Molecular-level design of alternative media for energy-saving pilot-scale fibrillation of nanocellulose.

The outstanding mechanical properties, light weight, and biodegradability of cellulose nanofibrils (CNFs) make them promising components of renewable and sustainable next-generation reinforced composite biomaterials and bioplastics. Manufacturing CNFs at a pilot scale requires disc-refining fibrillation of dilute cellulose fibers in aqueous pulp suspensions to shear the fibers apart into their nanodimensional forms, which is, however, an energy-intensive process. Here, we used atomistic molecular dynamics (MD) simulation to examine media that might facilitate the reduction of interactions between cellulose fibers, thereby reducing energy consumption in fibrillation. The most suitable medium found by the simulations was an aqueous solution with 0.007:0.012 wt.% NaOH:urea, and indeed this was found in pilot-scale experiments to reduce the fibrillation energy by ~21% on average relative to water alone. The NaOH:urea-mediated CNFs have similar crystallinity, morphology, and mechanical strength to those formed in water. The NaOH and urea act synergistically on CNFs to aid fibrillation but at different length scales. NaOH deprotonates hydroxyl groups leading to mesoscale electrostatic repulsion between fibrils, whereas urea forms hydrogen bonds with protonated hydroxyl groups thus disrupting interfibril hydrogen bonds. This suggests a general mechanism in which an aqueous medium that contains a strong base and a small organic molecule acting as a hydrogen-bond acceptor and/or donor may be effectively employed in materials processes where dispersion of deprotonable polymers is required. The study demonstrates how atomic-detail computer simulation can be integrated with pilot-scale experiments in the rational design of materials processes for the circular bioeconomy.

Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning.

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties. However, the question of which machine-learning approaches are most appropriate remains. In the present study, we examine how different ML architectures, ranging from tree-based approaches to feed-forward artificial neural networks, perform in generating nonlinear multivariate quantitative structure-property relationship models for the prediction of the temperature- and pressure-dependent surface tension of and speed of sound in ILs over a wide range of surface tensions (16.9-76.2 mN/m) and speeds of sound (1009.7-1992 m/s). The ML models are further interrogated using the powerful interpretation method, shapley additive explanations. We find that several different ML models provide high accuracy, according to traditional statistical metrics. The decision tree-based approaches appear to be the most accurate and precise, with extreme gradient-boosting trees and gradient-boosting trees being the best performers. However, our results also indicate that the promise of using machine-learning to gain deep insights into the underlying physics driving structure-property relationships in ILs may still be somewhat premature.

Deconstruction of biomass enabled by local demixing of cosolvents at cellulose and lignin surfaces.

A particularly promising approach to deconstructing and fractionating lignocellulosic biomass to produce green renewable fuels and high-value chemicals pretreats the biomass with organic solvents in aqueous solution. Here, neutron scattering and molecular-dynamics simulations reveal the temperature-dependent morphological changes in poplar wood biomass during tetrahydrofuran (THF):water pretreatment and provide a mechanism by which the solvent components drive efficient biomass breakdown. Whereas lignin dissociates over a wide temperature range (>25 °C) cellulose disruption occurs only above 150 °C. Neutron scattering with contrast variation provides direct evidence for the formation of THF-rich nanoclusters (Rg ∼ 0.5 nm) on the nonpolar cellulose surfaces and on hydrophobic lignin, and equivalent water-rich nanoclusters on polar cellulose surfaces. The disassembly of the amphiphilic biomass is thus enabled through the local demixing of highly functional cosolvents, THF and water, which preferentially solvate specific biomass surfaces so as to match the local solute polarity. A multiscale description of the efficiency of THF:water pretreatment is provided: matching polarity at the atomic scale prevents lignin aggregation and disrupts cellulose, leading to improvements in deconstruction at the macroscopic scale.

A Multifunctional Cosolvent Pair Reveals Molecular Principles of Biomass Deconstruction.

