The Journal of Physical Chemistry B

N-linked glycosylation is known to modulate the catalytic function of human carbonic anhydrase (HCA) IX, yet its influence on the underlying free-energy landscape remains largely unexplored. In the present work, we combine extensive all-atom molecular dynamics simulations with kinetic transition network analysis to investigate the effect of glycosylation on the conformational organization of the catalytic domain of HCA IX in both monomeric and dimeric forms. The multidimensional conformational space is discretized into distinct free energy minima using the distribution of reciprocal interatomic distances (DRID), and the effective barriers separating them are estimated using the max flow-min cut formalism. The corresponding free energy landscapes are visualized in terms of disconnectivity graphs, which provide a faithful representation of underlying kinetics. Minimum free energy paths, mean first passage times, as well as frustration metrics are computed to further quantify the effect of glycosylation on landscape topography. Unglycosylated systems are found to exhibit predominantly funnel-like landscapes, with a limited number of metastable states in the vicinity of the native protein fold. In contrast, glycosylation enhances landscape complexity, resulting in a wide array of relaxation timescales. Strikingly, the two glycan chains affect the landscape topography in distinct ways, despite having closely matching sequences. Dimerization couples the glycan chain dynamics, with transitions between key metastable states involving coordinated motions of both the chains. Our work illustrates that interpretation in terms of disconnectivity graphs and transition networks could reveal important insights into the organization of glycoprotein energy landscapes.

Molecular flexibility governs how molecules behave, reorganize, and respond to their environment. Although experiments measure molar entropy for small molecules and molecular dynamics (MD) simulations capture molecular motions, quantifying configuration entropy and the concerted internal motions such as torsion rotations, angle bending, and their couplings are central to understanding thermodynamic behavior but remains challenging. To dissect these contributions, we used MD trajectories and developed an internal coordinate PC-entropy (iPC-entropy) method to probe the origins of entropy and reveal how specific motions shape the thermodynamic landscape. The studies accurately captured molar entropy, identified key torsional motions as major contributors, and uncovered a critical angle-torsion coupling in which angle bending was strongly correlated with torsional rotation, a coupling that increases nonlinearly with molecular size. Evaluating entropic changes upon protein-ligand binding reveals that dominant entropic penalty arises from ligand dihedral rigidification rather than protein reorganization and highlights the specific dihedral rotations that become restricted. We also suggest systematic corrections for approaches considering solely rotamers to reliably reproduce the relative entropic penalty in computer-aided drug discovery. Together, our findings elucidate the molecular origins of entropy and entropy changes. In addition, we can quantify and illustrate the internal motions that strongly shape binding thermodynamics, thereby offering mechanistic insights to guide drug development.

This present work shows that E-hook fragments possess functional structure differences governed by electrostatic interactions and sequence composition. The acidic C-terminal tails of tubulin, known as E-hooks, play a central role in regulating interactions between microtubules and motor proteins, microtubule-associated proteins, and enzymatic modifiers. Despite their functional importance, the intrinsic structural properties of these peptide segments remain poorly characterized due to their intrinsically disordered nature. In this work, we present quantum-mechanically optimized structures of hexamer peptides derived from {beta}-tubulin E-hook sequences. Density functional theory calculations were used to optimize peptide geometries using progressively larger basis sets. From the optimized geometries we calculated theoretical Raman spectra, Ramachandran backbone dihedral distributions, and measured radii of gyration to resolve composition dependent structural tendencies. The combined Raman and conformational analyses provide a systematic computational approach for comparing simulated and experimental Raman spectra of tubulin E-hooks and other intrinsically disordered proteins and offer insight into how E-hooks contribute to the recognition mechanisms underlying the tubulin code.

