In Search of the Best Low-Cost Methods for Efficient Screening of Conformers.

Locating the lowest energy conformer is crucial for the accurate computation of equilibrium properties of molecular systems. This paper examines the performance of efficient low-cost methods in terms of the alignment and relative energies of their energy minima against the benchmark revDSD-PBEP86-D4/def2-TZVPP//MP2/cc-pVTZ potential energy surface. The low-cost methods considered include GFN-FF, GFN2-xTB, DFTB3, HF-3c, B97-3c, PBEh-3c, and r2SCAN-3c composite methods against a diverse test set of 20 compounds including alkanes, perfluoroalkyl molecules, peptides, open-shell radicals, and Zn(II) complexes of varying sizes. The "3c" composite methods are generally more accurate, but are at least 2-3 orders of magnitude more expensive than tight-binding methods which have energy minima that align well with the benchmark potential energy surface. The findings of this paper were further exploited to introduce a simple strategy involving Grimme's CENSO energy-sorting algorithm that resulted in up to an order of magnitude reduction in computational time for locating the lowest energy conformer on the revDSD-PBEP86-D4/def2-TZVPP//MP2/cc-pVTZ surface.

Binding of Per- and Polyfluoroalkyl Substances to ß-Lactoglobulin from Bovine Milk.

Per- and polyfluoroalkyl substances (PFAS) are known for their high environmental persistence and potential toxicity. The presence of PFAS has been reported in many dairy products. However, the mechanisms underlying the accumulation of PFAS in these products remain unclear. Here, we used native mass spectrometry and molecular dynamics simulations to probe the interactions between 19 PFAS of environmental concern and two isoforms of the major bovine whey protein ß-lactoglobulin (ß-LG). We observed that six of these PFAS bound to both protein isoforms with low- to mid-micromolar dissociation constants. Based on quantitative, competitive binding experiments with endogenous ligands, PFAS can bind orthosterically and preferentially to ß-LG's hydrophobic ligand-binding calyx. ß-Cyclodextrin can also suppress binding of PFAS to ß-LG owing to the ability of ß-cyclodextrin to directly sequester PFAS from solution. This research sheds light on PFAS-ß-LG binding, suggesting that such interactions could impact lipid-fatty acid transport in bovine mammary glands at high PFAS concentrations. Furthermore, our results highlight the potential use of ß-cyclodextrin in mitigating PFAS binding, providing insights toward the development of strategies to reduce PFAS accumulation in dairy products and other biological systems.

Maximizing Aqueous Drug Encapsulation: Small Nanoparticles Formation Enabled by Glycopolymers Combining Glucose and Tyrosine.

Highly potent heterocyclic drugs are frequently poorly water soluble, leading to limited or abandoned further drug development. Nanoparticle technology offers a powerful delivery approach by enhancing the solubility and bioavailability of hydrophobic therapeutics. However, the common usage of organic solvents causes unwanted toxicity and process complexity, therefore limiting the scale-up of nanomedicine technology for clinical translation. Here, we show that an organic-solvent-free methodology for hydrophobic drug encapsulation can be obtained using polymers based on glucose and tyrosine. An aqueous solution based on a tyrosine-containing glycopolymer is able to dissolve solid dasatinib directly without adding an organic solvent, resulting in the formation of very small nanoparticles of around 10 nm loaded with up to 16 wt % of drug. This polymer is observed to function as both a drug solubilizer and a nanocarrier at the same time, offering a simple route for the delivery of insoluble drugs.

Simple Composite Approach to Efficiently Estimate Basis Set Limit CCSD(T) Harmonic Frequencies and Reaction Thermochemistry.

