Protein-protein interaction site prediction based on conditional random fields

Protein-protein interaction site prediction based on conditional random fields
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基于条件随机场的蛋白质-蛋白质相互作用位点预测

DOI：
10.1093/bioinformatics/btl660
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发表时间：
2007-03-01
期刊：
BIOINFORMATICS
影响因子：
5.8
通讯作者：
Liu, Tao
Liu, Tao
中科院分区：
生物学3区
文献类型：
--
作者：
Li, Ming-Hui;Lin, Lei;Liu, Tao

文献摘要

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动机 我们的动机是快速增长的蛋白质结构在蛋白质数据库与必要的信息预测蛋白质-蛋白质相互作用的网站，以开发用于识别参与蛋白质-蛋白质相互作用的残基的方法。我们想比较基于条件随机场（CRFs）的方法与传统的分类为基础的方法，省略了相邻残基的两个标签之间的关系，以显示基于CRFs的方法在预测蛋白质-蛋白质相互作用位点的优势。 结果 蛋白质-蛋白质相互作用位点的预测被解决为一个顺序标记问题，通过应用CRF的功能，包括蛋白质序列的轮廓和残基可及表面积。当使用1276个非冗余杂合蛋白质链作为训练集和测试集时，基于CRF的方法可以实现与最先进方法相当的性能。实验结果表明，基于条件随机场的蛋白质相互作用位点预测方法是一种功能强大、鲁棒性强的蛋白质相互作用位点预测方法，可用于指导生物学家对蛋白质进行具体实验。 可用性 http://www.insun.hit.edu.cn/~mhli/site_CRFs/index.html. 补充资料 补充数据可在Bioinformatics在线获得。
MOTIVATION We are motivated by the fast-growing number of protein structures in the Protein Data Bank with necessary information for prediction of protein-protein interaction sites to develop methods for identification of residues participating in protein-protein interactions. We would like to compare conditional random fields (CRFs)-based method with conventional classification-based methods that omit the relation between two labels of neighboring residues to show the advantages of CRFs-based method in predicting protein-protein interaction sites. RESULTS The prediction of protein-protein interaction sites is solved as a sequential labeling problem by applying CRFs with features including protein sequence profile and residue accessible surface area. The CRFs-based method can achieve a comparable performance with state-of-the-art methods, when 1276 nonredundant hetero-complex protein chains are used as training and test set. Experimental result shows that CRFs-based method is a powerful and robust protein-protein interaction site prediction method and can be used to guide biologists to make specific experiments on proteins. AVAILABILITY http://www.insun.hit.edu.cn/~mhli/site_CRFs/index.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.