title: Logistic regression models to predict solvent accessible residues using sequence- and homology-based qualitative and quantitative descriptors applied to a domain-complete X-ray structure learning set.
abstract: A working example of relative solvent accessibility (RSA) prediction for proteins is presented. Novel logistic regression models with various qualitative descriptors that include amino acid type and quantitative descriptors that include 20- and six-term sequence entropy have been built and validated. A domain-complete learning set of over 1300 proteins is used to fit initial models with various sequence homology descriptors as well as query residue qualitative descriptors. Homology descriptors are derived from BLASTp sequence alignments, whereas the RSA values are determined directly from the crystal structure. The logistic regression models are fitted using dichotomous responses indicating buried or accessible solvent, with binary classifications obtained from the RSA values. The fitted models determine binary predictions of residue solvent accessibility with accuracies comparable to other less computationally intensive methods using the standard RSA threshold criteria 20 and 25% as solvent accessible. When an additional non-homology descriptor describing Lobanov-Galzitskaya residue disorder propensity is included, incremental improvements in accuracy are achieved with 25% threshold accuracies of 76.12 and 74.79% for the Manesh-215 and CASP(8+9) test sets, respectively. Moreover, the described software and the accompanying learning and validation sets allow students and researchers to explore the utility of RSA prediction with simple, physically intuitive models in any number of related applications.
authors.affiliation: Department of Chemistry, San Jose State University , San Jose, CA 95192-0101, USA.
authors.forename: Reecha
authors.initials: R
authors.lastname: Nepal
authors.affiliation: Department of Mathematics and Statistics, San Jose State University , San Jose, CA 95192-0101, USA.
authors.forename: Joanna
authors.initials: J
authors.lastname: Spencer
authors.affiliation: Department of Biomedical, Chemical and Materials Engineering, San Jose State University , San Jose, CA 95192-0101, USA.
authors.forename: Guneet
authors.initials: G
authors.lastname: Bhogal
authors.affiliation: Department of General Engineering, San Jose State University , San Jose, CA 95192-0101, USA.
authors.forename: Amulya
authors.initials: A
authors.lastname: Nedunuri
authors.affiliation: Department of Chemistry and Biochemistry, Cal Poly San Luis Obispo , San Luis Obispo, CA 93407, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Poelman
authors.affiliation: Department of Bioengineering, University of California, San Diego , San Diego, CA 92093-0412, USA.
authors.forename: Thejas
authors.initials: T
authors.lastname: Kamath
authors.affiliation: Department of Biomedical, Chemical and Materials Engineering, San Jose State University , San Jose, CA 95192-0101, USA.
authors.forename: Edwin
authors.initials: E
authors.lastname: Chung
authors.affiliation: College of Science and Mathematics, California State University San Marcos , San Marcos, CA 92096-0001, USA.
authors.forename: Katherine
authors.initials: K
authors.lastname: Kantardjieff
authors.affiliation: Department of Mathematics and Statistics, San Jose State University , San Jose, CA 95192-0101, USA.
authors.forename: Andrea
authors.initials: A
authors.lastname: Gottlieb
authors.affiliation: Department of Chemistry, San Jose State University , San Jose, CA 95192-0101, USA.
authors.forename: Brooke
authors.initials: B
authors.lastname: Lustig
keywords: education; relative solvent accessibility prediction; teaching