BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques.
BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques.
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DOI：
10.1093/nar/gki588
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发表时间：
2005-07-01
影响因子：
14.9
通讯作者：
Raghava, GPS
中科院分区：
文献类型：
--
作者：
Kumar, M;Bhasin, M;Natt, NK;Raghava, GPS
本文描述了一种预测蛋白质序列中超二级结构基序β-发夹的方法。使用DSSP和PROMOTIF程序从2880个蛋白质的非冗余数据集获得5102个发夹和5131个非发夹的集合，对该方法进行训练和测试。两种机器学习技术，人工神经网络（ANN）和支持向量机（SVM），被用来预测β-发夹。当使用氨基酸序列作为输入时，使用ANN实现了65.5%的准确性。当使用进化信息（PSI-BLAST配置文件），观察到的二级结构和表面可及性作为输入时，准确度从65.5%提高到69.1%。当SVM用于分类而不是ANN时，该方法的准确性进一步提高，从69.1%提高到79.2%。开发的方法的性能在测试情况下进行了评估，其中预测的二级结构和表面可及性被用来代替观察到的结构。在测试用例中，基于SVM的方法实现的最高准确率为77.9%。在测试用例中，在X先前使用的数据集上获得了71.1%的最大准确度，Matthew相关系数为0.41。Cruz，E. G.哈钦森，A. Shephard和J.M. Thornton（2002）Proc. Natl Acad. Sci. USA，99，11157-11162.该方法的性能也进行了评估，在蛋白质结构预测技术的关键评估（CASP 6）的第6次社区范围内的实验中使用的蛋白质。基于所描述的算法，已经开发了一个Web服务器BhairPred（），其可以用于使用SVM方法预测蛋白质中的β-发夹。
This paper describes a method for predicting a supersecondary structural motif, β-hairpins, in a protein sequence. The method was trained and tested on a set of 5102 hairpins and 5131 non-hairpins, obtained from a non-redundant dataset of 2880 proteins using the DSSP and PROMOTIF programs. Two machine-learning techniques, an artificial neural network (ANN) and a support vector machine (SVM), were used to predict β-hairpins. An accuracy of 65.5% was achieved using ANN when an amino acid sequence was used as the input. The accuracy improved from 65.5 to 69.1% when evolutionary information (PSI-BLAST profile), observed secondary structure and surface accessibility were used as the inputs. The accuracy of the method further improved from 69.1 to 79.2% when the SVM was used for classification instead of the ANN. The performances of the methods developed were assessed in a test case, where predicted secondary structure and surface accessibility were used instead of the observed structure. The highest accuracy achieved by the SVM based method in the test case was 77.9%. A maximum accuracy of 71.1% with Matthew's correlation coefficient of 0.41 in the test case was obtained on a dataset previously used by X. Cruz, E. G. Hutchinson, A. Shephard and J. M. Thornton (2002) Proc. Natl Acad. Sci. USA, 99, 11157–11162. The performance of the method was also evaluated on proteins used in the ‘6th community-wide experiment on the critical assessment of techniques for protein structure prediction (CASP6)’. Based on the algorithm described, a web server, BhairPred (), has been developed, which can be used to predict β-hairpins in a protein using the SVM approach.
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影响因子：
64.8
作者：
RUMELHART, DE;HINTON, GE;WILLIAMS, RJ
通讯作者：
WILLIAMS, RJ
影响因子：
8
作者：
Kaur, H;Raghava, GPS
通讯作者：
Raghava, GPS
影响因子：
5.8
作者：
Kaur, H;Raghava, GPS
通讯作者：
Raghava, GPS
影响因子：
8
作者：
Kaur, H;Raghava, GPS
通讯作者：
Raghava, GPS
DOI：
10.1093/protein/10.7.763
发表时间：
1997-07-01
期刊：
PROTEIN ENGINEERING
影响因子：
--
作者：
Sun, ZR;Rao, XQ;Xu, D
通讯作者：
Xu, D