The solvation shell probed by resonant intermolecular Coulombic decay

Molecules involved in solvation shells have properties differing from those of the bulk solvent, which can in turn affect reactivity. Among key properties of these molecules are their nature and electronic structure. Widely used tools to characterize this type of property are X-ray-based spectroscopies, which, however, usually lack the capability to selectively probe the solvation-shell molecules. A class of X-ray triggered "non-local" processes has the recognized potential to provide this selectivity. Intermolecular Coulombic decay (ICD) and related processes involve neighbouring molecules in the decay of the X-ray-excited target, and are thus naturally sensitive to its immediate environment. Applying electron spectroscopy to aqueous solutions, we explore the resonant flavours of ICD and demonstrate how it can inform on the first solvation shell of excited solvated cations. One particular ICD process turns out to be a potent marker of the formation of ion pairs. Another gives a direct access to the electron binding energies of the water molecules in the first solvation shell, a quantity previously elusive to direct measurements. The resonant nature of the processes makes them readily measurable, providing powerful new spectroscopic tools.

Single-particle rotational microrheology enables pathological staging of macrophage foaming and antiatherosclerotic studies

The formation of macrophage-derived foam cells has been recognized as the pathological hallmark of atherosclerotic diseases. However, the pathological evolution dynamics and underlying regulatory mechanisms remain largely unknown. Herein, we introduce a single-particle rotational microrheology method for pathological staging of macrophage foaming and antiatherosclerotic explorations by probing the dynamic changes of lysosomal viscous feature over the pathological evolution progression. The principle of this method involves continuous monitoring of out-of-plane rotation-caused scattering brightness fluctuations of the gold nanorod (AuNR) probe-based microrheometer and subsequent determination of rotational relaxation time to analyze the viscous feature in macrophage lysosomes. With this method, we demonstrated the lysosomal viscous feature as a robust pathological reporter and uncovered three distinct pathological stages underlying the evolution dynamics, which are highly correlated with a pathological stage-dependent activation of the NLRP3 inflammasome-involved positive feedback loop. We also validated the potential of this positive feedback loop as a promising therapeutic target and revealed the time window-dependent efficacy of NLRP3 inflammasome-targeted drugs against atherosclerotic diseases. To our knowledge, the pathological staging of macrophage foaming and the pathological stage-dependent activation of the NLRP3 inflammasome-involved positive feedback mechanism have not yet been reported. These findings provide insights into in-depth understanding of evolutionary features and regulatory mechanisms of macrophage foaming, which can benefit the analysis of effective therapeutical drugs as well as the time window of drug treatment against atherosclerotic diseases in preclinical studies.

Topology Effect on Order-Disorder Transition of High-χ Block Copolymers

This work aims to systematically examine the topology effect on the self-assembly of block copolymers. Compositionally, symmetric polystyrene-block-polydimethylsiloxane block copolymers (BCPs) with different chain topologies (diblock, three-arm star-block, and four-arm star-block) and various molecular weights are synthesized. These purposely designed block copolymers are used as a model system to investigate the topology effect on order-to-disorder transition temperature (T ODT) by temperature-resolved small-angle X-ray scattering experiments. An increase of the T ODT is observed when the arm number of BCPs with equivalent arm length (i.e., molecular weight) is increased from one to four. Based on the random-phase approximation (RPA), Flory-Huggins interaction parameter (χ) is determined from the regression of the measured T ODT. The observation by differential scanning calorimetry also demonstrates the shifting of the endothermic peak from the order-to-disorder transition of star-blocks to the higher temperature region, consistent with the scattering experiments and the RPA prediction.

Leveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach

Protein phase transitions (PPTs) from the soluble state to a dense liquid phase (forming droplets via liquid-liquid phase separation) or to solid aggregates (such as amyloids) play key roles in pathological processes associated with age-related diseases such as Alzheimer's disease. Several computational frameworks are capable of separately predicting the formation of droplets or amyloid aggregates based on protein sequences, yet none have tackled the prediction of both within a unified framework. Recently, large language models (LLMs) have exhibited great success in protein structure prediction; however, they have not yet been used for PPTs. Here, we fine-tune a LLM for predicting PPTs and demonstrate its usage in evaluating how sequence variants affect PPTs, an operation useful for protein design. In addition, we show its superior performance compared to suitable classical benchmarks. Due to the "black-box" nature of the LLM, we also employ a classical random forest model along with biophysical features to facilitate interpretation. Finally, focusing on Alzheimer's disease-related proteins, we demonstrate that greater aggregation is associated with reduced gene expression in Alzheimer's disease, suggesting a natural defense mechanism.

A pH-sensitive motif in an outer membrane protein activates bacterial membrane vesicle production

Outer membrane vesicles (OMVs) produced by Gram-negative bacteria have key roles in cell envelope homeostasis, secretion, interbacterial communication, and pathogenesis. The facultative intracellular pathogen Salmonella Typhimurium increases OMV production inside the acidic vacuoles of host cells by changing expression of its outer membrane proteins and modifying the composition of lipid A. However, the molecular mechanisms that translate pH changes into OMV production are not completely understood. Here, we show that the outer membrane protein PagC promotes OMV production through pH-dependent interactions between its extracellular loops and surrounding lipopolysaccharide (LPS). Structural comparisons and mutational studies indicate that a pH-responsive amino acid motif in PagC extracellular loops, containing PagC-specific histidine residues, is crucial for OMV formation. Molecular dynamics simulations suggest that protonation of histidine residues leads to changes in the structure and flexibility of PagC extracellular loops and their interactions with the surrounding LPS, altering membrane curvature. Consistent with that hypothesis, mimicking acidic pH by mutating those histidine residues to lysine increases OMV production. Thus, our findings reveal a mechanism for sensing and responding to environmental pH and for control of membrane dynamics by outer membrane proteins.

Chirality-Induced Magnetic Polarization by Charge Localization in a Chiral Supramolecular Crystal

The chirality-induced spin selectivity (CISS) effect is a fascinating phenomenon that correlates the molecular structure with electron spin-polarization (SP). Experimental procedures to quantify the spin-filtering magnitude have extensively used magnetic-field-dependent conductive AFM. In this work chiral crystals of imide-substituted coronene bisimide ((S)-CBI-GCH) are studied to explain the dynamics of the current-voltage I - V spectra and the origin of superimposed peaks are investigated. A dynamic voltage-sweep rate-dependent phenomenon can give rise to complex I - V curves. The redox group, capable of localization of charge, acts as a localized state that interferes with the continuum of the π - π stacking, giving rise to Fano resonances. A novel mechanism for dynamic transport is introduced, which provides insight into the origin of spin-polarized charge in crystallized CBI-GCH molecules after absorption on a metallic substrate, guided by transient charge polarization. Crucially, interference between charge localization and delocalization during transport may be important properties in understanding the magnetochiral phenomena observed by electrostatic force microscopy. Finally, it is observed that charge trapping sensitively modifies the injection barrier from direct tunneling to Fowler-Nordheim tunneling transport supporting nonlinearity in CISS for this class of molecules.

Machine Learning Approach to Vertical Energy Gap in Redox Processes

A straightforward approach to calculating the free energy change (ΔG) and reorganization energy of a redox process is linear response approximation (LRA). However, accurate prediction of redox properties is still challenging due to difficulties in conformational sampling and vertical energy-gap sampling. Expensive hybrid quantum mechanical/molecular mechanical (QM/MM) calculations are typically employed in sampling energy gaps using conformations from simulations. To alleviate the computational cost associated with the expensive QM method in the QM/MM calculation, we propose machine learning (ML) methods to predict the vertical energy gaps (VEGs). We tested several ML models to predict the VEGs and observed that simple models like linear regression show excellent performance (mean absolute error ∼0.1 eV) in predicting VEGs in all test systems, even when using features extracted from cheaper semiempirical methods. Our best ML model (extra trees regressor) shows a mean absolute error of around 0.1 eV while using features from the cheapest QM method. We anticipate our approach can be generalized to larger macromolecular systems with more complex redox centers.

