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Sci Adv. 2016 Jul 20;2(7):e1600692. doi: 10.1126/sciadv.1600692. eCollection 2016 Jul.

Protein engineering by highly parallel screening of computationally designed variants.

Author information

1
Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada.; Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
2
Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
3
Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada.; Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.

Abstract

Current combinatorial selection strategies for protein engineering have been successful at generating binders against a range of targets; however, the combinatorial nature of the libraries and their vast undersampling of sequence space inherently limit these methods due to the difficulty in finely controlling protein properties of the engineered region. Meanwhile, great advances in computational protein design that can address these issues have largely been underutilized. We describe an integrated approach that computationally designs thousands of individual protein binders for high-throughput synthesis and selection to engineer high-affinity binders. We show that a computationally designed library enriches for tight-binding variants by many orders of magnitude as compared to conventional randomization strategies. We thus demonstrate the feasibility of our approach in a proof-of-concept study and successfully obtain low-nanomolar binders using in vitro and in vivo selection systems.

KEYWORDS:

Computational protein design; protein engineering; structural biology

PMID:
27453948
PMCID:
PMC4956399
DOI:
10.1126/sciadv.1600692
[Indexed for MEDLINE]
Free PMC Article
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