Enhancing PET Degrading Enzymes: A Combinatory Approach.
Ancestral sequence reconstruction
Biocatalysis
Machine learning
PET hydrolase (PETase)
Protein engineering
Journal
Chembiochem : a European journal of chemical biology
ISSN: 1439-7633
Titre abrégé: Chembiochem
Pays: Germany
ID NLM: 100937360
Informations de publication
Date de publication:
07 Apr 2024
07 Apr 2024
Historique:
revised:
02
04
2024
received:
30
01
2024
accepted:
04
04
2024
medline:
8
4
2024
pubmed:
8
4
2024
entrez:
7
4
2024
Statut:
aheadofprint
Résumé
Plastic waste has become a substantial environmental issue. A potential strategy to mitigate this problem is to use enzymatic hydrolysis of plastics to depolymerize post-consumer waste and allow it to be reused. Over the last few decades, the use of enzymatic PET-degrading enzymes has shown promise as a great solution for creating a circular plastic waste economy. PsPETase from Piscinibacter sakaiensis has been identified as an enzyme with tremendous potential for such applications. But to improve its efficiency, enzyme engineering has been applied aiming at enhancing its thermal stability, enzymatic activity, and ease of production. Here, we combine different strategies such as structure-based rational design, ancestral sequence reconstruction and machine learning to engineer more highly active Combi-PETase variants with a melting temperature of 70°C and optimal performance at 60°C. Furthermore, this study demonstrates that these approaches, commonly used in other works of enzyme engineering, are most effective when utilized in combination, enabling the improvement of enzymes for industrial applications.
Identifiants
pubmed: 38584134
doi: 10.1002/cbic.202400084
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e202400084Informations de copyright
© 2024 Wiley‐VCH GmbH.