Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids

Biparatopic binding of ISB 1442 to CD38 in trans enables increased cell antibody density and increased avidity

ISB 1442 is a bispecific biparatopic antibody in clinical development to treat hematological malignancies. It consists of two adjacent anti-CD38 arms targeting non-overlapping epitopes that preferentially drive binding to tumor cells and a low-affinity anti-CD47 arm to enable avidity-induced blocking of proximal CD47 receptors. We previously reported the pharmacology of ISB 1442, designed to reestablish synthetic immunity in CD38+ hematological malignancies. Here, we describe the discovery, optimization and characterization of the ISB 1442 antigen binding fragment (Fab) arms, their assembly to 2 + 1 format, and present the high-resolution co-crystal structures of the two anti-CD38 Fabs, in complex with CD38. This, with biophysical and functional assays, elucidated the underlying mechanism of action of ISB 1442. In solution phase, ISB 1442 forms a 2:2 complex with CD38 as determined by size-exclusion chromatography with multi-angle light scattering and electron microscopy. The predicted antibody-antigen stoichiometries at different CD38 surface densities were experimentally validated by surface plasmon resonance and cell binding assays. The specific design and structural features of ISB 1442 enable: 1) enhanced trans binding to adjacent CD38 molecules to increase Fc density at the cancer cell surface; 2) prevention of avid cis binding to monomeric CD38 to minimize blockade by soluble shed CD38; and 3) greater binding avidity, with a slower off-rate at high CD38 density, for increased specificity. The superior CD38 targeting of ISB 1442, at both high and low receptor densities, by its biparatopic design, will enhance proximal CD47 blockade and thus counteract a major tumor escape mechanism in multiple myeloma patients.

Effects of the physico-chemical properties of amino acids and chemically functionalized surfaces on DIOS-MS analysis

Desorption ionization on silicon mass spectrometry (DIOS-MS) allows for the detection of low molecular weight species from fluid samples. However, this method remains scarcely used for clinical diagnosis likely because of a lack of knowledge about the desorption/ionization mechanism as well as about the interplay between the surface and analyte properties which are effective in desorption/ionization, impeding the optimization of the DIOS-MS analysis. Herein, the normalized intensity of the DIOS-MS peaks at [M+H]+ of seven amino acids on four different porous silicon modified surfaces are investigated. These amino acids (arginine, phenylalanine, methionine, glutamine, leucine, cysteine and valine) have different isoelectric points, proton affinities, and octanol-water partition coefficients. The four selected surfaces were oxidized porous silicon (SiO2), the same porous silicon modified with a propyl dimethyl ethoxy silane, octadecyl dimethyl ethoxy silane or 3 amino propyl dimethyl ethoxy silane (CH3-short, CH3-long and NH3+, respectively). These surfaces present different electrical charges, alkyl chain lengths, and hydrophilic/hydrophobic properties. For each surface, the intensities of the protonated molecules ([M+H]+) are discussed with respect to the electrical charge and proton affinity of the amino acids, their z-distributions inside the pores (determined by time of flight secondary ion mass spectrometry profiling), their surface interaction energies (calculated by molecular dynamics simulations), the interfacial water content and the proton availability for each surface.

Computational approaches and experimental investigation for identification of potential inhibitors targeting cysteine synthase in Leishmania donovani

Visceral leishmaniasis poses a significant health challenge due to limited treatment options, drug resistance, and lack of vaccine. Targeting essential proteins of Leishmania parasites, either absent or distinct from human, is imperative for developing new chemotherapeutic strategies. The cysteine synthase (CS) and serine O-acetyltransferase (SAT) involved in the de novo cysteine biosynthetic pathway of L. donovani may represent an attractive drug target. This pathway is absent in humans and controls the trypanothione-based redox metabolism; crucial for parasite survival and drug resistance. The C-terminal SAT-peptides strongly bind to CS creating a regulatory CS-SAT complex, leading to partial or complete inhibition of CS activity. In this study, CS in complex with SAT was utilized as a framework to screen inhibitors against LdCS. Structure-based virtual screening and molecular docking against LdCS protein with varying precisions (SP and XP modes) were performed to identify potential novel inhibitors. We have identified 17 top-ranked hits exhibiting inhibitory activity based on docking score against LdCS. Four of these compounds were further evaluated through molecular dynamics simulations and biological assays. Compounds (ASN05106249) and (ASN03069898) showed significant inhibitory effect on CS enzymatic activity and growth of parasite that highlight the potential of LdCS to develop new therapies against Leishmaniasis.

Solvation layer effects on lithium migration in localized High-Concentration Electrolytes: Analyzing the diverse antisolvent Contributions

Localized high-concentration electrolytes (LHCEs) offer a new methodology to improve the functionality of conventional electrolytes. Understanding the impact of antisolvents on bulk electrolytes is critical to the construction of sophisticated LHCEs. However, the mechanism of how antisolvent modulates the electrochemical reactivity of the solvation structure in LHCEs remains unclear. In this work, the key correlation between the physicochemical properties of antisolvents and their corresponding Lithium-ion battery (LIBs) systems has been elucidated by comprehensive multiscale theoretical simulations combined with experimental characterizations. Nine antisolvents (chain ethers and cyclic non-ethers) are investigated in a typical lithium bis(fluorosulfonyl)imide/1,2-dimethoxymethane (LiFSI/DME) system. It is highlighted that the relative molecular masses of antisolvents in the same class are positively correlated with the density. The viscosity of a liquid mixture consisting of DME and antisolvent in the same class is positively correlated with the magnitude of the interaction energy between them. Additionally, the self-diffusion coefficient of Li+ is also positively correlated with the sum of the interaction energies between Li+-DME and Li+-FSI-, which is also affected by the class of antisolvent. These results provide deep insights into the behavior and properties of LHCEs, which help to advance the design of high-performance LIBs.

Interaction of L-proline with water and ice: Implications for Litopenaeus Vannamei Cryoprotection during temperature fluctuation

Temperature fluctuations can negatively affect the quality of frozen shrimp. Research on novel cryoprotectants to replace traditional agents (phosphate, etc.) has become a hotspot. Our results indicated that L-Proline could reduce thawing losses, delay texture deterioration and improve the functional properties of myofibrillar proteins of shrimp. Thawing loss in the proline group (3.2 %) was significantly lower than that in the control (5.4 %) after 3 freeze-thaw cycles (p < 0.05). Compared to Na4P2O7, proline had better permeability and greater ability to inhibit ice crystal growth and volume expansion. Through molecular simulations, we found that proline might inhibit ice crystal formation by forming glassy states with water. Hydrogen bonding between proline and water/ice played a major role, and only a small amount of proline was required to significantly reduce the ice crystal growth rate from 0.16 m/s to 0.06 m/s. Briefly, proline exhibited potential as a cryoprotectant for shrimp in temperature fluctuations.

Molecular docking, molecular dynamics simulation, and MM/PBSA analysis of ginger phytocompounds as a potential inhibitor of AcrB for treating multidrug-resistant Klebsiella pneumoniae infections

The emergence of Multidrug resistance (MDR) in human pathogens has defected the existing antibiotics and compelled us to understand more about the basic science behind alternate anti-infective drug discovery. Soon, proteome analysis identified AcrB efflux pump protein as a promising drug target using plant-driven phytocompounds used in traditional medicine systems with lesser side effects. Thus, the present study aims to explore the novel, less toxic, and natural inhibitors of Klebsiella pneumoniae AcrB pump protein from 69 Zingiber officinale phyto-molecules available in the SpiceRx database through computational-biology approaches. AcrB protein's homology-modelling was carried out to get a 3D structure. The multistep-docking (HTVS, SP, and XP) were employed to eliminate less-suitable compounds in each step based on the docking score. The chosen hit-compounds underwent induced-fit docking (IFD). Based on the XP GScore, the top three compounds, epicatechin (-10.78), 6-gingerol (-9.71), and quercetin (-9.09) kcal/mol, were selected for further calculation of binding free energy (MM/GBSA). Furthermore, the short-listed compounds were assessed for their drug-like properties based on in silico ADMET properties and Pa, Pi values. In addition, the molecular dynamics simulation (MDS) studies for 250 ns elucidated the binding mechanism of epicatechin, 6-gingerol, and quercetin to AcrB. From the dynamic binding free energy calculations using MM/PBSA, 6-gingerol exhibited a strong binding affinity towards AcrB. Further, the 6-gingerol complex's energy fluctuation was observed from the free energy landscape. In conclusion, 6-gingerol has a promising inhibiting potential against the AcrB efflux pump and thus necessitates further validation through in vitro and in vivo experiments.

