| Grant number: | 22/04695-2 |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| Start date: | July 01, 2022 |
| End date: | December 31, 2024 |
| Field of knowledge: | Physical Sciences and Mathematics - Chemistry - Physical-Chemistry |
| Principal Investigator: | Munir Salomao Skaf |
| Grantee: | Alberto Monteiro dos Santos |
| Host Institution: | Instituto de Química (IQ). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
| Associated research grant: | 13/08293-7 - CCES - Center for Computational Engineering and Sciences, AP.CEPID |
Abstract The subject of this postdoctoral research is the study of reaction mechanisms and catalytic efficiency of active enzymes on lignocellulosic substrates using advanced molecular modelling techniques. The enzymes of interest are primarily Esterases (e.g., Glucuronyl Esterases - GEs, Feruloyl Esterases - FAEs) and ²-glycosidases Glycoside Hydrolases (GHs). GEs/FAEs and GHs are involved in lignocellulosic biomass pre-treatment and the saccharification stage, respectively. The catalytic improvement of these enzymes has a potential impact on reducing the production costs of new generation biofuels. We propose using hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) methods to describe the reaction mechanism of these enzymes against lignocellulosic substrates and subsequently apply machine learning approaches to propose mutations that might result in an improved catalytic efficiency of the enzymes. The obtained transition state structures obtained from QM/MM will be used as starting points for design with the Rosetta enzyme protocol and machine learning strategies. The proposed models will be further analysed using neural networks. First, we will focus on obtaining a detailed molecular description of the reaction mechanism and estimated catalytic yield of the enzymes using QM/MM. We will then combine the data obtained for the electrostatic fields created by key residues responsible for the stabilization of the Transition States (TS) into machine learning algorithms to propose new catalytic models. (AU) | |
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