Machine Learning for Efficient Prediction of Protein Redox Potential: The Flavoproteins Case

Machine Learning Approach to Vertical Energy Gap in Redox Processes

A straightforward approach to calculating the free energy change (ΔG) and reorganization energy of a redox process is linear response approximation (LRA). However, accurate prediction of redox properties is still challenging due to difficulties in conformational sampling and vertical energy-gap sampling. Expensive hybrid quantum mechanical/molecular mechanical (QM/MM) calculations are typically employed in sampling energy gaps using conformations from simulations. To alleviate the computational cost associated with the expensive QM method in the QM/MM calculation, we propose machine learning (ML) methods to predict the vertical energy gaps (VEGs). We tested several ML models to predict the VEGs and observed that simple models like linear regression show excellent performance (mean absolute error ∼0.1 eV) in predicting VEGs in all test systems, even when using features extracted from cheaper semiempirical methods. Our best ML model (extra trees regressor) shows a mean absolute error of around 0.1 eV while using features from the cheapest QM method. We anticipate our approach can be generalized to larger macromolecular systems with more complex redox centers.

Predicting redox potentials by graph-based machine learning methods

The evaluation of oxidation and reduction potentials is a pivotal task in various chemical fields. However, their accurate prediction by theoretical computations, which is a complementary task and sometimes the only alternative to experimental measurement, may be often resource-intensive and time-consuming. This paper addresses this challenge through the application of machine learning techniques, with a particular focus on graph-based methods (such as graph edit distances, graph kernels, and graph neural networks) that are reviewed to enlighten their deep links with theoretical chemistry. To this aim, we establish the ORedOx159 database, a comprehensive, homogeneous (with reference values stemming from density functional theory calculations), and reliable resource containing 318 one-electron reduction and oxidation reactions and featuring 159 large organic compounds. Subsequently, we provide an instructive overview of the good practice in machine learning and of commonly utilized machine learning models. We then assess their predictive performances on the ORedOx159 dataset through extensive analyses. Our simulations using descriptors that are computed in an almost instantaneous way result in a notable improvement in prediction accuracy, with mean absolute error (MAE) values equal to 5.6 kcal mol- 1 $$ {}^{-1} $$ for reduction and 7.2 kcal mol- 1 $$ {}^{-1} $$ for oxidation potentials, which paves a way toward efficient in silico design of new electrochemical systems.

Retuning the potential of the electrochemical leaf

The electrochemical leaf enables the electrification and control of multi-enzyme cascades by exploiting two discoveries: (i) the ability to electrify the photosynthetic enzyme ferredoxin NADP+ reductase (FNR), driving it to catalyse the interconversion of NADP+/NADPH whilst it is entrapped in a highly porous, metal oxide electrode, and (ii) the evidence that additional enzymes can be co-entrapped in the electrode pores where, through one NADP(H)-dependent enzyme, extended cascades can be driven by electrical connection to FNR, via NADP(H) recycling. By changing a critical active-site tyrosine to serine, FNR's exclusivity for NADP(H) is swapped for unphosphorylated NAD(H). Here we present an electrochemical study of this variant FNR, and show that in addition to the intended inversion of cofactor preference, this change to the active site has altered FNR's tuning of the flavin reduction potential, making it less reductive. Exploiting the ability to monitor the variant's activity with NADP(H) as a function of potential has revealed a trapped intermediate state, relieved only by applying a negative overpotential, which allows catalysis to proceed. Inhibition by NADP+ (very tightly bound) with respect to NAD(H) turnover was also revealed and interestingly, this inhibition changes depending on the applied potential. These findings are of critical importance for future exploitation of the electrochemical leaf.

