Designing evolvable libraries using multi-body potentials.
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.
Lappe
Michael
M
Max-Planck-Institute for Molecular Genetics, Structural Proteomics/Bioinformatics Group, Ihnestrasse 63-73, Berlin 14195, Germany. lappe@molgen.mpg.de
Bagler
Ganesh
G
Filippis
Ioannis
I
Stehr
Henning
H
Duarte
Jose M
JM
Sathyapriya
Rajagopal
R
D020650:Combinatorial Chemistry Techniques; D019020:Directed Molecular Evolution; D004353:Drug Evaluation, Preclinical; D008958:Models, Molecular; D011336:Probability; D013816:Thermodynamics