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Protein folds recognized by an intelligent predictor based‐on evolutionary and structural information
Author(s) -
Cheung Ngaam J.,
Ding XueMing,
Shen HongBin
Publication year - 2016
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.24232
Subject(s) - protein tertiary structure , benchmark (surveying) , protein structure prediction , computer science , artificial neural network , particle swarm optimization , artificial intelligence , protein structure , distance matrix , feature (linguistics) , pattern recognition (psychology) , data mining , machine learning , algorithm , biology , geography , biochemistry , linguistics , philosophy , geodesy
Protein fold recognition is an important and essential step in determining tertiary structure of a protein in biological science. In this study, a model termed NiRecor is developed for recognizing protein folds based on artificial neural networks incorporated in an adaptive heterogeneous particle swarm optimizer. The main contribution of NiRecor is that it is a data‐driven and highly‐performing predictor without manually tuning control parameters for different data sets. In biological science, since evolutionary‐ and structure‐based information of amino acid sequences is greatly important in determination of tertiary structure of a protein, accordingly, in NiRecor we employ two different feature sets, which involve position specific scoring matrix and secondary structure prediction matrix, to predict the structural classes of protein folds. The experimental results demonstrate the proposed method is powerful in predicting protein folds with higher precisions by improvements of 1.1 ∼7.8 percentages on three benchmark datasets by comparing with several existing predictors. © 2015 Wiley Periodicals, Inc.