3-State Protein Secondary Structure Prediction based on SCOPe Classes

Creative Commons License

Atasever S., AZGINOĞLU N., Erbay H., AYDIN Z.

BRAZILIAN ARCHIVES OF BIOLOGY AND TECHNOLOGY, vol.64, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 64
  • Publication Date: 2021
  • Doi Number: 10.1590/1678-4324-2021210007
  • Journal Name: BRAZILIAN ARCHIVES OF BIOLOGY AND TECHNOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Animal Behavior Abstracts, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Keywords: Protein secondary structure prediction, SCOPe, Support Vector Machine, Dynamic Bayesian Network, DATABASE, CLASSIFICATION
  • Abdullah Gül University Affiliated: Yes

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 Q(3) accuracy rate of 82.36% when trained and tested using proteins from all SCOPe classes. We compared our method with the popular PSI PRED on the SCOPe test datasets and found that our method outperformed PSI PRED.