title: Better library design: data-driven protein engineering.
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.
authors.affiliation: School of Chemical and Biomolecular Engineering, Parker H. Petit Institute of Bioengineering and Bioscience, Atlanta, GA, USA.
authors.forename: Javier F
authors.initials: JF
authors.lastname: Chaparro-Riggers
authors.forename: Karen M
authors.initials: KM
authors.lastname: Polizzi
authors.forename: Andreas S
authors.initials: AS
authors.lastname: Bommarius
mesh_terms: D019295:Computational Biology / Q000379:methods; D019020:Directed Molecular Evolution; D004798:Enzymes / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D015723:Gene Library; D015202:Protein Engineering / Q000379:methods*; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D020539:Sequence Analysis, Protein; D013329:Structure-Activity Relationship
chemical_list: D004798:Enzymes; D011506:Proteins