Identification of NAD interacting residues in proteins.
Identification of NAD interacting residues in proteins.
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DOI：
10.1186/1471-2105-11-160
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发表时间：
2010-03-30
影响因子：
3
通讯作者：
Raghava GP
中科院分区：
文献类型：
--
作者：
Ansari HR;Raghava GP
小分子辅因子或配体在细胞的正常功能中起着至关重要的作用。为了完全理解反应机制，需要对其靶蛋白和结合位点进行准确的注释。烟酰胺腺嘌呤二核苷酸（NAD+或NAD）是活细胞中最常用的有机辅因子之一，在细胞代谢、储存和调节过程中起着关键作用。在过去，几个NAD结合蛋白（NADBP）已在文献中报道，这是负责广泛的活动在细胞中。已经尝试推导出NAD+与其靶蛋白结合的规则。然而，到目前为止，由于结构确定的耗时过程和基于相似性的方法的局限性，无法导出有效的模型。因此，需要一种基于序列和非相似性的方法来表征NAD结合位点以帮助注释。本研究尝试利用生物信息学工具从氨基酸序列中预测NAD结合蛋白及其相互作用残基。我们从555个NAD结合蛋白中提取了1556条蛋白链，其结构可在蛋白质数据库中获得。然后我们去除所有冗余的蛋白质链，最终获得195条非冗余的NAD结合蛋白质链，其中没有两条链具有超过40%的序列同一性。在这项研究中，所有模型都是使用五重交叉验证技术对上述195种NAD结合蛋白的数据集进行开发和评估的。虽然在NAD相互作用中某些类型的残基是优选的（例如Gly、Tyr、Thr、His），但是残基如Ala、Glu、Leu、Lys不是优选的。利用不同窗口长度的氨基酸序列建立了一种基于支持向量机（SVM）的NAD相互作用残基预测方法，在窗口长度为17时，预测精度达到74.13%，最大相关系数（MCC）为0.47。我们还开发了一种基于SVM的方法，使用进化信息的形式的位置特定的评分矩阵（PSSM），并获得最大MCC 0.75，准确率为87.25%。首次开发了一种基于序列的方法，用于在没有任何先前结构信息的情况下预测NAD结合蛋白及其相互作用残基。本模型将有助于理解细胞中NAD+依赖性作用机制。为了向科学界提供服务，我们开发了一个用户友好的网络服务器，可从URL http://www.imtech.res.in/raghava/nadbinder/获得。
Small molecular cofactors or ligands play a crucial role in the proper functioning of cells. Accurate annotation of their target proteins and binding sites is required for the complete understanding of reaction mechanisms. Nicotinamide adenine dinucleotide (NAD+ or NAD) is one of the most commonly used organic cofactors in living cells, which plays a critical role in cellular metabolism, storage and regulatory processes. In the past, several NAD binding proteins (NADBP) have been reported in the literature, which are responsible for a wide-range of activities in the cell. Attempts have been made to derive a rule for the binding of NAD+ to its target proteins. However, so far an efficient model could not be derived due to the time consuming process of structure determination, and limitations of similarity based approaches. Thus a sequence and non-similarity based method is needed to characterize the NAD binding sites to help in the annotation. In this study attempts have been made to predict NAD binding proteins and their interacting residues (NIRs) from amino acid sequence using bioinformatics tools. We extracted 1556 proteins chains from 555 NAD binding proteins whose structure is available in Protein Data Bank. Then we removed all redundant protein chains and finally obtained 195 non-redundant NAD binding protein chains, where no two chains have more than 40% sequence identity. In this study all models were developed and evaluated using five-fold cross validation technique on the above dataset of 195 NAD binding proteins. While certain type of residues are preferred (e.g. Gly, Tyr, Thr, His) in NAD interaction, residues like Ala, Glu, Leu, Lys are not preferred. A support vector machine (SVM) based method has been developed using various window lengths of amino acid sequence for predicting NAD interacting residues and obtained maximum Matthew's correlation coefficient (MCC) 0.47 with accuracy 74.13% at window length 17. We also developed a SVM based method using evolutionary information in the form of position specific scoring matrix (PSSM) and obtained maximum MCC 0.75 with accuracy 87.25%. For the first time a sequence-based method has been developed for the prediction of NAD binding proteins and their interacting residues, in the absence of any prior structural information. The present model will aid in the understanding of NAD+ dependent mechanisms of action in the cell. To provide service to the scientific community, we have developed a user-friendly web server, which is available from URL http://www.imtech.res.in/raghava/nadbinder/.
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