Literature DB >> 21920789

A novel method for quantitatively predicting non-covalent interactions from protein and nucleic acid sequence.

Jiansheng Wu1, Dong Hu, Xin Xu, Yan Ding, Shancheng Yan, Xiao Sun.   

Abstract

Biochemical interactions between proteins and biological macromolecules are dominated by non-covalent interactions. A novel method is presented for quantitatively predicting the number of two most dominant non-covalent interactions, i.e., hydrogen bonds and van der Waals contacts, potentially forming in a hypothetical protein-nucleic acid complex from sequences using support vector machine regression models in conjunction with a hybrid feature. The hybrid feature consists of the sequence-length fraction information, conjoint triad for protein sequences and the gapped dinucleotide composition. The SVR-based models achieved excellent performance. The polarity of amino acids was also found to play a vital role in the formation of hydrogen bonds and van der Waals contacts. We have constructed a web server H-VDW (http://www.cbi.seu.edu.cn/H-VDW/H-VDW.htm) for public access to the SVR models.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21920789     DOI: 10.1016/j.jmgm.2011.08.001

Source DB:  PubMed          Journal:  J Mol Graph Model        ISSN: 1093-3263            Impact factor:   2.518


  2 in total

1.  Multi-instance multilabel learning with weak-label for predicting protein function in electricigens.

Authors:  Jian-Sheng Wu; Hai-Feng Hu; Shan-Cheng Yan; Li-Hua Tang
Journal:  Biomed Res Int       Date:  2015-05-05       Impact factor: 3.411

2.  Multi-Instance Metric Transfer Learning for Genome-Wide Protein Function Prediction.

Authors:  Yonghui Xu; Huaqing Min; Qingyao Wu; Hengjie Song; Bicui Ye
Journal:  Sci Rep       Date:  2017-02-06       Impact factor: 4.379

  2 in total

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