Profiling of antioxidant properties and identification of potential analgesic inhibitory activities of Allophylus villosus and Mycetia sinensis employing in vivo, in vitro, and computational techniques

ETHNOPHARMACOLOGICAL RELEVANCE

The traditional use of plants for medicinal purposes, called phytomedicine, has been known to provide relief from pain. In Bangladesh, the Chakma indigenous community has been using Allophylus villosus and Mycetia sinensis to treat various types of pain and inflammation.

AIM OF THE STUDY

The object of this research is to evaluate the effectiveness of these plants in relieving pain and their antioxidant properties using various approaches such as in vitro, in vivo, and computational techniques. Additionally, the investigation will also analyse the phytochemicals present in these plants.

MATERIALS AND METHODS

We conducted in vivo analgesic experiment on Swiss albino mice and in-silico inhibitory activities on COX-2 & 15-LOX-2 enzymes. Assessment of DPPH, Anti Radical Activities (ARA), FRAP, H2O2 Free Radical Scavenging, Reducing the power of both plants performed significant % inhibition with tolerable IC50. Qualitative screening of functional groups of phytochemicals was précised by FTIR and GC-MS analysis demonstrated phytochemical investigations.

RESULTS

The ethyl acetate (EtOAc) fractioned Mycetia sinensis extract as well as the ethanoic extract and all fractioned extracts of Allophylus villosus have reported a significant percentage (%) of writhing inhibition (p < 0.05) with the concentrated doses 250 mg as well as 500 mg among the Swiss albino mice for writhing observation of analgesic effect. In the silico observation, a molecular-docking investigation has performed according to GC-MS generated 43 phyto-compounds of both plants to screen their binding affinity by targeting COX-2 and 15-LOX-2 enzymes. Consequently, in order to assess and ascertain the effectiveness of the sorted phytocompounds, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) investigation, DFT (Density-functional theory) by QM (Quantum mechanics), and MDS (Molecular dynamics simulation) were carried out. As the outcome, compounds like 5-(2,4-ditert-butylphenoxy)-5-oxopentanoic acid; 2,4-ditert-butylphenyl 5-hydroxypentanoate; 3,3-diphenyl-5-methyl-3H-pyrazole; 2-O-(6-methylheptan-2-yl) 1-O-octyl benzene-1,2-dicarboxylate and dioctan-3-yl benzene-1,2-dicarboxylate derived from the ethnic plant A. villosus and another ethnic plant M. sinensis extracts enchants magnificent analgesic inhibitions and performed more significant drug like activities with the targeted enzymes.

CONCLUSIONS

Phytocompounds from A. villosus & M. sinensis exhibited potential antagonist activity against human 15-lipoxygenase-2 and cyclooxygenase-2 proteins. The effective ester compounds from these plants performed more potential anti-nociceptive activity which could be used as a drug in future.

The LOD paradox: When lower isn't always better in biosensor research and development

Biosensor research has long focused on achieving the lowest possible Limits of Detection (LOD), driving significant advances in sensitivity and opening up new possibilities in analysis. However, this intense focus on low LODs may not always meet the practical needs or suit the actual uses of these devices. While technological improvements are impressive, they can sometimes overlook important factors such as detection range, ease of use, and market readiness, which are vital for biosensors to be effective in real-world applications. This review advocates for a balanced approach to biosensor development, emphasizing the need to align technological advancements with practical utility. We delve into various applications, including the detection of cancer biomarkers, pathology-related biomarkers, and illicit drugs, illustrating the critical role of LOD within these contexts. By considering clinical needs and broader design aspects like cost-effectiveness, sustainability, and regulatory compliance, we argue that integrating technical progress with practicality will enhance the impact of biosensors. Such an approach ensures that biosensors are not only technically sound but also widely useable and beneficial in real-world applications. Addressing the diverse analytical parameters alongside user expectations and market demands will likely maximize the real-world impact of biosensors.

Distribution of Single-Particle Resonances Determines the Plasmonic Response of Disordered Nanoparticle Ensembles

Understanding how colloidal soft materials interact with light is crucial to the rational design of optical metamaterials. Electromagnetic simulations are computationally expensive and have primarily been limited to model systems described by a small number of particles-dimers, small clusters, and small periodic unit cells of superlattices. In this work we study the optical properties of bulk, disordered materials comprising a large number of plasmonic colloidal nanoparticles using Brownian dynamics simulations and the mutual polarization method. We investigate the far-field and near-field optical properties of both colloidal fluids and gels, which require thousands of nanoparticles to describe statistically. We show that these disordered materials exhibit a distribution of particle-level plasmonic resonance frequencies that determines their ensemble optical response. Nanoparticles with similar resonant frequencies form anisotropic and oriented clusters embedded within the otherwise isotropic and disordered microstructures. These collectively resonating morphologies can be tuned with the frequency and polarization of incident light. Knowledge of particle resonant distributions may help to interpret and compare the optical responses of different colloidal structures, correlate and predict optical properties, and rationally design soft materials for applications harnessing light.

Deciphering the Thermal Stability of Bacteriophage MS2-Derived Virus-like Particle and Its Engineered Variant

RNA bacteriophage MS2-derived virus-like particles (VLPs) have been widely used in biomedical research as model systems to study virus assembly, structure-function relationships, vaccine development, and drug delivery. Considering the diverse utility of these VLPs, a systemic engineering approach has been utilized to generate smaller particles with optimal serum stability and tissue penetrance. Additionally, it is crucial to demonstrate the overall stability of these mini MS2 VLPs, ensuring cargo protection until they reach their target cell/organ. However, no detailed analysis of the thermal stability and heat-induced disassembly of MS2 VLPs has yet been attempted. In this work, we investigated the thermal stability of both wild-type (WT) MS2 VLP and its "mini" variant containing S37P mutation (mini MS2 VLP). The mini MS2 VLP exhibits a higher capsid melting temperature (Tm) when compared to its WT MS2 VLP counterpart, possibly attributed to its smaller interdimer angle. Our study presents that the thermal unfolding of MS2 VLPs follows a sequential process involving particle destabilization, nucleic acid exposure/melting, and disassembly of VLP. This observation underscores the disruption of cooperative intersubunit interactions and protein-nucleic acid interactions, shedding light on the mechanism of heat-induced VLP disassembly.

Synthesis of nano-SiO2@PTPEG-VPA copolymer and its effects on early-age cement hydration

Incorporating nano-SiO2 particles into cement paste has garnered significant attention for enhancing the performance of hardened cement paste. However, the agglomeration of nanoparticles in the pore solution of cement-water system poses a challenge for cost-effective and efficient applications. Meanwhile, superplasticizers containing phosphate groups exhibit strong complexation with calcium ions and show promise in improving the dispersion performance. This study introduces a surface chemical modification technique to enhance the dispersibility of nano-SiO2. Firstly, poly(isoprenyl oxy poly(ethylene glycol) ether-random-vinylphosphonic acid) (PTPEG-VPA), a silanized superplasticizer containing phosphate moieties, is copolymerized and chemically grafted onto pristine nano-SiO2 surfaces through condensation and silanization processes. The resulting core-shell SiO2@PTPEG-VPA nanoparticles are comprehensively characterized using FT-IR spectroscopy, TGA, DLS, TEM, BET surface area analysis, and zeta potential measurements. The results indicate that introducing phosphate moieties improves the dispersion capacity of grafted copolymers, thereby reducing the severe agglomeration of nano-SiO2 in solution. Subsequently, the impact of SiO2@PTPEG-VPA on cement hydration and early-age strength development is investigated using microcalorimetry and TGA characterization. Finally, a mechanism is proposed to explain the observed retarding effects of grafted PTPEG-VPA on pristine SiO2. Overall, this study provides novel insights into the chemical design of nanoparticles, aimed at manipulating cement paste properties.

