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• W2215355231 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." @default.
• W2215355231 created "2016-06-24" @default.
• W2215355231 creator A5038805449 @default.
• W2215355231 date "2006-05-12" @default.
• W2215355231 modified "2023-10-17" @default.
• W2215355231 title "Bayesian Model Based Clustering Analysis: Application to a Molecular Dynamics Trajectory of the HIV-1 Integrase Catalytic Core" @default.
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• W2215355231 doi "https://doi.org/10.1021/ci050463u" @default.
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