The complex structure of plant cell walls resists chemical or biological degradation, challenging the breakdown of lignocellulosic biomass into renewable chemical precursors that could form the basis of future production of green chemicals and transportation fuels. Here, experimental and computational results reveal that the effect of the tetrahydrofuran (THF)-water cosolvents on the structure of lignin and on its interactions with cellulose in the cell wall drives multiple synergistic mechanisms leading to the efficient breakdown and fractionation of biomass into valuable chemical precursors. Molecular simulations show that THF-water is an excellent "theta" solvent, such that lignin dissociates from itself and from cellulose and expands to form a random coil. The expansion of the lignin molecules exposes interunit linkages, rendering them more susceptible to depolymerization by acid-catalyzed cleavage of aryl-ether bonds. Nanoscale infrared sensors confirm cosolvent-mediated molecular rearrangement of lignin in the cell wall of micrometer-thick hardwood slices and track the disappearance of lignin. At bulk scale, adding dilute acid to the cosolvent mixture liberates the majority of the hemicellulose and lignin from biomass, allowing unfettered access of cellulolytic enzymes to the remaining cellulose-rich material, allowing them to sustain high rates of hydrolysis to glucose without enzyme deactivation. Through this multiscale analysis, synergistic mechanisms for biomass deconstruction are identified, portending a paradigm shift toward first-principles design and evaluation of other cosolvent methods to realize low cost fuels and bioproducts.

Relationship between lignocellulosic biomass dissolution and physicochemical properties of ionic liquids composed of 3-methylimidazolium cations and carboxylate anions.

The ionic liquid (IL) 1-ethyl-3-methylimidazolium acetate ([EMIM]Acetate) has been widely used for biomass processing, i.e., to pretreat, activate, or fractionate lignocellulosic biomass to produce soluble sugars and lignin. However, this IL does not achieve high biomass solubility, therefore minimizing the efficiency of biomass processing. In this study, [EMIM]Acetate and three other ILs composed of different 3-methylimidazolium cations and carboxylate anions ([EMIM]Formate, 1-allyl-3-methylimidazolium ([AMIM]) formate, and [AMIM]Acetate) were analyzed to relate their physicochemical properties to their biomass solubility performance. While all four ILs are able to dissolve hybrid poplar under fairly mild process conditions (80 °C and 100 RPM stirring), [AMIM]Formate and [AMIM]Acetate have particularly increased biomass solubility of 40 and 32%, respectively, relative to [EMIM]Acetate. Molecular dynamics simulations suggest that strong interactions between IL and specific plant biopolymers may contribute to this enhanced solubilization, as the calculated second virial coefficients between ILs and hemicellullose are most favorable for [AMIM]Formate, matching the trend of the experimental solubility measurements. The simulations also reveal that the interactions between the ILs and hemicellulose are an important factor in determining the overall biomass solubility, whereas lignin-IL interactions were not found to vary significantly, consistent with literature. The combined experimental and simulation studies identify [AMIM]Formate as an efficient biomass solvent and explain its efficacy, suggesting a new approach to rationally select ionic liquid solvents for lignocellulosic deconstruction.

Modeling of the Passive Permeation of Mercury and Methylmercury Complexes Through a Bacterial Cytoplasmic Membrane.

Cellular uptake and export are important steps in the biotransformation of mercury (Hg) by microorganisms. However, the mechanisms of transport across biological membranes remain unclear. Membrane-bound transporters are known to be relevant, but passive permeation may also be involved. Inorganic HgII and methylmercury ([CH3HgII]+) are commonly complexed with thiolate ligands. Here, we have performed extensive molecular dynamics simulations of the passive permeation of HgII and [CH3HgII]+ complexes with thiolate ligands through a model bacterial cytoplasmic membrane. We find that the differences in free energy between the individual complexes in bulk water and at their most favorable position within the membrane are ∼2 kcal mol-1. We provide a detailed description of the molecular interactions that drive the membrane crossing process. Favorable interactions with carbonyl and tail groups of phospholipids stabilize Hg-containing solutes in the tail-head interface region of the membrane. The calculated permeability coefficients for the neutral compounds CH3S-HgII-SCH3 and CH3HgII-SCH3 are on the order of 10-5 cm s-1. We conclude that small, nonionized Hg-containing species can permeate readily through cytoplasmic membranes.