1.Surface functionality and pH play a decisive role in governing the structural dynamics, hydration, and drug-binding behaviour of dendrimers. Here, all-atom molecular dynamics (MD) simulations were performed on five generations of PAMAM (G1-G5) and PETIM (G2-G6) dendrimers with O-core and N-core architectures, functionalized with amine, carboxylic acid, or sugar terminal groups under different protonation states. Protonation of the tertiary branch-point amines expands the dendrimer structure, increases internal porosity and hydration, and enhances structural fluctuations across both families. In contrast, non-protonated amine -NH2 (NP) and carboxylic acid -COOH (NP) terminated dendrimers, together with deprotonated carboxylate-COO- (DeP) systems, retain comparatively compact conformations. Sugar-functionalized dendrimers ({beta}-galactose-terminated PETIM and D-glucose-terminated PAMAM) are most hydrated and structurally rigid, whereas amine-terminated dendrimers exhibit the greatest conformational dynamics. PAMAM dendrimers with -NH2, -NH3+, and -COO- terminal groups are generally more hydrated than their PETIM counterparts. However, {beta}-galactose-terminated PETIM dendrimers are more hydrophilic than D-glucose-terminated PAMAM dendrimers. N-core PETIM dendrimers also adopt more compact and spherical conformations than equivalent O-core PETIM dendrimers. Drug-binding MD simulations show that curcumin binding is dominated by van der Waals (vdW) interactions, whereas doxorubicin complexation is primarily driven by electrostatic interactions. Among the investigated surface functionalities, -NH2 (NP), -NH3+ (P), -COOH (NP), and -COO- (DeP) terminations exhibit the most favourable drug-binding characteristics. Except for deprotonated carboxylate systems, curcumin binds more strongly than doxorubicin. Overall, these findings establish molecular-level relationships between surface functionality, protonation state, dendrimer architecture, and drug-binding behaviour, providing design principles for pH-responsive dendrimer nanocarriers with enhanced drug-loading and controlled-release performance. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/742721v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@119bf29org.highwire.dtl.DTLVardef@1554d86org.highwire.dtl.DTLVardef@154a254org.highwire.dtl.DTLVardef@16d5c5b_HPS_FORMAT_FIGEXP M_FIG C_FIG

Biophysics is a rapidly advancing field with an incredible breadth of topics. Thus, undergraduate biophysics instructors have to strategize and decide what topics they will cover in their courses. Educational institutions utilize a variety of biophysics textbooks. A common deficiency of each of the existing texts is that it serves well a given set of topics (theory, illustrations, practice problems) and leaves out other areas. A typical example includes good theory and problems for thermodynamics and kinetics while presenting molecular dynamics and various spectroscopic methods in a lacking or outdated way. The authors of this manuscript teach a capstone Biophysical Chemistry three-quarter series (Western Washington University/WWU, Bellingham, WA) which ideally should resonate with the general and major-specific courses the students take within their major at WWU. To achieve this goal and to enrich the traditional lecture-based delivery, the instructors have developed and brought together key pedagogical elements: purpose-built online textbook with a uniform structure of the academic content and practice problems, a study sample (oligopeptide) of biophysical significance with a growing set of experimental and computational data and student-centric in-class activities including computer labs. Our Biophysical series emphasizes concepts and methods of computational structural biology (Molecular Dynamics) and spectroscopic approaches (IR, UV and NMR). Here we describe the details of our integrative approach, summarize key outcomes and chart ways to advance the biophysical chemistry series further. Our textbook can be found through LibreText.

Directional anisotropy in electron density provides key information about chemical bonding that is not readily accessible from conventional electron-density maps. Here, a model-independent framework is presented for decomposing experimental structure factors into angular components using spherical harmonics. Reciprocal-space projection onto spherical harmonics followed by standard Fourier synthesis yields angularly filtered density maps. The{ell} = 0 component captures the isotropic part of the density, while the{ell} = 1 components resemble px, py and pz-like dipolar functions that highlight directional electronic structure. Applications to high-resolution datasets, including urea, the Gly-Ala dipeptide and a 0.97 [A]{beta}-lactamase structure, reveal chemically interpretable dipolar features associated with carbonyl and amide bonds, N-H interactions and aromatic{pi} systems. Quantitative analysis using bond-centred sampling demonstrates stable dipolar signatures that remain detectable under moderate resolution truncation. These results establish spherical-harmonic angular decomposition as a practical framework for extracting directional electronic information from crystallographic electron-density maps. SynopsisAngular decomposition of experimental structure factors reveals dipolar anisotropy and directional electron-density features that are directly meaningful for chemical interpretation.

