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Better library design: data‐driven protein engineering
Author(s) -
ChaparroRiggers Javier F.,
Polizzi Karen M.,
Bommarius Andreas S.
Publication year - 2007
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.200600170
Subject(s) - protein engineering , limiting , computer science , rational design , protein design , identification (biology) , limit (mathematics) , computational biology , data science , protein structure , nanotechnology , biology , engineering , mathematics , biochemistry , mechanical engineering , mathematical analysis , botany , materials science , enzyme
Abstract Data‐driven protein engineering is increasingly used as an alternative to rational design and combinatorial engineering because it uses available knowledge to limit library size, while still allowing for the identification of unpredictable substitutions that lead to large effects. Recent advances in computational modeling and bioinformatics, as well as an increasing databank of experiments on functional variants, have led to new strategies to choose particular amino acid residues to vary in order to increase the chances of obtaining a variant protein with the desired property. Strategies for limiting diversity at each position, design of small sub‐libraries, and the performance of scouting experiments, have also been developed or even automated, further reducing the library size.