Computational Design of Stable and Soluble Biocatalysts

J 2019

Computational Design of Stable and Soluble Biocatalysts

MUSIL, Miloš; Hannes KONEGGER; Jiří HON; David BEDNÁŘ; Jiří DAMBORSKÝ et al.

Základní údaje

Originální název

Computational Design of Stable and Soluble Biocatalysts

Autoři

MUSIL, Miloš; Hannes KONEGGER; Jiří HON; David BEDNÁŘ a Jiří DAMBORSKÝ

Vydání

ACS Catalysis, WASHINGTON, AMER CHEMICAL SOC, 2019, 2155-5435

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10403 Physical chemistry

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 12.350

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/19:00113346

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

aggregation; computational design; force field; expressibility; machine learning; phylogenetic analysis; enzyme stability; enzyme solubility

Štítky

H23, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 15. 2. 2023 22:29, Mgr. Michaela Hylsová, Ph.D.

Anotace

V originále

Natural enzymes are delicate biomolecules possessing only marginal thermodynamic stability. Poorly stable, misfolded, and aggregated proteins lead to huge economic losses in the biotechnology and biopharmaceutical industries. Consequently, there is a need to design optimized protein sequences that maximize stability, solubility, and activity over a wide range of temperatures and pH values in buffers of different composition and in the presence of organic cosolvents. This has created great interest in using computational methods to enhance biocatalysts' robustness and solubility. Suitable methods include (i) energy calculations, (ii) machine learning, (iii) phylogenetic analyses, and (iv) combinations of these approaches. We have witnessed impressive progress in the design of stable enzymes over the last two decades, but predictions of protein solubility and expressibility are scarce. Stabilizing mutations can be predicted accurately using available force fields, and the number of sequences available for phylogenetic analyses is growing. In addition, complex computational workflows are being implemented in intuitive web tools, enhancing the quality of protein stability predictions. Conversely, solubility predictors are limited by the lack of robust and balanced experimental data, an inadequate understanding of fundamental principles of protein aggregation, and a dearth of structural information on folding intermediates. Here we summarize recent progress in the development of computational tools for predicting protein stability and solubility, critically assess their strengths and weaknesses, and identify apparent gaps in data and knowledge. We also present perspectives on the computational design of stable and soluble biocatalysts.

Návaznosti

EF16_013/0001761, projekt VaV

Název: RECETOX RI

LM2015047, projekt VaV

Název: Česká národní infrastruktura pro biologická data (Akronym: ELIXIR-CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Česká národní infrastruktura pro biologická data

LM2015051, projekt VaV

Název: Centrum pro výzkum toxických látek v prostředí (Akronym: RECETOX RI)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Výzkumná infrastruktura RECETOX

LM2015055, projekt VaV

Název: Centrum pro systémovou biologii (Akronym: C4SYS)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, The national infrastructure C4SYS - Centre for Systems Biology

Přiložené soubory

acscatal.8b03613.pdf

Citovat

MUSIL, Miloš; Hannes KONEGGER; Jiří HON; David BEDNÁŘ a Jiří DAMBORSKÝ. Computational Design of Stable and Soluble Biocatalysts. ACS Catalysis. WASHINGTON: AMER CHEMICAL SOC, 2019, roč. 9, č. 2, s. 1033-1054. ISSN 2155-5435. Dostupné z: https://doi.org/10.1021/acscatal.8b03613.

@article{1638956,
   author = {Musil, Miloš and Konegger, Hannes and Hon, Jiří and Bednář, David and Damborský, Jiří},
   article_location = {WASHINGTON},
   article_number = {2},
   doi = {https://doi.org/10.1021/acscatal.8b03613},
   keywords = {aggregation; computational design; force field; expressibility; machine learning; phylogenetic analysis; enzyme stability; enzyme solubility},
   language = {eng},
   issn = {2155-5435},
   journal = {ACS Catalysis},
   title = {Computational Design of Stable and Soluble Biocatalysts},
   url = {https://pubs.acs.org/doi/10.1021/acscatal.8b03613},
   volume = {9},
   year = {2019}
}

TY  - JOUR
ID  - 1638956
AU  - Musil, Miloš - Konegger, Hannes - Hon, Jiří - Bednář, David - Damborský, Jiří
PY  - 2019
TI  - Computational Design of Stable and Soluble Biocatalysts
JF  - ACS Catalysis
VL  - 9
IS  - 2
SP  - 1033-1054
EP  - 1033-1054
PB  - AMER CHEMICAL SOC
SN  - 21555435
KW  - aggregation
KW  - computational design
KW  - force field
KW  - expressibility
KW  - machine learning
KW  - phylogenetic analysis
KW  - enzyme stability
KW  - enzyme solubility
UR  - https://pubs.acs.org/doi/10.1021/acscatal.8b03613
L2  - https://pubs.acs.org/doi/10.1021/acscatal.8b03613
N2  - Natural enzymes are delicate biomolecules possessing only marginal thermodynamic stability. Poorly stable, misfolded, and aggregated proteins lead to huge economic losses in the biotechnology and biopharmaceutical industries. Consequently, there is a need to design optimized protein sequences that maximize stability, solubility, and activity over a wide range of temperatures and pH values in buffers of different composition and in the presence of organic cosolvents. This has created great interest in using computational methods to enhance biocatalysts' robustness and solubility. Suitable methods include (i) energy calculations, (ii) machine learning, (iii) phylogenetic analyses, and (iv) combinations of these approaches. We have witnessed impressive progress in the design of stable enzymes over the last two decades, but predictions of protein solubility and expressibility are scarce. Stabilizing mutations can be predicted accurately using available force fields, and the number of sequences available for phylogenetic analyses is growing. In addition, complex computational workflows are being implemented in intuitive web tools, enhancing the quality of protein stability predictions. Conversely, solubility predictors are limited by the lack of robust and balanced experimental data, an inadequate understanding of fundamental principles of protein aggregation, and a dearth of structural information on folding intermediates. Here we summarize recent progress in the development of computational tools for predicting protein stability and solubility, critically assess their strengths and weaknesses, and identify apparent gaps in data and knowledge. We also present perspectives on the computational design of stable and soluble biocatalysts.
ER  -

MUSIL, Miloš; Hannes KONEGGER; Jiří HON; David BEDNÁŘ a Jiří DAMBORSKÝ. Computational Design of Stable and Soluble Biocatalysts. \textit{ACS Catalysis}. WASHINGTON: AMER CHEMICAL SOC, 2019, roč.~9, č.~2, s.~1033-1054. ISSN~2155-5435. Dostupné z: https://doi.org/10.1021/acscatal.8b03613.

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