Protein secondary structure prediction with dihedral angles

被引：73

作者：

Wood, MJ ^{[1
]}

Hirst, JD ^{[1
]}

机构：

[1] Univ Nottingham, Sch Chem, Nottingham NG7 2RD, England

来源：

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS | 2005年 / 59卷 / 03期

关键词：

structure prediction; sequence representation; neural networks; cascade-correlation; CASP;

D O I：

10.1002/prot.20435

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：

We present DESTRUCT, a new method of protein secondary structure prediction, which achieves a three-state accuracy (Q(3)) of 79.4% in a cross-validated trial on a nonredundant set of 513 proteins. An iterative set of cascade-correlation neural networks is used to predict both secondary structure and psi dihedral angles, with predicted values enhancing the subsequent iteration. Predictive accuracies of 80.7% and 81.7% are achieved on the CASP4 and CASP5 targets, respectively. Our approach is significantly more accurate than other contemporary methods, due to feedback and a novel combination of structural representations. (c) 2005 Wiley-Liss, Inc.

引用

页码：476 / 481

页数：6

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