The Journal of Physical Chemistry Letters

Cells communicate with their environment by integrating signals, often chemical in nature, triggered by specific molecules bind to specific membrane-bound receptors, resulting in a downstream signaling cascade. Arguably, G-protein-coupled receptors (GPCRs) constitute the most pharmacologically important family of such receptors, binding small molecules, peptides, lipids, and hormones with high specificity. However, despite a highly conserved fold and sequence similarity, GPCRs are still mostly studied on a case-by-case basis. Here, we infer a general, evolutionarily conserved mechanism of class A GPCR activation. By leveraging coevolution and machine learning methods applied to all class A GPCRs structures, we derive a mathematical description (a so-called collective variable - CV) of the receptor's activation state which is independent of its sequence. Then, we bias molecular dynamics simulations along this CV to obtain transitions between activation states of a diverse set of class A GPCR family members. To demonstrate that our model generalizes beyond GPCRs in our training set, we obtain conformational transitions of an orphan receptor, GPR183. Finally, we show that we can model ligand effect on the receptors by converging Free Energy Surfaces of activation of the {beta}2-adrenergic receptor within this common mechanism framework. These results, to our knowledge, prove for the first time the existence of a mechanism uniting all class A GPCRs. Our approach thus facilitates direct comparisons between receptors and opens up the possibility of structural and dynamical studies of many orphan and understudied GPCRs. It also serves as a blueprint for inferring family-wide protein mechanisms.

The inhibition of A{beta}42 ({beta}-amyloid) fibril formation is a key therapeutic strategy in Alzheimer's disease research. Paddlewheel diruthenium complexes have shown promising activity against A{beta}42 aggregation and preformed fibril disaggregation, yet their molecular mode of action remains poorly understood. In this work, we perform atomistic simulations to explore how charge modulation influences the interactions of three analogous paddlewheel diruthenium complexes, the parent neutral complex [Ru2Cl(D-p-FPhF)(O2CCH3)3], and its anionic [Ru2Cl2(D-p-FPhF)(O2CCH3)3]- and cationic [Ru2(D-p-FPhF)(O2CCH3)3]+ counterparts (D-p-FPhF- is the N,N' -bis(4-fluorophenyl)formamidinato ligand) with A{beta}42. Our results indicate that electrostatic tuning governs binding affinity and the extent of interaction across the A{beta}42 fibril surface. As the complexes' charge changes from -1 to +1, the interaction pattern shifts from localized contacts to widespread, multi-site engagement encompassing key charged, aromatic, and hydrophobic regions of A{beta}42. This enhanced binding correlates with longer-lived, thermodynamically stable interactions at the fibril interface, which effectively lower the free energy penalty for fibril disassembly. Overall, our findings propose a mechanism in which charge-dependent activation through ligand exchange enhances fibril recognition and promotes disruptive binding modes, demonstrating the potential of charge-tunable diruthenium complexes as therapeutic modulators of A{beta}42 fibril stability.

Biomolecular condensates exhibit spontaneous electrochemical microenvironments characterized by asymmetric ion distributions and pH gradients that emerge from protein-sequence-dependent charge regulation. Despite their biological importance, mechanistic understanding of these microenvironments has been constrained by the absence of computationally tractable frameworks capable of treating proton exchange, counterion partitioning, and buffer equilibria on consistent thermodynamic footing. Here, we introduce the buffered Charge-Regulation Monte Carlo (b-CR-MC) framework, which couples grand-canonical exchange of ions and buffer species with explicit charge regulation of titratable residues. By extending the CR-MC ion-merging strategy to multicomponent reservoirs and employing the Restricted Primitive Model, b-CR-MC achieves computational efficiency while maintaining thermodynamic rigor, with quantitative agreement to the more expensive generalized G-RxMC approach. Applied to full-length FUS (net positive) and PGL-3 (net negative) under physiological conditions, the framework reveals sequence-dependent pH gradients: the dense phase of FUS exhibits an alkaline shift, while PGL-3 exhibits an acidic shift, in both cases driving the condensate interior toward the protein's isoelectric point. Slab-geometry simulations further resolve the Donnan potential and continuous ion profiles across the condensate interface, confirming the direction and magnitude of these electrochemical shifts. Additionally, we identify spatially resolved buffer depletion within dense phases, establishing that dynamic charge regulation is a primary determinant rather than a secondary correction to condensate electrochemistry. By establishing a sequence-resolved, thermodynamically consistent computational platform, b-CR-MC enables quantitative prediction of how mutations and post-translational modifications reprogram condensate microenvironments across biological and pathophysiological contexts.

