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Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation
Author(s) -
Li Guangyue,
Qin Youcai,
Fontaine Nicolas T.,
Ng Fuk Chong Matthieu,
MariaSolano Miguel A.,
Feixas Ferran,
Cadet Xavier F.,
Pandjaitan Rudy,
GarciaBorràs Marc,
Cadet Frederic,
Reetz Manfred T.
Publication year - 2021
Publication title -
chembiochem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.05
H-Index - 126
eISSN - 1439-7633
pISSN - 1439-4227
DOI - 10.1002/cbic.202000612
Subject(s) - epistasis , mutant , protein stability , selection (genetic algorithm) , protein engineering , enzyme , stability (learning theory) , chemistry , directed evolution , computational biology , biochemistry , biology , computer science , machine learning , gene
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.