Accurately Modeling RNA Stem-Loops in an Implicit Solvent Environment

Ribonucleic acid (RNA) molecules can adopt a variety of secondary and tertiary structures in solution, with stem-loops being one of the more common motifs. Here, we present a systematic analysis of 15 RNA stem-loop sequences simulated with molecular dynamics simulations in an implicit solvent environment. Analysis of RNA cluster ensembles showed that the stem-loop structures can generally adopt the A-form RNA in the stem region. Loop structures are more sensitive, and experimental structures could only be reproduced with modification of CH···O interactions in the force field, combined with an implicit solvent nonpolar correction to better model base stacking interactions. Accurately modeling RNA with current atomistic physics-based models remains challenging, but the RNA systems studied herein may provide a useful benchmark set for testing other RNA modeling methods in the future.

Aqueous Ionic Liquid Mixtures as Minimal Models of Lipid Bilayer Membranes

We introduce aqueous ionic liquid (IL) mixtures, specifically mixtures of 1-butyl-3-imidazoliumtetrafluoroborate (BMImBF4), with water as a minimal model of lipid bilayer membranes. Imidazolium-based ILs are known to form clustered nanoscale structures in which local inhomogeneities, micellar or lamellar structures, are formed to shield hydrophobic parts of the cation from the polar cosolvent (water). To investigate these nanostructures, dynamic light scattering (DLS) on samples with different mixing ratios of water and BMImBF4 was performed. At mixing ratios of 50% and 45% (v/v), small and homogeneous nanostructures can indeed be detected. To test whether, in particular, these stable nanostructures in aqueous mixtures may mimic the effects of phospholipid bilayer membranes, we further investigated their interaction with myelin basic protein (MBP), a peripheral, intrinsically disordered membrane protein of the myelin sheath. Using dynamic light scattering (DLS), continuous wave (CW) and pulse electron paramagnetic resonance (EPR), and small-angle X-ray scattering (SAXS) on recombinantly produced, "healthy" charge variants rmC1WT and double cysteine variant C1S17CH85C, we find that the size and the shape of the determined nanostructures in an optimum mixture offer model membranes in which the protein exhibits native behavior. SAXS measurements illuminate the size and shape of the nanostructures and indicate IL-rich "beads" clipped together by functional MBP, one of the in vivo roles of the protein in the myelin sheath. All the gathered data combined indicate that the 50% and 45% aqueous IL mixtures can be described as offering minimal models of a lipid mono- or bilayer that allow native processing and potential study of at least peripheral membrane proteins like MBP.

Five-Site Water Models for Ice and Liquid Water Generated by a Series-Parallel Machine Learning Strategy

Icing, a common natural phenomenon, always originates from a molecule. Molecular simulation is crucial for understanding the relevant process but still faces a great challenge in obtaining a uniform and accurate description of ice and liquid water with limited model parameters. Here, we propose a series-parallel machine learning (ML) approach consisting of a classification back-propagation neural network (BPNN), parallel regression BPNNs, and a genetic algorithm to establish conventional TIP5P-BG and temperature-dependent TIP5P-BGT models. The established water models exhibit a comprehensive balance among the crucial physical properties (melting point, density, vaporization enthalpy, self-diffusion coefficient, and viscosity) with mean absolute percentage errors of 2.65 and 2.40%, respectively, and excellent predictive performance on the related properties of liquid water. For ice, the simulation results on the critical nucleus size and growth rate are in good accordance with experiments. This work offers a powerful molecular model for phase transition and icing in nanoconfinement and a construction strategy for a complex molecular model in the extreme case.

Catalyst-Free Transformation of Carbon Dioxide to Small Organic Compounds in Water Microdroplets Nebulized by Different Gases

A straightforward nebulized spray system is designed to explore the hydrogenation of carbon dioxide (CO2) within water microdroplets surrounded by different gases such as carbon dioxide, nitrogen, oxygen, and compressed air. The collected droplets are analyzed using water-suppressed nuclear magnetic resonance (NMR). Formate anion (HCOO-), acetate anion (CH3COO-), ethylene glycol (HOCH2CH2OH), and methane (CH4) are detected when water is nebulized. This pattern persisted when the water is saturated with CO2, indicating that CO2 in the nebulizing gas triggers the formation of these small organics. In a pure CO2 atmosphere, the formate anion concentration is determined to be ≈70 µm, referenced to dimethyl sulfoxide, which has been introduced as an internal standard in the collected water droplets. This study highlights the power of water microdroplets to initiate unexpected chemistry for the transformation of CO2 to small organic compounds.

Study of chemical reactivity and molecular interactions of the hydrochlorothiazide-4-aminobenzoic acid cocrystal using spectroscopic and quantum chemical approaches

In this study, the molecular, electronic, and chemical properties of the drug hydrochlorothiazide (HCTZ) are determined after cocrystallization with 4-aminobenzoic acid (4-ABA). Analysis has been performed to understand how those variations lead to alteration of physical properties and chemical reactivity in the cocrystal HCTZ-4ABA. IR and Raman characterizations were performed along with quantum chemical calculations. A theoretical investigation of hydrogen bonding interactions in HCTZ-4ABA has been conducted using two functionals: B3LYP and wB97X-D. The results obtained by B3LYP and wB97X-D are compared which leads to the conclusion that B3LYP is the best applied function (density functional theory) to obtain suitable results for spectroscopy. The chemical reactivity descriptors are used to understand various aspects of pharmaceutical properties. Natural bond orbital (NBO) analysis and quantum theory of atoms (QTAIM) are used to analyze nature and strength of hydrogen bonding in HCTZ-4ABA. QTAIM analyzed moderate role of hydrogen bonding interactions in HCTZ-4ABA. The calculated HOMO-LUMO energy gap shows that HCTZ-4ABA is chemically more active than HCTZ drug. These chemical parameters suggest that HCTZ-4ABA is chemically more reactive and softer than HCTZ. The results of this study suggest that cocrystals can be a good alternative for enhancing physicochemical properties of a drug without altering its therapeutic properties.

A low-barrier proton shared between two aspartates acts as a conformational switch that changes the substrate specificity of the β-lactamase BlaC

Serine β-lactamases inactivate β-lactam antibiotics in a two-step mechanism comprising acylation and deacylation. For the deacylation step, a water molecule is activated by a conserved glutamate residue to release the adduct from the enzyme. The third-generation cephalosporin ceftazidime is a poor substrate for the class A β-lactamase BlaC from Mycobacterium tuberculosis but it can be hydrolyzed faster when the active site pocket is enlarged, as was reported for mutant BlaC P167S. The conformational change in the Ω-loop of the P167S mutant displaces the conserved glutamate (Glu166), suggesting it is not required for deacylation of the ceftazidime adduct. Here, we report the characterization of wild type BlaC and BlaC E166A at various pH values. The presence of Glu166 strongly enhances activity against nitrocefin but not ceftazidime, indicating it is indeed not required for deacylation of the adduct of the latter substrate. At high pH wild type BlaC was found to exist in two states, one of which converts ceftazidime much faster, resembling the open state previously reported for the BlaC mutant P167S. The pH-dependent switch between the closed and open states is caused by the loss at high pH of a low-barrier hydrogen bond, a proton shared between Asp172 and Asp179. These results illustrate how readily shifts in substrate specificity can occur as a consequence of subtle changes in protein structure.