In silico and in vitro evaluation of newly synthesized pyrazolo-pyridine fused tetrazolo-pyrimidines derivatives as potential anticancer and antimicrobial agents

Diversely functionalized pyrazolo-pyridine fused tetrazolo-pyrimidines 10aa-am and 10ba-bn were successfully synthesized via a catalyst-free synthetic protocol with moderate to very good yields. The compounds were evaluated for cytotoxicity against MCF-7 and HEK-293 cells using MTT assay. Among the tested compounds, 10ab (IC50- 23.83 µM) and 10ah (IC50- 23.30 µM) demonstrated the highest potency against MCF-7 cells, while 10bc (IC50- 14.46 µM) and 10bh (IC50- 2.53 µM) exhibited excellent cytotoxicity against HEK-293 cells. Additionally, antibacterial screening was performed against three Gram-negative bacteria (E. coli, P. aeruginosa, and S. enterica) and three Gram-positive bacteria (S. aureus, B. megaterium, and B. subtilis) using broth dilution method, while antifungal activity was assessed against three fungal strains (A. niger, Penicillium, and S. cerevisiae) using agar well diffusion method. In antimicrobial screening, the majority of the compounds demonstrated significant antibacterial efficacy compared to antifungal activity. We also conducted comprehensive computational studies, including DFT calculations, molecular docking and dynamics, and drug-likeness assessments. In the DFT study, compounds 10ac and 10bc displayed stable conformations, indicating their potential for higher therapeutic activity. Molecular docking analyses revealed compelling interactions, with compound 10ah demonstrating docking score -7.42 kcal/mol against catalytical domain PARP1 (PDB ID: 7KK4) and 10bh exhibiting a best docking score -10.77 kcal/mol against human corticotropin-releasing factor receptor 1 (PDB ID: 4Z9G). A 100 ns molecular dynamics (MD) simulation study of compounds 10ah and 10bh revealed the stable conformation and binding energy in a stimulating environment. In drug-likeness assessments, both the compounds 10ah and 10bh adhere all the established guidelines.

Identification of potential inhibitors against Escherichia coli Mur D enzyme to combat rising drug resistance: an in-silico approach

Indiscriminate use of anti-microbial agents has resulted in the inception, frequency, and spread of antibiotic resistance among targeted bacterial pathogens and the commensal flora. Mur enzymes, playing a crucial role in cell-wall synthesis, are one of the most appropriate targets for developing novel inhibitors against antibiotic-resistant bacterial pathogens. In the present study, in-silico high-throughput virtual (HTVS) and Standard-Precision (SP) screening was carried out with 0.3 million compounds from several small-molecule libraries against the E. coli Mur D enzyme (PDB ID 2UUP). The docked complexes were further subjected to extra-precision (XP) docking calculations, and highest Glide-score compound was further subjected to molecular simulation studies. The top six virtual hits (S1-S6) displayed a glide score (G-score) within the range of -9.013 to -7.126 kcal/mol and compound S1 was found to have the highest stable interactions with the Mur D enzyme (2UUP) of E. coli. The stability of compound S1 with the Mur D (2UUP) complex was validated by a 100-ns molecular dynamics simulation. Binding free energy calculation by the MM-GBSA strategy of the S1-2UUP (Mur D) complex established van der Waals, hydrogen bonding, lipophilic, and Coulomb energy terms as significant favorable contributors for ligand binding. The final lead molecules were subjected to ADMET predictions to study their pharmacokinetic properties and displayed promising results, except for certain modifications required to improve QPlogHERG values. So, the compounds screened against the Mur D enzyme can be further studied as preparatory points for in-vivo studies to develop potential drugs. HIGHLIGHTSE.coli is a common cause of urinary tract infections.E.coli MurD enzyme is a suitable target for drug development.Novel inhibitors against E.coli MurD enzyme were identified.Molecular dynamics studies identified in-silico potential of identified compound.ADMET predictions and Lipinski's rule of five studies showed promising results.

The solvation of Na+ ions by ethoxylate moieties enhances adsorption of sulfonate surfactants at the air-water interface

HYPOTHESIS

Experiments show pronounced synergy in the reduction of surface tension when the nonionic surfactant Poly(oxy-1,2-ethanediyl), .alpha.-tris(1-phenylethyl)phenyl-.omega.-hydroxy- (Ethoxylated tristyrylphenol, EOT) is mixed with the anionic surfactant Sodium 4-dodecylbenzenesulfonate (NaDDBS). We hypothesize that the synergism is due to counterion (cation) effects. This would be unusual as one of the surfactants is nonionic. To test this hypothesis, the molecular mechanisms responsible need to be probed using experiments and simulations.

APPROACH

The interfacial properties of mixtures comprising EOT and NaDDBS are investigated using equilibrium molecular dynamics (MD) simulations. Free energy calculations using thermodynamic integration and umbrella sampling methods are employed to analyze the molecular interactions at surface and reveal the role of counterion solvation on the results observed. Simulation snapshots and trajectories are interrogated to confirm the findings.

FINDINGS

Simulation results indicate that the ethoxylate moieties solvate Na+ ions, forming long-lasting cation-EOT complexes. Free energy calculations suggest that these complexes are more stable at the interface than in the bulk, likely because of changes in the dielectric properties of water. The cation-EOT complexes, in turn, cause a stronger affinity between the interface and NaDDBS when EOT is present. Similar studies conducted for mixtures of EOT and cationic surfactant Dodecylammonium chloride (DAC) do not show evidence of Cl- ions solvation via the ethoxylate moieties, while the DAC headgroup was found to form hydrogen bonds with the EOT headgroup. This suggests that the mechanisms observed are likely ion specific.

Unveiling the drug delivery mechanism of graphene oxide dots at the atomic scale

Graphene oxide (GO) is an amphiphilic and versatile graphene-based nanomaterial that is extremely promising for targeted drug delivery, which aims to administer drugs in a spatially and temporally controlled manner. A typical GO nanocarrier features a polyethylene glycol coating and conjugation to an active targeting ligand. However, it is challenging to accurately model GO dots, because of their intrinsically complex and not unique structure. Here, realistic atomistic GO models are designed as homogeneously/inhomogeneously oxidized flakes and then coated with stealth polymeric chains conjugated to an active targeting ligand (PEG-cRGD). Doxorubicin (DOX) adsorption is investigated by metadynamics simulations for accelerated loading/release events. The presence of PEG and cRGD are found not to affect the DOX adsorption, whereas the homogeneity of oxidation plays a crucial role. We also proved that a change in pH towards acidic conditions causes a reduction in the GO/DOX affinity in line with a pH-triggered release mechanism. Based on this study, the ideal graphene-based DOX carrier is identified as a homogeneously highly oxidized GO where graphitic regions with strong DOX π-π stacking are limited. Such interactions excessively stabilize DOX and are not weakened by a pH-change. On the contrary, DOX interactions with surface oxidized groups are H-bonding and electrostatic, which can effectively be modified by a pH reduction. Our findings are useful to the experimental community to further develop successful drug delivery systems.

Repurposing FDA-approved drugs to target G-quadruplexes in breast cancer

Breast cancer, a leading cause of cancer-related mortality in women, is characterized by genomic instability and aberrant gene expression, often influenced by noncanonical nucleic acid structures such as G-quadruplexes (G4s). These structures, commonly found in the promoter regions and 5'-untranslated RNA sequences of several oncogenes, play crucial roles in regulating transcription and translation. Stabilizing these G4 structures offers a promising therapeutic strategy for targeting key oncogenic pathways. In this study, we employed a drug repurposing approach to identify FDA-approved drugs capable of binding and stabilizing G4s in breast cancer-related genes. Using ligand-based virtual screening and biophysical methods, we identified several promising compounds, such as azelastine, belotecan, and irinotecan, as effective G4 binders, with significant antiproliferative effects in breast cancer cell lines. Notably, belotecan and irinotecan exhibited a synergistic mechanism, combining G4 stabilization with their established topoisomerase I inhibition activity to enhance cytotoxicity in cancer cells. Our findings support the therapeutic potential of G4 stabilization in breast cancer, validate drug repurposing as an efficient strategy to identify G4-targeting drugs, and highlight how combining G4 stabilization with other established drug activities may improve anticancer efficacy.

Humic Acid with Vertical Adsorption Conformation Enhanced the Transport of Petroleum Hydrocarbon-Contaminated Colloids

Humic acid (HA) enhances colloidal transport in porous media, yet the mechanisms by which the HA adsorption conformation affects colloid transport remain unclear. This study investigated the influence of HA on the transport of petroleum-hydrocarbon-contaminated soil colloids (TPHs-SC) in saturated sand columns. The presence of TPHs on the colloidal surface occupied adsorption sites, hindering HA from forming a horizontal adsorption conformation, as observed on uncontaminated soil colloids (SC). Instead, a vertical adsorption conformation was formed, reducing the overall adsorption of HA. Vertically adsorbed HA increased the colloidal diffuse double-layer potential and extended the Derjaguin-Landau-Verwey-Overbeek energies between colloids and water-bearing media. This was evidenced by higher ζ potentials (-28.5 to -34.0 mV) and enhanced TPHs-SC transport compared to SC (ζ potentials ranging from -25.2 to -29.5 mV) in the presence of HA, particularly under alkaline conditions. Additionally, weak van der Waals and electrostatic interactions between TPHs near colloidal surfaces and free HA/TPHs formed a zonal distribution, facilitating the cotransport of colloids with TPHs. These findings underscore the significance of the HA adsorption conformation in TPHs-SC transport and provide insights into the critical mechanisms from an environmental structural chemistry perspective.

