MEnTaT: A machine-learning approach for the identification of mutations to increase protein stability.
PLS-DA
esterase
kinase
oxidase
thermostability
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
05 Dec 2023
05 Dec 2023
Historique:
pmc-release:
01
06
2024
medline:
4
12
2023
pubmed:
1
12
2023
entrez:
1
12
2023
Statut:
ppublish
Résumé
Enhancing protein thermal stability is important for biomedical and industrial applications as well as in the research laboratory. Here, we describe a simple machine-learning method which identifies amino acid substitutions that contribute to thermal stability based on comparison of the amino acid sequences of homologous proteins derived from bacteria that grow at different temperatures. A key feature of the method is that it compares the sequences based not simply on the amino acid identity, but rather on the structural and physicochemical properties of the side chain. The method accurately identified stabilizing substitutions in three well-studied systems and was validated prospectively by experimentally testing predicted stabilizing substitutions in a polyamine oxidase. In each case, the method outperformed the widely used bioinformatic consensus approach. The method can also provide insight into fundamental aspects of protein structure, for example, by identifying how many sequence positions in a given protein are relevant to temperature adaptation.
Identifiants
pubmed: 38039271
doi: 10.1073/pnas.2309884120
pmc: PMC10710055
doi:
Substances chimiques
Mentat
0
Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2309884120Déclaration de conflit d'intérêts
Competing interests statement:The authors declare no competing interest.
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