About: Protein Conformational States: A First Principles Bayesian Method

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Protein Conformational States: A First Principles Bayesian Method
Creator	Rogers, David
Source	ArXiv
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 naive 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 the number of atoms and time-samples are varied over 1.5 orders of magnitude. Further, the method's derivation from Bayesian analysis on the set of inter-atomic contacts makes it easy to understand and extend to more complex cases.
has issue date	2020-08-05(xsd:dateTime)
has license	arxiv
sha1sum (hex)	ce83eb80894662dfdb77ed95381f8697907091c2
resource representing a document's title	Protein Conformational States: A First Principles Bayesian Method
resource representing a document's body	covid:ce83eb80894662dfdb77ed95381f8697907091c2#body_text
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