Computational insights into the targeted inhibition of lipoxygenase in Pseudomonas aeruginosa: hints for drug design

Pseudomonas aeruginosa is regarded as the most opportunistic pathogen. It can induce ferroptosis in humans. It secretes a unique lipoxygenase (LOX) isoform, pLoxA that can oxidize polyenoic fatty acids. Unlike other lipoxygenases, pLoxA can oxygenate membrane phospholipids like phosphatidylethanolamine, leading to hemolysis of red blood cells (RBC). This functional overlap with human 15-LOX that uses the same substrate has provided a bottleneck to the discovery of pLoxA-specific inhibitors and there is an immediate need to find pLoxA specific drugs. The active site of pLoxA is much larger than LOX enzymes, reflecting its ability to accommodate bulky substrates, such as phospholipids. The molecular docking of two experimentally established inhibitors and the further molecular dynamics simulations provided possible key residues in the active site of pLoxA. Our study found that this region is essentially hydrophobic including His 377 and His 382 that are placed to the non-heme iron atom and help to stabilize the inhibitors in the binding site along with hydrophobic residues contribute well toward ligand interactions that involve Phe 415, Ile 416 and Leu 424. MD simulations showed that interactions with those residues were dynamic in nature. Main contribution to binding stability arose via π-π stacking, π-cation, and alkyl interactions.

Micellar Solvent Accessibility of Esterified Polyoxyethylene Chains as Crucial Element of Polysorbate Oxidation: A Density Functional Theory, Molecular Dynamics Simulation and Liquid Chromatography/Mass Spectrometry Investigation

Given that the amphiphilicity of polysorbates represents a key factor in the protection of proteins from particle formation, the loss of this property through degradative processes is a significant concern. Therefore, the present study sought to identify the factors that contribute to the oxidative cleavage of the polysorbate (PS) molecule and to ascertain the preferred sites of degradation. In order to gain insight into the radical susceptibility of the individual polysorbate segments and their accessibility to water, conceptual density functional theory calculations and molecular dynamics simulations were performed. The behavior of monoesters and diesters was examined in both monomer form and within the context of micelles. The theoretical results were corroborated by experimental findings, wherein polysorbate 20 was subjected to 50 ppb Fe2+ and 100,000 lx·h of visible light, and subsequently stored at 25 °C/60% r.h. or 40 °C/75% r.h. for a period of 3 months. Molecular dynamics simulations demonstrated that unesterified polyoxyethylene(POE) chains within a polysorbate 20 molecule exhibited the greatest water accessibility, indicating their heightened susceptibility to oxidation. Nevertheless, the oxidative cleavage of esterified polyoxyethylene chains of a polysorbate 20 molecule is highly detrimental to the protective effect on protein particle formation. This occurs presumably at the oxyethylene (OE) units in the vicinity of the sorbitan ring, leaving a nonamphiphilic molecule in the worst case. Consequently, the critical degradation sites were identified, resulting in the formation of degradation products that indicate a loss of amphiphilicity in PS.

Multifunctional Carbon Dots In Situ Confined Hydrogel for Optical Communication, Drug Delivery, pH Sensing, Nanozymatic Activity, and UV Shielding Applications

Inspired by the emerging potential of photoluminescent hydrogels, this work unlocks new avenues for advanced biosensing, bioimaging, and drug delivery applications. Carbon quantum dots (CDs) are deemed particularly promising among various optical dyes, for enhancing polymeric networks with superior physical and chemical properties. This study presents the synthesis of CDs derived from Prunella vulgaris, a natural plant resource, through a single-step hydrothermal process, followed by their uniform integration into hydrogel matrices via an in situ free radical graft polymerization. The resulting CD-integrated hydrogels exhibit multifunctionality in biomedical applications, featuring a diffusion-controlled drug release mechanism, permit concurrent delivery of photoluminescent CDs and therapeutic agents, enabling real-time monitoring over 32 h. In addition, these hydrogels function as a broad-range optical pH sensor (pH 3-11), provide robust ultraviolet (UV) shielding, and demonstrate nanozyme-like peroxidase activity. Critically, biocompatibility tests confirm their non-cytotoxicity toward fibroblast cells, establishing these hydrogels as promising candidates for diverse biomedical applications. These include advanced wound dressings that monitor the healing process and detect infection through pH sensing, and promote healing through the nanozymatic activity, all while maintaining a moist wound microenvironment. These hydrogels demonstrate exceptional suitability for advanced smart drug delivery, effective UV-blocking, and as innovative platforms for in vivo sensing and bioimaging.

Silicene-Based Quantum Dots Nanocomposite Coated Functional UV Protected Textiles With Antibacterial and Antioxidant Properties: A Versatile Solution for Healthcare and Everyday Protection

The predominant adverse health effects in care delivery result from hospital-acquired (nosocomial) infections, which impose a substantial financial burden on global healthcare systems. Integrating contact-killing antibacterial action, gas permeability, and antioxidant properties into textile coatings offers a transformative solution, significantly enhancing both medical and everyday protective applications. This study presents an innovative, pollution-free physical compounding method for creating a fluorescent biopolymer composite embedded with silicene-based heteroatom-doped carbon quantum dots for the production of functional textiles. The resulting coated fabric shows superior ultraviolet (UV) protection behavior (UVA and UVB), thermal stability, breathability, mechanical strength, and antioxidant capabilities as demonstrated by the 2,2-diphenyl-1-picrylhydrazyl (DPPH) experiment (>78%) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) ABTS assay (>90%). Rigorous testing against both gram positive and gram negative bacteria confirms that the coated fabric has excellent antibacterial activity. Results from time-dependent antibacterial assays indicate that the nanocomposite can markedly inhibit bacterial proliferation within a few hours. Molecular dynamics modeling, in conjunction with experimental investigations, is employed to elucidate the intermolecular interactions influencing the components of the treated cotton fabrics. The ongoing research can result in the creation of cost-effective smart textile substrates aimed at inhibiting microbial contamination in healthcare and medical applications, possibly rendering them commercially viable.

Mimosa pudica Linn. extract improves aphrodisiac performance in diabetes-induced male Wister rats

Male sexual dysfunction is considered one of the major consequences of diabetes mellitus. The medicinal plant, Mimosa pudica Linn. is believed to have numerous therapeutic effects, including anti-diabetic, anti-obesity, aphrodisiac, and a sexual behaviour-enhancing properties. In the present study, the significant effect of ethanolic extract of M. pudica L. to scavenge excessive free radicals and alleviate the deleterious effects of alloxan-induced diabetes on the male sexual system of rats was demonstrated. The rats treated with the M. pudica L. extract recovered their body weight, the weight of their reproductive organs, the characteristics of the sperm and the histocellular arrangement of the testes. In addition, significant levels of hormones (testosterone, follicle-stimulating hormone and luteinising hormone) increased in both serum and testicular homogenates of male diabetic rats treated with M. pudica L. extract. Further, antioxidant enzymes, SOD, CAT, GSH, and GPx levels are increased, and oxidative stress markers MDA and ROS are reduced in both serum and testicular homogenates of M. pudica L. extract treated male rats. Furthermore, an in silico molecular docking study was performed to predict high potential compounds of M. pudica L. extract against the PDE5 receptor. Two bioactive compounds, namely 3-Dibenzofuranamine (-11.1 kcal × mol-1), Stigmasta-7,16-dien-3-ol (-10.4 kcal × mol-1) showed the highest binding affinities with PDE5 enzyme, much higher than the reference drug sildenafil (-9.9 kcal × mol-1). According to these findings, bioactive compounds rich in ethanolic extract of M. pudica L. have significant aphrodisiac performance in diabetic rats.Communicated by Ramaswamy H. Sarma.

Assessment of Phage-Displayed Peptides Targeting Cancer Cell Surface Proteins: A Comprehensive Molecular Docking Study

Peptides binding overexpressed breast and cervical cancer cell surface proteins can be isolated by phage display technology, and their affinity to their potential receptors can be assessed by molecular docking. We isolated 44 phage clones displaying dodecapeptides with high affinity to HeLa cervical cancer and MDA-MB-231 (MDA) breast cancer cells by repeated biopanning of an MK13 phage library and explored their affinity to specific proteins by molecular docking. Six peptides appeared repeatedly during biopanning: two with affinity to HeLa (H5/H21), and four with affinity to MDA cells (M3/M7/M15/M17). Peptide pairs M3/H5 and H1/M17 had affinity to both cell lines. A systematic review identified Annexin A2, EGFR, CD44, CD146, and Integrin alpha V as potential protein targets in HeLa cells, and Vimentin, Galectin-1, and Annexins A1 and A5 in MDA cells. Via virtual screening, we selected six peptides with the highest total docking scores: H1 (-916.32), H6 (-979.21), H19 (-1093.24), M6 (-732.21), M16 (-745.5), and M19 (-739.64), and identified that docking scores were strengthened by the protein type, the interacting amino acid side chains, and the polarity of peptides. This approach facilitates the selection of relevant peptides that could be further explored for active targeting in cancer diagnosis and treatment.

