Zhai Jing-Xuan, Cao Tian-Jie, An Ji-Yong, Bian Yong-Tao
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 21116, China.
J Theor Biol. 2017 Nov 7;432:80-86. doi: 10.1016/j.jtbi.2017.08.009. Epub 2017 Aug 9.
It is a challenging task for fundamental research whether proteins can interact with their partners. Protein self-interaction (SIP) is a special case of PPIs, which plays a key role in the regulation of cellular functions. Due to the limitations of experimental self-interaction identification, it is very important to develop an effective biological tool for predicting SIPs based on protein sequences. In the study, we developed a novel computational method called RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) for detecting SIPs from protein sequences. Firstly, Average Blocks (AB) feature extraction method is employed to represent protein sequences on a Position Specific Scoring Matrix (PSSM). Secondly, Principal Component Analysis (PCA) method is used to reduce the dimension of AB vector for reducing the influence of noise. Then, by employing the Relevance Vector Machine (RVM) algorithm, the performance of RVM-AB is assessed and compared with the state-of-the-art support vector machine (SVM) classifier and other exiting methods on yeast and human datasets respectively. Using the fivefold test experiment, RVM-AB model achieved very high accuracies of 93.01% and 97.72% on yeast and human datasets respectively, which are significantly better than the method based on SVM classifier and other previous methods. The experimental results proved that the RVM-AB prediction model is efficient and robust. It can be an automatic decision support tool for detecting SIPs. For facilitating extensive studies for future proteomics research, the RVMAB server is freely available for academic use at http://219.219.62.123:8888/SIP_AB.
对于基础研究而言，蛋白质是否能与它们的伙伴相互作用是一项具有挑战性的任务。蛋白质自相互作用（SIP）是蛋白质-蛋白质相互作用（PPI）的一种特殊情况，在细胞功能调节中起关键作用。由于实验性自相互作用识别存在局限性，开发一种基于蛋白质序列预测SIP的有效生物学工具非常重要。在本研究中，我们开发了一种名为RVM-AB的新型计算方法，该方法结合了相关向量机（RVM）模型和平均模块（AB）来从蛋白质序列中检测SIP。首先，采用平均模块（AB）特征提取方法在位置特异性评分矩阵（PSSM）上表示蛋白质序列。其次，使用主成分分析（PCA）方法降低AB向量的维度，以减少噪声的影响。然后，通过采用相关向量机（RVM）算法，分别在酵母和人类数据集上评估RVM-AB的性能，并与最先进的支持向量机（SVM）分类器和其他现有方法进行比较。通过五重测试实验，RVM-AB模型在酵母和人类数据集上分别达到了93.01%和97.72%的非常高的准确率，这明显优于基于SVM分类器的方法和其他先前的方法。实验结果证明，RVM-AB预测模型是高效且稳健的。它可以成为检测SIP的自动决策支持工具。为了便于未来蛋白质组学研究的广泛开展，RVMAB服务器可在http://219.219.62.123:8888/SIP_AB上免费用于学术用途。
