ArticleChembiochem : a European journal of chemical biology2021
Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation.
Article in Chembiochem : a European journal of chemical biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-DependentACS synthetic biology · 2025Article
- Second-order allosteric control as a mechanism for compensatory mutations in B-cell translocation gene 2.Protein science : a publication of the Protein Society · 2025Article
- Ultrahigh-Throughput Activity Engineering of Promiscuous Amidases through a Fluorescence-Activated Cell Sorting Assay.ACS catalysis · 2025Article
- Mapping Biomaterial Complexity by Machine Learning.Tissue engineering. Part A · 2024Review
- Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning.ACS central science · 2024Article
- Computational peptide discovery with a genetic programming approach.Journal of computer-aided molecular design · 2024Article
- Machine Learning-Guided Protein Engineering.ACS catalysis · 2023Review
- Computational Peptide Discovery with a Genetic Programming Approach.Research square · 2023Article
- Protein dynamics provide mechanistic insights about epistasis among common missense polymorphisms.Biophysical journal · 2023Article
- Epoxide Hydrolases: Multipotential Biocatalysts.International journal of molecular sciences · 2023Review
- How can we discover developable antibody-based biotherapeutics?Frontiers in molecular biosciences · 2023Review
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- Mutation-Specific Differences in Kv7.1 (International journal of molecular sciences · 2022Review
- High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering.Chemical communications (Cambridge, England) · 2022Review
- Learning Strategies in Protein Directed Evolution.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Article
- Intelligent host engineering for metabolic flux optimisation in biotechnology.The Biochemical journal · 2021Review
- Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation.Chembiochem : a European journal of chemical biology · 2021Article
- Machine learning for enzyme engineering, selection and design.Protein engineering, design & selection : PEDS · 2021Review
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Authors and funding
11 authors.
Funding
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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Registered trials
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