Protein Thermal Stability Engineering Using HoTMuSiC.
Artificial neural network
Lipase
Protein design
Protein melting temperature
Statistical potentials
Thermal stability
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2020
2020
Historique:
entrez:
2
2
2020
pubmed:
2
2
2020
medline:
28
1
2021
Statut:
ppublish
Résumé
The rational design of enzymes is a challenging research field, which plays an important role in the optimization of a wide series of biotechnological processes. Computational approaches allow screening all possible amino acid substitutions in a target protein and to identify a subset likely to have the desired properties. They can thus be used to guide and restrict the huge, time-consuming search in sequence space to reach protein optimality. Here we present HoTMuSiC, a tool that predicts the impact of point mutations on the protein melting temperature, which uses the experimental or modeled protein structure as sole input and is available at the dezyme.com website. Its main advantages include accuracy and speed, which makes it a perfect instrument for thermal stability engineering projects aiming at designing new proteins that feature increased heat resistance or remain active and stable in nonphysiological conditions. We set up a HoTMuSiC-based pipeline, which uses additional information to avoid mutations of functionally important residues, identified as being too well conserved among homologous proteins or too close to annotated functional sites. The efficiency of this pipeline is successfully demonstrated on Rhizomucor miehei lipase.
Identifiants
pubmed: 32006278
doi: 10.1007/978-1-0716-0270-6_5
doi:
Substances chimiques
Proteins
0
Lipase
EC 3.1.1.3
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM