Document Detail


Prediction of the parallel/antiparallel orientation of beta-strands using amino acid pairing preferences and support vector machines.
MedLine Citation:
PMID:  20035768     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In principle, structural information of protein sequences with no detectable homology to a protein of known structure could be obtained by predicting the arrangement of their secondary structural elements. Although some ab initio methods for protein structure prediction have been reported, the long-range interactions required to accurately predict tertiary structures of beta-sheet containing proteins are still difficult to simulate. To remedy this problem and facilitate de novo prediction of beta-sheet containing protein structures, we developed a support vector machine (SVM) approach that classified parallel and antiparallel orientation of beta-strands by using the information of interstrand amino acid pairing preferences. Based on a second-order statistics on the relative frequencies of each possible interstrand amino acid pair, we defined an average amino acid pairing encoding matrix (APEM) for encoding beta-strands as input in the prediction model. As a result, a prediction accuracy of 86.89% and a Matthew's correlation coefficient value of 0.71 have been achieved through 7-fold cross-validation on a non-redundant protein dataset from PISCES. Although several issues still remain to be studied, the method presented here to some extent could indicate the important contribution of the amino acid pairs to the beta-strand orientation, and provide a possible way to further be combined with other algorithms making a full 'identification' of beta-strands.
Authors:
Ning Zhang; Guangyou Duan; Shan Gao; Jishou Ruan; Tao Zhang
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2009-12-24
Journal Detail:
Title:  Journal of theoretical biology     Volume:  263     ISSN:  1095-8541     ISO Abbreviation:  J. Theor. Biol.     Publication Date:  2010 Apr 
Date Detail:
Created Date:  2010-02-23     Completed Date:  2010-06-01     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0376342     Medline TA:  J Theor Biol     Country:  England    
Other Details:
Languages:  eng     Pagination:  360-8     Citation Subset:  IM    
Copyright Information:
Copyright 2009 Elsevier Ltd. All rights reserved.
Affiliation:
Key Laboratory of Bioactive Materials, Ministry of Education and the College of Life Sciences, Nankai University, PR China.
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MeSH Terms
Descriptor/Qualifier:
Amino Acids / chemistry*
Models, Theoretical*
Proteins / chemistry
Chemical
Reg. No./Substance:
0/Amino Acids; 0/Proteins

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