Predicting the secondary structure of globular proteins
using neural network models
Ning Qian and Terrence J. Sejnowski, J. Mol. Biol. 1988,
202:865-884. Download the
full paper (PDF file)
Abstract
We present a new method for predicting the secondary structure of
globular proteins based on non-linear neural network models. Network
models learn from existing protein structures how to predict the
secondary structure of local sequences of amino acids. The average
success rate of our method on a testing set of proteins non-homologous
with the corresponding training set was 64.3% on three types of
secondary structure (alpha-helix, beta-sheet, and coil), with
correlation coefficients of C_alpha = 0.41, C_beta = 0.31 and C_coil =
0.41. These quality indices are all higher than those of previous
methods. The prediction accuracy for the first 25 residues of the
N-terminal sequence was significantly better. We conclude from
computational experiments on real and artificial structures that no
method based solely on local information in the protein sequence is
likely to produce significantly better results for non-homologous
proteins. The performance of our method of homologous proteins is much
better than for non-homologous proteins, but is not as good as simply
assuming that homologous sequences have identical structures.
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