Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference

Proceedings of the National Academy of Sciences of the United States of America

Volumn 112, Issue 22, 2015, Pages 6985-6990

  MacCallum, Justin L ^a   Perez, Alberto ^b   Dill, Ken A ^b  

^a UNIVERSITY OF CALGARY   (Canada)

^b STONY BROOK UNIVERSITY   (United States)

Author keywords

Bayesian inference; Integrative structural biology; Molecular modeling; Protein structure

Indexed keywords

ARTICLE; ATOMIC PARTICLE; BAYES THEOREM; DATA ANALYSIS; INFORMATION PROCESSING; MATHEMATICAL COMPUTING; MODELING EMPLOYING LIMITED DATA; PHYSICAL CHEMISTRY; PHYSICAL MODEL; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN STRUCTURE; SEQUENCE ANALYSIS; STRUCTURE ANALYSIS; CHEMICAL STRUCTURE; CHEMISTRY; MOLECULAR BIOLOGY; PROCEDURES; PROTEIN CONFORMATION;

PROTEIN;

BAYES THEOREM; MODELS, MOLECULAR; MOLECULAR BIOLOGY; PROTEIN CONFORMATION; PROTEINS;

EID: 84930959314     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1506788112     Document Type: Article

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