Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation.
Chembiochem
; 22(5): 904-914, 2021 03 02.
Article
em En
| MEDLINE
| ID: mdl-33094545
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Rhodococcus
/
Dobramento de Proteína
/
Epóxido Hidrolases
/
Multimerização Proteica
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Aprendizado de Máquina
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Mutação
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Chembiochem
Assunto da revista:
BIOQUIMICA
Ano de publicação:
2021
Tipo de documento:
Article