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...

Descripción completa

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
Descripción
Sumario: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

Ejemplares similares