fRMSDPred: Predicting Local RMSD Between Structural Fragments Using Sequence Information

Huzefa Rangwala*, George Karypis

Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455, USA. rangwala@cs.umn.edu

Proc LSS Comput Syst Bioinform Conf. August, 2007. Vol. 6, p. 311-322. Full-Text PDF

*To whom correspondence should be addressed.

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.