Computational Design of Stable and Soluble Biocatalysts

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F19%3A00071086" target="_blank" >RIV/00159816:_____/19:00071086 - isvavai.cz</a>

Alternative codes found

RIV/00216305:26230/18:PU130812 RIV/00216224:14310/19:00113346

Result on the web

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

Result language

angličtina

Original language name

Computational Design of Stable and Soluble Biocatalysts

Original language description

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&apos; 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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10403 - Physical chemistry

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

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

Name of the periodical

ACS Catalysis

ISSN

2155-5435

e-ISSN

—

Volume of the periodical

9

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

22

Pages from-to

1033-1054

UT code for WoS article

000458707000028

EID of the result in the Scopus database

—