Computational Design of Stable and Soluble Biocatalysts

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F18%3APU130812" target="_blank" >RIV/00216305:26230/18:PU130812 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00159816:_____/19:00071086 RIV/00216224:14310/19:00113346

Výsledek na webu

<a href="https://pubs.acs.org/doi/10.1021/acscatal.8b03613" target="_blank" >https://pubs.acs.org/doi/10.1021/acscatal.8b03613</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acscatal.8b03613" target="_blank" >10.1021/acscatal.8b03613</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Computational Design of Stable and Soluble Biocatalysts

Popis výsledku v původním jazyce

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 co-solvents. 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, the number of sequences available for phylogenetic analyses is growing, and 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.

Název v anglickém jazyce

Computational Design of Stable and Soluble Biocatalysts

Popis výsledku anglicky

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 co-solvents. 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, the number of sequences available for phylogenetic analyses is growing, and 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

ACS Catalysis

ISSN

2155-5435

e-ISSN

—

Svazek periodika

2019

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

22

Strana od-do

1033-1054

Kód UT WoS článku

000458707000028

EID výsledku v databázi Scopus

2-s2.0-85059802317