title: Bayesian model of protein primary sequence for secondary structure prediction.
abstract: Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein's function in the cell. Understanding a protein's secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob-socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation.
authors.affiliation: Department of Statistics, Rice University, Houston, Texas, United States of America.
authors.forename: Qiwei
authors.initials: Q
authors.lastname: Li
authors.affiliation: Department of Statistics, Brigham Young University, Provo, Utah, United States of America.
authors.forename: David B
authors.initials: DB
authors.lastname: Dahl
authors.affiliation: Department of Statistics, Rice University, Houston, Texas, United States of America.
authors.forename: Marina
authors.initials: M
authors.lastname: Vannucci
authors.affiliation: Department of Chemistry, University of the Pacific, Stockton, California, United States of America.
authors.lastname: Hyun Joo
authors.affiliation: Department of Chemistry, University of the Pacific, Stockton, California, United States of America.
authors.forename: Jerry W
authors.initials: JW
authors.lastname: Tsai
mesh_terms: D000595:Amino Acid Sequence; D001499:Bayes Theorem; D003198:Computer Simulation; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D012984:Software
chemical_list: D011506:Proteins