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  Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation

Li, G., Qin, Y., Fontaine, N. T., Ng Fuk Chong, M., Maria-Solano, M. A., Feixas, F., et al. (2021). Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation. Chembiochem, 22(5), 904-914. doi:10.1002/cbic.202000612.

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 Creators:
Li, Guangyue1, Author
Qin, Youcai1, Author
Fontaine, Nicolas T.2, Author
Ng Fuk Chong, Matthieu2, Author
Maria-Solano, Miguel A.3, Author
Feixas, Ferran3, Author
Cadet, Xavier F.2, Author
Pandjaitan, Rudy2, Author
Garcia‐Borràs, Marc3, Author
Cadet, Frederic2, Author
Reetz, Manfred T.4, 5, 6, Author           
Affiliations:
1State Key Laboratory for Biology of Plant Diseases and Insect Pests Key Laboratory of Control of Biological Hazard Factors (Plant Origin) for Agri-product Quality and Safety Ministry of Agriculture, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100081 P. R. China, ou_persistent22              
2PEACCEL, Artificial Intelligence Department, 6 Square Albin Cachot, Box 42, 75013 Paris, France, ou_persistent22              
3Institut de Química Computacional i Catàlisi and Departament de Química, Universitat de Girona Campus Montilivi, 17003 Girona, Catalonia, Spain, ou_persistent22              
4Department of Chemistry, Philipps-Universität, 35032 Marburg, Germany, ou_persistent22              
5Research Department Reetz, Max-Planck-Institut für Kohlenforschung, Max Planck Society, ou_1445588              
6Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West 7th Avenue, Tianjin Airport Economic Area, 300308 Tianjin, P. R. China, ou_persistent22              

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Free keywords: machine learning; inoov'SAR; epistasis; artificial intelligence; epoxide hydrolase; molecular dynamics simulations
 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.

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Language(s): eng - English
 Dates: 2020-08-312020-10-222021-03-02
 Publication Status: Published online
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1002/cbic.202000612
 Degree: -

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Title: Chembiochem
  Abbreviation : Chembiochem
Source Genre: Journal
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Publ. Info: Weinheim, Germany : Wiley-VCH
Pages: - Volume / Issue: 22 (5) Sequence Number: - Start / End Page: 904 - 914 Identifier: ISSN: 1439-4227
CoNE: https://pure.mpg.de/cone/journals/resource/110978984568897