Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation. (17th November 2020)

• Record Type: Journal Article
• Authors: Li, Guangyue; Qin, Youcai; Fontaine, Nicolas T.; Ng Fuk Chong, Matthieu; Maria‐Solano, Miguel A.; Feixas, Ferran; Cadet, Xavier F.; Pandjaitan, Rudy; Garcia‐Borràs, Marc; Cadet, Frederic; Reetz, Manfred T.
• Is Part Of: Chembiochem. Volume 22:Number 5(2021)
• Page Start: 904
• Page End: 914
• Publication Date: 2020-11-17
• Subjects: machine learning -- innov'SAR -- epistasis -- artificial intelligence -- epoxide hydrolase -- molecular dynamics simulations; Biochemistry -- Periodicals; Molecular biology -- Periodicals; Pharmaceutical chemistry -- Periodicals; 572
• DOI: 10.1002/cbic.202000612
• Languages: English
• ISSNs: 1439-4227

Abstract

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. Abstract : A quick learner : Machine learning based on the innov'SAR algorithm leads to efficient selection of highly robust limonene epoxide hydrolase mutants with enhanced unfolding stability and resistance to aggregation by recognizing epistatic mutational interactions.