title: Enzyme optimization: moving from blind evolution to statistical exploration of sequence-function space.
abstract: Directed evolution is a powerful tool for the creation of commercially useful enzymes, particularly those approaches that are based on in vitro recombination methods, such as DNA shuffling. Although these types of search algorithms are extraordinarily efficient compared with purely random methods, they do not explicitly represent or interrogate the genotype-phenotype relationship and are essentially blind in nature. Recently, however, researchers have begun to apply multivariate statistical techniques to model protein sequence-function relationships and guide the evolutionary process by rapidly identifying beneficial diversity for recombination. In conjunction with state-of-the-art library generation methods, the statistical approach to sequence optimization is now being used routinely to create enzymes efficiently for industrial applications.
authors.affiliation: Codexis, Inc., 200 Penobscot Drive, Redwood City, CA 94063, USA. richard.fox@codexis.com
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Fox
authors.forename: Gjalt W
authors.initials: GW
authors.lastname: Huisman
mesh_terms: D001185:Artificial Intelligence; D019020:Directed Molecular Evolution / Q000639:trends*; D004798:Enzymes / Q000737:chemistry* / Q000235:genetics*; D015202:Protein Engineering / Q000639:trends*; D011994:Recombinant Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D004798:Enzymes; D011994:Recombinant Proteins