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 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.
authors.affiliation: Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 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: D000465:Algorithms; D030562:Databases, Protein; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein
chemical_list: D011506:Proteins