.png){kind=icon}
Return
Designing evolvable libraries using multi-body potentials
.png){kind=icon}
PRE
AI
DOI：10.1016/j.copbio.2009.07.008.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
PROTEIN-STRUCTURE PREDICTION
STATISTICAL POTENTIALS
EVOLUTION
STABILITY
CONTACT
RECOMBINATION
FORCE
IDENTIFICATION
OPTIMIZATION
CONNECTIVITY
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7
Papers:
5.0K
Citations:
1.9W
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSave
View more