Journal Article

Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates

Yuedong Yang, Eshel Faraggi, Huiying Zhao and Yaoqi Zhou

in Bioinformatics

Volume 27, issue 15, pages 2076-2082
Published in print August 2011 | ISSN: 1367-4803
Published online June 2011 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/btr350
Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates

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Motivation: In recent years, development of a single-method fold-recognition server lags behind consensus and multiple template techniques. However, a good consensus prediction relies on the accuracy of individual methods. This article reports our efforts to further improve a single-method fold recognition technique called SPARKS by changing the alignment scoring function and incorporating the SPINE-X techniques that make improved prediction of secondary structure, backbone torsion angle and solvent accessible surface area.

Results: The new method called SPARKS-X was tested with the SALIGN benchmark for alignment accuracy, Lindahl and SCOP benchmarks for fold recognition, and CASP 9 blind test for structure prediction. The method is compared to several state-of-the-art techniques such as HHPRED and BoostThreader. Results show that SPARKS-X is one of the best single-method fold recognition techniques. We further note that incorporating multiple templates and refinement in model building will likely further improve SPARKS-X.

Availability: The method is available as a SPARKS-X server at http://sparks.informatics.iupui.edu/

Contact: yqzhou@iupui.edu

Journal Article.  5065 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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