3-State Protein Secondary Structure Prediction based on SCOPe Classes
Braz. arch. biol. technol
; 64: e21210007, 2021. tab, graf
Artigo
em Inglês
| LILACS
| ID: biblio-1339314
Biblioteca responsável:
BR1.1
ABSTRACT
Abstract Improving the accuracy of protein secondary structure prediction has been an important task in bioinformatics since it is not only the starting point in obtaining tertiary structure in hierarchical modeling but also enhances sequence analysis and sequence-structure threading to help determine structure and function. Herein we present a model based on DSPRED classifier, a hybrid method composed of dynamic Bayesian networks and a support vector machine to predict 3-state secondary structure information of proteins. We used the SCOPe (Structural Classification of Proteins-extended) database to train and test the model. The results show that DSPRED reached a Q3 accuracy rate of 82.36% when trained and tested using proteins from all SCOPe classes. We compared our method with the popular PSIPRED on the SCOPe test datasets and found that our method outperformed PSIPRED.
Texto completo:
Disponível
Coleções:
Bases de dados internacionais
Base de dados:
LILACS
Assunto principal:
Estrutura Secundária de Proteína
/
Máquina de Vetores de Suporte
Tipo de estudo:
Estudo prognóstico
/
Fatores de risco
Idioma:
Inglês
Revista:
Braz. arch. biol. technol
Assunto da revista:
Biologia
Ano de publicação:
2021
Tipo de documento:
Artigo
País de afiliação:
Turquia
Instituição/País de afiliação:
Abdullah Gul University/TR
/
Kayseri University/TR
/
Nevsehir Haci Bektas Veli University/TR
/
University of Turkish Aeronautical Association/TR