Harnessing iron‑sulfur enzymes for synthetic biology

Reactions catalysed by iron-sulfur (Fe-S) enzymes appear in a variety of biosynthetic pathways that produce valuable natural products. Harnessing these biosynthetic pathways by expression in microbial cell factories grown on an industrial scale would yield enormous economic and environmental benefits. However, Fe-S enzymes often become bottlenecks that limits the productivity of engineered pathways. As a consequence, achieving the production metrics required for industrial application remains a distant goal for Fe-S enzyme-dependent pathways. Here, we identify and review three core challenges in harnessing Fe-S enzyme activity, which all stem from the properties of Fe-S clusters: 1) limited Fe-S cluster supply within the host cell, 2) Fe-S cluster instability, and 3) lack of specialized reducing cofactor proteins often required for Fe-S enzyme activity, such as enzyme-specific flavodoxins and ferredoxins. We highlight successful methods developed for a variety of Fe-S enzymes and electron carriers for overcoming these difficulties. We use heterologous nitrogenase expression as a grand case study demonstrating how each of these challenges can be addressed. We predict that recent breakthroughs in protein structure prediction and design will prove well-suited to addressing each of these challenges. A reliable toolkit for harnessing Fe-S enzymes in engineered metabolic pathways will accelerate the development of industry-ready Fe-S enzyme-dependent biosynthesis pathways.

The energetics and evolution of oxidoreductases in deep time

The core metabolic reactions of life drive electrons through a class of redox protein enzymes, the oxidoreductases. The energetics of electron flow is determined by the redox potentials of organic and inorganic cofactors as tuned by the protein environment. Understanding how protein structure affects oxidation-reduction energetics is crucial for studying metabolism, creating bioelectronic systems, and tracing the history of biological energy utilization on Earth. We constructed ProtReDox (https://protein-redox-potential.web.app), a manually curated database of experimentally determined redox potentials. With over 500 measurements, we can begin to identify how proteins modulate oxidation-reduction energetics across the tree of life. By mapping redox potentials onto networks of oxidoreductase fold evolution, we can infer the evolution of electron transfer energetics over deep time. ProtReDox is designed to include user-contributed submissions with the intention of making it a valuable resource for researchers in this field.

Assessing the Performance of Non-Equilibrium Thermodynamic Integration in Flavodoxin Redox Potential Estimation

Flavodoxins are enzymes that contain the redox-active flavin mononucleotide (FMN) cofactor and play a crucial role in numerous biological processes, including energy conversion and electron transfer. Since the redox characteristics of flavodoxins are significantly impacted by the molecular environment of the FMN cofactor, the evaluation of the interplay between the redox properties of the flavin cofactor and its molecular surroundings in flavoproteins is a critical area of investigation for both fundamental research and technological advancements, as the electrochemical tuning of flavoproteins is necessary for optimal interaction with redox acceptor or donor molecules. In order to facilitate the rational design of biomolecular devices, it is imperative to have access to computational tools that can accurately predict the redox potential of both natural and artificial flavoproteins. In this study, we have investigated the feasibility of using non-equilibrium thermodynamic integration protocols to reliably predict the redox potential of flavodoxins. Using as a test set the wild-type flavodoxin from Clostridium Beijerinckii and eight experimentally characterized single-point mutants, we have computed their redox potential. Our results show that 75% (6 out of 8) of the calculated reaction free energies are within 1 kcal/mol of the experimental values, and none exceed an error of 2 kcal/mol, confirming that non-equilibrium thermodynamic integration is a trustworthy tool for the quantitative estimation of the redox potential of this biologically and technologically significant class of enzymes.

Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks

Redox-active organic molecules, i.e., molecules that can relatively easily accept and/or donate electrons, are ubiquitous in biology, chemical synthesis, and electronic and spintronic devices, such as solar cells and rechargeable batteries, etc. Choosing the best candidates from an essentially infinite chemical space for experimental testing in a target application requires efficient screening approaches. In this Review, we discuss modern in silico techniques for predicting reduction and oxidation potentials of organic molecules that go beyond conventional first-principles computations and thermodynamic cycles. Approaches ranging from simple linear fits based on molecular orbital energy approximation and energy difference approximation to advanced regression and neural network machine learning algorithms employing complex descriptors of molecular compositions, geometries, and electronic structures are examined in conjunction with relevant literature examples. We discuss the interplay between ab initio data and machine learning (ML), i.e., whether it is better to base predictions on low-level quantum-chemical results corrected with ML or to bypass first-principles computations entirely and instead rely on elaborate deep learning architectures. Finally, we list currently available data sets of redox-active organic molecules and their experimental and/or computed properties to facilitate the development of screening platforms and rational design of redox-active organic molecules.