title: Bayesian model based clustering analysis: application to a molecular dynamics trajectory of the HIV-1 integrase catalytic core.
abstract: This work describes the application of a Bayesian method for clustering protein conformations sampled during a molecular dynamics simulation of the HIV-1 integrase catalytic core. A clustering analysis is carried out under the assumption of normal distribution without fixing the number of clusters in advance. Some performance measures, such as posterior probability and class cross entropy, are used to determine the most probable set of clusters. The Bayesian clustering method results in meaningful groups identifying transitions between conformational ensembles. The dihedral angles involved in such transitions are also examined in detail. The conformations in high dimensional space are projected into 3D space employing a multidimensional scaling technique to provide a visual inspection.
authors.affiliation: Department of Applied Chemistry, Ocean University of China, Qingdao 266003, P. R. China. liyan311@hotmail.com
authors.forename: Yan
authors.initials: Y
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D020134:Catalytic Domain; D016000:Cluster Analysis; D019427:HIV Integrase / Q000737:chemistry* / Q000378:metabolism; D011487:Protein Conformation
chemical_list: D019427:HIV Integrase