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Co‐Evolutionary Fitness Landscapes for Sequence Design
Author(s) -
Tian Pengfei,
Louis John M.,
Baber James L.,
Aniana Annie,
Best Robert B.
Publication year - 2018
Publication title -
angewandte chemie
Language(s) - English
Resource type - Journals
eISSN - 1521-3757
pISSN - 0044-8249
DOI - 10.1002/ange.201713220
Subject(s) - fitness landscape , protein design , sequence (biology) , computational biology , sh3 domain , monte carlo method , domain (mathematical analysis) , protein structure , computer science , biology , genetics , mathematics , biochemistry , statistics , population , demography , kinase , sociology , proto oncogene tyrosine protein kinase src , mathematical analysis
Efficient and accurate models to predict the fitness of a sequence would be extremely valuable in protein design. We have explored the use of statistical potentials for the coevolutionary fitness landscape, extracted from known protein sequences, in conjunction with Monte Carlo simulations, as a tool for design. As proof of principle, we created a series of predicted high‐fitness sequences for three different protein folds, representative of different structural classes: the GA (all‐α) and GB (α/β) binding domains of streptococcal protein G, and an SH3 (all‐β) domain. We found that most of the designed proteins can fold stably to the target structure, and a structure for a representative of each for GA, GB and SH3 was determined. Several of our designed proteins were also able to bind to native ligands, in some cases with higher affinity than wild‐type. Thus, a search using a statistical fitness landscape is a remarkably effective tool for finding novel stable protein sequences.