A potential therapeutic strategy by fungal laccase targeting novel binding sites on human cytomegalovirus DNA polymerase

Human cytomegalovirus (HCMV) is a common herpesvirus that can severely affect transplant recipients, those with AIDS, and newborns. Existing synthetic medications face limitations, including toxicity, processing issues, and viral resistance. As part of this study, the efficacy of the extracellular enzyme laccase isolated from a widely available mushroom (Pleurotus pulmonarius) was compared to that of ganciclovir, a common antiviral, used against HCMV. The study found that laccase can synergistically inhibit HCMV replication by targeting new inhibitory sites on the UL54 protein. Viral replication requires significant energy, increasing cellular respiration. The antiviral effect of laccase was linked to reduced expression of genes regulating cellular respiration, which coincided with decreased viral DNA copies. Additionally, in silico analysis has identified a novel binding site for the laccase enzyme in the C-terminal region of HCMV DNA polymerase specifically between amino acids 1004 and 1242, which effectively obstructs the binding of the essential viral replication regulatory accessory protein UL44, thereby hindering successful replication. Molecular dynamics simulations were performed under standardized conditions mimicking a cellular environment, revealing a stable protein-protein docking complex. This study may aid in developing novel antiviral strategies by utilizing laccase's target specificity to regulate host cellular pathways against Herpesviridae.

Revitalizing interphase in all-solid-state Li metal batteries by electrophile reduction

All-solid-state lithium metal batteries promise high levels of safety and energy density, but their practical realization is limited by low Li reversibility, limited cell loading and demand for high-temperature and high-pressure operation, stemming from solid-state electrolyte (SSE) low-voltage reduction and high-voltage decomposition, and from lithium dendrite growth. Here we concurrently address these challenges by reporting that a family of reductive electrophiles gain electrons and cations from metal-nucleophile materials (here a Li sulfide SSE) upon contact to undergo electrochemical reduction and form interphase layers (named solid reductive-electrophile interphase) on material surfaces. The solid reductive-electrophile interphase is electron blocking and lithiophobic, prevents SSE reduction, suppresses Li dendrites and supports high-voltage cathodes. Consequently, a reductive-electrophile-treated SSE exhibits high critical capacity and Li reversibility at the anode, and enables Li(1% Mg)/SSE/LiNi0.8Co0.15Al0.05O2 all-solid-state lithium metal batteries to achieve a high coulombic efficiency (>99.9%), long cycle life (~10,000 h) and high loading (>7 mAh cm-2) at 30 °C and 2.5 MPa. This concept also extends to cathodes of other materials (for example, metal oxides), boosting the high-nickel cathode's cycle life and expanding the operational voltage up to 4.5 V. Such solid reductive-electrophile interphase tailoring of material surfaces holds promise to accelerate all-solid-state lithium metal battery commercialization and offer solutions for a wide range of materials.

Prediction of hydration energies of adsorbates at Pt(111) and liquid water interfaces using machine learning

Aqueous phase heterogeneous catalysis is important to various industrial processes, including biomass conversion, Fischer-Tropsch synthesis, and electrocatalysis. Accurate calculation of solvation thermodynamic properties is essential for modeling the performance of catalysts for these processes. Explicit solvation methods employing multiscale modeling, e.g., involving density functional theory and molecular dynamics have emerged for this purpose. Although accurate, these methods are computationally intensive. This study introduces machine learning (ML) models to predict solvation thermodynamics for adsorbates on a Pt(111) surface, aiming to enhance computational efficiency without compromising accuracy. In particular, ML models are developed using a combination of molecular descriptors and fingerprints and trained on previously published water-adsorbate interaction energies, energies of solvation, and free energies of solvation of adsorbates bound to Pt(111). These models achieve root mean square error values of 0.09 eV for interaction energies, 0.04 eV for energies of solvation, and 0.06 eV for free energies of solvation, demonstrating accuracy within the standard error of multiscale modeling. Feature importance analysis reveals that hydrogen bonding, van der Waals interactions, and solvent density, together with the properties of the adsorbate, are critical factors influencing solvation thermodynamics. These findings suggest that ML models can provide rapid and reliable predictions of solvation properties. This approach not only reduces computational costs but also offers insights into the solvation characteristics of adsorbates at Pt(111)-water interfaces.

Ozonated Monolayer Graphene for Extended Performance and Durability in Hydrogen Fuel Cell Electric Vehicles

In the landscape of proton exchange membrane fuel cells (PEMFCs), there is a strong need for durable, low hydrogen crossover membranes that retain high current output and proton conductivity during operation. This study presents the use of UV-Ozone induced defects in graphene to eliminate the proton conductivity penalty commonly associated with traditional crossover mitigation strategies. We report a defect engineered graphene material that demonstrates an increase in hydrogen/proton selectivity of 27%, a decrease in H2 crossover of 24%, with limited to no impact on current output. Furthermore, we demonstrate a membrane that is 39% more durable than state of the art GORE Select membranes and shows no loss in performance after a 100 h accelerated stress test (AST). This study illustrates the viability of 2D material membranes to sieve between H2 and H3O+ in industrial testing conditions and serve as highly scalable and durable fuel cell membranes that represent a significant upgrade over current state of the art membranes for hydrogen fuel cell vehicles and clean energy generation.

Structure and Zeta Potential of Gold Nanoparticles with Coronas of Varying Size and Composition

The structure of the soft ligand shell in engineered nanoparticles is related to their physical and chemical properties. The variation in that structre is critical for extending the diversity of functions in a wide variety of applications. To uncover the structure of soft PAH coronas wrapped on gold nanoparticles (AuNPs), in particular, we used atomistic simulations in this work. We found that increasing the number of PAH chains can increase both the size of the soft PAH corona and the magnitude of the electric potential of the PAH-wrapped cit-AuNPs (PAH-AuNPs). We also found that when the salt concentration increases, both the soft corona size and the electric potential decrease due to Debye screening. We compared the ligand structures, ion distributions, and electric potentials of 5 different nanoparticles-viz. citrate, PAH, 3-mercapto-propionic acid (MPA), 16-mercapto-hexadecyl-trimethylammonium bromide (MTAB), and hexadecyl-trimethylammonium bromide (CTAB) capped AuNPs. We found that when the surface charge densities are similar, these 5 different nanoparticles have similar electric potential profiles, but their ligand structures differ. Using Debye-Hückel theory, we determine the slipping planes (at the hydrodynamic radius, R H) and calculate the ζ-potentials of different AuNPs. We compared several machine learning (ML) models to predict the ζ-potential values learned from our simulation data and found that the Extra Trees model is the best at rationalizing the experimental data.

Theoretical Study on the Solvent-Dependent Optical Properties of 2-Aryl-3H-1,3-benzazaphosphole Oxide

Photoresponsive molecules that respond to the surrounding environment are expected to be utilized as optical functional materials, such as sensors. Recently reported 2-aryl-3H-1,3-benzazaphosphole oxide with a diphenylamino group (ABPO) is one such molecule having interesting solvent-dependent properties. The absorption spectra of ABPO in nonpolar and polar solvents are almost identical in shape and excitation energy, whereas the fluorescence spectrum is red-shifted as the solvent polarity increases. In addition, the fluorescence quantum yield drastically decreases in methanol and acetonitrile solvents. In this study, the solvent-dependent optical properties of ABPO were investigated using quantum chemical calculations with solvent models. To describe the solvation structures explicitly, the QM/MM reweighting free energy self-consistent field method was used. The calculated absorption and fluorescence energies qualitatively reproduced the experimental trends. The S1 excited state has charge-transfer (CT) character with a large dipole moment, which is responsible for the solvent dependency of fluorescence spectra. Furthermore, in addition to the CT state, the twisted intramolecular charge transfer (TICT) state plays an important role in the fluorescence quenching. It was indicated that the stabilization of the TICT state, which has a larger dipole moment than the CT state, affects the decrease in fluorescence quantum yield in polar solvents.

Molecular Insights into the EOR Mechanism of Water, CO2, and CO2-WAG Flooding in Heterogeneous Nanochannels: A Molecular Dynamic Simulation

In tight oil exploitation, the water flooding, CO2 flooding, and CO2 water gas alternate (CO2-WAG) flooding are three commonly adopted methods to enhance the oil recovery (EOR), and the heterogeneous of reservoir has crucial influence on the oil sweep volume and oil displacement efficiency. In this work, molecular dynamic simulation was employed to investigate the displacement behavior in these three flooding methods in the heterogeneous tight reservoir. First, a single nanochannel was used to investigate the different displacement performances in these three flooding methods. Then a double nanochannel model were constructed to mimic the heterogeneous tight reservoir. The threshold injection pressure of three flooding modes was calculated. The number of displaced oil molecules was used to evaluate the oil displacement efficiency. Simulation results showed that the threshold injection pressure gave the following order: CO2 flooding < CO2-WAG flooding < water flooding. In double nanochannel systems, the injecting water passed through the large-sized nanochannel, and the oil inside the small-sized nanochannel could not be displaced in water flooding and CO2-WAG flooding. In CO2 flooding, some oil molecules inside two nanochannels were displaced, and the oil displacement efficiency in the large-size nanochannel was higher than that in the small-size nanochannel. The comparison of these three flooding methods showed that the CO2-WAG flooding has priority over the other two flooding methods, exhibiting both low threshold injection pressure of gas flooding and high oil displacement efficiency of water flooding; consequently, high EOR could be achieved. Our work was helpful to deeply understand the microscopic oil displacement processes of different flooding methods, and it has reference value for the development of tight oil reservoirs.

