Prediction of protein structural classes for low-homology sequences based on predicted secondary structure.

BMC Bioinformatics
Jian-Yi YangXin Chen

Abstract

Prediction of protein structural classes (alpha, beta, alpha + beta and alpha/beta) from amino acid sequences is of great importance, as it is beneficial to study protein function, regulation and interactions. Many methods have been developed for high-homology protein sequences, and the prediction accuracies can achieve up to 90%. However, for low-homology sequences whose average pairwise sequence identity lies between 20% and 40%, they perform relatively poorly, yielding the prediction accuracy often below 60%. We propose a new method to predict protein structural classes on the basis of features extracted from the predicted secondary structures of proteins rather than directly from their amino acid sequences. It first uses PSIPRED to predict the secondary structure for each protein sequence. Then, the chaos game representation is employed to represent the predicted secondary structure as two time series, from which we generate a comprehensive set of 24 features using recurrence quantification analysis, K-string based information entropy and segment-based analysis. The resulting feature vectors are finally fed into a simple yet powerful Fisher's discriminant algorithm for the prediction of protein structural classes. We tested...Continue Reading

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Citations

Mar 5, 2013·Amino Acids·Dong-Jun YuJing-Yu Yang
Nov 13, 2010·Nucleic Acids Research·Robbie P JoostenGert Vriend
Jun 23, 2012·PloS One·Xia-Yu XiaXian-Ming Pan
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Dec 30, 2015·International Journal of Molecular Sciences·Taigang LiuChunhua Wang
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Jun 19, 2013·Biochimie·Lichao ZhangLiang Kong
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May 2, 2012·Journal of Biomolecular Structure & Dynamics·Hao LinWei Chen
May 31, 2020·Medical & Biological Engineering & Computing·Kushal Kanti GhoshUjjwal Maulik

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Software Mentioned

SCPRED
BLAST
PSIPRED
RQA
ZLP
PSI

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