Journal Article

A novel method for accurate one-dimensional protein structure prediction based on fragment matching

Tuping Zhou, Nanjiang Shu and Sven Hovmöller

in Bioinformatics

Volume 26, issue 4, pages 470-477
Published in print February 2010 | ISSN: 1367-4803
Published online December 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp679
A novel method for accurate one-dimensional protein structure prediction based on fragment matching

More Like This

Show all results sharing this subject:

  • Bioinformatics and Computational Biology

GO

Show Summary Details

Preview

Motivation: The precise prediction of one-dimensional (1D) protein structure as represented by the protein secondary structure and 1D string of discrete state of dihedral angles (i.e. Shape Strings) is a prerequisite for the successful prediction of three-dimensional (3D) structure as well as protein–protein interaction. We have developed a novel 1D structure prediction method, called Frag1D, based on a straightforward fragment matching algorithm and demonstrated its success in the prediction of three sets of 1D structural alphabets, i.e. the classical three-state secondary structure, three- and eight-state Shape Strings.

Results: By exploiting the vast protein sequence and protein structure data available, we have brought secondary-structure prediction closer to the expected theoretical limit. When tested by a leave-one-out cross validation on a non-redundant set of PDB cutting at 30% sequence identity containing 5860 protein chains, the overall per-residue accuracy for secondary-structure prediction, i.e. Q3 is 82.9%. The overall per-residue accuracy for three- and eight-state Shape Strings are 85.1 and 71.5%, respectively. We have also benchmarked our program with the latest version of PSIPRED for secondary structure prediction and our program predicted 0.3% better in Q3 when tested on 2241 chains with the same training set. For Shape Strings, we compared our method with a recently published method with the same dataset and definition as used by that method. Our program predicted at 2.2% better in accuracy for three-state Shape Strings. By quantitatively investigating the effect of data base size on 1D structure prediction we show that the accuracy increases by ∼1% with every doubling of the database size.

Availability: The program is available for download at http://www.fos.su.se/∼nanjiang/Frag1D/download. Supplementary data are available at http://www.fos.su.se/∼nanjiang/Frag1D/supplement/suppl.html

Contact: svenh@struc.su.se

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6613 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.