Prediction of protein-RNA residue-base contacts using two-dimensional conditional random field with the lasso.
Prediction of protein-RNA residue-base contacts using two-dimensional conditional random field with the lasso.
复制标题
使用二维条件随机场和套索预测蛋白质-RNA 残基接触
DOI：
10.1186/1752-0509-7-s2-s15
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发表时间：
2013
影响因子：
--
通讯作者：
Akutsu T
中科院分区：
文献类型：
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作者：
Hayashida M;Kamada M;Song J;Akutsu T
研究背景为了揭示生物细胞系统中的分子功能和网络，研究蛋白质和RNA之间的相互作用是非常重要的。许多研究已经进行了调查和分析蛋白质氨基酸残基和RNA碱基之间的相互作用。就蛋白质中残基之间的相互作用而言，通常认为相互作用位点上的氨基酸残基与伴侣残基一起共同进化，以保持蛋白质中残基之间的相互作用。基于这一假设，我们在以前的研究中识别相互作用蛋白质中的残基-残基接触对，我们从同源蛋白质的多序列比对中计算氨基酸残基之间的互信息，并将其与判别随机场（DRF）方法相结合，其是一种特殊类型的条件随机场（CRF），并且已经证明在图像处理领域中对于从照片中提取区别区域的目的是有用的。最近，残基和DNA碱基之间的相互作用的进化相关性也被发现在某些转录因子和DNA-binding sites.ResultsIn this paper，我们采用更通用的二维CRF比这样的DRFs来预测蛋白质氨基酸残基和RNA碱基之间的相互作用。此外，我们引入标签代表的氨基酸和碱基的CRF的本地功能。此外，我们研究的效用ofL 1范数正则化（套索）的CRF。为了评估我们的方法，我们使用几个Pfam结构域和Rfam条目之间的残基相互作用，进行交叉验证，并计算平均AUC（ROC曲线下面积）得分。结果表明，我们的CRF为基础的方法，使用互信息和标签的套索是有用的，进一步提高性能，特别是提供CRF的功能成功地减少了套索approach.ConclusionsWe提出了简单和通用的二维CRF模型使用标签和互信息的套索。结合使用基于CRF的方法与套索对于预测蛋白质-RNA相互作用中的残基-碱基接触特别有用。
BackgroundTo uncover molecular functions and networks in biological cellular systems, it is important to dissect interactions between proteins and RNAs. Many studies have been performed to investigate and analyze interactions between protein amino acid residues and RNA bases. In terms of interactions between residues in proteins, it is generally accepted that an amino acid residue at interacting sites has coevolved together with the partner residue in order to keep the interaction between residues in proteins. Based on this hypothesis, in our previous study to identify residue-residue contact pairs in interacting proteins, we made calculations of mutual information (M I) between amino acid residues from some multiple sequence alignment of homologous proteins, and combined it with a discriminative random field (DRF) approach, which is a special type of conditional random fields (CRFs) and has been proved useful for the purpose of extracting distinguishing areas from a photograph in the image processing field. Recently, the evolutionary correlation of interactions between residues and DNA bases has also been found in certain transcription factors and the DNA-binding sites.ResultsIn this paper, we employ more generic two-dimensional CRFs than such DRFs to predict interactions between protein amino acid residues and RNA bases. In addition, we introduce labels representing kinds of amino acids and bases as local features of a CRF. Furthermore, we examine the utility ofL1-norm regularization (lasso) for the CRF. For evaluation of our method, we use residue-base interactions between several Pfam domains and Rfam entries, conduct cross-validation, and calculate the average AUC (Area under ROC Curve) score. The results suggest that our CRF-based method using mutual information and labels with the lasso is useful for further improving the performance, especially provided that the features of CRF are successfully reduced by the lasso approach.ConclusionsWe propose simple and generic two-dimensional CRF models using labels and mutual information with the lasso. Use of the CRF-based method in combination with the lasso is particularly useful for predicting the residue-base contacts in protein-RNA interactions.