Core fucosylation of the IgG1 Fc N297 glycan is known to reduce binding affinity to the Fc{gamma}RIIIa (CD16a) receptor and attenuate antibody-dependent cellular cytotoxicity (ADCC), yet the structural mechanisms underlying this effect remain incompletely understood. Here, we use extensive all-atom molecular dynamics simulations to systematically investigate how Fc glycosylation modulates the structural, energetic, and dynamical landscape of the IgG1 Fc-CD16a complex across multiple systems with fucosylation and galactosylation. Relative binding free energy calculations reproduce experimentally established trends, showing that afucosylation consistently strengthens Fc-CD16a interactions. Mechanistically, dual fucosylation (on both Fc arms) increases inter-glycan packing between the Fc N297 glycans, restricts Fc glycan conformational sampling, and destabilizes the conformational organization of the CD16a N162 glycan. These glycan-mediated perturbations propagate to the protein interface. The result is reduced Fc-CD16a contact persistence, redistribution of energetically important residues away from the canonical binding interface, and broader, less stable receptor-bound conformational states. Dynamic cross-correlation analysis further reveals that afucosylated systems maintain substantially stronger coordinated motions across the Fc-CD16a assembly, whereas fucosylation disrupts long-range dynamic coupling between the receptor and antibody domains. Across these different energetic, structural, conformational, and dynamical readouts, fucosylation systematically shifts the Fc-CD16a assembly from a compact, interface-stabilized binding mode toward a more heterogeneous and weakly coupled receptor-bound ensemble. Together, our findings set forth a mechanistic basis for Fc glycosylation regulating receptor engagement through ensemble-level conformational and dynamical reorganization rather than simple local steric effects. These results provide mechanistic design principles for rational Fc glycoengineering and the development of therapeutic antibodies with enhanced effector functions. More broadly, this work highlights how glycan composition can be leveraged as a tunable molecular design parameter for engineering protein recognition, conformational stability, and immune effector function in therapeutic glycoproteins.

The molecular mechanics-generalized Born surface area method (MMGBSA) is one of the most commonly used end state approaches used for the calculation of the binding free energy towards computational drug design and screening studies. It is customary to break up the free energy into van der Waals, electrostatic, polar solvation (GB), and nonpolar solvation (SA) terms and then either correlate these terms with experiment or assign physical meaning to each term. Here, we demonstrate that this assumption of independent fitting coefficients for decomposed energy terms could be invalid. Through analytic derivation and large-scale molecular dynamics simulations, we show that (i) the protein and ligand Coulomb interaction energy and the GB solvation correction are almost perfectly collinear (R2[≥]0.99) reflecting their designed role as vacuum electrostatics plus solvent screening, and (ii) the van der Waals interaction and SA term likewise exhibit strong correlation, as both depend primarily on buried surface area. Interaction entropy and C2 entropy corrections are also found to be strongly dependent on underlying electrostatic fluctuations, further reinforcing redundancy. These findings hold both at the level of instantaneous trajectory fluctuations and when averaged across a diverse set of 139 protein-protein complexes and persist in both single-trajectory and three trajectory MMGBSA protocols. Our results caution against using decomposed MMGBSA terms as independent predictors in regression models and suggest instead combining correlated terms into effective polar, nonpolar, and entropic contributions. Our study provides a systematic diagnosis of collinearity in MMGBSA and highlights pathways toward more interpretable and statistically robust predictive modeling.

