Prediction of transmembrane regions of β-barrel proteins using ANN- and SVM-based methods
Prediction of transmembrane regions of β-barrel proteins using ANN- and SVM-based methods
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DOI：
10.1002/prot.20092
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发表时间：
2004-07-01
影响因子：
2.9
通讯作者：
Raghava, GPS
中科院分区：
文献类型：
--
作者：
Natt, NK;Kaur, H;Raghava, GPS
本文描述了一种使用机器学习技术预测膜蛋白中跨膜 β-桶区域的方法：人工神经网络 (ANN) 和支持向量机 (SVM)。本研究中使用的 ANN 是具有标准反向传播训练算法的前馈神经网络。当将进化信息添加到单个序列作为从 PSI-BLAST 获得的多序列比对时，基于 ANN 的方法的准确性显着提高，从 70.4% 提高到 80.5%。我们还开发了一种基于 SVM 的方法，使用主序列作为输入，准确率达到 77.4%。通过在氨基酸序列信息中添加36个理化参数来修改SVM模型。最后，将基于 ANN 和基于 SVM 的方法结合起来，以充分利用这两种技术的潜力。 SVM、ANN 和组合方法的准确度和马修斯相关系数 (MCC) 值分别为 78.5%、80.5% 和 81.8%，以及 0.55、0.63 和 0.64。这些方法在 16 种蛋白质的非冗余数据集上进行训练和测试，并使用“留一交叉验证”(LOOCV) 评估性能。基于这项研究，我们开发了一个 Web 服务器 TBBPred，用于预测蛋白质中的跨膜 β-桶区域（可从 http://www.imtech.res.in/raghava/tbbpred 获取）。蛋白质 (C) 2004 Wiley-Liss, Inc.
This article describes a method developed for predicting transmembrane beta-barrel regions in membrane proteins using machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). The ANN used in this study is a feed-forward neural network with a standard back-propagation training algorithm. The accuracy of the ANN-based method improved significantly, from 70.4% to 80.5%, when evolutionary information was added to a single sequence as a multiple sequence alignment obtained from PSI-BLAST. We have also developed an SVM-based method using a primary sequence as input and achieved an accuracy of 77.4%. The SVM model was modified by adding 36 physicochemical parameters to the amino acid sequence information. Finally, ANN- and SVM-based methods were combined to utilize the full potential of both techniques. The accuracy and Matthews correlation coefficient (MCC) value of SVM, ANN, and combined method are 78.5%, 80.5%, and 81.8%, and 0.55, 0.63, and 0.64, respectively. These methods were trained and tested on a nonredundant data set of 16 proteins, and performance was evaluated using "leave one out cross-validation" (LOOCV). Based on this study, we have developed a Web server, TBBPred, for predicting transmembrane beta-barrel regions in proteins (available at http://www.imtech.res.in/raghava/tbbpred). Proteins (C) 2004 Wiley-Liss, Inc.