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.

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.

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.

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 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.

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.

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.

Dissecting Phenotype from Genotype with Clinical Isolates of SARS-CoV-2 First Wave Variants.

The emergence and availability of closely related clinical isolates of SARS-CoV-2 offers a unique opportunity to identify novel nonsynonymous mutations that may impact phenotype. Global sequencing efforts show that SARS-CoV-2 variants have emerged and then been replaced since the beginning of the pandemic, yet we have limited information regarding the breadth of variant-specific host responses. Using primary cell cultures and the K18-hACE2 mouse, we investigated the replication, innate immune response, and pathology of closely related, clinical variants circulating during the first wave of the pandemic. Mathematical modeling of the lung viral replication of four clinical isolates showed a dichotomy between two B.1. isolates with significantly faster and slower infected cell clearance rates, respectively. While isolates induced several common immune host responses to infection, one B.1 isolate was unique in the promotion of eosinophil-associated proteins IL-5 and CCL11. Moreover, its mortality rate was significantly slower. Lung microscopic histopathology suggested further phenotypic divergence among the five isolates showing three distinct sets of phenotypes: (i) consolidation, alveolar hemorrhage, and inflammation, (ii) interstitial inflammation/septal thickening and peribronchiolar/perivascular lymphoid cells, and (iii) consolidation, alveolar involvement, and endothelial hypertrophy/margination. Together these findings show divergence in the phenotypic outcomes of these clinical isolates and reveal the potential importance of nonsynonymous mutations in nsp2 and ORF8.

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.

Tuning Proton Transfer Thermodynamics in SARS-CoV-2 Main Protease: Implications for Catalysis and Inhibitor Design.

The catalytic reaction in SARS-CoV-2 main protease is activated by a proton transfer (PT) from Cys145 to His41. The same PT is likely also required for the covalent binding of some inhibitors. Here we use a multiscale computational approach to investigate the PT thermodynamics in the apo enzyme and in complex with two potent inhibitors, N3 and the α-ketoamide 13b. We show that with the inhibitors the free energy cost to reach the charge-separated state of the active-site dyad is lower, with N3 inducing the most significant reduction. We also show that a few key sites (including specific water molecules) significantly enhance or reduce the thermodynamic feasibility of the PT reaction, with selective desolvation of the active site playing a crucial role. The approach presented is a cost-effective procedure to identify the enzyme regions that control the activation of the catalytic reaction and is thus also useful to guide the design of inhibitors.

Inhibitor binding influences the protonation states of histidines in SARS-CoV-2 main protease.

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an attractive target for antiviral therapeutics. Recently, many high-resolution apo and inhibitor-bound structures of Mpro, a cysteine protease, have been determined, facilitating structure-based drug design. Mpro plays a central role in the viral life cycle by catalyzing the cleavage of SARS-CoV-2 polyproteins. In addition to the catalytic dyad His41-Cys145, Mpro contains multiple histidines including His163, His164, and His172. The protonation states of these histidines and the catalytic nucleophile Cys145 have been debated in previous studies of SARS-CoV Mpro, but have yet to be investigated for SARS-CoV-2. In this work we have used molecular dynamics simulations to determine the structural stability of SARS-CoV-2 Mpro as a function of the protonation assignments for these residues. We simulated both the apo and inhibitor-bound enzyme and found that the conformational stability of the binding site, bound inhibitors, and the hydrogen bond networks of Mpro are highly sensitive to these assignments. Additionally, the two inhibitors studied, the peptidomimetic N3 and an α-ketoamide, display distinct His41/His164 protonation-state-dependent stabilities. While the apo and the N3-bound systems favored N δ (HD) and N ϵ (HE) protonation of His41 and His164, respectively, the α-ketoamide was not stably bound in this state. Our results illustrate the importance of using appropriate histidine protonation states to accurately model the structure and dynamics of SARS-CoV-2 Mpro in both the apo and inhibitor-bound states, a necessary prerequisite for drug-design efforts.

Tuning Proton Transfer Thermodynamics in SARS-Cov-2 Main Protease: Implications for Catalysis and Inhibitor Design.

In this comutational work a hybrid quantum mechanics/molecular mechanics approach, the MD-PMM approach, is used to investigate the proton transfer reaction the activates the catalytic activity of SARS-CoV-2 main protease. The proton transfer thermodynamics is investigated for the apo ensyme (i.e., without any bound substrate or inhibitor) and in the presence of a inhibitor, N3, which was previously shown to covalently bind SARS-CoV-2 main protease.

Effects of sodium and calcium chloride ionic stresses on model yeast membranes revealed by molecular dynamics simulation.

As efforts to move a renewable economy grow, it will be necessary to make use of microbial conversion strategies for the production of novel materials or the upgrading of waste to high-value products. One critical technical challenge currently limiting waste upgrading remains the difficulty in obtaining single-pot conversion techniques where physical, chemical, and biological conversion are performed in a single step. To overcome this challenge, a detailed understanding of how different stresses impact microbial membrane stability will be necessary. Using all-atom molecular dynamics simulations, we examine the impacts of moderate concentrations of NaCl and CaCl2on a model yeast plasma membrane. Weak, though statistically significant, changes in membrane morphology and dynamics functions are observed that are consistent with swelling and stiffening. Additionally, an examination of the ion-lipid contacts and the behavior of water at the water-membrane interface suggests that the impacts of these common salts may, in part, be mediated through changes to water-membrane hydrogen-bonding and hydration water dynamics.

