Computational Tools for Designing Smart Libraries

Authors

Sebestova, E., Bendl, J., Brezovsky, J., Damborsky, J.

Source

METHODS IN MOLECULAR BIOLOGY 1179: 291-314 (2014)

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.

Citation

Sebestova, E., Bendl, J., Brezovsky, J., Damborsky, J., 2014: Computational Tools for Designing Smart Libraries. Methods in Molecular Biology 1179: 291-314.

ELIXIR CZ annual conference 15 – 16 Nov, 2017
New video explaining the goals of Rafts4Biotech EU project
Invitation to a presentation by Synthetic Genomics
Andrea Schenkmayerova received a grant to attend an EMBO Practical Course
Open Postdoc Position: Protein Engineering
FireProt in MUNI
Jan Stourac won Dean’s Award