Near-infrared pH-switchable BODIPY photosensitizers for dual biotin/cRGD targeted photodynamic therapy

Photodynamic therapy (PDT) is a clinically-approved cancer treatment that is based on production of cytotoxic reactive oxygen species to induce cell death. However, its efficiency depends on distribution of photosensitizer (PS) and depth of light penetration through the tissues. Tendency of pathological cancer tissues to exhibit lower pH than healthy tissues inspired us to explore dual-targeted pH-activatable photosensitizers based on tunable near-infrared (NIR) boron-dipyrromethene (BODIPY) dyes. Our BODIPY PSs were designed to carry three main attributes: (i) biotin or cRGD peptide as an effective cancer cell targeting unit, (ii) amino moiety that is protonated in acidic (pH <6.5) conditions for pH-activation of the PS based on photoinduced electron transfer (PET) and (iii) hydrophilic groups enhancing the water solubility of very hydrophobic BODIPY dyes. Illumination of such compounds with suitable light (>640nm) allowed for high phototoxicity against HeLa (αvβ3 integrin and biotin receptor positive) and A549 (biotin receptor positive) cells compared to healthy MRC-5 (biotin negative) cells. Moreover, no dark toxicity was observed on selected cell lines (>10 μM) providing promising photosensitizers for tumour-targeted photodynamic therapy.

Dealing with plasmonic crystal biosensors: Sensitivity assessment of nanodisks/nanoholes arrayed plasmonic system for label-free DNA detection

Label-free optical deoxyribonucleic acid (DNA) sensing with arrayed plasmonic nanostructures (plasmonic crystals) is a promising technology for biomedical diagnosis and bioanalytical science. Plasmonic biosensors can detect target biomolecules by utilizing the shift in plasmonic resonance caused by changes in the surrounding refractive index (RI) attributed to the capture of target biomolecules using a recognizer. Conventional explanations for the sensitivity of plasmonic crystals are based on bulk (BRIS) and surface RI sensitivities (SRIS) for basic plasmonic nanoparticles despite their unique properties such as surface lattice resonances (SLRs), wherein localized surface plasmons (LSPs) cooperatively oscillate with their pitch. Therefore, investigating the sensitivity of SLRs is imperative for improving sensing performance. In this study, the sensitivity of adenomatous polyposis coli (APC) gene-related DNA hybridization detection of complementary plasmonic crystals composed of nanodisks (PNDs) on or under plasmonic nanoholes (PNHs) was investigated considering the SLR properties. The BRIS was measured using the conventional definition of the peak wavelength shift per unit RI increment (nm/RIU) followed by the SRIS measurement using the layer-by-layer method. The BRIS and SRIS measurements reflect the practical sensitivity for DNA detection. PNHs had higher sensitivity than PNDs, with a limit of detection of 0.30 nM. Further, only the SLR-based mode responded to localized RI changes because of DNA hybridization, whereas both the LSPs- and SLR-based modes responded to uniform RI changes caused by layer-by-layer coating. Our investigation will open up possibilities and opportunities for plasmonic crystal biosensors.

The physico-chemical and antimicrobial properties of nano ZnO functionalised tannic acid

Tannic acid (TA) has been reported as an efficient plant-based compound with inhibitory activity against viruses and bacteria. The combination of TA with Zinc Oxide (ZnO) nanostructures with ZnO is one of the most widely used nanoparticles for antimicrobial properties, have not yet fully elucidate especially their mechanisms of overall physicochemical and antimicrobial actions. Hence, to observe the influence of TA adsorption on ZnO, the investigations on the TA concentration and the effect of pH towards the physicochemical, optical and antimicrobial properties are demonstrated. The pure ZnO are synthesised via the chemical reduction method and the ZnO-TA nanostructures are further prepared using the dropwise methods to form variations of pH samples, which causes the formation of different mean particle size distribution, d m . The findings reveal that the performance of physicochemical and optical properties of pure ZnO and ZnO-TA are different due to the wrapped layers of TA which change the charged surface of all the particles. The protonation reactions yield strong pH dependence (pH 3 and 5), with uptake performance becoming more dominant at higher TA concentration loading (pH 3). The detailed optical energy bandgap and Urbach energy that concluded the nanoparticle growth and disorder condition of produced particles are presented. For antimicrobial efficiency, ZnO-TA shows improved effectiveness in growth inhibitions of S. aureus 99.69% compared to pure ZnO nanostructure (99.39%). This work reveals that the TA concentration increases the overall performance, and the discussion gives added support to their potential performance related to the field of ZnO compound.

In vivo ElectroChromic Shift measurements of photosynthetic activity in far-red absorbing cyanobacteria

Some cyanobacteria can do photosynthesis using not only visible but also far-red light that is unused by most other oxygenic photoautotrophs because of its lower energy content. These species have a modified photosynthetic apparatus containing red-shifted pigments. The incorporation of red-shifted pigments decreases the photochemical efficiency of photosystem I and, especially, photosystem II, and it might affect the distribution of excitation energy between the two photosystems with possible consequences on the activity of the entire electron transport chain. To investigate the in vivo effects on photosynthetic activity of these pigment changes, we present here the adaptation of a spectroscopic method, based on a physical phenomenon called ElectroChromic Shift (ECS), to the far-red absorbing cyanobacteria Acaryochloris marina and Chroococcidiopsis thermalis PCC7203. ECS measures the electric field component of the trans-thylakoid proton motive force generated by photosynthetic electron transfer. We show that ECS can be used in these cyanobacteria to investigate in vivo the stoichiometry of photosystem I and photosystem II and their absorption cross-section, as well as the overall efficiency of light energy conversion into electron transport. Our results indicate that both species use visible and far-red light with similar efficiency, despite significant differences in their light absorption characteristics. ECS thus represents a new non-invasive tool to study the performance of naturally occurring far-red photosynthesis.

Magnesium induced structural reorganization in the active site of adenylate kinase

Phosphoryl transfer is a fundamental reaction in cellular signaling and metabolism that requires Mg2+ as an essential cofactor. While the primary function of Mg2+ is electrostatic activation of substrates, such as ATP, the full spectrum of catalytic mechanisms exerted by Mg2+ is not known. In this study, we integrate structural biology methods, molecular dynamic (MD) simulations, phylogeny, and enzymology assays to provide molecular insights into Mg2+-dependent structural reorganization in the active site of the metabolic enzyme adenylate kinase. Our results demonstrate that Mg2+ induces a conformational rearrangement of the substrates (ATP and ADP), resulting in a 30° adjustment of the angle essential for reversible phosphoryl transfer, thereby optimizing it for catalysis. MD simulations revealed transitions between conformational substates that link the fluctuation of the angle to large-scale enzyme dynamics. The findings contribute detailed insight into Mg2+ activation of enzymes and may be relevant for reversible and irreversible phosphoryl transfer reactions.

Chemography-guided analysis of a reaction path network for ethylene hydrogenation with a model Wilkinson's catalyst

Visualization and analysis of large chemical reaction networks become rather challenging when conventional graph-based approaches are used. As an alternative, we propose to use the chemical cartography ("chemography") approach, describing the data distribution on a 2-dimensional map. Here, the Generative Topographic Mapping (GTM) algorithm - an advanced chemography approach - has been applied to visualize the reaction path network of a simplified Wilkinson's catalyst-catalyzed hydrogenation containing some 105 structures generated with the help of the Artificial Force Induced Reaction (AFIR) method using either Density Functional Theory or Neural Network Potential (NNP) for potential energy surface calculations. Using new atoms permutation invariant 3D descriptors for structure encoding, we've demonstrated that GTM possesses the abilities to cluster structures that share the same 2D representation, to visualize potential energy surface, to provide an insight on the reaction path exploration as a function of time and to compare reaction path networks obtained with different methods of energy assessment.

