Title

Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition

Publication Type

Report

Publication Date

2010

Abstract

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.

Discipline

Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

Technical Report University of Minnesota Department of Computer Science and Engineering

Issue

TR10-015

First Page

1

Last Page

12

Publisher

University of Minnesota

City or Country

Minneapolis, MN

Additional URL

http://www.cs.umn.edu/research/technical_reports.php?page=report&report_id=10-015