Density Estimation for Protein Conformation Angles Using a Bivariate von Mises Distribution and Bayesian Nonparametrics

• 成果类型: Article
• 署名作者: Lennox, Kristin P.; Dahl, David B.; Vannucci, Marina; Tsai, Jerry W.
• 署名单位: Texas A&M University System; Texas A&M University College Station; Rice University; University of the Pacific
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.0024
• 发表日期: 2009
• 页码: 586-596
• 关键词: inference; prediction; dipeptide; mixtures; backbone

摘要

Interest in predicting protein backbone conformational angles has prompted the development of modeling and inference procedures for bivariate angular distributions. We present it Bayesian approach to density estimation for bivariate angular data that uses a Dirichlet process mixture model and a bivariate von Mises distribution. We derive the necessary full conditional distributions 10 fit the model, as well as the details for sampling from the posterior predictive distribution, We show how our density estimation method makes it possible to improve current approaches for protein structure prediction by comparing the performance of the so-called whole and half position distributions. Current methods in the field are based on whole position distributions. its density estimation for (he half positions requires techniques. such as ours, that can provide good estimates for small datasets. With our method we are able to demonstrate that half position data provides it better approximation for the distribution of confirmational angles at it given sequence position, therefore providing increased efficiency and accuracy in structure prediction.