SIDE-CHAIN PREDICTION BY NEURAL NETWORKS AND SIMULATED ANNEALING OPTIMIZATION

被引：40

作者：

HWANG, JK

LIAO, WF

机构：

[1] Institute of Life Sciences, National Tsing-Hua University

来源：

PROTEIN ENGINEERING | 1995年 / 8卷 / 04期

关键词：

MONTE CARLO OPTIMIZATION; NEURAL NETWORKS; PROTEINS; SIDE CHAINS; SIMULATED ANNEALING;

D O I：

10.1093/protein/8.4.363

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：

The prediction of the side-chain positions of proteins of known tertiary backbone structure was accomplished by a combination of neural networks and a simulated annealing method, Neural networks were used to generate distributions of side-chain dihedral angles, By eliminating network outputs with low activities, we were able to generate a reduced conformational space in which Monte Carlo-simulated annealing was carried out to optimize side-chain positions, In this study of 12 proteins, the average fractions of correct chi(1), chi(2) and combined chi(1) and chi(2) (to within 40 degrees of actual structure) were 82, 72 and 68% respectively.

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页码：363 / 370

页数：8

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