3-State Protein Secondary Structure Prediction based on SCOPe Classes
Braz. arch. biol. technol
;
64: e21210007, 2021. tab, graf
Artículo
en Inglés
| LILACS
| ID: biblio-1339314
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:
Disponible
Índice:
LILACS (Américas)
Asunto principal:
Estructura Secundaria de Proteína
/
Máquina de Vectores de Soporte
Tipo de estudio:
Estudio pronóstico
/
Factores de riesgo
Idioma:
Inglés
Revista:
Braz. arch. biol. technol
Asunto de la revista:
Biologia
Año:
2021
Tipo del documento:
Artículo
País de afiliación:
Turquía
Institución/País de afiliación:
Abdullah Gul University/TR
/
Kayseri University/TR
/
Nevsehir Haci Bektas Veli University/TR
/
University of Turkish Aeronautical Association/TR
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