Multiple linear regression for protein secondary structure prediction

被引：17

作者：

Pan, XM ^{[1
]}

机构：

[1] Acad Sinica, Inst Biophys, Natl Lab Biomacromol, Beijing 100101, Peoples R China

来源：

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS | 2001年 / 43卷 / 03期

关键词：

protein folding; secondary structure prediction; multiple linear regression; consensus; jackknife; amino acid sequence;

D O I：

10.1002/prot.1036

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：

In the present work, a novel method was proposed for prediction of secondary structure. Over a database of 396 proteins (CB396) with a three-state-defining secondary structure, this method with jackknife procedure achieved an accuracy of 68.8% and SOV score of 71.4% using single sequence and an accuracy of 73.7% and SOV score of 77.3% using multiple sequence alignments. Combination of this method with DSC, PHD, PREDATOR, and NNSSP gives Q(3) = 76.2% and SOV = 79.8%. (C) 2001 Wiley-Liss, Inc.

引用

页码：256 / 259

页数：4

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