Physical Sciences and Mathematics Commons^™

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, Reecha Nepal, Joanna Spencer, Guneet Bhogal, Amulya Nedunuri, Thomas Poelman, Thejas Kamath, Edwin Chung, Katherine Kantardjieff, Andrea Gottlieb, Brooke Lustig

Faculty Publications, Chemistry

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, …