title: Putting engineering back into protein engineering: bioinformatic approaches to catalyst design.
abstract: Complex multivariate engineering problems are commonplace and not unique to protein engineering. Mathematical and data-mining tools developed in other fields of engineering have now been applied to analyze sequence-activity relationships of peptides and proteins and to assist in the design of proteins and peptides with specified properties. Decreasing costs of DNA sequencing in conjunction with methods to quickly synthesize statistically representative sets of proteins allow modern heuristic statistics to be applied to protein engineering. This provides an alternative approach to expensive assays or unreliable high-throughput surrogate screens.
authors.affiliation: DNA 2.0, Inc., 1455 Adams Drive, Menlo Park, CA 94025, USA. cgustafsson@dnatwopointo.com
authors.forename: Claes
authors.initials: C
authors.lastname: Gustafsson
authors.forename: Sridhar
authors.initials: S
authors.lastname: Govindarajan
authors.forename: Jeremy
authors.initials: J
authors.lastname: Minshull
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019943:Amino Acid Substitution; D002384:Catalysis; D019295:Computational Biology / Q000379:methods*; D017076:Computer-Aided Design; D015195:Drug Design; D004798:Enzymes / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D007700:Kinetics; D015233:Models, Statistical; D008962:Models, Theoretical; D016296:Mutagenesis; D016571:Neural Networks, Computer; D015202:Protein Engineering / Q000639:trends*
chemical_list: D004798:Enzymes