Work

Co‐Evolutionary Fitness Landscapes for Sequence Design

Year: 2018

Type: article

Abstract: 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.

Source: Angewandte Chemie International Edition

DOI: 10.1002/anie.201713220

Authors: Pengfei Tian, John M. Louis, James L. Baber, Annie Aniana, Robert B. Best

Institutions: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases

Award: G2225345073