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Improved sequence‐based prediction of protein secondary structures by combining vacuum‐ultraviolet circular dichroism spectroscopy with neural network
Author(s) -
Matsuo Koichi,
Watanabe Hidenori,
Gekko Kunihiko
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.22055
Subject(s) - protein secondary structure , circular dichroism , spectroscopy , sequence (biology) , protein structure prediction , artificial neural network , biological system , protein structure , chemistry , crystallography , computer science , artificial intelligence , physics , biology , quantum mechanics , biochemistry
Abstract Synchrotron‐radiation vacuum‐ultraviolet circular dichroism (VUVCD) spectroscopy can significantly improve the predictive accuracy of the contents and segment numbers of protein secondary structures by extending the short‐wavelength limit of the spectra. In the present study, we combined VUVCD spectra down to 160 nm with neural‐network (NN) method to improve the sequence‐based prediction of protein secondary structures. The secondary structures of 30 target proteins (test set) were assigned into α‐helices, β‐strands, and others by the DSSP program based on their X‐ray crystal structures. Combining the α‐helix and β‐strand contents estimated from the VUVCD spectra of the target proteins improved the overall sequence‐based predictive accuracy Q 3 for three secondary‐structure components from 59.5 to 60.7%. Incorporating the position‐specific scoring matrix in the NN method improved the predictive accuracy from 70.9 to 72.1% when combining the secondary‐structure contents, to 72.5% when combining the numbers of segments, and finally to 74.9% when filtering the VUVCD data. Improvement in the sequence‐based prediction of secondary structures was also apparent in two other indices of the overall performance: the correlation coefficient (C) and the segment overlap value (SOV). These results suggest that VUVCD data could enhance the predictive accuracy to over 80% when combined with the currently best sequence‐prediction algorithms, greatly expanding the applicability of VUVCD spectroscopy to protein structural biology. Proteins 2008. © 2008 Wiley‐Liss, Inc.