Cavity-enhanced energy transport in molecular systems

Molecules are the building blocks of all of nature's functional components, serving as the machinery that captures, stores and releases energy or converts it into useful work. However, molecules interact with each other over extremely short distances, which hinders the spread of energy across molecular systems. Conversely, photons are inert, but they are fast and can traverse large distances very efficiently. Using optical resonators, these distinct entities can be mixed with each other, opening a path to new architectures that benefit from both the active nature of molecules and the long-range transport obtained by the coupling with light. In this Review, we present the physics underlying the enhancement of energy transfer and energy transport in molecular systems, and highlight the experimental and theoretical advances in this field over the past decade. Finally, we identify several key questions and theoretical challenges that remain to be resolved via future research.

Cracking the Code: Reprogramming the Genetic Script in Prokaryotes and Eukaryotes to Harness the Power of Noncanonical Amino Acids

Over 500 natural and synthetic amino acids have been genetically encoded in the last two decades. Incorporating these noncanonical amino acids into proteins enables many powerful applications, ranging from basic research to biotechnology, materials science, and medicine. However, major challenges remain to unleash the full potential of genetic code expansion across disciplines. Here, we provide an overview of diverse genetic code expansion methodologies and systems and their final applications in prokaryotes and eukaryotes, represented by Escherichia coli and mammalian cells as the main workhorse model systems. We highlight the power of how new technologies can be first established in simple and then transferred to more complex systems. For example, whole-genome engineering provides an excellent platform in bacteria for enabling transcript-specific genetic code expansion without off-targets in the transcriptome. In contrast, the complexity of a eukaryotic cell poses challenges that require entirely new approaches, such as striving toward establishing novel base pairs or generating orthogonally translating organelles within living cells. We connect the milestones in expanding the genetic code of living cells for encoding novel chemical functionalities to the most recent scientific discoveries, from optimizing the physicochemical properties of noncanonical amino acids to the technological advancements for their in vivo incorporation. This journey offers a glimpse into the promising developments in the years to come.

Ultrafast studies of elusive chemical reactions in the gas phase

The chemical composition of the interstellar medium and planetary atmospheres is constantly in flux as atoms and molecules collide and interact with high-energy particles such as electrons, protons, and photons. These transformative processes ultimately lead to the coalescence of molecules and eventually the birth of stars. Our understanding of these chemical ecosystems relies on models that synthesize data from gas-phase experiments, providing insights into reaction cross sections. This Review examines efforts to delve into the fundamental bond-forming and bond-breaking dynamics that occur during bimolecular and electron-initiated reactions. These experiments involve clever approaches to establish a time reference and the collision geometry necessary for tracking atomic motion with femtosecond time resolution. Findings from these efforts enhance present models and improve predictions for molecule-molecule and electron-molecule collisions.

Accurate and interpretable representation of correlated electronic structure via Tensor Product Selected CI

The task of computing wavefunctions that are accurate, yet simple enough mathematical objects to use for reasoning, has long been a challenge in quantum chemistry. The difficulty in drawing physical conclusions from a wavefunction is often related to the generally large number of configurations with similar weights. In Tensor Product Selected Configuration Interaction (TPSCI), we use a locally correlated tensor product state basis, which has the effect of concentrating the weight of a state onto a smaller number of physically interpretable degrees of freedom. In this paper, we apply TPSCI to a series of three molecular systems ranging in separability, one of which is the first application of TPSCI to an open-shell bimetallic system. For each of these systems, we obtain accurate solutions to large active spaces, and analyze the resulting wavefunctions through a series of different approaches including (i) direct inspection of the TPS basis coefficients, (ii) construction of Bloch effective Hamiltonians, and (iii) computation of cluster correlation functions.

Diffusion and Interaction Effects On Molecular Release Kinetics From Collapsed Microgels

The efficient transport of small molecules through dense hydrogel networks is crucial for various applications, including drug delivery, biosensing, catalysis, nanofiltration, water purification, and desalination. In dense polymer matrices, such as collapsed microgels, molecular transport follows the solution-diffusion principle: Molecules dissolve in the polymeric matrix and subsequently diffuse due to a concentration gradient. Employing dynamical density functional theory (DDFT), we investigate the nonequilibrium release kinetics of nonionic subnanometer-sized molecules from a microgel particle, using parameters derived from prior molecular simulations of a thermoresponsive hydrogel. The kinetics is primarily governed by the microgel radius and two intensive parameters: the diffusion coefficient and solvation free energy of the molecule. Our results reveal two limiting regimes: a diffusion-limited regime for large, slowly diffusing, and poorly soluble molecules within the hydrogel; and a reaction-limited regime for small, rapidly diffusing, and highly soluble molecules. These principles allow us to derive an analytical equation for release time, demonstrating excellent quantitative agreement with the DDFT results-a valuable and straightforward tool for predicting release kinetics from microgels.

Lewis Acid-Base Pairs: The Bonding Rule of Closed-Shell M···M' Interactions (M = HgII/PdII; M' = AgI/AuI)

Metallophilic interactions are the widespread interactions in multimetal clusters to orientate closed-shell metal self-assembly form linear, facet, or block clusters. The closed-shell metal cation does not have empty valence orbitals, but is able to attract each other. It is still a conundrum to understand the resource in balancing the strong Coulomb repulsion between two cations. Most traditional descriptions attribute the counterintuitive attractions to London dispersion, Pauli repulsions, and ambiguous orbital interactions. However, neither the dispersion nor the unsourced donor-acceptor interaction can be applied to explain the saturability and directionality in multimetal clusters, where the M···M' structure is the basic molecular unit. Here, we clarify the origination of the covalency in closed-shell metallophilic interactions based on the study of heterobimetallic compounds composed of d10-d8 species (AgI/AuI-PdII) and d10-d10 species (AgI/AuI-HgII) obtained from experiments. The inner d electrons not only participate in the metallophilic interactions but also show different Lewis acidity and basicity in the formation of M···M' structures. The present work not only provides us a novel covalent perspective to visualize the closed-shell M···M' interactions but also unveils the truth of metallophilic interactions.

Toward an ab Initio Description of Adsorbate Surface Dynamics

The advent of machine learning potentials (MLPs) provides a unique opportunity to access simulation time scales and to directly compute physicochemical properties that are typically intractable using density functional theory (DFT). In this study, we use an active learning curriculum to train a generalizable MLP using the DeepMD-kit architecture. By using sufficiently long MLP-based molecular dynamics (MD) simulations, which provide DFT-level accuracy, we investigate the diffusion of key surface-bound adsorbates on a Ag(111) facet. Detailed analysis of the MLP/MD-calculated diffusivities sheds light on the potential shortcomings of using DFT-based nudged elastic band to estimate surface diffusion barriers. More generally, while this study is focused on a specific system, we anticipate that the underlying workflows and the resulting models can be extended to other adsorbates and other materials in the future.

Microglial-derived C1q integrates into neuronal ribonucleoprotein complexes and impacts protein homeostasis in the aging brain

Neuroimmune interactions mediate intercellular communication and underlie critical brain functions. Microglia, CNS-resident macrophages, modulate the brain through direct physical interactions and the secretion of molecules. One such secreted factor, the complement protein C1q, contributes to complement-mediated synapse elimination in both developmental and disease models, yet brain C1q protein levels increase significantly throughout aging. Here, we report that C1q interacts with neuronal ribonucleoprotein (RNP) complexes in an age-dependent manner. Purified C1q protein undergoes RNA-dependent liquid-liquid phase separation (LLPS) in vitro, and the interaction of C1q with neuronal RNP complexes in vivo is dependent on RNA and endocytosis. Mice lacking C1q have age-specific alterations in neuronal protein synthesis in vivo and impaired fear memory extinction. Together, our findings reveal a biophysical property of C1q that underlies RNA- and age-dependent neuronal interactions and demonstrate a role of C1q in critical intracellular neuronal processes.

