Accession Number:

ADA467651

Title:

fRMSDPred: Predicting Local RMSD Between Structural Fragments Using Sequence Information

Descriptive Note:

Technical rept.

Corporate Author:

MINNESOTA UNIV MINNEAPOLIS DEPT OF ELECTRICAL AND COMPUTER ENGINEERING

Personal Author(s):

Report Date:

2007-04-04

Pagination or Media Count:

14.0

Abstract:

The effectiveness of comparative modeling approaches for protein structure prediction can be substantially improved by incorporating predicted structural information in the initial sequence-structure alignment. Motivated by the approaches used to align protein structures, this paper focuses on developing machine learning approaches for estimating the RMSD value of a pair of protein fragments. These estimated fragment-level RMSD values can be used to construct the alignment, assess the quality of an alignment, and identify high-quality alignment segments. We present algorithms to solve this fragment-level RMSD prediction problem using a supervised learning framework based on support vector regression and classification that incorporates protein profiles, predicted secondary structure, effective information encoding schemes, and novel second-order pairwise exponential kernel functions. Our comprehensive empirical study shows superior results compared to the profile-to-profile scoring schemes.

Subject Categories:

  • Biochemistry
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE