NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation

The predicted secondary structural states are not cross validated by any of the existing servers. Hence, information on the level of accuracy for every sequence is not reported by the existing servers. This was overcome by NNvPDB, which not only reported greater Q(3) but also validates every predict...

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Detalles Bibliográficos
Autores principales: Sakthivel, Seethalakshmi, S.K.M, Habeeb
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2015
Materias:
Web Server
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4574126/
https://www.ncbi.nlm.nih.gov/pubmed/26420924
http://dx.doi.org/10.6026/97320630011416
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author Sakthivel, Seethalakshmi
S.K.M, Habeeb
author_facet Sakthivel, Seethalakshmi
S.K.M, Habeeb
author_sort Sakthivel, Seethalakshmi
collection PubMed
description The predicted secondary structural states are not cross validated by any of the existing servers. Hence, information on the level of accuracy for every sequence is not reported by the existing servers. This was overcome by NNvPDB, which not only reported greater Q(3) but also validates every prediction with the homologous PDB entries. NNvPDB is based on the concept of Neural Network, with a new and different approach of training the network every time with five PDB structures that are similar to query sequence. The average accuracy for helix is 76%, beta sheet is 71% and overall (helix, sheet and coil) is 66%. AVAILABILITY: http://bit.srmuniv.ac.in/cgi-bin/bit/cfpdb/nnsecstruct.pl
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spelling pubmed-45741262015-09-29 NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation Sakthivel, Seethalakshmi S.K.M, Habeeb Bioinformation Web Server The predicted secondary structural states are not cross validated by any of the existing servers. Hence, information on the level of accuracy for every sequence is not reported by the existing servers. This was overcome by NNvPDB, which not only reported greater Q(3) but also validates every prediction with the homologous PDB entries. NNvPDB is based on the concept of Neural Network, with a new and different approach of training the network every time with five PDB structures that are similar to query sequence. The average accuracy for helix is 76%, beta sheet is 71% and overall (helix, sheet and coil) is 66%. AVAILABILITY: http://bit.srmuniv.ac.in/cgi-bin/bit/cfpdb/nnsecstruct.pl Biomedical Informatics 2015-08-31 /pmc/articles/PMC4574126/ /pubmed/26420924 http://dx.doi.org/10.6026/97320630011416 Text en © 2015 Biomedical Informatics This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
spellingShingle Web Server
Sakthivel, Seethalakshmi
S.K.M, Habeeb
NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation
title NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation
title_full NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation
title_fullStr NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation
title_full_unstemmed NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation
title_short NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation
title_sort nnvpdb: neural network based protein secondary structure prediction with pdb validation
topic Web Server
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4574126/
https://www.ncbi.nlm.nih.gov/pubmed/26420924
http://dx.doi.org/10.6026/97320630011416
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