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Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation |
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| Authors: | Prof Guangyue Li Dr Youcai Qin Dr Nicolas T Fontaine Dr Matthieu Ng Fuk Chong Miguel A Maria-Solano Dr Ferran Feixas Xavier F Cadet Dr Rudy Pandjaitan Dr Marc Garcia-Borràs Prof Frederic Cadet Prof Manfred T Reetz |
| Affiliation: | 1. 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;2. 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
These authors contributed equally to this work.;3. PEACCEL, Artificial Intelligence Department, 6 Square Albin Cachot, Box 42, 75013 Paris, France) . These authors contributed equally to this work.;4. PEACCEL, Artificial Intelligence Department, 6 Square Albin Cachot, Box 42, 75013 Paris, France) .;5. Institut de Química Computacional i Catàlisi and Departament de Química, Universitat de Girona Campus Montilivi, 17003 Girona, Catalonia, Spain) .;6. Department of Chemistry, Philipps-Universität, 35032 Marburg, Germany) . |
| 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. |
| Keywords: | machine learning innov'SAR epistasis artificial intelligence epoxide hydrolase molecular dynamics simulations |