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Analysis and prediction of protein local structure based on structure alphabets
Author(s) -
Dong Qiwen,
Wang Xiaolong,
Lin Lei,
Wang Yadong
Publication year - 2008
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.21904
Subject(s) - protein structure prediction , local structure , classifier (uml) , artificial intelligence , computer science , data structure , protein structure , artificial neural network , cartesian coordinate system , pattern recognition (psychology) , algorithm , mathematics , physics , geometry , nuclear magnetic resonance , programming language , chemical physics
In recent years, protein structure prediction using local structure information has made great progress. Many fragment libraries or structure alphabets have been developed. In this study, the entropies and correlations of local structures are first calculated. The results show that neighboring local structures are strongly correlated. Then, a dual‐layer model has been designed for protein local structure prediction. The position‐specific score matrix, generated by PSI‐BLAST, is inputted to the first‐layer classifier, whose output is further enhanced by a second‐layer classifier. The neural network is selected as the classifier. Two structure alphabets are explored, which are represented in Cartesian coordinate space and in torsion angles space respectively. Testing on the nonredundant dataset shows that the dual‐layer model is an efficient method for protein local structure prediction. The Q ‐scores are 0.456 and 0.585 for the two structure alphabets, which is a significant improvement in comparison with related works. Proteins 2008. © 2008 Wiley‐Liss, Inc.