Protein Structure Prediction Using String Kernels
Abstract
With recent advances in large-scale sequencing technologies, there has been an exponential growth in protein sequence information. Currently, the ability to produce sequence information far out-paces the rate at which one can produce structural and functional information. Consequently, researchers increasingly rely on computational techniques to extract useful information from known structures contained in large databases, though such approaches remain incomplete. As such, unraveling the relationship between pure sequence information and three-dimensional structure remains one of the great fundamental problems in molecular biology. In this report, the authors aim to show several ways in which researchers try to characterize the structural, functional, and evolutionary nature of proteins. Specifically, they focus on three common prediction problems: secondary structure prediction, remote homology, and fold prediction. They describe a class of methods employing large margin classifiers with novel kernel functions for solving these problems, supplemented with a thorough evaluation study.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 03, 2006
- Accession Number
- ADA444439
Entities
People
- George Karypis
- Huzefa Rangwala
- Kevin Deronne
Organizations
- University of Minnesota