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Designing evolvable libraries using multi-body potentials

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50406

Lappe,  Michael
Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50079

Bagler,  Ganesh
Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

Filippis,  Ioannis
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50570

Stehr,  Henning
Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50139

Duarte,  Jose M.
Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50511

Sathyapriya,  Rajagopal
Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Zitation

Lappe, M., Bagler, G., Filippis, I., Stehr, H., Duarte, J. M., & Sathyapriya, R. (2009). Designing evolvable libraries using multi-body potentials. Current Opinion in Biotechnology, 24(4), 437-446. doi:10.1016/j.copbio.2009.07.008.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-7D46-A
Zusammenfassung
Novel high-throughput technologies for directed evolution enable experimental coverage of an impressive number of sequences. Nevertheless, the success of such experiments hinges on the initial sequence libraries. Here we consider the computational design of smart focused libraries and review insights from experimental strategies and theoretic advances in modelling their energy landscapes. In library design as in structure prediction, the applied energy function is the key. Current knowledge-based potentials have proven more successful than purely physics-based ones. Here we summarize novel approaches that extend the classical pairwise treatment of residue contacts towards adaptive knowledge-based multi-body potentials. We suggest that minimal sets of probabilistic constraints will lead to much more efficient sampling of permissible conformations and sequence space.