Local Phase Separation of Co-solvents Enhances Pretreatment of Biomass for Bioenergy Applications.

Pretreatment facilitates more complete deconstruction of plant biomass to enable more economic production of lignocellulosic biofuels and byproducts. Various co-solvent pretreatments have demonstrated advantages relative to aqueous-only methods by enhancing lignin removal to allow unfettered access to cellulose. However, there is a limited mechanistic understanding of the interactions between the co-solvents and cellulose that impedes further improvement of such pretreatment methods. Recently, tetrahydrofuran (THF) has been identified as a highly effective co-solvent for the pretreatment and fractionation of biomass. To elucidate the mechanism of the THF-water interactions with cellulose, we pair simulation and experimental data demonstrating that enhanced solubilization of cellulose can be achieved by the THF-water co-solvent system at equivolume mixtures and moderate temperatures (≤445 K). The simulations show that THF and water spontaneously phase separate on the local surface of a cellulose fiber, owing to hydrogen bonding of water molecules with the hydrophilic cellulose faces and stacking of THF molecules on the hydrophobic faces. Furthermore, a single fully solvated cellulose chain is shown to be preferentially bound by water molecules in the THF-water mixture. In light of these findings, co-solvent reactions were performed on microcrystalline cellulose and maple wood to show that THF significantly enhanced cellulose deconstruction and lignocellulose solubilization at simulation conditions, enabling a highly versatile and efficient biomass pretreatment and fractionation method.

Enhanced sampling simulation analysis of the structure of lignin in the THF-water miscibility gap.

Using temperature replica-exchange molecular dynamics, we characterize a globule-to-coil transition for a softwood-like lignin biopolymer in a tetrahydrofuran (THF)-water cosolvent system at temperatures at which the cosolvent undergoes a de-mixing transition. The lignin is found to be in a coil state, similar to that in the high-temperature miscible region. Analysis of the transition kinetics indicates that THF acts in a surfactant-like fashion. The present study thus suggests that THF-water based pretreatments may efficiently remove lignin from biomass even at relatively low (non-water boiling) temperatures.

Force-Field Induced Bias in the Structure of Aß21-30: A Comparison of OPLS, AMBER, CHARMM, and GROMOS Force Fields.

In this work we examine the dynamics of an intrinsically disordered protein fragment of the amyloid ß, the Aß21-30, under seven commonly used molecular dynamics force fields (OPLS-AA, CHARMM27-CMAP, AMBER99, AMBER99SB, AMBER99SB-ILDN, AMBER03, and GROMOS53A6), and three water models (TIP3P, TIP4P, and SPC/E). We find that the tested force fields and water models have little effect on the measures of radii of gyration and solvent accessible surface area (SASA); however, secondary structure measures and intrapeptide hydrogen-bonding are significantly modified, with AMBER (99, 99SB, 99SB-ILDN, and 03) and CHARMM22/27 force-fields readily increasing helical content and the variety of intrapeptide hydrogen bonds. On the basis of a comparison between the population of helical and ß structures found in experiments, our data suggest that force fields that suppress the formation of helical structure might be a better choice to model the Aß21-30 peptide.

Spontaneous dimer states of the Aß(21-30) decapeptide.

Constant temperature and replica-exchange molecular dynamics simulations of two Aß21-30 decapeptides in explicit solvent reveal metastable dimer states that are abundant near physiological temperatures. As Alzheimer's disease is associated with the neurotoxic oligomers of amyloid ß-protein, the formation of these dimers provides insight into oligomer assembly.

Drugging the entire human proteome: Are we there yet?

Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.

Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents.

Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.15-385.25 K). Three ML models, including support vector regression (SVR), feed forward neural networks (FFNNs), and categorical boosting (CatBoost), were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two-factor polynomial regression baselines. Quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features were used as inputs for the ML models. The CatBoost model is excellent at externally predicting DES viscosity, as indicated by high R2 (0.99) and low root-mean-square-error (RMSE) and average absolute relative deviations (AARD) (5.22%) values for the testing data sets, and 98% of the data points lie within the 15% of AARD deviations. Furthermore, SHapley additive explanation (SHAP) analysis was employed to interpret the ML results and rationalize the viscosity predictions. The result is an ML approach that accurately predicts viscosity and will aid in accelerating the design of appropriate DESs for industrial applications.

Amantadine interactions with phase separated lipid membranes.

Amantadine, a small amphilphic organic compound that consists of an adamantane backbone and an amino group, was first recognized as an antiviral in 1963 and received approval for prophylaxis against the type A influenza virus in 1976. Since then, it has also been used to treat Parkinson's disease-related dyskinesia and is being considered as a treatment for corona viruses. Since amantadine usually targets membrane-bound proteins, its interactions with the membrane are also thought to be important. Biological membranes are now widely understood to be laterally heterogeneous and certain proteins are known to preferentially co-localize within specific lipid domains. Does amantadine, therefore, preferentially localize in certain lipid composition domains? To address this question, we studied amantadine's interactions with phase separating membranes composed of cholesterol, DSPC (1,2-distearoyl-sn-glycero-3-phosphocholine), POPC (1-palmitoyl-2-oleoyl-glycero-3-phosphocholine), and DOPC (1,2-dioleoyl-sn-glycero-3-phosphocholine), as well as single-phase DPhPC (1,2-diphytanoyl-sn-glycero-3-phos-phocholine) membranes. From Langmuir trough and differential scanning calorimetry (DSC) measurements, we determined, respectively, that amantadine preferentially binds to disordered lipids, such as POPC, and lowers the phase transition temperature of POPC/DSPC/cholesterol mixtures, implying that amantadine increases membrane disorder. Further, using droplet interface bilayers (DIBs), we observed that amantadine disrupts DPhPC membranes, consistent with its disordering properties. Finally, we carried out molecular dynamics (MD) simulations on POPC/DSPC/cholesterol membranes with varying amounts of amantadine. Consistent with experiment, MD simulations showed that amantadine prefers to associate with disordered POPC-rich domains, domain boundaries, and lipid glycerol backbones. Since different proteins co-localize with different lipid domains, our results have possible implications as to which classes of proteins may be better targets for amantadine.

Structural bases for Na+-Cl- cotransporter inhibition by thiazide diuretic drugs and activation by kinases.

The Na+-Cl- cotransporter (NCC) drives salt reabsorption in the kidney and plays a decisive role in balancing electrolytes and blood pressure. Thiazide and thiazide-like diuretics inhibit NCC-mediated renal salt retention and have been cornerstones for treating hypertension and edema since the 1950s. Here we determine NCC co-structures individually complexed with the thiazide drug hydrochlorothiazide, and two thiazide-like drugs chlorthalidone and indapamide, revealing that they fit into an orthosteric site and occlude the NCC ion translocation pathway. Aberrant NCC activation by the WNKs-SPAK kinase cascade underlies Familial Hyperkalemic Hypertension, but it remains unknown whether/how phosphorylation transforms the NCC structure to accelerate ion translocation. We show that an intracellular amino-terminal motif of NCC, once phosphorylated, associates with the carboxyl-terminal domain, and together, they interact with the transmembrane domain. These interactions suggest a phosphorylation-dependent allosteric network that directly influences NCC ion translocation.

Osteocalcin binds to a GPRC6A Venus fly trap allosteric site to positively modulate GPRC6A signaling.