Electronic Structure Methods for Simulating Flavin's Spectroscopy and Photophysics: Comparison of Multi-reference, TD-DFT, and Single-Reference Wave Function Methods

The use of flavins and flavoproteins in photocatalytic, sensing, and biotechnological applications has led to a growing interest in computationally modeling the excited-state electronic structure and photophysics of flavin. However, there is limited consensus regarding which computational methods are appropriate for modeling flavin's photophysics. We compare the energies of low-lying excited states of flavin computed with time-dependent density functional theory (TD-DFT), equation-of-motion coupled cluster (EOM-EE-CCSD), scaled opposite-spin configuration interaction [SOS-CIS(D)], multiconfiguration pair-density functional theory (MC-PDFT), and several multireference perturbation theory (MR-PT2) methods. In the first part, we focus on excitation energies of the first singlet excited state (S1) of five different redox and protonation states of flavin, with the goal of finding a suitable active space for MR-PT2 calculations. In the second part, we construct two sets of one-dimensional potential energy surfaces connecting the S0 and S1 equilibrium geometries (S0-S1 path) and the S1 (π,π*) and S2 (n,π*) equilibrium geometries (S1-S2 path). The first path therefore follows a Franck-Condon active mode of flavin while the second path maps crossings points between low-lying singlet and triplet states in flavin. We discuss the similarities and differences in the TD-DFT, EOM-EE-CCSD, SOS-CIS(D), MC-PDFT and MR-PT2 energy profiles along these paths. We find that (TD-)DFT methods are suitable for applications such as simulating the spectra of flavins but are inconsistent with several other methods when used for some geometry optimizations and when describing the energetics of dark (n,π*) states. MR-PT2 methods show promise for the simulation of flavin's low-lying excited states, but the selection of orbitals for the active space and the number of roots used for state averaging must be done carefully to avoid artifacts. Some properties, such as the intersystem crossing geometry and energy between the S1 (π,π*) and T2 (n,π*) states, may require additional benchmarking before they can be determined quantitatively.

Addressing spatiotemporal signal variations in pair correlation function analysis

Fluorescence correlation spectroscopy (FCS) is a cornerstone technique in optical microscopy to measure, for example, the concentration and diffusivity of fluorescent emitters and biomolecules in solution. The application of FCS to complex biological systems, however, is fraught with inherent intricacies that impair the interpretation of correlation patterns. Critical among these intricacies are temporal variations beyond diffusion in the quantity, intensity, and spatial distribution of fluorescent emitters. These variations introduce distortions into correlated intensity data, thus compromising the accuracy and reproducibility of the analysis. This issue is accentuated in imaging-based approaches such as pair correlation function (pCF) analysis due to their broader regions of interest compared with point-detector-based approaches. Despite ongoing developments in FCS, attention to systems characterized by a spatiotemporal-dependent probability distribution function (ST-PDF) has been lacking. To address this knowledge gap, we developed a new analytical framework for ST-PDF systems that introduces a dual-timescale model function within the conventional pCF analysis. Our approach selectively differentiates the signals associated with rapid processes, such as particle diffusion, from signals stemming from spatiotemporal variations in the distribution of fluorescent emitters occurring at extended delay timescales. To corroborate our approach, we conducted proof-of-concept experiments on an ST-PDF system, wherein the, initially, uniform distribution of fluorescent microspheres within a microfluidic channel changes into a localized accumulation of microspheres over time. Our framework is offering a comprehensive solution for investigating various phenomena such as biomolecular binding, sedimentation, and particle accumulation.

Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex

Dynamic processes involving biomolecules are essential for the function of the cell. Here, we introduce an integrative method for computing models of these processes based on multiple heterogeneous sources of information, including time-resolved experimental data and physical models of dynamic processes. We first compute integrative structure models at fixed time points and then optimally select and connect these snapshots into a series of trajectories that optimize the likelihood of both the snapshots and transitions between them. The method is demonstrated by application to the assembly process of the human Nuclear Pore Complex in the context of the reforming nuclear envelope during mitotic cell division, based on live-cell correlated electron tomography, bulk fluorescence correlation spectroscopy-calibrated quantitative live imaging, and a structural model of the fully-assembled Nuclear Pore Complex. Modeling of the assembly process improves the model precision over static integrative structure modeling alone. The method is applicable to a wide range of time-dependent systems in cell biology, and is available to the broader scientific community through an implementation in the open source Integrative Modeling Platform software.

Quantifying surface tension and viscosity in biomolecular condensates by FRAP-ID

Many proteins with intrinsically disordered regions undergo liquid-liquid phase separation under specific conditions in vitro and in vivo. These complex biopolymers form a metastable phase with distinct mechanical properties defining the timescale of their biological functions. However, determining these properties is nontrivial, even in vitro, and often requires multiple techniques. Here we report the measurement of both viscosity and surface tension of biomolecular condensates via correlative fluorescence microscopy and atomic force microscopy (AFM) in a single experiment (fluorescence recovery after probe-induced dewetting, FRAP-ID). Upon surface tension evaluation via regular AFM-force spectroscopy, controlled AFM indentations induce dry spots in fluorescent condensates on a glass coverslip. The subsequent rewetting exhibits a contact line velocity that is used to quantify the condensed-phase viscosity. Therefore, in contrast with fluorescence recovery after photobleaching (FRAP), where molecular diffusion is observed, in FRAP-ID fluorescence recovery is obtained through fluid rewetting and the subsequent morphological relaxation. We show that the latter can be used to cross-validate viscosity values determined during the rewetting regime. Making use of fluid mechanics, FRAP-ID is a valuable tool to evaluate the mechanical properties that govern the dynamics of biomolecular condensates and determine how these properties impact the temporal aspects of condensate functionality.

Kinetic monitoring of molecular interactions during surfactant-driven self-propelled droplet motion by high spatial resolution waveguide sensing

HYPOTHESIS

Self-driven actions, like motion, are fundamental characteristics of life. Today, intense research focuses on the kinetics of droplet motion. Quantifying macroscopic motion and exploring the underlying mechanisms are crucial in self-structuring and self-healing materials, advancements in soft robotics, innovations in self-cleaning environmental processes, and progress within the pharmaceutical industry. Usually, the driving forces inducing macroscopic motion act at the molecular scale, making their real-time and high-resolution investigation challenging. Label-free surface sensitive measurements with high lateral resolution could in situ measure both molecular-scale interactions and microscopic motion.

EXPERIMENTS

We employ surface-sensitive label-free sensors to investigate the kinetic changes in a self-assembled monolayer of the trimethyl(octadecyl)azanium chloride surfactant on a substrate surface during the self-propelled motion of nitrobenzene droplets. The adsorption-desorption of the surfactant at various concentrations, its removal due to the moving organic droplet, and rebuilding mechanisms at droplet-visited areas are all investigated with excellent time, spatial, and surface mass density resolution.

FINDINGS

We discovered concentration dependent velocity fluctuations, estimated the adsorbed amount of surfactant molecules, and revealed multilayer coverage at high concentrations. The desorption rate of surfactant (18.4 s-1) during the microscopic motion of oil droplets was determined by in situ differentiating between droplet visited and non-visited areas.

