Computational Tools for Designing Smart Libraries

C 2014

Computational Tools for Designing Smart Libraries

ŠEBESTOVÁ, Eva; Jaroslav BENDL; Jan BREZOVSKÝ a Jiří DAMBORSKÝ

Základní údaje

Originální název

Computational Tools for Designing Smart Libraries

Autoři

ŠEBESTOVÁ, Eva; Jaroslav BENDL; Jan BREZOVSKÝ a Jiří DAMBORSKÝ

Vydání

New York, Directed Evolution Library Creation, od s. 291-314, 24 s. 2nd edition, 1197, 2014

Nakladatel

Springer New York

Další údaje

Jazyk

angličtina

Typ výsledku

Kapitola resp. kapitoly v odborné knize

Obor

10600 1.6 Biological sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

URL

Kód RIV

RIV/00216224:14310/14:00093102

Organizační jednotka

Přírodovědecká fakulta

ISBN

978-1-4939-1052-6

DOI

https://doi.org/10.1007/978-1-4939-1053-3_20

UT WoS

000343250100021

EID Scopus

2-s2.0-84925883973

Klíčová slova anglicky

Computational tool; Rational design; Smart library; Focused library; Directed evolution; Mutation; Protein engineering; Software Web tool

Štítky

AKR, H19

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 23. 6. 2020 15:00, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

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.

Citovat

ŠEBESTOVÁ, Eva; Jaroslav BENDL; Jan BREZOVSKÝ a Jiří DAMBORSKÝ. Computational Tools for Designing Smart Libraries. In Gillam, E. M. J., Copp, J. N., Ackerley, D. F.,. Directed Evolution Library Creation. New York: Springer New York, 2014, s. 291-314. 2nd edition, 1197. ISBN 978-1-4939-1052-6. Dostupné z: https://doi.org/10.1007/978-1-4939-1053-3_20.

@inbook{1369169,
   author = {Šebestová, Eva and Bendl, Jaroslav and Brezovský, Jan and Damborský, Jiří},
   address = {New York},
   booktitle = {Directed Evolution Library Creation},
   doi = {https://doi.org/10.1007/978-1-4939-1053-3_20},
   editor = {Gillam, E. M. J., Copp, J. N., Ackerley, D. F.,},
   keywords = {Computational tool; Rational design; Smart library; Focused library; Directed evolution; Mutation; Protein engineering; Software Web tool},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {New York},
   isbn = {978-1-4939-1052-6},
   pages = {291-314},
   publisher = {Springer New York},
   title = {Computational Tools for Designing Smart Libraries},
   url = {http://link.springer.com/protocol/10.1007%2F978-1-4939-1053-3_20},
   year = {2014}
}

TY  - CHAP
ID  - 1369169
AU  - Šebestová, Eva - Bendl, Jaroslav - Brezovský, Jan - Damborský, Jiří
PY  - 2014
TI  - Computational Tools for Designing Smart Libraries
VL  - 2nd edition, 1197
PB  - Springer New York
CY  - New York
SN  - 9781493910526
KW  - Computational tool
KW  - Rational design
KW  - Smart library
KW  - Focused library
KW  - Directed evolution
KW  - Mutation
KW  - Protein engineering
KW  - Software Web tool
UR  - http://link.springer.com/protocol/10.1007%2F978-1-4939-1053-3_20
L2  - http://link.springer.com/protocol/10.1007%2F978-1-4939-1053-3_20
N2  - 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.
ER  -

ŠEBESTOVÁ, Eva; Jaroslav BENDL; Jan BREZOVSKÝ a Jiří DAMBORSKÝ. Computational Tools for Designing Smart Libraries. In Gillam, E. M. J., Copp, J. N., Ackerley, D. F.,. \textit{Directed Evolution Library Creation}. New York: Springer New York, 2014, s.~291-314. 2nd edition, 1197. ISBN~978-1-4939-1052-6. Dostupné z: https://doi.org/10.1007/978-1-4939-1053-3\_{}20.

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