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Biomolecular Modeling: Goals, Problems, Perspectives
Author(s) -
van Gunsteren Wilfred F.,
Bakowies Dirk,
Baron Riccardo,
Chandrasekhar Indira,
Christen Markus,
Daura Xavier,
Gee Peter,
Geerke Daan P.,
Glättli Alice,
Hünenberger Philippe H.,
Kastenholz Mika A.,
Oostenbrink Chris,
Schenk Merijn,
Trzesniak Daniel,
van der Vegt Nico F. A.,
Yu Haibo B.
Publication year - 2006
Publication title -
angewandte chemie international edition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.831
H-Index - 550
eISSN - 1521-3773
pISSN - 1433-7851
DOI - 10.1002/anie.200502655
Subject(s) - complement (music) , computer science , sampling (signal processing) , computation , field (mathematics) , theoretical computer science , management science , data science , statistical physics , algorithm , chemistry , mathematics , physics , engineering , biochemistry , filter (signal processing) , complementation , pure mathematics , computer vision , gene , phenotype
Computation based on molecular models is playing an increasingly important role in biology, biological chemistry, and biophysics. Since only a very limited number of properties of biomolecular systems is actually accessible to measurement by experimental means, computer simulation can complement experiment by providing not only averages, but also distributions and time series of any definable quantity, for example, conformational distributions or interactions between parts of systems. Present day biomolecular modeling is limited in its application by four main problems: 1) the force‐field problem, 2) the search (sampling) problem, 3) the ensemble (sampling) problem, and 4) the experimental problem. These four problems are discussed and illustrated by practical examples. Perspectives are also outlined for pushing forward the limitations of biomolecular modeling.