We introduce a simple strategy that combines the G3(MP2) composite method and explicitly correlated coupled cluster CCSD(T)-F12 method to efficiently estimate complete basis set CCSD(T) molecular geometries and harmonic vibrational frequencies at the cost of a double-ζ basis set calculation. Based on a large test set of 61 neutral, ionic, and open-shell molecules, and additionally 31 molecules in the HFREQ2014 data set, we demonstrate that this composite strategy has an average accuracy of 2 cm-1 or better relative to complete basis set CCSD(T) values. Using this approach, we estimated 696 CCSD(T)/CBS reaction energies of small to medium-sized systems containing up to 6 heavy atoms and confirmed existing approximations that use small basis set density functional theory methods [e.g., M06-2X/6-31+G(d)] to calculate thermal contributions to reaction enthalpies and Gibbs free energies that are accurate to within 0.2 kcal mol-1 on average.

High-Level Quantum Chemical Prediction of C-F Bond Dissociation Energies of Perfluoroalkyl Substances.

In this study, 550 C-F bond dissociation energies (BDEs) of a variety of per- and polyfluoroalkyl substances (PFASs) obtained from high-level DLPNO-CCSD(T)/CBS calculations were used to assess the accuracy of contemporary density functional theory (DFT) and semiempirical methods. DLPNO-CCSD(T)/CBS gas phase C-F BDEs fall between 404.9-550.7 kJ mol-1 and M06-2X and ωB97M-V in conjunction with the aug-cc-pVTZ basis set predicted BDEs closest to the benchmark level with a mean absolute deviation (MAD) of 7.3 and 8.3 kJ mol-1, respectively. It was observed that DFT prediction errors increase with the degree of fluorination and system size. As such, previous model chemistry recommendations based on smaller nonfluorinated systems may not be carried over to modeling the energetics of PFASs and related systems.

Molecular Geometries and Vibrational Contributions to Reaction Thermochemistry Are Surprisingly Insensitive to the Choice of Basis Sets.

Calculation of molecular geometries and harmonic vibrational frequencies are pre-requisites for thermochemistry calculations. Contrary to conventional wisdom, this paper demonstrates that quantum chemical predictions of the thermochemistry of many gas and solution phase chemical reactions appear to be very insensitive to the choice of basis sets. For a large test set of 80 diverse organic and transition-metal-containing reactions, variations in reaction free energy based on geometries and frequencies calculated using a variety of double and triple-zeta basis sets from the Pople, Jensen, Ahlrichs, and Dunning families are typically less than 4 kJ mol-1, especially when the quasiharmonic oscillator correction is applied to mitigate the effects of low-frequency modes. Our analysis indicates that for many organic molecules and their transition states, high-level revDSD-PBEP86-D4 and DLPNO-CCSD(T)/(aug-)cc-pVTZ single-point energies usually vary by less than 2 kJ mol-1 on density functional theory geometries optimized using basis sets ranging from 6-31+G(d) to aug-pcseg-2 and aug-cc-pVTZ. In cases where these single-point energies vary significantly, indicating sensitivity of molecular geometries to the choice of basis set, there is often substantial cancellation of errors when the reaction energy or barrier is calculated. The study concludes that the choice of basis set for molecular geometry and frequencies, particularly those considered in this study, is not critical for the accuracy of thermochemistry calculations in the gas or solution phase.

MM/GBSA prediction of relative binding affinities of carbonic anhydrase inhibitors: effect of atomic charges and comparison with Autodock4Zn.

Carbonic anhydrase is an attractive drug target for the treatment of many diseases. This paper examines the ability of end-state MM/GBSA methods to rank inhibitors of carbonic anhydrase in terms of their binding affinities. The MM/GBSA binding energies were evaluated using different atomic charge schemes (Mulliken, ESP and NPA) at different levels of theories, including Hartree-Fock, B3LYP-D3(BJ), and M06-2X with the 6-31G(d,p) basis set. For a large test set of 32 diverse inhibitors, the use of B3LYP-D3(BJ) ESP atomic charges yielded the strongest correlation with experiment (R2 = 0.77). The use of the recently enhanced Autodock Vina and zinc optimised AD4Zn force field also predicted ligand binding affinities with moderately strong correlation (R2 = 0.64) at significantly lower computational cost. However, the docked poses deviate significantly from crystal structures. Overall, this study demonstrates the applicability of docking to estimate ligand binding affinities for a diverse range of CA inhibitors, and indicates that more theoretically robust MM/GBSA simulations show promise for improving the accuracy of predicted binding affinities, as long as a validated set of parameters is used.