Small-angle X-ray or neutron scattering (SAXS/SANS) analysis of large unilamellar vesicles (LUVs) is often limited by high-dimensional bilayer models and the lack of dedicated, statistically rigorous workflows. Here, we introduce SAS_MoCa, an open-source Python package that integrates a compositional scattering density profile (SDP) description of lipid bilayers with a separated form factor (SFF) treatment of vesicle size and polydispersity, and couples these highly parameterized models to an adaptive thermodynamic simulated annealing algorithm formulated within a constrained Bayesian framework. SAS_MoCa enables users to incorporate quantitative prior information from, e.g., previous SAXS/SANS studies, dynamic light scattering, NMR, or molecular simulations, and returns full posterior parameter distributions, uncertainties (reported as medians and median absolute deviations) and correlations even from single SAXS curves. Validation on POPC, POPE and DMPC SAXS-only data demonstrates that the method yields reproducible structural parameters with uncertainties comparable to joint SAXS/contrast-variation SANS analyses. The modular architecture of SAS_MoCa facilitates extension to additional lipid systems and future joint SAXS/SANS or SANS-only applications.

A priori assessment of target proteins' druggability remains an unsolved problem in the field of drug development. The empirical approaches widely used to solve this problem demonstrate low efficiency. In this work, we investigated the factor of hydration of a representative set of 65 evolutionarily and structurally unrelated human enzymes in a water environment. This factor depends only on the structure of the proteins, and not on the physical and chemical properties of any potential ligands. The results show that, unlike the widely used approaches based on calculations of the accessible surface area (ASA), the content of low-entropy water molecules (LEW) in the active sites of human enzymes is systematically higher than that in other areas of their surface, including inactive cavities. Optimal criteria and a step-by-step procedure for identifying protein ligand binding sites are proposed. The proposed approach, based on the calculation of the LEW content in the first hydration layer of potentially interesting target proteins, makes it possible to evaluate their medicinal suitability even before the development of any ligands. The article also presents the results of a comparative analysis of experimental Raman spectroscopy data and the results of molecular dynamics simulations of water hydrogen bonds using three widely used water models (TIP3P, OPC3, and TIP5P) and standard algorithms for calculating hydrogen bond networks.

Proteases are an important class of proteolytic enzymes having great importance in both basic science and industrial applications. While cells tightly regulate the spatio-temporal activity of different proteases for cellular homeostasis, mis-regulation often leads to adverse effects. In this context, the protease activity of papain and its activation by L-cysteine is poorly understood in the literature. Herein, we discover that the protease activity of papain can be effectively regulated via a spontaneous liquid-liquid phase separation (LLPS) pathway. We show that papain undergoes biomolecular condensation via spontaneous LLPS under macromolecular crowding through the involvement of intermolecular hydrophobic interactions. Secondary structure analyses revealed a compact conformation of phase-separated papain with increased -helix content. Although native free papain is found to be active towards synthetic and protein substrates, the proteolytic digestion produces heterogeneous peptide aggregates. In contrast, we found that papain droplets remain dormant toward protein digestion due to the disulfide linkage of the active cysteine residue (Cys-25) in its compact conformational state. More importantly, we show that the protease activity of phase-separated papain can be reactivated in the presence of L-cysteine to produce uniform soluble peptide fragments. Our findings indicate that although disulfide linkages are not necessary for the phase separation of papain, upon phase separation, intermolecular interactions between phase-separated papain result in the formation of disulfide linkages involving active Cys-25 residues. The present discovery has tremendous technological importance to boost the efficacy of meat tenderization in the food industry.