Fmoc-conjugated dipeptide-based hydrogels and their pH-tuneable behaviour

In this work, we designed three dipeptide-based hydrogelators by attaching different hydrophilic amino acids (aspartic acid, glutamic acid, and glutamine) to Fmoc-conjugated phenylalanine. Self-assembly and gelation of the three dipeptides were studied in 50 mM phosphate buffer solutions. The gelation efficiency and kinetics of glutamine-based hydrogelators (FQ) were better than those of aspartic acid and glutamic acid-based hydrogelators FD and FE respectively at neutral pH. The lower gelation efficiency of FE and FD was due to the pH-responsive side chain (carboxylic acid) compared to FQ, where amide group was present as a side chain. Three hydrogelators exhibited better gelation efficiency at lower pHs as the anionic carboxylate group was protonated to the carboxylic group, facilitating better self-assembly and gelation processes. Thioflavin-T (ThT) binding study of hydrogels indicated the formation of β-sheet-like structure in the hydrogel state. The self-assembly process was inspected using molecular dynamic study, revealing that the newly developed FQ gelator possesses a higher aggregation tendency than FE and FD. Finally, these peptide-based injectable biomaterials were examined using fluorescence and FT-IR spectroscopy, scanning electron microscopy, and rheology.

Short-Ranged United-Atom Model for Efficient Simulations of Glycerol and Its Aqueous Mixtures

Glycerol, a versatile cryoprotectant, exhibits a complex conformational landscape governed by intra- and intermolecular hydrogen bonds. Capturing its structural and thermodynamic properties in liquid and glass states remains challenging due to discrepancies between NMR, neutron scattering experiments, and all-atom (AA) simulations. While AA simulations are widely used, they overestimate the α-conformation and incur significant computational costs. Coarse-grained (CG) models provide an efficient alternative but have yet to accurately describe glycerol's conformational distribution and thermodynamic behavior. Here, we introduce SR-UA glycerol, a short-ranged united-atom model parametrized to reproduce experimental density, enthalpy of vaporization, conformational distributions from NMR, and radial distribution functions from neutron scattering data. Inspired by the monatomic water (mW) model, SR-UA glycerol employs short-range anisotropic interactions to mimic hydrogen bonding, achieving about 100-fold computational speedup over AA models. The model captures the conformational shift from γγ to αα as glycerol transitions from gas to the liquid phase, emphasizing the role of intermolecular hydrogen bonds in stabilizing open conformations. When combined with mW water, SR-UA glycerol successfully reproduces key features of glycerol-water mixtures, including the decrease in the temperature of maximum density and the dynamical crossover, in agreement with AA simulations across a range of temperatures and concentrations. This work establishes a robust and efficient model to investigate glycerol's behavior in aqueous mixtures, opening the possibility of addressing with molecular simulations the competition between vitrification and crystallization at cryopreservation-relevant conditions.

Effect of Apolar Chain Grafting Density on Mobile-Phase Transport Properties Revealed by Molecular Simulations

The alkyl-modified surfaces are routinely utilized in high-performance liquid chromatography (HPLC). This study investigates the effect of grafting density on the transport and structure of acetonitrile (ACN) solutions across dimethyloctadecylsilane-modified surfaces, utilizing molecular dynamics (MD) simulations as a probing tool. Simulation results reveal that as the grafting density increases, the conformation of grafted chains transitions from a relatively disordered, reclining state to a more ordered, upright configuration. As a result, the end-to-end distance of the grafted chain rises with the increase of grafting density, subsequently influencing the position and magnitude of adsorption peaks, as well as the diffusion coefficient of ACN solution on the grafted surface. Further analysis indicates that under the influence of Couette flow, increased grafting density reduces both the effective viscosity and the hydrodynamic penetration length of the ACN solution, indicating the flow phase being constrained by the grafted chains and thus inhibiting its effective penetration into their interiors. Additionally, the effective viscosity shows shear-thinning behavior with an increasing shear rate. What is more is that a slip phenomenon emerges on the ungrafted surface, whereas no such slip is observed on the grafted surface, and the slip length increases in proportion to the rise in the applied shear rate. These simulation findings underscore the subtle interplay between the structure and transport properties of the molecular liquids at the grafted interface, which provide insights for improving the design of grafted functional materials employed in advanced separation technologies.

Active-Learning Assisted General Framework for Efficient Parameterization of Force-Fields

This work presents an efficient approach to optimizing force field parameters for sulfone molecules using a combination of genetic algorithms (GA) and Gaussian process regression (GPR). Sulfone-based electrolytes are of significant interest in energy storage applications, where accurate modeling of their structural and transport properties is essential. Traditional force field parametrization methods are often computationally expensive and require extensive manual intervention. By integrating GA and GPR, our active learning framework addresses these challenges by achieving optimized parameters in 12 iterations using only 300 data points, significantly outperforming previous attempts requiring thousands of iterations and parameters. We demonstrate the efficiency of our method through a comparison with state-of-the-art techniques, including Bayesian Optimization. The optimized GA-GPR force field was validated against experimental and reference data, including density, viscosity, diffusion coefficients, and surface tension. The results demonstrated excellent agreement between GA-GPR predictions and experimental values, outperforming the widely used OPLS force field. The GA-GPR model accurately captured both bulk and interfacial properties, effectively describing molecular mobility, caging effects, and interfacial arrangements. Furthermore, the transferability of the GA-GPR force field across different temperatures and sulfone structures underscores its robustness and versatility. Our study provides a reliable and transferable force field for sulfone molecules, significantly enhancing the accuracy and efficiency of molecular simulations. This work establishes a strong foundation for future machine learning-driven force field development, applicable to complex molecular systems.

Insights into interactions between taxanes and P-glycoprotein using biophysical and in silico methods

Multidrug resistance mediated by P-glycoprotein (Pgp) is a significant obstacle to cancer chemotherapy. Taxane drugs, including paclitaxel, docetaxel, and cabazitaxel, are used to treat multiple types of cancer. All taxane drugs are Pgp substrates, but cabazitaxel is also a Pgp inhibitor, indicating potential differential interactions between Pgp and different taxanes. Here, we showed for the first time that cabazitaxel had a partial inhibitory effect on the ATPase activity at concentrations higher than 10 µM. We found the KD of paclitaxel, docetaxel, and cabazitaxel to Pgp are 0.85 µM, 40.59 µM, and 13.53 µM, respectively. Based on acrylamide quenching, paclitaxel induced Pgp into a wide inward-facing open conformation at a high concentration but a slightly occluded conformation at lower concentrations. Both docetaxel and cabazitaxel shifted Pgp towards occluded states, each drug resulting in a unique degree of occlusion. Furthermore, molecular docking and energy calculations revealed that cabazitaxel binds with the "access tunnel" and blocks the subsequent nucleotide-binding domain dimerization. Our results indicate that the preference of taxanes for different binding sites on Pgp leads to distinct transport mechanisms. These results provide valuable insight into the interaction between taxanes and Pgp, which will enhance future drug development.

Identifying High Ionic Conductivity Compositions of Ionic Liquid Electrolytes Using Features of the Solvation Environment

Binary mixtures of ionic liquids with molecular solvents are gaining interest in electrochemical applications due to the improvement in their performance over neat ionic liquids. Dilution with suitable molecular solvents can reduce the viscosity and facilitate faster diffusion of ions, thereby yielding substantially higher ionic conductivity than that for a pure ionic liquid. Although viscosity and diffusion coefficients typically behave as monotonic functions of concentration, ionic conductivity often passes through a peak value at an optimum molar ratio of the molecular solvent to the ionic liquid. The ionic conductivity maximum is generally explained in terms of a balance between the ease of charge transport and the concentration of the charge carriers. In this work, fluctuation in the local environment surrounding an ion is invoked as a plausible explanation for the ionic conductivity mechanism with a binary mixture of 1-ethyl-3-methylimidazolium tetrafluoroborate and ethylene glycol as an example. The magnitude of the dynamism in the local environment is captured by measuring the spatial and temporal features of the solvation environment. Standard deviation in the number of ions in the solvation environment serves as a spatial feature, while the cage correlation lifetimes for oppositely charged ions within the first solvation shell serve as a temporal feature. Large standard deviations in the cluster ion population and short cage correlation lifetimes are indicators of highly dynamic ionic environment at the molecular level and consequently yield high ionic conductivity. Such compositions were found to be in good agreement with the optimum ionic liquid mole fractions obtained through experimental measurement. Short cage correlation lifetimes enable the identification of optimum mixture compositions using simulation trajectories significantly shorter than those required to implement the Nernst-Einstein or Einstein formalisms for calculating ionic conductivity. We validated the applicability of this approach across force fields and in six ionic liquid-molecular solvent electrolytes formed with combination of cations, anions, and solvents. We offer a computationally efficient approach of screening ionic liquid-molecular solvent binary mixture electrolytes to identify molar ratios that yield high ionic conductivity.

