3-State Protein Secondary Structure Prediction based on SCOPe Classes
Braz. arch. biol. technol
;
64: e21210007, 2021. tab, graf
Article
in English
| 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.
Full text:
Available
Index:
LILACS (Americas)
Main subject:
Protein Structure, Secondary
/
Support Vector Machine
Type of study:
Prognostic study
/
Risk factors
Language:
English
Journal:
Braz. arch. biol. technol
Journal subject:
Biology
Year:
2021
Type:
Article
Affiliation country:
Turkey
Institution/Affiliation country:
Abdullah Gul University/TR
/
Kayseri University/TR
/
Nevsehir Haci Bektas Veli University/TR
/
University of Turkish Aeronautical Association/TR
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