Simulation of the Pump-Probe Spectra and Excitation Energy Relaxation of the B850 Band of the LH2 Complex in Purple Bacteria

Ultrafast spectroscopic techniques have been vital in studying excitation energy transfer (EET) in photosynthetic light harvesting complexes. In this paper, we simulate the pump-probe spectra of the B850 band of the light harvesting complex 2 (LH2) of purple bacteria, by using the hierarchical equation of motion method and the optical response function approach. The ground state bleach, stimulated emission, and excited state absorption components of the pump-probe spectra are analyzed in detail. The laser pulse-induced population dynamics are also simulated to help understand the main features of the pump-probe spectra and the EET process. It is shown that the excitation energy relaxation is an ultrafast process with multiple time scales. The first 40 fs of the pump-probe spectra is dominated by the relaxation of the k = ±1 states to both the k = 0 and higher energy states. Dynamics on a longer time scale around 200 fs reflects the relaxation of higher energy states to the k = 0 state.

Combined Multireference-Multiscale Approach to the Description of Photosynthetic Reaction Centers

A first-principles description of the primary photochemical processes that drive photosynthesis and sustain life on our planet remains one of the grand challenges of modern science. Recent research established that explicit incorporation of protein electrostatics in excited-state calculations of photosynthetic pigments, achieved for example with quantum-mechanics/molecular-mechanics (QM/MM) approaches, is essential for a meaningful description of the properties and function of pigment-protein complexes. Although time-dependent density functional theory has been used productively so far in QM/MM approaches for the study of such systems, this methodology has limitations. Here we pursue for the first time a QM/MM description of the reaction center in the principal enzyme of oxygenic photosynthesis, Photosystem II, using multireference wave function theory for the high-level QM region. We identify best practices and establish guidelines regarding the rational choice of active space and appropriate state-averaging for the efficient and reliable use of complete active space self-consistent field (CASSCF) and the N-electron valence state perturbation theory (NEVPT2) in the prediction of low-lying excited states of chlorophyll and pheophytin pigments. Given that the Gouterman orbitals are inadequate as a minimal active space, we define specific minimal and extended active spaces for the NEVPT2 description of electronic states that fall within the Q and B bands. Subsequently, we apply our multireference-QM/MM protocol to the description of all pigments in the reaction center of Photosystem II. The calculations reproduce the electrochromic shifts induced by the protein matrix and the ordering of site energies consistent with the identity of the primary donor (ChlD1) and the experimentally known asymmetric and directional electron transfer. The optimized protocol sets the stage for future multireference treatments of multiple pigments, and hence for multireference studies of charge separation, while it is transferable to the study of any photoactive embedded tetrapyrrole system.

The Use of Effective Core Potentials with Multiconfiguration Pair-Density Functional Theory

The reliable and accurate prediction of chemical properties is a key goal in quantum chemistry. Transition-metal-containing complexes can often pose difficulties to quantum mechanical methods for multiple reasons, including many electron configurations contributing to the overall electronic description of the system and the large number of electrons significantly increasing the amount of computational resources required. Often, multiconfigurational electronic structure methods are employed for such systems, and the cost of these calculations can be reduced by the use of an effective core potential (ECP). In this work, we explore both theoretical considerations and performances of ECPs applied in the context of multiconfiguration pair-density functional theory (MC-PDFT). A mixed-basis set approach is used, using ECP basis sets for transition metals and all-electron basis sets for nonmetal atoms. We illustrate the effects that an ECP has on the key parameters used in the computation of MC-PDFT energies, and we explore how ECPs affect the prediction of physical observables for chemical systems. The dissociation curve for a metal dimer was explored, and ionization energies for transition metal-containing diatomic systems were computed and compared to experimental values. In general, we find that ECP approaches employed with MC-PDFT are able to predict ionization energies with improved accuracy compared to traditional Kohn-Sham density functional theory approaches.

SCN as a local probe of protein structural dynamics

The dynamics of lysozyme is probed by attaching -SCN to all alanine residues. The one-dimensional infrared spectra exhibit frequency shifts in the position of the maximum absorption of 4 cm-1, which is consistent with experiments in different solvents and indicates moderately strong interactions of the vibrational probe with its environment. Isotopic substitution 12C → 13C leads to a redshift by -47 cm-1, which agrees quantitatively with experiments for CN-substituted copper complexes in solution. The low-frequency, far-infrared part of the protein spectra contains label-specific information in the difference spectra when compared with the wild type protein. Depending on the position of the labels, local structural changes are observed. For example, introducing the -SCN label at Ala129 leads to breaking of the α-helical structure with concomitant change in the far-infrared spectrum. Finally, changes in the local hydration of SCN-labeled alanine residues as a function of time can be related to the reorientation of the label. It is concluded that -SCN is potentially useful for probing protein dynamics, both in the high-frequency part (CN-stretch) and in the far-infrared part of the spectrum.

Quantum chemical package Jaguar: A survey of recent developments and unique features

This paper is dedicated to the quantum chemical package Jaguar, which is commercial software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific features that are relevant to chemical research as well as describe those aspects of the program that are pertinent to the user interface, the organization of the computer code, and its maintenance and testing. Among the scientific topics that feature prominently in this paper are the quantum chemical methods grounded in the pseudospectral approach. A number of multistep workflows dependent on Jaguar are covered: prediction of protonation equilibria in aqueous solutions (particularly calculations of tautomeric stability and pKa), reactivity predictions based on automated transition state search, assembly of Boltzmann-averaged spectra such as vibrational and electronic circular dichroism, as well as nuclear magnetic resonance. Discussed also are quantum chemical calculations that are oriented toward materials science applications, in particular, prediction of properties of optoelectronic materials and organic semiconductors, and molecular catalyst design. The topic of treatment of conformations inevitably comes up in real world research projects and is considered as part of all the workflows mentioned above. In addition, we examine the role of machine learning methods in quantum chemical calculations performed by Jaguar, from auxiliary functions that return the approximate calculation runtime in a user interface, to prediction of actual molecular properties. The current work is second in a series of reviews of Jaguar, the first having been published more than ten years ago. Thus, this paper serves as a rare milestone on the path that is being traversed by Jaguar's development in more than thirty years of its existence.

Motility driven glassy dynamics in confluent epithelial monolayers

As wounds heal, embryos develop, cancer spreads, or asthma progresses, the cellular monolayer undergoes a glass transition between solid-like jammed and fluid-like flowing states. During some of these processes, the cells undergo an epithelial-to-mesenchymal transition (EMT): they acquire in-plane polarity and become motile. Thus, how motility drives the glassy dynamics in epithelial systems is critical for the EMT process. However, no analytical framework that is indispensable for deeper insights exists. Here, we develop such a theory inspired by a well-known glass theory. One crucial result of this work is that the confluency affects the effective persistence time-scale of active force, described by its rotational diffusivity, Deffr. Deffr differs from the bare rotational diffusivity, Dr, of the motile force due to cell shape dynamics, which acts to rectify the force dynamics: Deffr is equal to Dr when Dr is small and saturates when Dr is large. We test the theoretical prediction of Deffr and how it affects the relaxation dynamics in our simulations of the active Vertex model. This novel effect of Deffr is crucial to understanding the new and previously published simulation data of active glassy dynamics in epithelial monolayers.