Integral membrane proteins remodel the surrounding lipid bilayer, but quantifying the resulting deformations and linking them to protein density in the membrane has remained challenging. Here, we introduce an integrative methodology that combines all-atom molecular dynamics (MD) simulations with multiscale small-angle X-ray scattering (SAXS) analysis to connect membrane strain to the protein/lipid ratio in proteoliposomes. Using outer membrane phospholipase A (OmpLA) reconstituted into lipid bilayers with both increased and decreased hydrophobic thickness, we systematically probe the effects of positive and negative hydrophobic mismatch.MD simulations demonstrate that OmpLA causes anisotropic, oscillatory thickness deformations extending up to eight times the radius of the first lipid shell surrounding the protein, yet the net change in average membrane thickness remains below 1%. Through our multiscale SAXS analysis, we quantitatively extract structural parameters, ranging from proteoliposome size to internal membrane architecture, using constrained Bayesian inference, with priors derived from MD findings. Specifically, we determine the protein/lipid molar ratio and average membrane strain, revealing excellent agreement between experiment and simulation. In thinner bilayers, substantial protein loss limits the analysis, highlighting the role of bilayer stability in sample preparation. Moreover, the predominance of OmpLA monomers in the thicker membranes is consistent with weak, membrane-mediated repulsive interactions between protein inclusions. Collectively, this integrative approach establishes a framework for quantifying protein-lipid interactions across molecular and mesoscale dimensions.

Biomolecular condensates are widespread cellular self-assembled structures with essential functions. There are suggestions of condensates formed by different proteins being near criticality. However, systematic investigation of the criticality of condensates is absent, and critical exponents defining their universality class have not been found. Here, using long-time simulations, we show that condensates exhibit typical critical phenomena, including scale-free spatiotemporal correlations, critical slowing down, divergence of correlation length and dynamic scaling. From these scaling behaviors, a set of critical exponents is determined. Based on dynamic critical exponent, diverse condensates can be divided into two distinct universality classes, arising from differences in their molecular components and interaction types.

Protein folding is the process by which a polypeptide chain organizes into its three-dimensional structure through a balance of stabilizing and destabilizing interactions encoded by the sequence. A central question in protein biophysics is how thermodynamic factors guide a polypeptide toward its native folded state despite the rugged energy landscape and the competing influence of nonnative interactions. In many biomolecular processes, cooperativity provides a mechanism by which multiple weak interactions act collectively to generate a robust response. In the context of protein folding, such cooperative effects may arise when the formation of one native contact enhances the stability or likelihood of nearby native contacts, thereby promoting collective organization toward the folded state. At the same time, folding is opposed by the much larger number of non-native interactions, whose heterogeneity can introduce frustration and destabilize folding even when the average native bias favors the folded phase. The interplay of these competing effects in determining foldability remains unclear in statistical-mechanical models. Here, we address this problem using a one-dimensional spin-glass model of protein folding with explicit shared-residue cooperative interactions encoded through wedge-based motifs. We show that modest cooperative bias can stabilize folding even where the noncooperative system remains unfolded, whereas non-native energetic fluctuation suppresses folding and shifts the transition to higher cooperative strengths. We further find that partial cooperative coverage is sufficient to lower the folding threshold. Therefore, the model provides a mean-field framework for incorporating cooperative interaction strength into the native one-dimensional model of protein folding and for describing how local cooperativity reshapes the folding transition.