Inhibitor binding influences the protonation states of histidines in SARS-CoV-2 main protease.

The main protease (M pro ) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an attractive target for antiviral therapeutics. Recently, many high-resolution apo and inhibitor-bound structures of M pro , a cysteine protease, have been determined, facilitating structure-based drug design. M pro plays a central role in the viral life cycle by catalyzing the cleavage of SARS-CoV-2 polyproteins. In addition to the catalytic dyad His41-Cys145, M pro contains multiple histidines including His163, His164, and His172. The protonation states of these histidines and the catalytic nu-cleophile Cys145 have been debated in previous studies of SARS-CoV M pro , but have yet to be investigated for SARS-CoV-2. In this work we have used molecular dynamics simulations to determine the structural stability of SARS-CoV-2 M pro as a function of the protonation assignments for these residues. We simulated both the apo and inhibitor-bound enzyme and found that the conformational stability of the binding site, bound inhibitors, and the hydrogen bond networks of M pro are highly sensitive to these assignments. Additionally, the two inhibitors studied, the peptidomimetic N3 and an α -ketoamide, display distinct His41/His164 protonation-state-dependent stabilities. While the apo and the N3-bound systems favored N δ (HD) and N ϵ (HE) protonation of His41 and His164, respectively, the α -ketoamide was not stably bound in this state. Our results illustrate the importance of using appropriate histidine protonation states to accurately model the structure and dynamics of SARS-CoV-2 M pro in both the apo and inhibitor-bound states, a necessary prerequisite for drug-design efforts.

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.

Capturing Deuteration Effects in a Molecular Mechanics Force Field: Deuterated THF and the THF-Water Miscibility Gap.

Deuteration is a common chemical modification used in conjunction with experiments such as neutron scattering, NMR, and Fourier-transform infrared for the study of molecular systems. Under the Born-Oppenheimer (BO) approximation, while the underlying potential energy surface remains unchanged by isotopic substitutions, isotopic substitution still alters intramolecular vibrations, which in turn may alter intermolecular interactions. Molecular mechanics (MM) force fields used in classical molecular dynamics simulations are assumed to represent local approximations of the BO potential energy surfaces, and hence, MD simulations using simple isotopic mass substitutions should capture BO-compatible isotope effects. However, standard MM force-field parameterizations do not directly fit to the local harmonic quantum mechanical (QM) Hessian that describes the BO surface, but rather to QM normal-modes and/or mass-dependent internal-coordinate derived distortion energies. Here, using tetrahydrofuran (THF)-water mixtures as our model system, we show that not only does a simple mass-substitution approach fail to capture an experimentally characterized deuteration effect (the loss of the closed-loop miscibility gap associated with the complete deuteration of THF) but also it is necessary to generate new MM force-field parameters that correctly describe isotopic dependent vibrations to capture the experimental deuteration effect. We show that the origin of this failure is a result of using mass-dependent features to fit the THF MM force field, which unintentionally biases the bonded terms of the force field to represent only the isotopologue used during the original force-field parameterization. In addition, we make use of our isotopologue-corrected force field for D8THF to examine the molecular origins of the isotope-dependent loss of the THF-water miscibility gap.

Ligand-Dependent Sodium Ion Dynamics within the A2A Adenosine Receptor: A Molecular Dynamics Study.

Sodium ions have long been known to reduce the binding of agonists in many class-A GPCRs while having little effect on antagonist binding. Here, using long-time scale classical all-atom molecular dynamics simulations, we explore, in atomic detail, the motion of sodium ions within the ligand-binding pocket of the A2A adenosine receptor (A2A-AR) both in the presence and absence of ligands and in the active and inactive state. We identify novel secondary ion binding sites within the pocket and find that the types of ion motions within the pocket are highly dependent on the presence and type of ligand within the pocket. Our results provide a first step toward developing a molecular understanding of the impact of sodium ions on class-A GPCRs.

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.

Lipid Rafts: Buffers of Cell Membrane Physical Properties.

Lateral organization of lipids in the cell membrane appears to be an ancient feature of the cell, given the existence of lipid rafts in both eukaryotic and prokaryotic cells. Currently seen as platforms for protein partitioning, we posit that lipid rafts are capable of playing another role: stabilizing membrane physical properties over varying temperatures and other environmental conditions. Membrane composition defines the mechanical and viscous properties of the bilayer. The composition also varies strongly with temperature, with systematic changes in the partitioning of high and low melting temperature membrane components. In this way, rafts function as buffers of membrane physical properties, progressively counteracting environmental changes via compositional changes; i.e., more high melting lipids partition to the fluid phase with increasing temperature, increasing the bending modulus and viscosity, as thermal effects decrease these same properties. To provide an example of this phenomenon, we have performed neutron scattering experiments and atomistic molecular dynamics simulations on a phase separated model membrane. The results demonstrate a buffering effect in both the lateral diffusion coefficient and the bending modulus of the fluid phase upon changing temperature. This demonstration highlights the potentially advantageous stabilizing effect of complex lipid compositions in response to temperature and potentially other membrane destabilizing environmental conditions.

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.