The process of drug unbinding is of immense importance in the field of biophysics and therapeutics. The behavior of these systems is greatly influenced by their thermodynamic and kinetic properties. Therefore, it is crucial to accurately estimate the ligand binding free energies and rate of ligand dissociation, yet these processes are often governed by rare event transitions that lie beyond the reach of standard brute-force molecular dynamics simulations. While enhanced sampling simulations offer a solution, their efficacy is strictly contingent upon the selection of appropriate collective variables (CVs) which is non-trivial for complex systems like protein-ligand complexes. In this study, we present a method to derive optimized CV from transition state region (TS) via an interpretable machine learning (ML) model, Elastic Net. By employing some physically intuitive order parameters, the derived optimized CV from the TS-region greatly accelerate ligand binding-unbinding transitions and achieves rapid free energy surface (FES) convergence across diverse systems including buried and solvent exposed active sites such as Trpsin-benzamidine complex, host-guest systems and sodium epoxidase etc. Intriguingly significant contribution of the ligand hydration is found in the optimized CV which depicts crucial role of solvent in driving ligand binding-unbinding transitions. The estimated binding free energies for different protein-ligand complexes match quite well with experiments, while maintaining a low computational cost. The derived optimized CV is also used to calculate the ligand residence times across different systems and calculated residence times are within the experimental range for all systems, again with very little computational costs. Moreover, we show that the optimized CV constructed from TS region via an interpretable ML model is transferable across diverse systems, offering a robust and scalable framework for drug discovery and investigation of complex biomolecular recognition.

Many functional RNA motifs adopt structures that deviate from the canonical A-form helix and are emerging targets for RNA-directed therapeutics. The microtubule-associated protein tau (MAPT) A-bulge motif (5'-GCAGU/5'-ACGU) is one such motif. Because its structure is stabilized by a delicate balance of local interactions, its accurate modeling remains a major challenge for molecular dynamics (MD) simulations. The experimentally determined nuclear magnetic resonance (NMR) structure of the MAPT A-bulge motif provides a stringent test of whether RNA force fields can accurately reproduce the experimentally observed conformation. Most current AMBER-family RNA force-field models have incorrectly favored a non-native base-triple state of the MAPT A-bulge motif over the experimentally observed stacked state. Structural comparison of the stacked and base-triple conformations revealed that overly favorable NH-N hydrogen bonds between the bulged adenosine and an adjacent Watson-Crick base pair were the primary source of this imbalance. We developed gHBfix-18Ab, an 18-component hydrogen-bond correction that distinguishes NH and NH2; donors. gHBfix-18Ab was combined with the previously developed OL3CP and NBfix0BPh corrections to generate the composite model gHBfix-18Ab*. This model restored the experimentally observed stacked state as the global minimum in the calculated free-energy profile and improved agreement with NMR-derived distance data for the A-bulge region. Importantly, gHBfix-18Ab* did not produce marked structural destabilization of the cUUCGg tetraloop, a widely used benchmark for RNA force-field validation, suggesting that the refinement preserves the stability of the unrelated RNA motif. These results demonstrate that targeted refinement of hydrogen-bond interactions provides a practical strategy for systematic improvement of RNA force fields toward more accurate modeling of noncanonical RNA motifs.