BackgroundThe interaction between the anti-apoptotic protein Bcl-xL and the BH3-only sensitizer BAD represents a critical regulatory checkpoint in the intrinsic apoptotic pathway. Although this interaction is known to influence mitochondrial fate, its dynamic regulation and structural determinants in living cells remain poorly understood. Here, we developed a fluorescence lifetime imaging microscopy-based Forster resonance energy transfer (FLIM-FRET) platform to visualize and quantify Bcl-xL-BAD interactions in real-time. MethodsWe developed a quantitative fluorescence lifetime-based FRET (FLIM-FRET) approach to visualize and measure Bcl-xL-BAD interactions in single living glioblastoma cells. Stable GFP/Venus-Bcl-xL and mCherry-BAD FRET pairs were created, followed by acceptor photobleaching FRET, FLIM-FRET, Annexin V-BFP-based apoptosis assays, pharmacological perturbation using BH3 mimetics, and molecular dynamics simulations with MM/GBSA analysis. Statistical significance was assessed using appropriate parametric tests across multiple independent experiments. ResultsUsing this platform, we observed that apoptotic stress markedly enhances the engagement of Bcl-xL and BAD. Increased FRET efficiency coincided with Annexin V positivity and nuclear condensation, indicating that maximal BAD binding reflects a higher level of apoptotic commitment. Structure-function analysis using targeted Bcl-xL mutants revealed distinct binding requirements: disruption of the core hydrophobic groove (Y101K) abolished BAD binding and impaired BH3 mimetic sensitivity, whereas mutation within the BH1 domain (G138A) preserved BAD interaction and sensitivity to BH3 mimetics. Molecular dynamics simulations corroborated these observations by revealing preserved BAD-binding energetics in the G138A mutant, but destabilization in the Y101K mutant. ConclusionsTogether, these findings demonstrate the utility of a live-cell FLIM-FRET platform for resolving protein-protein interactions involving apoptotic proteins at the single-cell level. By linking interaction dynamics, structural determinants, and functional outcomes, this approach provides a broadly applicable framework for studying apoptotic priming, structural tolerance at BCL-2 family interfaces, and cellular responses to BH3-mimetic therapies.

I.Rapid developments in magic-angle-spinning (MAS) hardware over the past two decades have made possible the acquisition of high-resolution spectra of protons in solids, fuelling studies of small and large molecules alike. Nevertheless, proton resolution, limited by the strong dipole-dipole coupling network, remains a bottleneck even at MAS frequencies exceeding 100 kHz. We present here techniques based on phase-modulated homonuclear decoupling that dramatically improve proton coherence times and resolution compared to 60-95 kHz MAS alone using low average radio-frequency amplitudes (< 100 kHz). A relatively high sensitivity (40- 70%) and a straightforward optimization procedure directly on the sample being studied allows these gains to be realised in large biomolecules, as demonstrated here on a 326-residue cytoskeletal protein in its filamentous state. These techniques enable experiments with improved resolution on biomolecules while simultaneously taking advantage of the higher sensitivity available on probes with relatively large rotor volumes that cannot reach higher MAS frequencies.

Chemical exchange saturation transfer (CEST) has become an indispensable NMR method to characterize slow exchange affecting biomacromolecules, especially for cases involving exchange between a major state and a minor state, the latter of which is often invisible in the spectrum. The CEST method is based on successive irradiation of selective regions of the NMR spectrum using a weak radiofrequency field, B1, while observing the effect on the visible major state when the B1 field saturates the invisible minor state. The need for selective saturation of narrow spectral regions has to date required acquisition of many tens of two-dimensional CEST spectra to sample the entire spectrum with sufficient resolution. Here we present the ACCEST method which measures an entire CEST profile from a single two-dimensional accordion-CEST spectrum plus a reference spectrum. ACCEST is based on the concept of accordion spectroscopy, where in the present implementation the carrier frequency of the weak saturating B1 field is stepped in synchrony with the dwell-time incrementation in the indirect dimension of the two-dimensional spectrum. We benchmarked ACCEST against conventional CEST, resulting in excellent agreement for both backbone 15N and methyl 13C CEST profiles. ACCEST offers substantial time savings that scale linearly with the number of spectra required in the corresponding conventional CEST experiment. Thus, ACCEST can dramatically speed up lengthy serial experiments, such as ligand titrations or temperature-dependent studies, and enable studies of non-equilibrium systems or samples with limited lifetimes.

