fRMSDPred: Predicting local RMSD between structural fragments using sequence information

Huzefa Rangwala, George Karypis

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

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 article 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 with the profile-to-profile scoring schemes. We also show that for protein pairs with low sequence similarity (less than 12% sequence identity) these new local structural features alone or in conjunction with profile-based information lead to alignments that are considerably accurate than those obtained by schemes that use only profile and/or predicted secondary structure information.

Original languageEnglish (US)
Pages (from-to)1005-1018
Number of pages14
JournalProteins: Structure, Function and Genetics
Volume72
Issue number3
DOIs
StatePublished - Aug 15 2008

Keywords

  • Classification
  • Comparative modeling
  • Machine learning
  • Regression
  • Structure prediction

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