Nonbonded Force Field Parameters Derived from Atoms-in-Molecules Methods Reproduce Interactions in Proteins from First-Principles

Noncovalent interactions govern many chemical and biological phenomena and are crucial in protein-protein interactions, enzyme catalysis, and DNA folding. The size of these macromolecules and their various conformations demand computationally inexpensive force fields that can accurately mimic the quantum chemical nature of the atomic noncovalent interactions. Accurate force fields, coupled with increasingly longer molecular dynamics simulations, may empower us to predict conformational changes associated with the biochemical function of proteins. Here, we aim to derive nonbonded protein force field parameters from the partitioned electron density of amino acids, the fundamental units of proteins, via the atoms-in-molecules (AIM) approach. The AIM parameters are validated using a database of charged, aromatic, and hydrophilic side-chain interactions in 610 conformations, primarily involving π-π interactions, as recently reported by one of us (Carter-Fenk et al., 2023). Electrostatic and van der Waals interaction energies calculated with nonbonded force field parameters from different AIM methodologies were compared to first-principles interaction energies from absolute localized molecular orbital-energy decomposition analysis (ALMO-EDA) at the ωB97XV/def2-TZVPD level. Our findings show that electrostatic interactions between side chains are accurately reproduced by atomic charges from the minimal basis iterative stockholder (MBIS) scheme with mean absolute errors of 4-7 kJ/mol. Meanwhile, C6 coefficients from the MBIS AIM method effectively predict dispersion interactions with a mean error of -2 kJ/mol and a maximal error of -5 kJ/mol. As an outlook to use AIM methods in the development of protein force fields, we present the constrained AIM method that allows one to fix backbone parameters during the optimization of side-chain interactions. Backbone dihedral parameters have been optimized to reproduce secondary structure elements in proteins, and not altering them maintains compatibility with conventional protein force fields while improving the description of side-chain interactions. Our validated AIM methods allow for the depiction of noncovalent, long-range interactions in proteins using cost-effective force fields that achieve chemical precision.

IceCoder: Identification of Ice Phases in Molecular Simulation Using Variational Autoencoder

The identification and classification of different phases of ice within molecular simulations are challenging tasks due to the complex and varied phase space of ice, which includes numerous crystalline and amorphous forms. Traditional order parameters often struggle to differentiate between these phases, especially under the conditions of thermal fluctuations. In this work, we present a novel machine learning-based framework, IceCoder, which combines a variational autoencoder (VAE) with the smooth overlap of atomic position (SOAP) descriptor to classify a large number of ice phases effectively. Our approach compresses high-dimensional SOAP vectors into a two-dimensional latent space using VAE, facilitating the visualization and distinction of various ice phases. We trained the model on a comprehensive data set generated through molecular dynamics simulations and demonstrated its ability to accurately detect various phases of crystalline ice as well as liquid water at the molecular level. IceCoder provides a robust and generalizable tool for tracking ice phase transitions in simulations, overcoming the limitations of traditional methods. This approach may also be generalized to detect polymorphs in other molecular crystals, leading to new insights into the microscopic mechanisms underlying nucleation, growth, and phase transitions while maintaining computational efficiency.

CHARMM-GUI EnzyDocker for Protein-Ligand Docking of Multiple Reactive States along a Reaction Coordinate in Enzymes

Enzymes play crucial roles in all biological systems by catalyzing a myriad of chemical reactions. These reactions range from simple one-step processes to intricate multistep cascades. Predicting mechanistically appropriate binding modes along a reaction pathway for substrate, product, and all reaction intermediates and transition states is a daunting task. To address this challenge, special docking programs like EnzyDock have been developed. Yet, running such docking simulations is complicated due to the nature of multistep enzyme processes. This work presents CHARMM-GUI EnzyDocker, a web-based cyberinfrastructure designed to streamline the preparation and running of EnzyDock docking simulations. The development of EnzyDocker has been achieved through integration of existing CHARMM-GUI modules, such as PDB Reader and Manipulator, Ligand Designer, and QM/MM Interfacer. In addition, new functionalities have been developed to facilitate a one-stop preparation of multistate and multiscale docking systems and enable interactive and intuitive ligand modifications and flexible protein residues selections. A simple setup related to multiligand docking is automatized through intuitive user interfaces. EnzyDocker offers support for standard classical docking and QM/MM docking with CHARMM built-in semiempirical engines. Automated consensus restraints for incorporating experimental knowledge into the docking are facilitated via a maximum common substructure algorithm. To illustrate the robustness of EnzyDocker, we conducted docking simulations of three enzyme systems: dihydrofolate reductase, SARS-CoV-2 Mpro, and the diterpene synthase CotB2. In addition, we have created four tutorial videos about these systems, which can be found at https://www.charmm-gui.org/demo/enzydock. EnzyDocker is expected to be a valuable and accessible web-based tool that simplifies and accelerates the setup process for multistate docking for enzymes.

Developing Novel Lattice Mapping for Accurate and Efficient Charge Transport Modeling from Atomistic Morphology

This study focuses on numerical methods to compute charge carrier mobility in disordered materials, such as organic light-emitting diodes (OLEDs), based solely on molecular structures. The approach involves developing an ab initio method for calculating charge carrier mobility in organic materials using kinetic Monte Carlo (KMC) simulations. These simulations utilize Marcus rates derived from precise calculations of transfer integrals and site energies specific to the material's morphology. Going beyond the current approach to tackle a multicharge model system presents computational challenges, particularly in calculating site energies. To address this issue, a novel lattice mapping method was developed to efficiently determine transfer integrals and site energies from realistic morphologies while keeping computational costs manageable. Validation of the method was conducted by comparing mobility values computed using the KMC method with experimental data, showing a good agreement. Further insights into charge transport dynamics were gained through the analysis of charge carrier behavior using residence time calculations. Additionally, the model's applicability to multicharge systems was demonstrated by simulating exciton formation. In conclusion, the model has the potential to effectively and accurately simulate charge carrier trajectories in multicharge, multilayer models with minimal loss of information from realistic morphology, making it a valuable tool for designing and optimizing organic electronic devices.

Nanomolar inhibitor of the galectin-8 N-terminal domain binds via a non-canonical cation-π interaction

Galectin-8 is a tandem-repeat galectin consisting of two distinct carbohydrate recognition domains and is a potential drug target. We have developed a library of galectin-8N inhibitors that exhibit high nanomolar Kd values as determined by a competitive fluorescence polarization assay. A detailed thermodynamic analysis of the binding of D-galactosides to galectin-8N by isothermal titration calorimetry reveals important differences in enthalpic and/or entropic contributions to binding. Contrary to expectations, the binding of 2-O-propargyl-D-galactoside was found to strongly increase the binding enthalpy, whereas the binding of 2-O-carboxymethylene-D-galactoside was surprisingly less enthalpy-driven. The results of our work suggest that the ethynyl group can successfully replace the carboxylate group when targeting the water-exposed guanidine moiety of a critical arginine residue. This results in only a minor loss of affinity and an adjusted enthalpic contribution to the overall binding due to non-canonical cation-π interactions, as evidenced by the obtained crystal structure of 2-O-propargyl-D-galactoside in complex with the N-terminal domain of galectin-8. Such an interaction has neither been identified nor discussed to date in a small-molecule ligand-protein complex.

Reprofiling lamivudine as an antibiofilm and anti-pathogenic agent against Pseudomonas aeruginosa

Resistance to antibiotics is a critical growing public health problem that needs urgent action to combat. To avoid the stress on bacterial growth that evokes the development of resistance, anti-virulence agents can be an attractive strategy as they do not target bacterial growth. There are FDA approved drugs have been screened for their anti-virulence activities. Lamivudine (LAM) is a synthetic nucleoside analogue used as an antiretroviral in treatment of HIV and can be used in treatment of HBV. The present study aimed to assess the anti-virulence activities of LAM against a clinically important pathogen Pseudomonas aeruginosa. The LAM's antibiofilm and anti-virulence activities were evaluated. The impact of LAM on the quorum sensing (QS) systems which control the production of these virulence factors was assessed virtually and by quantification of the expression of QS-encoding genes. Furthermore, in vivo mice protection assay was conducted to attest the LAM's anti-pathogenic activity. The current findings elaborated the promising anti-pathogenic and anti-QS activities of LAM. LAM interfered with biofilm formation in P. aeruginosa PAO1 strain. Moreover, swarming motility and production of pyocyanin and protease were significantly diminished. At the molecular level, LAM downregulated the QS-encoding genes LasI, LasR, RhlR, PqsA and PqsR. Additionally, the detailed in silico docking and molecular simulation studies showed the considered high LAM's ability to bind and hinder the QS receptors in the P. aeruginosa. In an agreement with in vitro and in silico, the in vivo results showed the LAM full protection of mice against P. aeruginosa. In conclusion, LAM could be repurposed to be employed as adjunct therapy with traditional antibiotics for treating serious pseudomonal infections.

Predicting the Spurious Acceleration of Coarse-Grained Molecular Dynamics from Molecular Fluid Structure

Reproducing dynamical properties, such as diffusion coefficients, in coarse-grained (CG) molecular dynamics simulations can be challenging due to the loss of fine-grained details, such as atomic vibrations and local motions of particles in the parent all-atom (AA) system. In this study, we present a predictive tool for the mobility acceleration factor, defined as ratio of the CG diffusion coefficient to the AA diffusion coefficient. According to the well-established Green-Kubo formalism, the diffusion coefficient is related to integral of the velocity autocorrelation function. As integral of the velocity autocorrelation function is influenced by the particle's acceleration, key parameters affecting the acceleration differences between an AA molecule and its corresponding CG bead are identified to develop a predictive model. By conducting AA and CG simulations on 20 liquid hydrocarbons with varying masses and sizes, their mobility acceleration factors are determined, the largest being 62.78. This data is then used to fit a nonlinear functional form as the predictive model. The identified molecular descriptors for the predictive model are easy to calculate for new molecules, enabling the model to be readily applied to predict the mobility acceleration factor for different molecules in CG simulations.

