title: fRMSDPred: predicting local RMSD between structural fragments using sequence information.
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.
authors.affiliation: Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455, USA. rangwala@cs.umn.edu
authors.forename: Huzefa
authors.initials: H
authors.lastname: Rangwala
authors.forename: George
authors.initials: G
authors.lastname: Karypis
mesh_terms: D003198:Computer Simulation; D016018:Least-Squares Analysis; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D010446:Peptide Fragments / Q000737:chemistry*; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013499:Surface Properties
chemical_list: D010446:Peptide Fragments; D011506:Proteins