MotifQuest: An Automated Pipeline for Motif Database Creation to Improve Peptidomics Database Searching Programs

Endogenous peptides are an abundant and versatile class of biomolecules with vital roles pertinent to the functionality of the nervous, endocrine, and immune systems and others. Mass spectrometry stands as a premier technique for identifying endogenous peptides, yet the field still faces challenges due to the lack of optimized computational resources for reliable raw mass spectra analysis and interpretation. Current database searching programs can exhibit discrepancies due to the unique properties of endogenous peptides, which typically require specialized search considerations. Herein, we present a high throughput, novel scoring algorithm for the extraction and ranking of conserved amino acid sequence motifs within any endogenous peptide database. Motifs are conserved patterns across organisms, representing sequence moieties crucial for biological functions, including maintenance of homeostasis. MotifQuest, our novel motif database generation algorithm, is designed to work in partnership with EndoGenius, a program optimized for database searching of endogenous peptides and that is powered by a motif database to capitalize on biological context to produce identifications. MotifQuest aims to quickly develop motif databases without any prior knowledge, a laborious task not possible with traditional sequence alignment resources. In this work we illustrate the utility of MotifQuest to expand EndoGenius' identification utility to other endogenous peptides by showcasing its ability to identify antimicrobial peptides. Additionally, we discuss the potential utility of MotifQuest to parse out motifs from a FASTA database file that can be further validated as new peptide drug candidates.

Optimal tree tensor network operators for tensor network simulations: Applications to open quantum systems

Tree tensor network states (TTNS) decompose the system wavefunction to the product of low-rank tensors based on the tree topology, serving as the foundation of the multi-layer multi-configuration time-dependent Hartree method. In this work, we present an algorithm that automatically constructs the optimal and exact tree tensor network operators (TTNO) for any sum-of-product symbolic quantum operator. The construction is based on the minimum vertex cover of a bipartite graph. With the optimal TTNO, we simulate open quantum systems, such as spin relaxation dynamics in the spin-boson model and charge transport in molecular junctions. In these simulations, the environment is treated as discrete modes and its wavefunction is evolved on equal footing with the system. We employ the Cole-Davidson spectral density to model the glassy phonon environment and incorporate temperature effects via thermo-field dynamics. Our results show that the computational cost scales linearly with the number of discretized modes, demonstrating the efficiency of our approach.

Quantum nature of reactivity modification in vibrational polariton chemistry

In this work, we present a mixed quantum-classical open quantum system dynamics method for studying rate modifications of ground-state chemical reactions in an optical cavity under vibrational strong-coupling conditions. In this approach, the cavity radiation mode is treated classically with a mean-field nuclear force averaging over the remaining degrees of freedom, both within the system and the environment, which are handled quantum mechanically within the hierarchical equations of motion framework. Using this approach, we conduct a comparative analysis by juxtaposing the mixed quantum-classical results with fully quantum-mechanical simulations. After eliminating spurious peaks that can occur when not using the rigorous definition of the rate constant, we confirm the crucial role of the quantum nature of the cavity radiation mode in reproducing the resonant peak observed in the cavity frequency-dependent rate profile. In other words, it appears necessary to explicitly consider the quantized photonic states in studying reactivity modification in vibrational polariton chemistry (at least for the model systems studied in this work), as these phenomena stem from cavity-induced reaction pathways involving resonant energy exchanges between photons and molecular vibrational transitions.

Electronic dynamics through conical intersections via non-Markovian stochastic Schrödinger equation with complex modes

Conical intersections (CIs) play a crucial role in photochemical reactions, offering an efficient channel for ultrafast non-adiabatic relaxation of excited states. This significantly influences the reaction pathways and the resulting products. In this work, we utilize the non-Markovian stochastic Schrödinger equation with complex modes method to explore the dynamics of electronic transitions through conical intersections (CIs) in pyrazine. The linear vibronic coupling model serves as the foundational framework, incorporating both intra-state and inter-state electron-vibrational interactions. The dynamics of the excited electronic transitions are analyzed across varying strengths of system-bath coupling and different bath relaxation times. The accuracy of this method is demonstrated by comparing its predictions with those from the hierarchical equations of motion method.

Plasmon dephasing time and optical field enhancement in a plasmonic nanobowl substrate studied by scanning near-field optical microscopy

Plasmonic substrates have been extensively investigated due to their potential applications in fluorescence microscopy, chemical sensing, and photochemical reactions. The optical properties of the substrate depend on the spatial and temporal features of the plasmon excited. Hence, the ability to directly visualize plasmon dynamics is crucial. In this study, we investigated the spatial and temporal properties of plasmon excitation in a plasmonic nanobowl substrate consisting of a periodic hexagonal array of nanoscale bowl-like structures developed with self-assembly. Near-field transmission imaging revealed that multiple plasmon resonance bands are observed from visible to near-infrared spectral region, and the optical contrast of the image is dependent on the observed band. Near-field two-photon photoluminescence microscopy revealed that the probability of excitation inside each nanoscale bowl-like structure is greater than that in the surrounding area. Near-field time-resolved imaging revealed that the nanobowl substrate exhibited a substantially long plasmon dephasing time, exceeding 12 fs. Based on the spectral features of the near-field and far-field spectra, we found that optically dark plasmon mode is excited by the near-field illumination and only partly contributes to the long dephasing time observed. This fact indicates that the dephasing time is extended by some other mechanism in the periodic substrate. We revealed from this study that the enhanced optical fields induced in the nanobowl structure originate from the photosynergetic effect of the cavity mode and plasmon mode excited.

Data-driven and equation-free methods for neurological disorders: analysis and control of the striatum network

The striatum as part of the basal ganglia is central to both motor, and cognitive functions. Here, we propose a large-scale biophysical network for this part of the brain, using modified Hodgkin-Huxley dynamics to model neurons, and a connectivity informed by a detailed human atlas. The model shows different spatio-temporal activity patterns corresponding to lower (presumably normal) and increased cortico-striatal activation (as found in, e.g., obsessive-compulsive disorder), depending on the intensity of the cortical inputs. By applying equation-free methods, we are able to perform a macroscopic network analysis directly from microscale simulations. We identify the mean synaptic activity as the macroscopic variable of the system, which shows similarity with local field potentials. The equation-free approach results in a numerical bifurcation and stability analysis of the macroscopic dynamics of the striatal network. The different macroscopic states can be assigned to normal/healthy and pathological conditions, as known from neurological disorders. Finally, guided by the equation-free bifurcation analysis, we propose a therapeutic close loop control scheme for the striatal network.

Targeting spectroscopic accuracy for dispersion bound systems from ab initio techniques: Translational eigenstates of Ne@C70

We investigate the endofullerene system Ne@C70 by constructing a three-dimensional Potential Energy Surface (PES) describing the translational motion of the Ne atom. This is constructed from electronic structure calculations from a plethora of methods, including MP2, SCS-MP2, SOS-MP2, RPA@PBE, and C(HF)-RPA, which were previously used for He@C60 in Panchagnula et al. [J. Chem. Phys. 160, 104303 (2024)], alongside B86bPBE-25X-XDM and B86bPBE-50X-XDM. The reduction in symmetry moving from C60 to C70 introduces a double well potential along the anisotropic direction, which forms a test of the sensitivity and effectiveness of the electronic structure methods. The nuclear Hamiltonian is diagonalized using a symmetrized double minimum basis set outlined in Panchagnula and Thom [J. Chem. Phys. 159, 164308 (2023)], with translational energies having error bars ±1 and ±2 cm-1. We find no consistency between electronic structure methods as they find a range of barrier heights and minima positions of the double well and different translational eigenspectra, which also differ from the Lennard-Jones (LJ) PES given in Mandziuk and Bačić [J. Chem. Phys. 101, 2126-2140 (1994)]. We find that generating effective LJ parameters for each electronic structure method cannot reproduce the full PES nor recreate the eigenstates, and this suggests that the LJ form of the PES, while simple, may not be best suited to describe these systems. Even though MP2 and RPA@PBE performed best for He@C60, due to the lack of concordance between all electronic structure methods, we require more experimental data in order to properly validate the choice.