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On statistical energy functions for biomolecular modeling and design
Author(s) -
Liu Haiyan
Publication year - 2016
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.1007/s40484-015-0054-x
Subject(s) - computer science , statistical model , statistical physics , statistical analysis , energy (signal processing) , algorithm , mathematics , artificial intelligence , physics , statistics
Statistical energy functions are general models about atomic or residue‐level interactions in biomolecules, derived from existing experimental data. They provide quantitative foundations for structural modeling as well as for structure‐based protein sequence design. Statistical energy functions can be derived computationally either based on statistical distributions or based on variational assumptions. We present overviews on the theoretical assumptions underlying the various types of approaches. Theoretical considerations underlying important pragmatic choices are discussed.

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