Calcium oxalates are naturally occurring minerals, with the monohydrate form CaC2O4xH2O (COM) being the most stable. COM contains two crystallographically different water sites. We investigate the details of water internal dynamics in the high and low temperature phases of COM using 17O central transition solid-state NMR line shapes, as well as laboratory and rotating frame relaxation rates. The measurements were performed either under static or magic angle spinning conditions and in a wide temperature range from 343 to 180 K. The combination of all measurements allows for precise constraints on motional mechanisms, rate constants, and amplitudes of motions. The high temperature phase is dominated by large-angle fluctuations with an amplitude of about 100 degrees, identical in both sites. During the phase transition between 323 to 300 K, these large-angle jumps freeze out in one of the water sites, while remaining active in the other. In the low temperature phase from 280 to 180 K, small-angle fluctuations of 2-8 degrees in amplitude dominate the relaxation. Transverse relaxation rates also point to the existence of a very slow collective rocking motion down to about 220-200 K.

Hydrophobins are biosurfactant proteins that coat the cell surfaces of filamentous fungi. On the conidial surface, hydrophobins self-assemble into rodlets, forming a dense hydrophobic film that promotes air-dispersibility. Although rodlet formation is closely associated with the physiology of filamentous fungi, its underlying molecular mechanisms remain largely unknown. Previously, we revealed that RolA, a hydrophobin derived from Aspergillus oryzae, forms rodlets at the air-water interface. In this study, we focused on the flexible N-terminal region of RolA, which lacks a well-defined tertiary structure, and hypothesized that this intrinsically disordered region regulates rodlet formation. To investigate its role, we used RolA mutants with reduced charges in the N-terminal region and analyzed the rodlet formation process on the surface of a water-in-air sessile droplet using atomic force microscopy. In addition, we quantitatively characterized rodlet formation at the air-water interface by applying a kinetic perspective to the interfacial tension change profiles obtained from dynamic surface tension measurements. The results suggested that RolA first forms a monolayer at the air-water interface, then rodlet formation proceeds through the continuous supply of free RolA monomers from the bulk phase to the interfacial RolA film. Our molecular dynamics simulations of RolA at the interface supported a model in which RolA molecules within the interfacial film interact with free monomers in the bulk phase through their N-terminal regions. These results reveal a previously unidentified role of the N-terminal region in rodlet formation and provide a more comprehensive framework for understanding the molecular mechanism underlying RolA rodlet formation.

Membrane surfaces can accelerate amyloid $\beta$ (A$\beta$) aggregation, yet the role of membrane curvature in this process remains poorly understood. Here, we used multi-million atom all-atom molecular dynamics simulations to compare the adsorption, conformational dynamics, and oligomerization of four A$\beta$42 peptides at a planar neuronal membrane and a highly curved lipid vesicle. For both systems, all peptides adsorbed within the first 2 $\mu$s, but their subsequent behavior differed substantially. The curved membrane exhibited a larger area per lipid and more extensive hydrophobic packing defects, allowing A$\beta$42 to penetrate more deeply and form strong contacts with lipid tails through its central hydrophobic core and C-terminal region. These interactions disrupted a solution-formed dimer and limited peptide-peptide association during the simulated interval. Additionally, vesicle-bound peptides adopted more extended conformations with increased $\beta$-structure and $\beta$-hairpin formation compared with peptides at the planar membrane. A$\beta$42 adsorption was also corelated to lipid reorganization in the vesicle. In contrast, the planar membrane supported weaker adsorption and stable dimer-to-trimer growth but showed little large-scale lipid segregation. These findings reveal that curvature reshapes the early A$\beta$42 aggregation landscape by strengthening peptide-lipid interactions, altering aggregation-prone conformations, and reorganizing membrane domains. Membrane geometry should therefore be considered alongside lipid composition in mechanistic models of A$\beta$42 oligomerization and membrane-associated toxicity.

