Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation.
artificial intelligence
epistasis
epoxide hydrolase
innov'SAR
machine learning
molecular dynamics simulations
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:
02 03 2021
02 03 2021
Historique:
received:
31
08
2020
revised:
22
10
2020
pubmed:
24
10
2020
medline:
28
9
2021
entrez:
23
10
2020
Statut:
ppublish
Résumé
Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside-the-box, predictions not found in other state-of-the-art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness.
Identifiants
pubmed: 33094545
doi: 10.1002/cbic.202000612
pmc: PMC7984044
doi:
Substances chimiques
Limonene
9MC3I34447
Epoxide Hydrolases
EC 3.3.2.-
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
904-914Subventions
Organisme : National Natural Science Foundation of China
ID : 21807111
Organisme : Elite Youth Program of CAAS
Organisme : Agricultural Science and Technology Innovation Program of CAAS
ID : CAAS-ZDRW202011
Organisme : Central Public-interest Scientific Institution Basal Research Fund
ID : Y2019PT16
Organisme : European Union (UE)
Organisme : Region Reunion (FEDER)
Organisme : Generalitat de Catalunya AGAUR
ID : 2017 SGR-1707
Organisme : Generalitat de Catalunya AGAUR
ID : 2017 SGR-39
Organisme : Beatriu de Pinós H2020 MSCA-Cofund
ID : 2018-BP-00204
Organisme : MINECO-Spain
ID : BES-2015-074964
Organisme : MICINN-Spain
ID : RTI2018-101032-J-I00
Organisme : MICINN-Spain
ID : PID2019-111300GA-I00
Organisme : MICINN-Spain
ID : IJCI-2017-33411
Organisme : Barcelona Supercomputing Center-Centro Nacional de Supercomputación
Organisme : Max-Planck-Society
Informations de copyright
© 2020 The Authors. ChemBioChem published by Wiley-VCH GmbH.
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