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

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Authors

KUNKA Antonín MARQUES Sérgio Manuel HAVLÁSEK Martin VAŠINA Michal VELÁTOVÁ Nikola CENGELOVÁ Lucia KOVÁŘ David DAMBORSKÝ Jiří MAREK Martin BEDNÁŘ David PROKOP Zbyněk

Year of publication 2023
Type Article in Periodical
Magazine / Source ACS Catalysis
MU Faculty or unit

Faculty of Science

Citation
Web https://pubs.acs.org/doi/10.1021/acscatal.3c02575
Doi http://dx.doi.org/10.1021/acscatal.3c02575
Keywords biocatalysis; computational design; FireProt; machine learning; PROSS; proteinengineering; stabilization; thermostability
Attached files
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.
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