Chemically similar post-translational modifications can mediate distinct biological functions, but how proteins distinguish between them remains unclear. Sulfotyrosine (sTyr) and phosphotyrosine (pTyr) exemplify this problem because they have similar sizes, local geometries, and electrostatic properties but function in different biological contexts. Here, we used the monoclonal antibody PSG2, which recognizes sTyr independently of the surrounding peptide sequence, to examine how a protein distinguishes these modifications. The crystal structure of PSG2 bound to an sTyr-containing peptide revealed a deep electropositive pocket with no modeled water molecules in direct contact with the sulfate group. Gas-phase density functional theory calculations favored pTyr over sTyr, showing that direct protein-ligand interactions alone are insufficient to explain PSG2 selectivity. Explicit first-shell hydration calculations showed that pTyr has a larger desolvation penalty than sTyr, and accounting for this difference reversed the calculated energetic order. Isothermal titration calorimetry showed favorable enthalpic and entropic contributions to sTyr binding, whereas no detectable heat signal was observed for pTyr. These results show that PSG2 distinguishes sTyr from pTyr through the balance between direct protein-ligand interactions and ligand desolvation.

G protein-coupled receptors are major pharmacological targets, yet achieving subtype selectivity remains challenging when closely related receptors share highly conserved orthosteric binding sites. Here, we investigate the molecular determinants governing ligand recognition and unbinding at the adenosine A2A and A2B receptors, two closely related class A GPCRs with markedly different pharmacological profiles. We combine Funnel Metadynamics and adaptive infrequent metadynamics to characterize the thermodynamics and kinetics of three representative ligands: the non-selective antagonist theophylline (TEP), the A2A-selective inverse agonist ZM-241385 (ZMA), and the non-selective full agonist NECA. Across six ligand-receptor complexes, our simulations reproduce experimentally resolved binding modes, predict the unresolved binding poses of TEP and ZMA at A2B, and provide binding free energies consistent with experimental trends. Kinetic simulations further resolve ligand-specific unbinding pathways, metastable intermediates, residence times, rate-determining transitions, and their associated transition-state configurations. Comparison of A2A and A2B reveals how subtle differences within and around their highly conserved orthosteric sites are amplified into distinct thermodynamic and kinetic behaviors. In particular, we identify three major receptor-specific features: differences in hydration and polarity near TM1/TM2/TM7, differences in steric packing and pocket volume at the TM3/TM5/TM6 floor, and a more dynamic network of charged extracellular residues and lipids in A2B that modulates ligand egress. These features rationalize ligand-dependent differences in affinity, residence time, and subtype selectivity, including the preferential stabilization of ZMA-like antagonists at A2A. Overall, our results provide a dynamic atomistic map of the A2A and A2B orthosteric regions and demonstrate how thermodynamic and kinetic information can reveal pharmacologically relevant differences that are not apparent from static structures alone. This framework may support the rational design and repurposing of subtype-selective adenosine receptor ligands.

Interleukin-13 (IL-13) is an immunomodulatory cell signaling cytokine that has been implicated in neurodegenerative disease and chronic inflammation. IL-13 binds to its low and high affinity receptors, IL-13 receptor 1 (IL-13R1) and IL-13 receptor 2 (IL-13R2), respectively, with residence times that vary accordingly. As the binding kinetics of the cytokine-receptor complexes influence cellular responses, we employed the molecular dynamics (MD) simulation-based{tau} -random acceleration molecular dynamics method ({tau}RAMD) to compute relative residence times for wild-type (WT) IL-13 and 19 IL-13 mutants to the two receptors. Comparison with experimental kinetic data shows that the{tau} RAMD computations capture the trends in residence times. Analysis of simulated dissociation trajectories of the cytokine-receptor complexes reveals two distinct dissociation pathways of IL-13 from each of the receptors. This study thus pinpoints key determinants of the interaction of IL-13 with its receptors which could be targeted for therapeutic design. Statement of SignificanceCytokines are regulatory proteins that bind to cell surface receptors and thereby send signals to the cellular interior. Interleukin-13 (IL-13) is a cytokine that has a low and a high affinity receptor. It has important physiological roles, and its deregulation is involved in diseases such as atopic dermatitis and asthma. We employed a molecular dynamics simulation-based method to compute the effects of changes in the sequence of IL-13 on the lifetimes of complexes of IL-13 and its receptors. Comparison with experiments supports the validity of the computational approach and analysis of the simulations reveals two distinct ways in which IL-13 dissociates from each receptor. These results thus provide a map for targeting IL-13 - receptor interactions for the design of therapeutics.