The SMN2 exon 7 5' splice-site/U1 snRNA duplex contains an A$_{-1}$ bulge that weakens splice-site recognition and represents a therapeutically relevant RNA connectivity defect, yet its conformational landscape and coupling to solvation remain poorly understood. Here, we performed enhanced-sampling Hamiltonian replica-exchange molecular dynamics simulations of the SMN2 splice-site duplex using four explicit-solvent models (OPC, TIP4P-Ew, TIP3P, and SPC/E) and characterized the sampled ensemble using linear and machine-learned latent representations. Across representations, the A$_{-1}$ defect consistently populated three metastable conformational states distinguished by local duplex geometry, base stacking, hydrogen-bonding patterns, and solvent exposure. The relative populations of these states, together with first-shell hydration and Na$^+$ distributions around the defect, varied substantially across water models, demonstrating that hydration and ion organization actively shape the equilibrium between locally accommodated and solvent-exposed conformations of the SMN2 splice-site bulge. Our results shed light on the conformational components of this therapeutic RNA target and highlight the impact of solvation model as an important consideration for molecular simulations of RNA splice-site recognition and small-molecule repair.

Plexin-B1 is a transmembrane receptor that integrates signals from Rho-family and Ras-family (Rap1b) GTPases to regulate cellular processes. While ligand simulated activation of the receptor is largely understood, the role of membrane composition and GTPase allosteric effects on plexin structure, internal protein dynamics, and function is still to be elucidated. Here, we performed multi-replica, 1 s all-atom simulations of Plexin-B1-GTPase complexes on PIP2- and PIP3-containing membranes to investigate the effects of these two signaling lipids, as well as on the GTPases. We found that both Rap1b and Rnd1 stably associate with the membrane, with PIP2 promoting broader lipid engagement and stronger Rap1b-Plexin-B1 interactions, whereas PIP3 enhances Rnd1-Plexin contacts and induces a membrane proximal orientation of Plexins juxtamembrane helix and makes contacts with a previously discovered activation switch loop. Contact map and network analyses revealed lipid-dependent shifts in allosteric communication, with PIP2 favoring Rap1b-centric hotspots and PIP3 favoring Rnd1-centric pathways. These predictions allow us to suggest a model for plexin intracellular region activation where both the identity of phosphoinositides and GTPase context synergistically stabilize Plexin-B1 membrane engagement, alter structural dynamics, and allosteric networks. Thus, we propose that the membrane is an active modulator of plexin receptor signaling.

Protein structures encode non-local contact organization, but static coordinates do not directly quantify how a contact network responds when effective stabilizing interactions are strengthened or weakened. Here we introduce Contact-Network Responsiveness (CNR), a structure-derived framework that converts residue-level protein coordinates into density-controlled and topology-corrected response descriptors. The method is structure-source agnostic and can be applied to experimentally determined PDB structures, AlphaFold models, or other predicted structures; here, human AlphaFold models serve as the high-coverage structural substrate. Across 22,167 valid human protein structures, hydrophobic non-local contact density defined a nearly exact Bethe mean-field baseline for the conformational susceptibility threshold. A graph-aware residue-level extension then revealed systematic topology-dependent deviations from this density-only prediction. We define a topology correction ratio, [Formula], which separates topology-facilitated, density-dominated and topology-suppressed contact-network response regimes. CNR descriptors were associated with curated DisProt disorder annotations and broad-coverage UniProt/MobiDB-lite disorder fractions, supporting the interpretation that CNR captures a structural organization axis related to non-local contact availability and responsiveness.