Quantum Chemical Prediction of the Acidities of Sulfonamide Inhibitors of Carbonic Anhydrase.

This study examined two pKa calculation approaches (direct and proton exchange schemes) that employ high-level quantum chemical methods and implicit solvent models to predict aqueous Brønsted acidities of a large set of sulfonamides. For gas-phase deprotonation energies, the DSD-PBEP86-D3(BJ) double-hybrid functional provided the best agreement with the LNO-CCSD(T)/CBS benchmark with a mean absolute deviation less than 2 kJ mol-1 when the aug-cc-pVTZ or larger basis sets are used. For a large test set of 54 primary and secondary sulfonamides, the use of the DSD-PBEP86-D3(BJ)/aug-cc-pVTZ level of theory in conjunction with SM12 solvation free energies predict their pKa values with a mean accuracy of 0.9 units. In comparison, the SMD and ADF-COSMO-RS models have slightly higher mean errors of 1.4 and 1.1 pKa units provided that the proton exchange scheme was employed to cancel the systematic errors in these models. The performance of these protocols was less ideal when applied to sulfonic acids, sulfamates, and N-substituted sulfonamides, indicating that the degree of error cancellation is sensitive to the chemical environment around the -NH2 head group. The validated protocols were then used to estimate the pKa values of arylsulfonamide carbonic anhydrase inhibitors, which are used to correct their experimentally measured binding free energies to account for deprotonation of the sulfonamide group upon binding to the enzyme. These corrected values did not have a significant impact on the correlation with MMGBSA binding free energies obtained from classical MD simulations where the ligand is usually considered in the deprotonated form.

Predicting Solvent Effects on SN2 Reaction Rates: Comparison of QM/MM, Implicit, and MM Explicit Solvent Models.

Solvents are one of the key variables in the optimization of a synthesis yield or properties of a synthesis product. In this paper, contemporary solvent models are applied to predict the rates of SN2 reactions in a range of aqueous and non-aqueous solvents. High-level CCSD(T)/CBS//M06-2X/6-31+G(d) gas phase energies were combined with solvation free energies from SMD, SM12, and ADF-COSMO-RS continuum solvent models, as well as molecular mechanics (MM) explicit solvent models with different atomic charge schemes to predict the rate constants of three SN2 reactions in eight protic and aprotic solvents. It is revealed that the prediction of rate constants in organic solvents is not necessarily less challenging than in water and popular solvent models struggle to predict their rate constants to within 3 log units of experimental values. Among the continuum solvent models, the ADF-COSMO-RS model performed the best in predicting absolute rate contants while the SM12 model was best at predicting relative rate constants with an average accuracy of about 1.5 and 0.8 log units, respectively. The use of computationally more demanding MM explicit solvent models did not translate to improvements in absolute rate constants but was quite effective at predicting relative rate constants due to systematic error cancellation. Free energy barriers obtained from umbrella sampling with explicit solvent QM/MM simulations led to excellent agreement with experimental values, provided that a validated level of theory is used to treat the QM region.

Molecular Crowding Electrolytes for Stable Proton Batteries.

Proton electrochemistry is promising for developing post-lithium energy storage devices with high capacity and rate capability. However, some electrode materials are vulnerable because of the co-intercalation of free water molecules in traditional acid electrolytes, resulting in rapid capacity fading. Here, the authors report a molecular crowding electrolyte with the usage of poly(ethylene glycol) (PEG) as a crowding agent, achieving fast and stable electrochemical proton storage and expanded working potential window (3.2 V). Spectroscopic characterisations reveal the formation of hydrogen bonds between water and PEG molecules, which is beneficial for confining the activity of water molecules. Molecular dynamics simulations confirm a significant decrease of free water fraction in the molecular crowding electrolyte. Dynamic structural evolution of the MoO3 anode is studied by in-situ synchrotron X-ray diffraction (XRD), revealing a reversible multi-step naked proton (de)intercalation mechanism. Surficial adsorption of PEG molecules on MoO3 anode works in synergy to alleviate the destructive effect of concurrent water desolvation, thereby achieving enhanced cycling stability. This strategy offers possibilities of practical applications of proton electrochemistry thanks to the low-cost and eco-friendly nature of PEG additives.

