Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition
Optimally combining available information is one of the key challenges in knowledge-driven prediction techniques. In this study, we evaluate six Phi and Psi-based backbone alphabets. We show that the addition of predicted backbone conformations to SVM classifiers can improve fold recognition. Our experimental results show that the inclusion of predicted backbone conformations in our feature representation leads to higher overall accuracy compared to when using amino acid residues alone.
Databases and Information Systems
Data Management and Analytics
Technical Report University of Minnesota Department of Computer Science and Engineering
University of Minnesota
City or Country
SHIM, Kyong Jin.
Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition. (2010). Technical Report University of Minnesota Department of Computer Science and Engineering. 1-12. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1527