ArticleJournal of chemical information and modeling2022
Machine Learning for Efficient Prediction of Protein Redox Potential: The Flavoproteins Case.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Toward Relative Redox Potential Predictions in Flavoproteins: Treatment of a Charge-Changing Mutation in Alchemical Free Energy Simulations.Journal of chemical theory and computation · 2026Article
- Predicting Metalloprotein Redox Potentials with Machine Learning: A Focus on Iron-Sulfur Systems.Journal of chemical information and modeling · 2025Article
- Machine Learning Approach to Vertical Energy Gap in Redox Processes.Journal of chemical theory and computation · 2024Article
- Assessing the Performance of Non-Equilibrium Thermodynamic Integration in Flavodoxin Redox Potential Estimation.Molecules (Basel, Switzerland) · 2023Article
- Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks.Journal of chemical theory and computation · 2023Review
- Prediction of Redox Power for Photocatalysts: Synergistic Combination of DFT and Machine Learning.Journal of chemical theory and computation · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Determining the redox potentials of protein cofactors and how they are influenced by their molecular neighborhoods is essential for basic research and many biotechnological applications, from biosensors and biocatalysis to bioremediation and bioelectronics. The laborious determination of redox potential with current experimental technologies pushes forward the need for computational approaches that can reliably predict it. Although current computational approaches based on quantum and molecular mechanics are accurate, their large computational costs hinder their usage. In this work, we explored the possibility of using more efficient QSPR models based on machine learning (ML) for the prediction of protein redox potential, as an alternative to classical approaches. As a proof of concept, we focused on flavoproteins, one of the most important families of enzymes directly involved in redox processes. To train and test different ML models, we retrieved a dataset of flavoproteins with a known midpoint redox potential (
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