Improving the Accuracy of Quantum Mechanics/Molecular Mechanics (QM/MM) Models with Polarized Fragment Charges.

This paper introduces an economical approach for improving the accuracy and convergence of quantum mechanics/molecular mechanics (QM/MM) models. The approach is tested on a series of neutral and charged amino acids embedded in a 160-water cluster, where their intramolecular proton transfer energies (neutral amino acid → zwitterionic amino acid) were previously obtained at the ωB97X-D/6-31G(d) level of theory. When the charges on the MM atoms were replaced with those obtained at the same QM level of theory used to treat the QM atoms, this significantly improved the accuracy and convergence of the QM/MM models. In particular, the QM/MM model converged to within 1.4 kcal mol-1 of directly calculated DFT energies for smaller (by as many as 20 waters) QM regions. The use of atomic charges obtained from the natural population analysis yielded the most significant improvement, while other charge schemes such as Mulliken, electrostatic potential, or CM5 led to poorer outcomes. It is further demonstrated that the QM atomic charges can be accurately estimated in a highly efficient manner using an iterative fragmentation approach based on the moving-domain QM/MM method. Similar observations were made when the approach was used to predict the barrier of an SN2 reaction. Thus, the use of QM-quality atomic charges on MM atoms represents a simple and easy-to-implement strategy for improving the accuracy of QM/MM models.

Hydrogen-Bond Disrupting Electrolytes for Fast and Stable Proton Batteries.

Rechargeable aqueous proton batteries are promising competitors for the next generation of energy storage systems with the fast diffusion kinetics and wide availability of protons. However, poor cycling stability is a big challenge for proton batteries due to the attachment of water molecules to the electrode surface in acid electrolytes. Here, a hydrogen-bond disrupting electrolyte strategy to boost proton battery stability via simultaneously tuning the hydronium ion solvation sheath in the electrolyte and the electrode interface is reported. By mixing cryoprotectants such as glycerol with acids, hydrogen bonds involving water molecules are disrupted leading to a modified hydronium ion solvation sheaths and minimized water activity. Concomitantly, glycerol absorbs on the electrode surface and acts to protect the electrode surface from water. Fast and stable proton storage with high rate capability and long cycle life is thus achieved, even at temperatures as low as -50 °C. This electrolyte strategy may be universal and is likely to pave the way toward highly stable aqueous energy storage systems.

Development of an Albumin-Polymer Bioconjugate via Covalent Conjugation and Supramolecular Interactions.

Preexisting serum albumin-polymer bioconjugates have been formed either through covalent conjugation or supramolecular interactions. However, the viability of producing a bioconjugate where both covalent conjugation and supramolecular interactions have been adopted is yet to be explored. In this work, the noncovalent interaction of two polymers bearing fatty acid-based end-functionalities were compared and the superior binder was carried forward for testing with serum albumin that possessed a polymer conjugated to its Cys34 residue. The studies demonstrated that an albumin-polymer bioconjugate equipped with polymers via both covalent and supramolecular interactions can be successfully achieved.

Accurate Quantum Chemical Prediction of Gas-Phase Anion Binding Affinities and Their Structure-Binding Relationships.