Carbapenems are the most potent {beta}-lactams, key antibiotics for healthcare-associated infections by Gram-negative bacteria and evade hydrolysis by most {beta}-lactamases, but are increasingly threatened by emergence of enzymes exhibiting hydrolytic activity towards them. Of the four recognised {beta}-lactamase subclasses, class A (active-site serine enzymes that hydrolyse {beta}-lactams via a covalent acylenzyme intermediate) is the most widely disseminated and, while the majority of such enzymes react with carbapenems to form long-lasting acylenzyme complexes, several possess carbapenem-hydrolyzing activity (carbapenemases). Here, we investigate the basis for these differences in a panel of class A {beta}-lactamases using molecular dynamics (MD) simulations of the respective acylenzyme complexes and tetrahedral intermediates (TI). The simulations reveal multiple features associated with catalytic activity across the spectrum of enzymes tested, including more extensive interactions of the carbapenem acylenzyme carbonyl and generally increased lifetimes of active site water molecules positioned for deacylation. Analysis of the dynamic trajectories shows carbapenemases to have reduced root mean-squared fluctuation (RMSF) differences between the acylenzyme and TI, that are not limited to the active site, indicating that the acylenzyme complex is pre-organised for reaction in carbapenemases but not in carbapenem-inhibited enzymes. Similarly, Principal Component Analysis (PCA) of acylenzyme and TI dynamics shows greater overlap between the two states in carbapenemases, providing further evidence for acylenzyme pre-organisation. Such simulations may represent an effective computational assay able to identify enzymes with carbapenemase activity at relatively modest computational cost.

Proteins are critical biomolecular machines that populate ensembles of interconverting conformations. Many biological processes depend on transitions between metastable states. Although molecular dynamics (MD) simulations provide a physically grounded route to characterize these motions, routine sampling of large-scale conformational transitions remains computationally demanding. Recent advances in protein structure prediction have created new opportunities for ensemble generation, but many existing approaches require noising inputs, task-specific training, supervised fitting on extensive MD data, or experimentally-informed restraints. Here, we introduce Pi-Ensemble (Predicting Interpolated Ensemble), a sequence-guided framework for generating protein conformational ensembles interpolating between two structural anchor states. Unlike previous methods, Pi-Ensemble alternately leverages inverse-folding and structure-prediction models to propose intermediate conformations between known protein states, generating diverse ensembles without additional training. We evaluate Pi-Ensemble across diverse protein systems, including enzymes, transporters, receptors, and benchmark cases with reference MD simulations or experimental Double Electron-Electron Resonance (DEER) data. Pi-Ensemble recovers physically plausible intermediate conformations, captures transition pathways observed in large-scale MD simulations, and generates structures consistent with experimental distance distributions. Furthermore, Pi-Ensemble-generated conformations provide effective starting seeds for parallel MD simulations, improving conformational exploration and accelerating convergence relative to simulations initiated only from endpoint structures. These results establish sequence-guided structural interpolation as a practical strategy for probing protein conformational landscapes. By generating diverse and physically reasonable conformational proposals without long-timescale MD or model retraining, Pi-Ensemble provides an extensible framework for studying protein flexibility, guiding adaptive sampling, and accelerating mechanistic investigations of protein function.

