A novel protein structural classes prediction method based on predicted secondary structure

Biochimie
Shuyan DingTianming Wang

Abstract

Knowledge of structural classes plays an important role in understanding protein folding patterns. In this paper, features based on the predicted secondary structure sequence and the corresponding E-H sequence are extracted. Then, an 11-dimensional feature vector is selected based on a wrapper feature selection algorithm and a support vector machine (SVM). Among the 11 selected features, 4 novel features are newly designed to model the differences between α/β class and α + β class, and other 7 rational features are proposed by previous researchers. To examine the performance of our method, a total of 5 datasets are used to design and test the proposed method. The results show that competitive prediction accuracies can be achieved by the proposed method compared to existing methods (SCPRED, RKS-PPSC and MODAS), and 4 new features are demonstrated essential to differentiate α/β and α + β classes. Standalone version of the proposed method is written in JAVA language and it can be downloaded from http://web.xidian.edu.cn/slzhang/paper.html.

References

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Citations

Sep 11, 2014·Interdisciplinary Sciences, Computational Life Sciences·Hui DingNini Rao
May 13, 2015·IEEE Transactions on Nanobioscience· Hong-Liang Dai
Jun 19, 2013·Biochimie·Lichao ZhangLiang Kong
Mar 13, 2014·Journal of Theoretical Biology·Shuyan DingYuhua Yao
Apr 16, 2013·Biochimie·Jaroslav KubrychtJiří Hudeček
Jun 21, 2016·Journal of Theoretical Biology·Mohammad H OlyaeeMahdi Yaghoobi
Sep 30, 2017·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Wenzheng BaoDe-Shuang Huang
Aug 29, 2018·Current Computer-aided Drug Design·Bishnupriya PandaAbhimanyu Thakur
May 31, 2020·Medical & Biological Engineering & Computing·Kushal Kanti GhoshUjjwal Maulik
Feb 9, 2021·Molecular Diversity·Mahsa Torkamanian-AfsharAli Masoudi-Nejad
Feb 25, 2020·Genomics·Mahsa Torkamanian-AfsharAli Masoudi-Nejad

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