This paper systematically examines the performance of contemporary wavefunction and density functional theory methods to identify robust and cost-efficient methods for predicting gas-phase anion binding energies. This includes the local coupled cluster LNO-CCSD(T) and DLPNO-CCSD(T), as well as double-hybrid DSD-PBEP86-D3(BJ) and various hybrid functionals M06-2X, B3LYP-D3(BJ), ωB97M-V, and ωB97X-V. The focus is on dual-hydrogen-bonding anion receptors that are commonly found in supramolecular chemistry and organocatalysis, namely, (thio)ureas, deltamides, (thio)squaramides, and croconamides as well as the yet-to-be-explored rhodizonamides. Of the methods examined, M06-2X emerged as the overall best performing method as the other functionals including DSD-PBEP86-D3(BJ) and the local coupled cluster DLPNO-CCSD(T) method displayed systematic errors that increase with the degree of carbonylation of the receptors. Hybrid ONIOM models that employed semiempirical methods (PM7, GFN1-xTB, and GFN2-xTB) and "threefold"-corrected small-basis set potentials (HF-3c, B97-3c, and PBEh-3c) were explored, and the best models resulted in 50- to 500-fold reduction in CPU time compared to W1-local. These calculations provide important insight into the structure-binding relationships where there is a direct correlation between Brønsted acidity and anion binding affinity, though the strength of the correlation also depends on other factors such as hydrogen-bonding geometry and the geometrical distortion that the receptor needs to undergo to bind the anion.

"Water-in-Sugar" Electrolytes Enable Ultrafast and Stable Electrochemical Naked Proton Storage.

Proton is an ideal charge carrier for rechargeable batteries due to its small ionic radius, ultrafast diffusion kinetics and wide availability. However, in commonly used acid electrolytes, the co-interaction of polarized water and proton (namely hydronium) with electrode materials often causes electrode structural distortions. The hydronium adsorption on electrode surfaces also facilitates hydrogen evolution as an unwanted side reaction. Here, a "water-in-sugar" electrolyte with high concentration of glucose dissolved in acid to enable the naked proton intercalation, as well as an extended 3.9 V working potential window, is shown. A glucose-derived organic thin film is formed on electrode surface upon cycling. Molecular dynamics simulations reveal the significant decrease of free water in bulk electrolytes, while density functional theory calculations indicate that glucose preferentially binds to the electrode surface which can inhibit water adsorption. The scarcity of free water and the protective organic film work in synergy to suppress water interactions with the electrode surface, which enables the naked proton (de)intercalation. The "water-in-sugar" electrolyte significantly enhances a MoO3 electrode for stable cycling over 100 000 times. This facile electrolyte approach opens new avenues to aqueous electrochemistry and energy storage devices.

Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions.

Despite recent advances in the development of machine learning potentials (MLPs) for biomolecular simulations, there has been limited effort on developing stable and accurate MLPs for enzymatic reactions. Here we report a protocol for performing machine-learning-assisted free energy simulation of solution-phase and enzyme reactions at the ab initio quantum-mechanical/molecular-mechanical (ai-QM/MM) level of accuracy. Within our protocol, the MLP is built to reproduce the ai-QM/MM energy and forces on both QM (reactive) and MM (solvent/enzyme) atoms. As an alternative strategy, a delta machine learning potential (ΔMLP) is trained to reproduce the differences between the ai-QM/MM and semiempirical (se) QM/MM energies and forces. To account for the effect of the condensed-phase environment in both MLP and ΔMLP, the DeePMD representation of a molecular system is extended to incorporate the external electrostatic potential and field on each QM atom. Using the Menshutkin and chorismate mutase reactions as examples, we show that the developed MLP and ΔMLP reproduce the ai-QM/MM energy and forces with errors that on average are less than 1.0 kcal/mol and 1.0 kcal mol-1 Å-1, respectively, for representative configurations along the reaction pathway. For both reactions, MLP/ΔMLP-based simulations yielded free energy profiles that differed by less than 1.0 kcal/mol from the reference ai-QM/MM results at only a fraction of the computational cost.

On the Accuracy of QM/MM Models: A Systematic Study of Intramolecular Proton Transfer Reactions of Amino Acids in Water.

