title: Computational tools for designing smart libraries.
abstract: Traditional directed evolution experiments are often time-, labor- and cost-intensive because they involve repeated rounds of random mutagenesis and the selection or screening of large mutant libraries. The efficiency of directed evolution experiments can be significantly improved by targeting mutagenesis to a limited number of hot-spot positions and/or selecting a limited set of substitutions. The design of such "smart" libraries can be greatly facilitated by in silico analyses and predictions. Here we provide an overview of computational tools applicable for (a) the identification of hot-spots for engineering enzyme properties, and (b) the evaluation of predicted hot-spots and selection of suitable amino acids for substitutions. The selected tools do not require any specific expertise and can easily be implemented by the wider scientific community.
authors: Loschmidt Laboratories, Masaryk University, Kamenice 5/A13, 625 00, Brno, Czech Republic.
authors: Eva
authors: E
authors: Sebestova
authors: Jaroslav
authors: J
authors: Bendl
authors: Jan
authors: J
authors: Brezovsky
authors: Jiri
authors: J
authors: Damborsky
mesh_terms: D019295:Computational Biology / Q000379:methods*; D019020:Directed Molecular Evolution; D015723:Gene Library