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Prediction of protein structural class for the twilight zone sequences.

Abstract：

:Structural class characterizes the overall folding type of a protein or its domain. This paper develops an accurate method for in silico prediction of structural classes from low homology (twilight zone) protein sequences. The proposed LLSC-PRED method applies linear logistic regression classifier and a custom-designed, feature-based sequence representation to provide predictions. The main advantages of the LLSC-PRED are the comprehensive representation that includes 58 features describing composition and physicochemical properties of the sequences and transparency of the prediction model. The representation also includes predicted secondary structure content, thus for the first time exploring synergy between these two related predictions. Based on tests performed with a large set of 1673 twilight zone domains, the LLSC-PRED's prediction accuracy, which equals over 62%, is shown to be better than accuracy of over a dozen recently published competing in silico methods and similar to accuracy of other, non-transparent classifiers that use the proposed representation.

journal_name

Biochem Biophys Res Commun

journal_title

Biochemical and biophysical research communications

authors

Kurgan L,Chen K

doi

10.1016/j.bbrc.2007.03.164

subject

Has Abstract

pub_date

2007-06-01 00:00:00

pages

453-60

issue

2

eissn

0006-291X

issn

1090-2104

pii

S0006-291X(07)00654-7

journal_volume

357

pub_type

杂志文章

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