Protein Conformational States-A First Principles Bayesian Method.

ABSTRACT: Automated identification of protein conformational states from simulation of an ensemble of structures is a hard problem because it requires teaching a computer to recognize shapes. We adapt the naïve Bayes classifier from the machine learning community for use on atom-to-atom pairwise contacts. The result is an unsupervised learning algorithm that samples a 'distribution' over potential classification schemes. We apply the classifier to a series of test structures and one real protein, showing that it identifies the conformational transition with >95% accuracy in most cases. A nontrivial feature of our adaptation is a new connection to information entropy that allows us to vary the level of structural detail without spoiling the categorization. This is confirmed by comparing results as th

SUBMITTER: Rogers DM 

PROVIDER: S-EPMC7712966 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature