EVALUATION OF SECONDARY STRUCTURE OF PROTEINS FROM UV CIRCULAR-DICHROISM SPECTRA USING AN UNSUPERVISED LEARNING NEURAL-NETWORK

被引：936

作者：

ANDRADE, MA ^{[1
]}

CHACON, P ^{[1
]}

MERELO, JJ ^{[1
]}

MORAN, F ^{[1
]}

机构：

[1] UNIV GRANADA, FAC CIENCIAS, DEPT ELECTR & TECNOL COMP, E-18071 GRANADA, SPAIN

来源：

PROTEIN ENGINEERING | 1993年 / 6卷 / 04期

关键词：

NEURAL NETWORKS; PREDICTION; SECONDARY STRUCTURE; UNSUPERVISED LEARNING;

D O I：

10.1093/protein/6.4.383

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

An optimized self-organizing map algorithm has been used to obtain protein topological (proteinotopic) maps. A neural network is able to arrange a set of proteins depending on their ultraviolet circular dichroism spectra in a completely unsupervised learning process. Analysis of the proteinotopic map reveals that the network extracts the main secondary structure features even with the small number of examples used. Some methods to use the proteinotopic map for protein secondary structure prediction are tested showing a good performance in the 200-240 nm wavelength range that is likely to increase as new protein structures are known.

引用

页码：383 / 390

页数：8

共 31 条

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