GPRC6A, a member of the Family C G-protein coupled receptors, regulates energy metabolism and sex hormone production and is activated by diverse ligands, including cations, L-amino acids, the osteocalcin (Ocn) peptide and the steroid hormone testosterone. We sought a structural framework for the ability of multiple distinct classes of ligands to active GPRC6A. We created a structural model of GPRC6A using Alphafold2. Using this model we explored a putative orthosteric ligand binding site in the bilobed Venus fly trap (VFT) domain of GPRC6A and two positive allosteric modulator (PAM) sites, one in the VFT and the other in the 7 transmembrane (7TM) domain. We provide evidence that Ocn peptides act as a PAM for GPRC6A by binding to a site in the VFT that is distinct from the orthosteric site for calcium and L-amino acids. In agreement with this prediction, alternatively spliced GPRC6A isoforms 2 and 3, which lack regions of the VFT, and mutations in the computationally predicted Ocn binding site, K352E and H355P, prevent Ocn activation of GPRC6A. These observations explain how dissimilar ligands activate GPRC6A and set the stage to develop novel molecules to activate and inhibit this previously poorly understood receptor.

GPRC6A is a Potential Therapeutic Target for Metformin Regulation of Glucose Homeostasis in Mice.

Understanding the mechanism of metformin actions in treating type 2 diabetes is limited by an incomplete knowledge of the specific protein targets mediating its metabolic effects. Metformin has structural similarities to L-Arginine (2-amino-5-guanidinopentanoic acid), which is a ligand for GPRC6A, a Family C G-protein coupled receptor that regulates energy metabolism. Ligand activation of GPRC6A results in lowering of blood glucose and other metabolic changes resembling the therapeutic effect of metformin. In the current study, we tested if metformin activates GPRC6A. We used Alphafold2 to develop a structural model for L-Arginine (L-Arg) binding to the extracellu-lar bilobed venus flytrap domain (VFT) of GPRC6A. We found that metformin docked to the site in the VFT that overlaps the binding site for L-Arg. Metformin resulted in a dose-dependent stimulation of GPRC6A activity in HEK-293 cells transfected with full-length wild-type GPRC6A but not in untransfected control cells. In addition, metformin failed to activate an alternatively spliced GPRC6A isoform lacking the putative binding site in the VFT. More specifically, mutation of the predicted metformin key binding residues Glu170 and Asp303 in the GPRC6A VFT resulted in loss of metformin receptor activation in vitro. The in vivo role of GPRC6A in mediating the effects of metformin was tested in Gprc6a-/- mice. Administration of therapeutic doses of metformin lowered blood glucose levels following a glucose tolerance test in wild-type but not Gprc6a-/- mice. Finally, we EN300, created by adding a carboxymethyl group from L-Arg to the biguanide backbone of metformin. EN300 showed dose-dependent stimulation of GPRC6A activity in vitro with greater potency than L-Arginine, but less than metformin. Thus, we suggest that GPRC6A is a potential molecular target for metformin which may be used to understand the therapeutic actions of metformin and develop novel small molecules to treat T2D.

Ambient-densified and polymer-free transparent wood film for smart food packaging window.

Wood, with its inherent hierarchical structure, presents opportunities for creating eco-friendly and cost-effective alternatives to petroleum-based plastics. We introduced a top-down and polymer-free method for engineering natural balsa wood into transparent wood film, demonstrating its potential use in food packaging windows. The wood was delignified and then proceeded with 2,2,6,6-tetramethyl-1-piperidinyloxy oxidation to soften the wood structure and introduce carboxyl groups. A robust and transparent wood film was produced by drying the wood under ambient condition without the need for additional polymers or mechanical force. Curcumin was also integrated into the wood using vacuum impregnation. The functionalized wood film with curcumin (WFC) exhibited a distinguishable redness shift in alkaline conditions. We then applied the WFC as an intelligent food packaging window to sense the freshness of shrimp based on the pH-responsive color change. This study provides a simple and scalable approach for developing sustainable and smart food packaging using wood.

Genetic interactions between Polycystin-1 and TAZ in osteoblasts define a novel mechanosensing mechanism regulating bone formation in mice.