On the complex hydrogen-bond network structural dynamics of liquid methanol: Chains, rings, bifurcations, and lifetimes

Solvation plays a pivotal role in chemistry to effectively steer chemical reactions. While liquid water has been extensively studied, our molecular-level knowledge of other associated liquids capable of forming H-bond networks, such as liquid methanol, remains surprisingly scarce. We use large-scale ab initio molecular dynamics simulations to comprehensively study the structural, dynamical, and electronic properties of bulk methanol under ambient conditions. Methanol is an interesting species in the liquid state since it can only donate one H-bond while a significant fraction accepts two H-bonds, which imprints one-dimensional linear and cyclic H-bonding patterns subject to significant bifurcations. After validation of radial distribution functions and the self-diffusion coefficient with respect to experimental data, we carried out detailed analyses of the H-bond network topology in terms of chain-like, ring-like, and branched H-bonded aggregates, including lifetime assessment. The analysis revealed that nearly all methanol molecules are actively engaged in filamentary H-bonding, predominantly forming branched linear chains with a significant contribution arising from tetrameric to hexameric rings-in stark contrast to the compact three-dimensional H-bond network of water. Five-membered rings turned out to be the most long-lived cyclic structures with an intermittent lifetime of 4 ps, while rings consisting of only three methanol molecules as well as very large cyclic structures are merely transient motifs. Detailed analyses of the effective electric molecular dipoles disclose a pronounced sensitivity of non-additive polarization and charge transfer effects of the individual methanol molecules to the particular H-bond network structure they are a member of, including its topology, be it linear or cyclic.

Amber ff24EXP-GA, Based on Empirical Ramachandran Distributions of Glycine and Alanine Residues in Water

Molecular dynamics (MD) offers important insights into intrinsically disordered peptides and proteins (IDPs) at a level of detail that often surpasses that available through experiments. Recent studies indicate that MD force fields do not reproduce intrinsic conformational ensembles of amino acid residues in water well, which limits their applicability to IDPs. We report a new MD force field, Amber ff24EXP-GA, derived from Amber ff14SB by optimizing the backbone dihedral potentials for guest glycine and alanine residues in cationic GGG and GAG peptides, respectively, to best match the guest residue-specific spectroscopic data. Amber ff24EXP-GA outperforms Amber ff14SB with respect to conformational ensembles of all 14 guest residues x (G, A, L, V, I, F, Y, Dp, Ep, R, C, N, S, T) in GxG peptides in water, for which complete sets of spectroscopic data are available. Amber ff24EXP-GA captures the spectroscopic data for at least 7 guest residues (G, A, V, F, C, T, Ep) better than CHARMM36m and exhibits more amino acid specificity than both the parent Amber ff14SB and CHARMM36m. Amber ff24EXP-GA reproduces the experimental data on three folded proteins and three longer IDPs well, while outperforming Amber ff14SB on short unfolded peptides.

Quantum Mechanics-Calibrated Classical Simulation of Earth-Abundant Divalent Metal Bis(trifluoromethanesulfonyl)imide Molar Conductivity in Organic Cosolvents with Onsager Transport Formalism

Molar conductivities are one of the fundamental transport properties of electrolytes that reflect the complex and dynamic interactions between ions and solvents comprehensively. The quantitative accuracy of experimental input-free simulations of molar conductivities is strongly influenced by the underlying interaction parameters employed in the model. When validated with experimental molar conductivities, the developed model could be used to reveal further atomistic level details about the solvation structures and correlated ion pair formation, providing in-depth knowledge about solution physical chemistry and shedding light on electrolyte-solvent system design rules. Divalent cations are more challenging to model than monovalent cations due to their higher charge densities and stronger interactions with the environment. Yet, they started attracting significant attention for next-generation energy storage purposes. In this work, we focus on two earth-abundant divalent cation electrolytes, Mg(TFSI)2 and Ca(TFSI)2 in a dimethylacetamide-tetrahydrofuran (DMA-THF) cosolvent system. We used quantum mechanical cluster models to optimize the force field parameters (including the pairwise nonbonded interaction parameters and atomic charges) to be applied in classical simulations. With the reliable force field model, we discussed the importance of including ion correlation explicitly in predicting the molar conductivities via the Onsager formalism and showed that the conventional Nernst-Einstein formula overestimates ionic mobilities due to its intrinsic independent and uncorrelated particle assumption. Further, we investigated the solvation structures and ion pair formations. We concluded that the suitability of the interaction potentials utilized in a classical model for particular systems needs to be assessed not solely by directly comparing the simulated molar conductivities with the measured ones but, more importantly, by using the correct formalism (Onsager) to deduce the simulated result from dynamics trajectories.

Structural Sensitivity of N 1s Excitations in N-Methylacetamide Solutions

Interpreting the X-ray spectra of liquids is complicated by their selective structural sensitivity and ensemble averaging. We report nitrogen K-edge spectra of liquid N-methylacetamide and its water solutions at temperatures of 305 and 350 K. The pre-peak in the spectrum shows a shift with an increase in the temperature or N-methylacetamide concentration. The effect is reproduced by our classical molecular dynamics simulations and subsequent spectrum calculations using density functional theory. We apply a data-driven method, emulator-based component analysis, to the computational data to identify the decisive structural degrees of freedom behind spectral variation. This representation in reduced dimensions accounts for the involved loss of structural information and reveals that the effect is indicative of weakening of the hydrogen bonds.

Phosphoryl-Graphene for High-Efficiency Uranium Separation and Recycling

To enhance the sustainability of nuclear energy and protect the environment, the efficient extraction of uranium from various water sources has emerged as an essential strategy for addressing the long-term challenges of nuclear waste management. In this study, we designed phosphoryl-functionalized graphene (PG) for efficient uranyl adsorption and synthesized the material from fluorinated graphene using phosphoryl ethanolamine under solvothermal conditions. The resultant PG features a unique 2D structure equipped with solvent-exposed phosphoryl groups, making it highly suitable for uranium adsorption in aqueous solutions. Notably, PG demonstrated a high sorption efficiency (∼77%) with rapid extraction capability (∼5 min) for U(VI) from aqueous media at pH 7, achieving an adsorption capacity of 316 mg U g-1. It also demonstrates good recyclability and stability even after 3 cycles and exhibits a significant seawater adsorption capacity of 117.8 mg U g-1. Both X-ray photoelectron spectroscopy analysis and molecular dynamics simulations revealed a preferential binding of uranyl ions to the phosphoryl groups of PG. This work paves the way for designing and developing functional graphene derivatives for efficient uranium extraction from various water resources, with promising potential for the recovery of other radioactive elements.

Molecular Simulation of CO2/CH4 Transport and Separation in Polystyrene-block-poly(ethylene oxide)/Ionic Liquid (IL) Membranes: Insights into Nanoconfined IL Effects

The phenomenon of ionic liquid (IL) nanoconfinement within a copolymer/IL membrane reportedly enhances membrane selectivity, solubility, and transport in gas separations. Also, the copolymer/IL membrane morphology has been found to affect IL stability at high transmembrane pressures. In this work, a combined mesoscopic dynamics simulation and hybrid grand canonical Monte Carlo/molecular dynamics (GCMC-MD) simulations were carried out to investigate the morphologies, as well as CO2/CH4 gas diffusivities, solubilities, and selectivities of polystyrene-b-poly(ethylene oxide) (PS-b-PEO)/1-Ethyl-3-methylimidazolium thiocyanate ([EMIM][SCN]) and PS-b-PEO/1-Ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([EMIM][Tf2N]) membranes. The latter simulations focused on nanoconfined ILs in the copolymer/IL phase boundaries at 2.5 and 5 nm confinement lengths. The investigated systems were four nanoconfined ILs, i.e., PS/[EMIM][SCN]/PEO (the IL forming a separate microphase, denoted IL-Micro), PS/[EMIM][Tf2N]/PEO, PS/[EMIM][SCN]-PEO/PS (the IL distributed in the PEO phase, denoted IL-PEO), and PS/[EMIM][Tf2N]-PEO/PS, and five control systems, i.e., PS/PEO/PS, bulk PS, bulk PEO, bulk [EMIM][SCN], and bulk [EMIM][Tf2N]. Based on the mesoscopic dynamics simulation results, the dominant membrane morphologies at IL loadings of <50 vol % were lamellar or cylindrical (favorable for both IL stability at high transmembrane pressures if the bedding planes are horizontal, i.e. at 90° to the nominal direction of the transmembrane pressure gradient) with the IL-PEO or IL-Micro phases. Also, there was an overall 50% match between the observed PS-b-PEO/[EMIM][SCN] and PS-b-PEO/[EMIM][Tf2N] membrane morphologies. Based on the MD simulation results, both CO2 and CH4 diffusivities were the smallest in the bulk PS (control) and highest in the PS/[EMIM][Tf2N]/PEO system (IL-Micro between the PS and PEO phases) at both confinement lengths. The CO2 diffusivities were, on average, larger when the confinement length increased to 5 nm. The GCMC-MD results indicated that the CO2 solubility in the IL-Micro phases was higher than in the corresponding bulk ILs at both confinement lengths, with the PS/[EMIM][Tf2N]/PEO system exhibiting the highest CO2 solubility, followed by the PS/[EMIM][SCN]/PEO system. Additionally, the permselectivities of the nanoconfined IL systems were, on average, 40-50% larger than those of the bulk systems, with the highest permselectivity observed for PS/[EMIM][Tf2N]/PEO at the confinement length of 5 nm. Overall, the IL nanoconfinement between the PS and PEO phases (IL-Micro) leads to significant improvements in the CO2/CH4 permselectivities, suggesting that strategies to create nanoconfined IL morphologies in the copolymer/IL membranes are very promising for optimizing the membrane gas separation performance.

