title: Innovation by homologous recombination.
abstract: Swapping fragments among protein homologs can produce chimeric proteins with a wide range of properties, including properties not exhibited by the parents. Computational methods that use information from structures and sequence alignments have been used to design highly functional chimeras and chimera libraries. Recombination has generated proteins with diverse thermostability and mechanical stability, enzyme substrate specificity, and optogenetic properties. Linear regression, Gaussian processes, and support vector machine learning have been used to model sequence-function relationships and predict useful chimeras. These approaches enable engineering of protein chimeras with desired functions, as well as elucidation of the structural basis for these functions.
authors.affiliation: Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
authors.forename: Devin L
authors.initials: DL
authors.lastname: Trudeau
authors.forename: Matthew A
authors.initials: MA
authors.lastname: Smith
authors.forename: Frances H
authors.initials: FH
authors.lastname: Arnold
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D001185:Artificial Intelligence; D006801:Humans; D008958:Models, Molecular; D008969:Molecular Sequence Data; D015202:Protein Engineering / Q000379:methods*; D055550:Protein Stability; D011994:Recombinant Proteins / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism; D011995:Recombination, Genetic
chemical_list: D011994:Recombinant Proteins