Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F23%3A00079746" target="_blank" >RIV/00159816:_____/23:00079746 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14310/23:00132030

Result on the web

<a href="https://pubs.acs.org/doi/10.1021/acscatal.3c02575" target="_blank" >https://pubs.acs.org/doi/10.1021/acscatal.3c02575</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acscatal.3c02575" target="_blank" >10.1021/acscatal.3c02575</a>

Result language

angličtina

Original language name

Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning

Original language description

Thermostability is an essential requirement for the use of enzymes in the bioindustry. Here, we compare different protein stabilization strategies using a challenging target, a stable haloalkane dehalogenase DhaA115. We observe better performance of automated stabilization platforms FireProt and PROSS in designing multiple-point mutations over the introduction of disulfide bonds and strengthening the intra- and the inter-domain contacts by in silico saturation mutagenesis. We reveal that the performance of automated stabilization platforms was still compromised due to the introduction of some destabilizing mutations. Notably, we show that their prediction accuracy can be improved by applying manual curation or machine learning for the removal of potentially destabilizing mutations, yielding highly stable haloalkane dehalogenases with enhanced catalytic properties. A comparison of crystallographic structures revealed that current stabilization rounds were not accompanied by large backbone re-arrangements previously observed during the engineering stability of DhaA115. Stabilization was achieved by improving local contacts including protein-water interactions. Our study provides guidance for further improvement of automated structure-based computational tools for protein stabilization.

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

—

Publication year

2023

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

13

Issue of the periodical within the volume

19

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

12506-12518

UT code for WoS article

001065993100001

EID of the result in the Scopus database

—