M. Kamada, M. Hayashida, Jiangning Song, T. Akutsu
{"title":"Discriminative random field approach to prediction of protein residue contacts","authors":"M. Kamada, M. Hayashida, Jiangning Song, T. Akutsu","doi":"10.1109/ISB.2011.6033167","DOIUrl":null,"url":null,"abstract":"Understanding of interactions of proteins is important to reveal networks and functions of molecules. Many investigations have been conducted to analyze interactions and contacts between residues. It is supported that residues at interacting sites have co-evolved with those at the corresponding residues in the partner protein to keep the interactions between the proteins. Therefore, mutual information (MI) between residues calculated from multiple sequence alignments of homologous proteins is considered to be useful for identifying contact residues in interacting proteins. In our previous work, we proposed a prediction method for protein-protein interactions using mutual information and conditional random fields (CRFs), and confirmed its usefulness. The discriminative random field (DRF) is a special type of CRFs, and can recognize some specific characteristic regions in an image. Since the matrix consisted of mutual information between residues in two interacting proteins can be regarded as an image, we propose a prediction method for protein residue contacts using DRF models with mutual information. To validate our method, we perform computational experiments for several interactions between Pfam domains. The results suggest that the proposed DRF-based method with MI is useful for predicting protein residue contacts compared with that using the corresponding Markov random field (MRF) model.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Systems Biology (ISB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISB.2011.6033167","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Understanding of interactions of proteins is important to reveal networks and functions of molecules. Many investigations have been conducted to analyze interactions and contacts between residues. It is supported that residues at interacting sites have co-evolved with those at the corresponding residues in the partner protein to keep the interactions between the proteins. Therefore, mutual information (MI) between residues calculated from multiple sequence alignments of homologous proteins is considered to be useful for identifying contact residues in interacting proteins. In our previous work, we proposed a prediction method for protein-protein interactions using mutual information and conditional random fields (CRFs), and confirmed its usefulness. The discriminative random field (DRF) is a special type of CRFs, and can recognize some specific characteristic regions in an image. Since the matrix consisted of mutual information between residues in two interacting proteins can be regarded as an image, we propose a prediction method for protein residue contacts using DRF models with mutual information. To validate our method, we perform computational experiments for several interactions between Pfam domains. The results suggest that the proposed DRF-based method with MI is useful for predicting protein residue contacts compared with that using the corresponding Markov random field (MRF) model.
了解蛋白质的相互作用对揭示分子的网络和功能具有重要意义。已经进行了许多研究来分析残留物之间的相互作用和接触。相互作用位点的残基与伴侣蛋白中相应残基的残基共同进化,以保持蛋白质之间的相互作用。因此,从同源蛋白的多个序列比对中计算出的残基之间的互信息(MI)被认为对鉴定相互作用蛋白中的接触残基是有用的。在我们之前的工作中,我们提出了一种利用互信息和条件随机场(CRFs)预测蛋白质-蛋白质相互作用的方法,并证实了它的实用性。判别随机场(discriminative random field, DRF)是一种特殊类型的随机场,它可以识别图像中特定的特征区域。由于两个相互作用蛋白残基间互信息构成的矩阵可以看作是一幅图像,我们提出了一种基于互信息的DRF模型的蛋白残基接触预测方法。为了验证我们的方法,我们对Pfam域之间的几个相互作用进行了计算实验。结果表明,与使用相应的马尔可夫随机场(MRF)模型相比,所提出的基于drf的MI方法可用于预测蛋白质残基接触。