RNA is a highly charged polyelectrolyte whose folding into functional architectures depends on an ionic atmosphere that screens strong electrostatic repulsion along the phosphate backbone. Whereas monovalent ions primarily stabilize secondary structure, divalent magnesium (Mg2+) drives tertiary folding often via site-specific and adopting various dynamic coordination modes. Current RNA structure-prediction frameworks rely largely on static direct-contact information, overlooking ion-mediated interactions and the dynamic exchange between distinct coordination modes-particularly the dynamic exchange between direct (inner) and solvent-separated (outer-sphere) Mg2+-phosphate coordination that often controls RNA's conformational transition. Here, we introduce the Structural-based Electrostatic Model (STEM), a hybrid implicit-explicit framework that explicitly captures how the dynamic exchange between distinct ion-coordination modes dictates folding pathways. STEM combines explicit Mg2+ ions to resolve site-specific interactions with implicit K+ ions to describe counter-ion condensation mediated electrostatic screening through generalized Manning counter-ion condensation model, enabling computationally efficient exploration of RNA folding landscapes. The model accurately reproduces crystallographic ion-binding sites, experimental preferential ion-interaction coefficients, and Small-Angle X-ray Scattering (SAXS)-derived radii of gyration across diverse RNA systems. Applied to a 58-nt rRNA fragment, STEM reveals that folding from an intermediate to the native state is driven by a chelated Mg2+-mediated tertiary contact and captures the resulting coordination-dependent conformational breathing. By shifting the paradigm from static direct-contact descriptions to ion-mediated dynamic interactions, STEM provides a physically grounded framework for predicting dynamic ensembles of RNA structures, resolving their folding free-energy landscapes, and elucidating the mechanisms of RNA folding and function beyond native conformations across physiological salt conditions.

Single-particle tracking (SPT) over time enables direct observation of molecular transport and interactions in living cells. Fluorescence-based SPT has provided insights into intracellular processes such as endocytosis, receptor signaling, and drug delivery. Extending the observation window to several hours and beyond is critical for capturing slow intracellular dynamics, including the full course of endosomal trafficking, the long-term accumulation of particles within subcellular compartments, and transitions between transport modes that occur on hour-scale timescales. However, long-term intracellular SPT under visible-wavelength excitation remains challenging because fluorescence probes generally suffer from photobleaching and phototoxicity. While near-infrared (NIR) excitation can simultaneously mitigate these issues, generally weak emission of NIR-emitting dyes has hindered its wide application in long-term SPT. Here, we demonstrate long-term NIR SPT using atomically precise gold quantum needles, Au42(PET)32 (PET = 2-phenylethanethiolate). Continuous tracking of intracellular particles in living HEK293 cells was achieved for up to 12 h. Trajectory analysis revealed temporal transitions between directional and diffusive transport, as well as the accumulation of multiple particles within localized intracellular domains over several-hour timescales. The high photostability of Au42, combined with low phototoxicity of NIR excitation, enables visualization of intracellular transport dynamics over timescales difficult to access using conventional visible fluorescent probes. These results establish Au42-based NIR imaging as a platform for long-term, low-phototoxicity intracellular SPT and provide a framework for investigating slow intracellular dynamics in living systems.

Glycation, a non-enzymatic reaction occurring between sugars and biological macromolecules, plays a critical role in ageing and disease pathogenesis. Methylglyoxal (MG) is a highly reactive -oxoaldehyde that leads to the formation of endogenous advanced glycation end products (AGEs). These AGEs are associated with diabetes and many other diseases, including neurodegeneration and cancer. This is often through interactions with the receptor for advanced glycation end products (RAGE). Inhibition of glycation/AGEs formation using natural products to target cancer is an area of recent interest. In vitro AGEs formation was observed by browning of samples, increased fluorescence, and carbonyl stress. MG induced changes in the structure of BSA were analysed using electrophoresis, spectroscopy, TEM, AFM, DLS, and CD spectroscopy. Our results show that AGEs form random structures, oligomeric aggregates, and {beta}-sheets. Thioflavin T and Congo red staining further validated these findings. Galangin and Caffeic acid demonstrated significant antiglycation activity, suppressing AGEs formation in vitro. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/737425v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@113b391org.highwire.dtl.DTLVardef@7208a1org.highwire.dtl.DTLVardef@94c2e1org.highwire.dtl.DTLVardef@867b85_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMethylglyoxal-induced Advanced Glycation End Products were prepared in vitro C_LIO_LIMethylglyoxal -induced structural modifications in BSA C_LIO_LIAGEs were characterised using various parameters C_LIO_LIBoth fluorescent and non-fluorescent AGEs were formed. C_LIO_LIPhytochemical treatment induced inhibition of AGEs formation C_LI