Roles of basic amino acid residues in substrate binding and transport of the light-driven anion pump Synechocystis halorhodopsin (SyHR)

Microbial rhodopsins are photoreceptive seven-transmembrane α-helical proteins, many of which function as ion transporters, primarily for small monovalent ions such as Na+, K+, Cl-, Br-, and I-. Synechocystis halorhodopsin (SyHR), identified from the cyanobacterium Synechocystis sp. PCC 7509, uniquely transports the polyatomic divalent SO42- inward, in addition to monovalent anions (Cl- and Br-). In this study, we conducted alanine-scanning mutagenesis on twelve basic amino acid residues to investigate the anion transport mechanism of SyHR. We quantitatively evaluated the Cl- and SO42- transport activities of the wild-type SyHR and its mutants. The results showed a strong correlation between the Cl- and SO42- transport activities among them (R = 0.94), suggesting a shared pathway for both anions. Notably, the R71A mutation selectively abolished SO42- transport activity while maintaining Cl- transport, whereas the H167A mutation significantly impaired both Cl- and SO42- transport. Further spectroscopic analysis revealed that the R71A mutant lost its ability to bind SO42- due to the absence of a positive charge, while the H167A mutant failed to accumulate the O intermediate during the photoreaction cycle (photocycle) due to reduced hydrophilicity. Additionally, computational analysis revealed the SO42- binding modes and clarified the roles of residues involved in its binding around the retinal chromophore. Based on these findings and previous structural information, we propose that the positive charge and hydrophilicity of Arg71 and His167 are crucial for the formation of the characteristic initial and transient anion-binding site of SyHR, enabling its unique ability to bind and transport both Cl- and SO42-.

Optimizing oil detachment from silica surfaces using gemini surfactants and functionalized silica nanoparticles: a combined molecular dynamics and machine learning approach

The decline in the exploration of new oil sites necessitates the development of efficient strategies to maximize recovery from existing reservoirs. This study employs a molecular dynamics (MD) approach to investigate oil detachment from silica surfaces of varying hydrophobicity using a combination of bis-cationic gemini surfactants (GS) and functionalized silica nanoparticles (SNPs). Density profiles and radial distribution function (rdf) plots revealed a multilayered oil adsorption model. A reduction in oil-silica interaction energy was observed with an increase in surface hydrophobicity, highlighting the importance of polar interactions. Standard waterflooding studies, involving oil detachment solely with water, were conducted to assess baseline recovery efficiency. All the GS-SNP combinations outperformed standard waterflooding methods. SNPs significantly mitigated GS adsorption on reservoir beds, as evidenced by center-of-mass measurements. However, the effectiveness of the added injectants (GS-SNP) went downhill with increasing surface hydrophobicity, further validating the existence of a potential barrier for oil detachment, as known previously. Finally, supervised machine learning (ML) models were generated to predict the GS-SNP combination for a given silica surface, with MD generated descriptors. In most cases, boosting models, viz., XGBoost and AdaBoost yielded the best correlation with the observed data. However, for the complex oil model, ridge regression and support vector regression (SVR) outperformed other ML models in SNP prediction, pointing to the existence of a simpler correlation between the descriptors and the output variable. With these findings, the study attempts to streamline the data-driven design of chemical injectants for enhanced oil recovery purposes.

Surfactant-Free Emulsion Polymerization of Styrene in Ethanol-Water Mixtures: The Role of Mesostructures in the Formation of Polystyrene

Although the synthesis of monodisperse polystyrene nanoparticles (MPSPs) in surfactant-free systems has been widely investigated, the role of mesostructures in forming MPSPs is still unclear. Herein, the styrene/ethanol-water (St/EtOH-H2O) ternary system with certain mesostructures was employed as a model to investigate the correlation between the mesostructure of systems and the properties of polystyrene products. The mesostructures of the ternary system, including styrene droplets, a sponge-like structure, and water droplets, were investigated by transmission electron microscope (TEM) with negative staining, dynamic light scattering (DLS), and nanoparticle tracking analysis (NTA). Classical molecular dynamics (MD) simulations showed the spontaneous aggregation of ethanol at the styrene-water interface, which may be one of the factors stabilizing the mesostructures. We demonstrated that the formation of MPSPs can only occur in the systems containing styrene droplets. Cooling the reactants of styrene droplet systems in the early stages of polymerization resulted in incomplete polystyrene nanospheres (PSNSs), indicating that the formation of MPSPs originated from the interface of styrene droplets. Subsequently, the MPSPs were gradually formed through self-templating polymerization. This study offers valuable insights into the preparation and understanding of the formation process of polymer nanoparticles in other similar surfactant-free systems.

Fluid Transport and Storage Capabilities of Carbon Dioxide through Organic and Inorganic Nanochannels: The Main Influence of Water Saturation

Underground carbon dioxide storage in confined systems becomes a viable alternative to diminish atmospheric concentrations of this gas. Shale reservoirs exhibit mineralogical and pore size heterogeneities that are not deeply analyzed to evaluate the transport and adsorption capacities of carbon dioxide inside their matrix. Functionalized carbon nanotubes and inorganic nanochannels composed of calcite or silicon dioxide are excellent approximations to model the poral throats of the organic and inorganic matrices of shale reservoirs, respectively. In this work, through an extensive molecular dynamics study, we assess the impact on adsorption and transport properties of carboxylic functionalization of the nanochannel surfaces and oxidized inorganic nanochannels, considering only silicon dioxide on pure carbon dioxide and water and carbon dioxide mixtures. We find that the presence of a relevant concentration of carboxylic groups and silicon dioxide on both types of nanochannels significantly reduces the axial velocity of carbon dioxide, owing mainly to their geometrical contributions. Regarding carbon dioxide and water mixtures at different molar fractions, simulations show that there is a relevant increase in water adsorption for both organic and inorganic nanochannels due to strong Coulombic interactions, which partially occlude the available space where carbon dioxide molecules could be adsorbed and displaced. In Figure 1a, we observe how the water molecules nucleate, self-owing to their own Coulombic interactions. On the other hand, in Figure 1b, we observe how this fluid interacts with SiO2, owing to its chemical affinity with the hydrophilic surface. Additionally, based on our findings, the mineralogical composition, the O/C relationship of kerogen, and residual water saturation confined in the nanopores all play a relevant role in defining the storage capacity of carbon dioxide.

Excess density as a descriptor for electrolyte solvent design

Electrolytes mediate interactions between the cathode and anode and determine the performance characteristics of batteries. The mixtures of multiple solvents are often used in electrolytes to achieve the desired properties, such as viscosity, dielectric constant, boiling point, and melting point. Conventionally, multi-component electrolyte properties are approximated with linear mixing, but in practice, significant deviations are observed. Excess quantities can provide insights into the molecular behavior of the mixture and could form the basis for designing high-performance electrolytes. Here, we investigate the excess density of commonly used Li-ion battery solvents, such as cyclic carbonates, linear carbonates, ethers, and nitriles with molecular dynamics simulations. We additionally investigate electrolytes consisting of these solvents and a salt. The results smoothly vary with mole percent and are fit to permutation-invariant Redlich-Kister polynomials. The mixtures of similar solvents, such as cyclic-cyclic carbonate mixtures, tend to have excess properties that are lower in magnitude compared to the mixtures of dissimilar substances, such as carbonate-nitrile mixtures. We perform experimental testing using our automated test stand, Clio, to provide validation to the observed simulation trends. We quantify the structure similarity using smooth overlap of atomic position fingerprints to create a descriptor for excess density, enabling the design of electrolyte properties. To a first approximation, this will allow us to estimate the deviation of a mixture from ideal behavior based solely upon the structural dissimilarity of the components.

Molecular Dynamics Insights into Cyrene's Vapor-Liquid Equilibria and Transport Properties

Since its inception in 2014, Cyrene has emerged as a promising biobased solvent derived from renewable cellulose waste, offering a sustainable alternative to conventional toxic solvents. However, experimental data on its thermodynamic and transport properties remain scarce. This study addresses this critical gap by employing state-of-the-art molecular dynamics simulations. The results provide novel data on Cyrene's phase behavior and fluid dynamics over a wide temperature range (300-700 K) and pressure conditions, including the prediction of critical properties (801 K, 81.04 bar, and 415.389 kg/m3). By leveraging advanced computational techniques, this research elucidates Cyrene's density, diffusion coefficients, and viscosity, with accuracy validated against experimental data where available. These findings enhance our theoretical understanding of Cyrene, supporting its adoption in industrial applications and contributing to the broader agenda of green chemistry. Future work will extend these models to study solvent mixtures and coarse-grained representations, driving further innovation in sustainable solvent design.