Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information
Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information
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DOI：
10.1186/1471-2105-11-301
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发表时间：
2010-06-03
影响因子：
3
通讯作者：
Raghava, Gajendra P. S.
中科院分区：
文献类型：
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作者：
Chauhan, Jagat S.;Mishra, Nitish K.;Raghava, Gajendra P. S.
背景：三磷酸鸟苷（GTP）结合蛋白在G蛋白的调节中发挥重要作用。因此，预测蛋白质中GTP相互作用残基是计算生物学领域的主要挑战之一。在这项研究中，已作出了尝试，以开发一种计算方法来预测GTP相互作用的蛋白质中的残基具有高准确度（Acc），精度（Prec）和召回（Rc）。结果：在这项研究中开发的所有模型已经训练和测试的非冗余（40%相似性）数据集上使用五倍交叉验证。首先，我们已经开发了基于神经网络的模型，使用单一序列和PSSM配置文件，并取得最大马修斯相关系数（MCC）分别为0.24（Acc 61.30%）和0.39（Acc 68.88%）。其次，我们已经开发了一个基于支持向量机（SVM）的模型，使用单一序列和PSSM概况，并实现了最大MCC 0.37（Prec 0.73，Rc 0.57，Acc 67.98%）和0.55（Prec 0.80，Rc 0.73，Acc 77.17%）分别。在这项工作中，我们首次提出了预测GTP相互作用二肽（两个连续的GTP相互作用残基）和三肽（三个连续的GTP相互作用残基）的新概念。我们开发了基于支持向量机的模型，用于使用PSSM图谱预测与GTP相互作用的二肽，并获得MCC 0.64，精确度0.87，召回率0.74，准确率81.37%。同样，基于支持向量机（SVM）模型，利用PSSM模型预测GTP相互作用三肽，其MCC为0.70，准确率为0.93，召回率为0.73，准确率为83.98%。基于二肽或三肽的预测模型比传统的基于单残基的预测模型更准确。已经开发了基于上述模型的网络服务器“GTPBinder”http://www.imtech.res.in/raghava/gtpbinder/based，用于预测蛋白质中的GTP相互作用残基。
Background: Guanosine triphosphate (GTP)-binding proteins play an important role in regulation of G-protein. Thus prediction of GTP interacting residues in a protein is one of the major challenges in the field of the computational biology. In this study, an attempt has been made to develop a computational method for predicting GTP interacting residues in a protein with high accuracy (Acc), precision (Prec) and recall (Rc).Result: All the models developed in this study have been trained and tested on a non-redundant (40% similarity) dataset using five-fold cross-validation. Firstly, we have developed neural network based models using single sequence and PSSM profile and achieved maximum Matthews Correlation Coefficient (MCC) 0.24 (Acc 61.30%) and 0.39 (Acc 68.88%) respectively. Secondly, we have developed a support vector machine (SVM) based models using single sequence and PSSM profile and achieved maximum MCC 0.37 (Prec 0.73, Rc 0.57, Acc 67.98%) and 0.55 (Prec 0.80, Rc 0.73, Acc 77.17%) respectively. In this work, we have introduced a new concept of predicting GTP interacting dipeptide (two consecutive GTP interacting residues) and tripeptide (three consecutive GTP interacting residues) for the first time. We have developed SVM based model for predicting GTP interacting dipeptides using PSSM profile and achieved MCC 0.64 with precision 0.87, recall 0.74 and accuracy 81.37%. Similarly, SVM based model have been developed for predicting GTP interacting tripeptides using PSSM profile and achieved MCC 0.70 with precision 0.93, recall 0.73 and accuracy 83.98%.Conclusion: These results show that PSSM based method performs better than single sequence based method. The prediction models based on dipeptides or tripeptides are more accurate than the traditional model based on single residue. A web server "GTPBinder" http://www.imtech.res.in/raghava/gtpbinder/based on above models has been developed for predicting GTP interacting residues in a protein.