fRMSDPred: predicting local RMSD between structural fragments using sequence information.
Publication Type:Journal Article
Source:Comput Syst Bioinformatics Conf, Volume 6, Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455, USA. firstname.lastname@example.org, p.311–322 (2007)
Keywords:*Models, Chemical, Computer Simulation, Least-Squares Analysis, Models, Molecular, Peptide Fragments/*chemistry, Protein Conformation, Protein Folding, Protein/*methods, Proteins/*chemistry/*ultrastructure, Sequence Analysis, Statistical, Surface Properties
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.