提供可靠的候选对象。
