Large-Scale Prediction of Human Protein-Protein Interactions from Amino Acid Sequence Based on Latent Topic Features
Large-Scale Prediction of Human Protein-Protein Interactions from Amino Acid Sequence Based on Latent Topic Features
复制标题
基于潜在主题特征的氨基酸序列大规模预测人类蛋白质-蛋白质相互作用
DOI：
10.1021/pr100618t
复制
发表时间：
2010-10-01
影响因子：
4.4
通讯作者：
Shen, Hong-Bin
中科院分区：
文献类型：
--
作者：
Pan, Xiao-Yong;Zhang, Ya-Nan;Shen, Hong-Bin
蛋白质-蛋白质相互作用（PPI）是任何生物体的整个相互作用系统的核心。虽然有许多人类蛋白质-蛋白质相互作用链接正在实验确定，但与人类中大约有30万种蛋白质-蛋白质相互作用的估计相比，数量仍然相对很少。因此，开发自动化计算方法来准确有效地预测蛋白质-蛋白质相互作用仍然是迫切和具有挑战性的。本文提出了一种新的层次LDA-RF（latent dirichlet allocation-random forest）模型，直接从蛋白质一级序列预测人类蛋白质-蛋白质相互作用。该模型通过挖掘低维潜在语义空间中隐藏在噪声氨基酸序列中的内部结构，具有较高的成功率和较强的处理大规模数据集的能力。首先，从序列中构造由联合三元组表示的局部序列特征。然后利用生成式LDA模型将原始特征空间投影到潜在语义空间，得到反映蛋白质间隐藏结构的低维潜在主题特征。最后，利用随机森林模型预测了两种蛋白质相互作用的概率。我们的研究结果表明，所提出的潜在主题特征是非常有前途的PPI预测，也可能成为一个强大的战略，以处理许多其他生物信息学问题。作为一个Web服务器，LDA-RF可以在http://www.csbio.sjtu.edu.cn/bioinf/LR_PPI上免费获得，供学术使用。
Protein-protein interaction (PPI) is at the core of the entire interactomic system of any living organism. Although there are many human protein-protein interaction links being experimentally determined, the number is still relatively very few compared to the estimation that there are similar to 300 000 protein-protein interactions in human beings. Hence, it is still urgent and challenging to develop automated computational methods to accurately and efficiently predict protein-protein interactions. In this paper, we propose a novel hierarchical LDA-RF (latent dirichlet allocation-random forest) model to predict human protein-protein interactions from protein primary sequences directly, which is featured by a high success rate and strong ability for handling large-scale data sets by digging the hidden internal structures buried into the noisy amino acid sequences in low dimensional latent semantic space. First, the local sequential features represented by conjoint triads are constructed from sequences. Then the generative LDA model is used to project the original feature space into the latent semantic space to obtain low dimensional latent topic features, which reflect the hidden structures between proteins. Finally, the powerful random forest model is used to predict the probability for interaction of two proteins. Our results show that the proposed latent topic feature is very promising for PPI prediction and could also become a powerful strategy to deal with many other bioinformatics problems. As a web server, LDA-RF is freely available at http://www.csbio.sjtu.edu.cn/bioinf/LR_PPI for academic use.