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Metabolons are protein assemblies that perform a series of reactions in a metabolic pathway. However, the general importance and aptitude of metabolons for enzyme catalysis remain poorly understood. In animals, biosynthesis of coenzyme Q is currently attributed to ten different proteins, with COQ3, COQ4, COQ5, COQ6, COQ7 and COQ9 forming the iconic COQ metabolon. Yet several reaction steps conducted by the metabolon remain enigmatic. To elucidate the prerequisites for animal coenzyme Q biosynthesis, we sought to construct the entire metabolon in vitro. Here we show that this approach, rooted in ancestral sequence reconstruction, reveals the enzymes responsible for the uncharacterized steps and captures the biosynthetic pathway in vitro. We demonstrate that COQ8, a kinase, increases and streamlines coenzyme Q production. Our findings provide crucial insight into how biocatalytic efficiency is regulated and enhanced by these biosynthetic engines in the context of the cell. Coenzyme Q has several important biological functions, but the understanding of the biosynthesis of coenzyme Q in humans remains incomplete. Now, by constructing the entire COQ metabolon in vitro, the enzymes and reactions underlying coenzyme Q biosynthesis are characterized.

Metabolons - transient assemblies of sequential metabolic enzymes - facilitate the reactions of multi-step metabolic pathways, yet, how they mechanistically bolster metabolic flux remains unknown. Here, we investigate the molecular determinants of metabolon formation in coenzyme Q (CoQ) biosynthesis using coarse-grained molecular dynamics simulations and biochemical experiments. We show that the COQ metabolon forms at the critical region of a phase transition, where both metabolon clustering and metabolic flux exhibit coordinated sigmoidal responses to changes in protein-protein interaction strength. These complete metabolons enable substrate channeling between sequential enzymes, leading to a crucial enhancement of CoQ production efficiency. Selectively disrupting protein-protein interactions and randomly shuffling the interaction network demonstrate that protein-proximity rather than fine structure of the metabolon clusters is imperative for substrate channeling. Grounded in both experiment and simulation, these findings provide a framework for understanding the organization and function of metabolons across diverse metabolic pathways.

Identifying potent lead molecules for specific targets remains a major bottleneck in drug discovery. As structural information about proteins becomes increasingly available, ultra-large virtual screenings (ULVSs) which computationally evaluate billions of molecules offer a powerful way to accelerate early-stage drug discovery. Here, we introduce AdaptiveFlow, an open-source platform designed to make ULVSs more accessible, scalable, and efficient. AdaptiveFlow provides free access to a screening-ready version of the Enamine REAL Space (1), the largest library of ready-to-dock, drug-like molecules, containing 69 billion compounds that we prepared using the ligand preparation module of the platform. A key innovation of the platform is its use of a multi-dimensional grid of molecular properties, which helps researchers explore and prioritize chemical space more effectively and reduce the computational costs by a factor of approximately 1000. This grid forms the basis of a new method for identifying promising regions of chemical space, enabling systematic exploration and prioritization of compound libraries. An optional active learning component can further accelerate this process by adaptively steering the search toward molecules most likely to bind a given target. To support a broad range of applications, AdaptiveFlow is compatible with over 1,500 docking methods. The platform achieves near-linear scaling on up to 5.6 million CPUs in the AWS Cloud, setting a new benchmark for large-scale cloud computing in drug discovery. Using this approach, we identified nanomolar inhibitors of two disease-relevant targets: ferroptosis suppressor protein 1 (FSP1) and poly(ADP-ribose) polymerase 1 (PARP-1) (2, 3). By leveraging newly solved crystal structures of FSP1 in complex with NAD +, FAD, and coenzyme Q1, we validated these hits experimentally and determined the first co-crystal structures of FSP1 bound to small-molecule inhibitors, enabling insights into inhibitor binding mechanisms previously unknown. With its high scalability, flexibility, and open accessibility, AdaptiveFlow offers a powerful new resource for discovering and optimizing drug candidates at an unprecedented scale and speed.