This work presents a systematic assessment of QM/QM' and QM/MM models with respect to direct QM calculations for the tautomerization (neutral to zwitterion) reactions of amino acids (glycine, alanine, valine, aspartate, and neutral and protonated histidine) solvated in a 160 water cluster. The effect of varying QM region size and choice of embedding potentials, including fixed-charge and polarizable molecular mechanics force fields (TIP3P and EFP) and various semiempirical QM methods (PM7, GFN2-xTB, DFTBA, DFTB3, HF-3c, and PBEh-3c), on the accuracy of the models was examined. A surprising finding was that molecular mechanics force fields outperformed many of the semiempirical methods. Generally, the errors in the QM/QM' and QM/MM models converge slowly with respect to the QM region size, requiring 50 or more waters to be included in the QM region before the error in the model falls below 1 kcal mol-1 of its pure QM result. Different QM region selection schemes were also compared, and it was found that selection based on Natural Population Analysis (NPA) atomic charges significantly reduced the error in the QM/QM' and QM/MM models particularly if a low-quality embedding potential was used. It is envisaged that these results will be useful for the development of future hybrid QM models.

The Effect of Vicinal Difluorination on the Conformation and Potency of Histone Deacetylase Inhibitors.

Histone deacetylase enzymes (HDACs) are potential targets for the treatment of cancer and other diseases, but it is challenging to design isoform-selective agents. In this work, we created new analogs of two established but non-selective HDAC inhibitors. We decorated the central linker chains of the molecules with specifically positioned fluorine atoms in order to control the molecular conformations. The fluorinated analogs were screened against a panel of 11 HDAC isoforms, and minor differences in isoform selectivity patterns were observed.

Polyphenylglyoxamide-Based Amphiphilic Small Molecular Peptidomimetics as Antibacterial Agents with Anti-Biofilm Activity.

The rapid emergence of drug-resistant bacteria is a major global health concern. Antimicrobial peptides (AMPs) and peptidomimetics have arisen as a new class of antibacterial agents in recent years in an attempt to overcome antibiotic resistance. A library of phenylglyoxamide-based small molecular peptidomimetics was synthesised by incorporating an N-alkylsulfonyl hydrophobic group with varying alkyl chain lengths and a hydrophilic cationic group into a glyoxamide core appended to phenyl ring systems. The quaternary ammonium iodide salts 16d and 17c showed excellent minimum inhibitory concentration (MIC) of 4 and 8 µM (2.9 and 5.6 µg/mL) against Staphylococcus aureus, respectively, while the guanidinium hydrochloride salt 34a showed an MIC of 16 µM (8.5 µg/mL) against Escherichia coli. Additionally, the quaternary ammonium iodide salt 17c inhibited 70% S. aureus biofilm formation at 16 µM. It also disrupted 44% of pre-established S. aureus biofilms at 32 µM and 28% of pre-established E. coli biofilms 64 µM, respectively. A cytoplasmic membrane permeability study indicated that the synthesised peptidomimetics acted via disruption and depolarisation of membranes. Moreover, the quaternary ammonium iodide salts 16d and 17c were non-toxic against human cells at their therapeutic dosages against S. aureus.

Diaminomethylenemalononitriles and Diaminomethyleneindanediones as Dual Hydrogen Bond Donors for Anion Recognition.

Diaminomethylenemalononitriles (DMMs) and diaminomethyleneindanediones (DMIs) are dual H-bond donors that have previously been used as organocatalysts, but their anion binding ability has not been investigated. We report the synthesis of both alkyl- and aryl-substituted DMMs and DMIs, together with a comparison of their anion binding ability with that of the analogous thioureas. The DMMs display affinity for monovalent anions, with similar anion binding affinities observed to that of the thioureas in acetonitrile, albeit with differing trends for the N,N'-dialkyl versus N,N'-diaryl compounds. In contrast, the DMIs do not bind to monovalent anions under similar conditions as a result of conformational locking through the formation of intramolecular H-bonds. This can be overcome upon addition of sulfate ions, and binding of sulfate is enhanced in a more competitive solvent (DMSO).