Open Access
Bioinformatics, volume 27, issue 15, pages 2076-2082
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
Publication type: Journal Article
Publication date: 2011-06-11
Journal:
Bioinformatics
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 5.8
ISSN: 13674803, 13674811, 14602059
Biochemistry
Computer Science Applications
Molecular Biology
Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Abstract
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.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.The method is available as a SPARKS-X server at http://sparks.informatics.iupui.edu/
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- We do not take into account publications without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Yang Y. et al. 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 // Bioinformatics. 2011. Vol. 27. No. 15. pp. 2076-2082.
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Yang Y., Faraggi E., Zhao H., Zhou Y. 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 // Bioinformatics. 2011. Vol. 27. No. 15. pp. 2076-2082.
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TY - JOUR
DO - 10.1093/bioinformatics/btr350
UR - https://doi.org/10.1093/bioinformatics/btr350
TI - 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
T2 - Bioinformatics
AU - Yang, Yuanhao
AU - Faraggi, Eshel
AU - Zhao, Huiying
AU - Zhou, Yaoqi
PY - 2011
DA - 2011/06/11 00:00:00
PB - Oxford University Press
SP - 2076-2082
IS - 15
VL - 27
SN - 1367-4803
SN - 1367-4811
SN - 1460-2059
ER -
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@article{2011_Yang,
author = {Yuanhao Yang and Eshel Faraggi and Huiying Zhao and Yaoqi Zhou},
title = {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},
journal = {Bioinformatics},
year = {2011},
volume = {27},
publisher = {Oxford University Press},
month = {jun},
url = {https://doi.org/10.1093/bioinformatics/btr350},
number = {15},
pages = {2076--2082},
doi = {10.1093/bioinformatics/btr350}
}
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MLA
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Yang, Yuanhao, et al. “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.” Bioinformatics, vol. 27, no. 15, Jun. 2011, pp. 2076-2082. https://doi.org/10.1093/bioinformatics/btr350.