Molecular mechanisms transducing physical forces in the bone microenvironment to regulate bone mass are poorly understood. Here, we used mouse genetics, mechanical loading, and pharmacological approaches to test the possibility that polycystin-1 and TAZ have interdependent mechanosensing functions in osteoblasts. We created and compared the skeletal phenotypes of control Pkd1flox/+;TAZflox/+, single Pkd1Oc-cKO, single TAZOc-cKO, and double Pkd1/TAZOc-cKO mice to investigate genetic interactions. Consistent with an interaction between polycystins and TAZ in bone in vivo, double Pkd1/TAZOc-cKO mice exhibited greater reductions of BMD and periosteal MAR than either single TAZOc-cKO or Pkd1Oc-cKO mice. Micro-CT 3D image analysis indicated that the reduction in bone mass was due to greater loss in both trabecular bone volume and cortical bone thickness in double Pkd1/TAZOc-cKO mice compared to either single Pkd1Oc-cKO or TAZOc-cKO mice. Double Pkd1/TAZOc-cKO mice also displayed additive reductions in mechanosensing and osteogenic gene expression profiles in bone compared to single Pkd1Oc-cKO or TAZOc-cKO mice. Moreover, we found that double Pkd1/TAZOc-cKO mice exhibited impaired responses to tibia mechanical loading in vivo and attenuation of load-induced mechanosensing gene expression compared to control mice. Finally, control mice treated with a small molecule mechanomimetic MS2 had marked increases in femoral BMD and periosteal MAR compared to vehicle control. In contrast, double Pkd1/TAZOc-cKO mice were resistant to the anabolic effects of MS2 that activates the polycystin signaling complex. These findings suggest that PC1 and TAZ form an anabolic mechanotransduction signaling complex that responds to mechanical loading and serve as a potential novel therapeutic target for treating osteoporosis.

Genetic interactions between polycystin-1 and Wwtr1 in osteoblasts define a novel mechanosensing mechanism regulating bone formation in mice.

Molecular mechanisms transducing physical forces in the bone microenvironment to regulate bone mass are poorly understood. Here, we used mouse genetics, mechanical loading, and pharmacological approaches to test the possibility that polycystin-1 and Wwtr1 have interdependent mechanosensing functions in osteoblasts. We created and compared the skeletal phenotypes of control Pkd1flox/+;Wwtr1flox/+, Pkd1Oc-cKO, Wwtr1Oc-cKO, and Pkd1/Wwtr1Oc-cKO mice to investigate genetic interactions. Consistent with an interaction between polycystins and Wwtr1 in bone in vivo, Pkd1/Wwtr1Oc-cKO mice exhibited greater reductions of BMD and periosteal MAR than either Wwtr1Oc-cKO or Pkd1Oc-cKO mice. Micro-CT 3D image analysis indicated that the reduction in bone mass was due to greater loss in both trabecular bone volume and cortical bone thickness in Pkd1/Wwtr1Oc-cKO mice compared to either Pkd1Oc-cKO or Wwtr1Oc-cKO mice. Pkd1/Wwtr1Oc-cKO mice also displayed additive reductions in mechanosensing and osteogenic gene expression profiles in bone compared to Pkd1Oc-cKO or Wwtr1Oc-cKO mice. Moreover, we found that Pkd1/Wwtr1Oc-cKO mice exhibited impaired responses to tibia mechanical loading in vivo and attenuation of load-induced mechanosensing gene expression compared to control mice. Finally, control mice treated with a small molecule mechanomimetic, MS2 that activates the polycystin complex resulted in marked increases in femoral BMD and periosteal MAR compared to vehicle control. In contrast, Pkd1/Wwtr1Oc-cKO mice were resistant to the anabolic effects of MS2. These findings suggest that PC1 and Wwtr1 form an anabolic mechanotransduction signaling complex that mediates mechanical loading responses and serves as a potential novel therapeutic target for treating osteoporosis.

SARS-CoV2 billion-compound docking.

This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens.