Computational Tools for Designing Smart Libraries

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F14%3A00093102" target="_blank" >RIV/00216224:14310/14:00093102 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/protocol/10.1007%2F978-1-4939-1053-3_20" target="_blank" >http://link.springer.com/protocol/10.1007%2F978-1-4939-1053-3_20</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-1-4939-1053-3_20" target="_blank" >10.1007/978-1-4939-1053-3_20</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Computational Tools for Designing Smart Libraries

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Computational Tools for Designing Smart Libraries

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

CEP obor

CE - Biochemie

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Directed Evolution Library Creation

ISBN

9781493910526

Počet stran výsledku

24

Strana od-do

291-314

Počet stran knihy

369

Název nakladatele

Springer New York

Místo vydání

New York

Kód UT WoS kapitoly

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