A major cause of heart failure in elderly patients are deposits of Transthyretin (TTR) fibrils. Using molecular dynamic simulations, we explore how the stability of TTR fibrils can be modulated by D-Retro-Inverso (DRI) Peptides, built from D-amino acids with the sequence of the parent peptide switched, and describe a mechanism by which one of these peptides, DRI-K6V, disrupts TTR fibrils. Our results may open the way to design of peptide drugs targeting established TTR amyloidosis.

Biomolecular condensates function as membraneless compartments, and some protein condensates can selectively concentrate single-stranded nucleic acids while excluding double-stranded nucleic acids. Understanding how nucleic acid structure affects partitioning into condensates has important implications for nucleic acid activity and function within condensates. Here, we present a set of coarse-grained two-bead-per-nucleotide models for simulations of double-stranded RNA and DNA in the CALVADOS framework. Our models separately represent the backbone and base, and maintain the helical structures using an elastic network potential tuned to capture chain stiffness. For dsRNA, the base stickiness was tuned using experimental data on differential partitioning of single- and double-stranded RNA into Ddx4N1 condensates in order to account for reduced base accessibility upon duplex formation. This RNA structural selectivity varied with the balance of electrostatic and non-electrostatic interactions, as revealed by simulations of condensates of the CAPRIN1 disordered region at varying ionic concentrations and with an R-to-K sequence variant. Finally, we developed parameters for double-stranded DNA using a similar approach. We envision that the CALVADOS models for double-stranded RNA and DNA will be useful for studying co-condensates of proteins and structured nucleic acids.

Polyglutamine (polyQ) aggregation plays a central role in several neurodegenerative diseases, including Huntington's disease. DNAJB6b, a molecular chaperone involved in protein quality control, is known to efficiently suppress polyQ aggregation, but its anti-aggregation mechanism remains unclear. In this work we investigate the interaction between DNAJB6b and the polyQ region (Q48) of mutant Huntingtin Exon 1 (mHttEx1) using a custom-built coarse-grained molecular dynamics model. The model incorporates a two-bead-per-amino-acid representation with hydrogen bonding (termed 2BPA-HB), and is calibrated against all-atom molecular dynamics data in terms of geometry, hydrophobicity, and hydrogen bonding. The model reproduces the tertiary structure of DNAJB6b and its interactions with Q48, and reveals an inverse correlation between DNAJB6b concentration and Q48 aggregation propensity. Our simulations show that DNAJB6b co-condensates with polyQ molecules, thereby shielding the polyQ from forming the intermolecular hydrogen bonds necessary for amyloid formation. The 2BPA-HB CGMD model en- ables efficient exploration of DNAJB6b conformations, supporting future studies of chaperone-mediated aggregation suppression and therapeutic development.

The rising incidence of invasive fungal diseases emphasizes the need for novel therapeutic strategies, including membrane-targeting antifungal agents, which require representative lipid models for detailed molecular-level studies. In this work, we propose a consensus quinary fungal plasma membrane model based on lipidomic literature data, specifically PC:PE:PI:PA:PS phospholipid model with ratio of 44:29:13:8:6. Using a bottom-up approach, we characterized the biophysical properties of this system - with particular emphasis on mechanical parameters such as bending rigidity and area compressibility - by combining molecular dynamics simulations with experimental flicker-noise and ATR-FTIR spectroscopies. Furthermore, we investigated the effect of two key non-phospholipid components: ergosterol and triacylglycerols. Biophysical analysis revealed that DPPI and its specific interactions with DSPS induced the most substantial deviations in baseline membrane parameters, particularly area per lipid, membrane thickness, and area compressibility, while DSPS influenced bending rigidity change and DLiPA primarily affected lipid packing defects. In addition, ergosterol and TGs were found to influence all of the investigated parameters to different degree. Notably, the overall biophysical profile of the proposed FPMM closely mimicked that of natural vesicles derived from yeast lipid extracts, establishing this model may provide a reliable platform for studying fungal membrane biophysics and lipid-targeting interactions.