Cargando…

Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information

Deciphering three dimensional structure of a protein sequence is a challenging task in biological science. Protein fold recognition and protein secondary structure prediction are transitional steps in identifying the three dimensional structure of a protein. For protein fold recognition, evolutionar...

Descripción completa

Detalles Bibliográficos
Autores principales: Paliwal, Kuldip K, Sharma, Alok, Lyons, James, Dehzangi, Abdollah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4290640/
https://www.ncbi.nlm.nih.gov/pubmed/25521502
http://dx.doi.org/10.1186/1471-2105-15-S16-S12
_version_ 1782352278293839872
author Paliwal, Kuldip K
Sharma, Alok
Lyons, James
Dehzangi, Abdollah
author_facet Paliwal, Kuldip K
Sharma, Alok
Lyons, James
Dehzangi, Abdollah
author_sort Paliwal, Kuldip K
collection PubMed
description Deciphering three dimensional structure of a protein sequence is a challenging task in biological science. Protein fold recognition and protein secondary structure prediction are transitional steps in identifying the three dimensional structure of a protein. For protein fold recognition, evolutionary-based information of amino acid sequences from the position specific scoring matrix (PSSM) has been recently applied with improved results. On the other hand, the SPINE-X predictor has been developed and applied for protein secondary structure prediction. Several reported methods for protein fold recognition have only limited accuracy. In this paper, we have developed a strategy of combining evolutionary-based information (from PSSM) and predicted secondary structure using SPINE-X to improve protein fold recognition. The strategy is based on finding the probabilities of amino acid pairs (AAP). The proposed method has been tested on several protein benchmark datasets and an improvement of 8.9% recognition accuracy has been achieved. We have achieved, for the first time over 90% and 75% prediction accuracies for sequence similarity values below 40% and 25%, respectively. We also obtain 90.6% and 77.0% prediction accuracies, respectively, for the Extended Ding and Dubchak and Taguchi and Gromiha benchmark protein fold recognition datasets widely used for in the literature.
format Online
Article
Text
id pubmed-4290640
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-42906402015-01-15 Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information Paliwal, Kuldip K Sharma, Alok Lyons, James Dehzangi, Abdollah BMC Bioinformatics Research Deciphering three dimensional structure of a protein sequence is a challenging task in biological science. Protein fold recognition and protein secondary structure prediction are transitional steps in identifying the three dimensional structure of a protein. For protein fold recognition, evolutionary-based information of amino acid sequences from the position specific scoring matrix (PSSM) has been recently applied with improved results. On the other hand, the SPINE-X predictor has been developed and applied for protein secondary structure prediction. Several reported methods for protein fold recognition have only limited accuracy. In this paper, we have developed a strategy of combining evolutionary-based information (from PSSM) and predicted secondary structure using SPINE-X to improve protein fold recognition. The strategy is based on finding the probabilities of amino acid pairs (AAP). The proposed method has been tested on several protein benchmark datasets and an improvement of 8.9% recognition accuracy has been achieved. We have achieved, for the first time over 90% and 75% prediction accuracies for sequence similarity values below 40% and 25%, respectively. We also obtain 90.6% and 77.0% prediction accuracies, respectively, for the Extended Ding and Dubchak and Taguchi and Gromiha benchmark protein fold recognition datasets widely used for in the literature. BioMed Central 2014-12-08 /pmc/articles/PMC4290640/ /pubmed/25521502 http://dx.doi.org/10.1186/1471-2105-15-S16-S12 Text en Copyright © 2014 Paliwal et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Paliwal, Kuldip K
Sharma, Alok
Lyons, James
Dehzangi, Abdollah
Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information
title Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information
title_full Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information
title_fullStr Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information
title_full_unstemmed Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information
title_short Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information
title_sort improving protein fold recognition using the amalgamation of evolutionary-based and structural based information
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4290640/
https://www.ncbi.nlm.nih.gov/pubmed/25521502
http://dx.doi.org/10.1186/1471-2105-15-S16-S12
work_keys_str_mv AT paliwalkuldipk improvingproteinfoldrecognitionusingtheamalgamationofevolutionarybasedandstructuralbasedinformation
AT sharmaalok improvingproteinfoldrecognitionusingtheamalgamationofevolutionarybasedandstructuralbasedinformation
AT lyonsjames improvingproteinfoldrecognitionusingtheamalgamationofevolutionarybasedandstructuralbasedinformation
AT dehzangiabdollah improvingproteinfoldrecognitionusingtheamalgamationofevolutionarybasedandstructuralbasedinformation