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

Journal: Chembiochem : a European journal of chemical biology
PMID: 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.

Authors

  • Guangyue Li
    Department of Chemistry, Philipps-University, 35032, Marburg, Germany.
  • Youcai Qin
    State 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.
  • Nicolas T Fontaine
    PEACCEL, Protein Engineering ACCELerator, 6 Square Albin Cachot, box 42, 75013 Paris, France.
  • Matthieu Ng Fuk Chong
    PEACCEL, Protein Engineering Accelerator, Paris, France.
  • Miguel A Maria-Solano
    Institut de Química Computacional i Catàlisi and Departament de Química, Universitat de Girona Campus Montilivi, 17003, Girona, Catalonia, Spain) .
  • Ferran Feixas
    Institut de Química Computacional i Catàlisi and Departament de Química, Universitat de Girona Campus Montilivi, 17003, Girona, Catalonia, Spain) .
  • Xavier F Cadet
    PEACCEL, Protein Engineering ACCELerator, 6 Square Albin Cachot, box 42, 75013 Paris, France.
  • Rudy Pandjaitan
    PEACCEL, Protein Engineering Accelerator, Paris, France.
  • Marc Garcia-Borràs
    Institut de Química Computacional i Catàlisi and Departament de Química, Universitat de Girona Campus Montilivi, 17003, Girona, Catalonia, Spain) .
  • Frédéric Cadet
    PEACCEL, Protein Engineering Accelerator, Paris, France. frederic.cadet@peaccel.com.
  • Manfred T Reetz
    Department of Chemistry, Philipps-University, 35032, Marburg, Germany.

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