Cells obtain their energy from the synthesis of ATP, generated in mitochondrial membranes by a series of proteins known as the OXPHOS complexes. The OXPHOS complexes catalyze intricate proton and electron reactions that generate a proton motive force (pmf) needed for oxidative phosphorylation. Despite major structural, biochemical and computational advances, the energy transduction mechanism remains poorly understood and is a major challenge for modern life sciences. Moreover, dysfunction of these complexes due to point mutations is responsible for half of mitochondrial disorders such as cancer and neurogenerative diseases. Mechanistic understanding of these proteins rely on the accurate prediction of pKa values and redox potentials responsible for the charge transfer processes. However, the accurate computation of these properties is challenging due to the large number of titratable groups, protein conformations and complex long-range interactions.
Here we combine machine learning approaches with biochemical and structural data to understand how proteins power the energy metabolism of our cells and how human disease-related mutations alter the protein function. By combining molecular dynamics simulations (MD) and machine learning (ML) models, we aim to predict structure-based chemical reactivity at a low computational cost. We derive a model to calculate pKa and redox potentials in proteins based on quantum chemical and physico-chemical properties that we train using a ML model based on conformations sampled in MD simulations. This approach tends to reduce bias obtained from using different types of force fields, solvation models and initial conditions. The predictions provide a basis for identifying key residues responsible for the protein function and disease-related mutations.
References
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