The MARTINI Force Field: Coarse Grained Model for Biomolecular Simulations

GPx4 is bound to peroxidized membranes by a hydrophobic anchor

Ferroptosis is a form of cell death discovered in recent years, induced by excessive peroxidation of phospholipids. Glutathione peroxidase 4 (GPx4) is an intracellular enzyme that can repair the peroxidized phospholipids on membranes, thus regulating ferroptosis. By combining multiscale molecular dynamics (MD) simulations and experimental assays, we investigate the binding mechanisms of GPx4 on membranes. Using coarse-grained MD simulations, we found that L130 and its adjacent residues on GPx4 can form a stable and unique binding interface with PE/PS-rich and peroxidized membranes. Subsequent all-atom MD simulations verified the stability of the binding interface. The critical residue on the interface, L130, was inserted deeply into the membrane as a hydrophobic anchor and guided the reaction center toward the membrane surface. Enzyme activity assays and in vitro cell experiments showed that mutations of L130 resulted in weaker activities of the enzyme, probably caused by less efficient binding of GPx4 on membranes, as revealed by in silico simulations. This study highlights the crucial role of the hydrophobic residue, L130, in the proper anchoring of GPx4 on membranes, the first step of its membrane-repairing function.

NMR Structural Characterization of SARS-CoV-2 ORF6 Reveals an N-Terminal Membrane Anchor

SARS-CoV-2, the virus responsible for the COVID-19 pandemic, encodes several accessory proteins, among which ORF6, a potent interferon inhibitor, is recognized as one of the most cytotoxic. Here, we investigated the structure, oligomeric state, and membrane interactions of ORF6 using NMR spectroscopy and molecular dynamics simulations. Using chemical-shift-ROSETTA, we show that ORF6 in proteoliposomes adopts a straight α-helical structure with an extended, rigid N-terminal part and flexible C-terminal residues. Cross-linking experiments indicate that ORF6 forms oligomers within lipid bilayers, and paramagnetic spin labeling suggests an antiparallel arrangement in its multimers. The amphipathic ORF6 helix establishes multiple contacts with the membrane surface with its N-terminal residues acting as membrane anchors. Our work demonstrates that ORF6 is an integral monotopic membrane protein and provides key insights into its conformation and the importance of the N-terminal region for the interaction with the membrane.

Computer-Aided Drug Discovery for Undruggable Targets

Undruggable targets are those of therapeutical significance but challenging for conventional drug design approaches. Such targets often exhibit unique features, including highly dynamic structures, a lack of well-defined ligand-binding pockets, the presence of highly conserved active sites, and functional modulation by protein-protein interactions. Recent advances in computational simulations and artificial intelligence have revolutionized the drug design landscape, giving rise to innovative strategies for overcoming these obstacles. In this review, we highlight the latest progress in computational approaches for drug design against undruggable targets, present several successful case studies, and discuss remaining challenges and future directions. Special emphasis is placed on four primary target categories: intrinsically disordered proteins, protein allosteric regulation, protein-protein interactions, and protein degradation, along with discussion of emerging target types. We also examine how AI-driven methodologies have transformed the field, from applications in protein-ligand complex structure prediction and virtual screening to de novo ligand generation for undruggable targets. Integration of computational methods with experimental techniques is expected to bring further breakthroughs to overcome the hurdles of undruggable targets. As the field continues to evolve, these advancements hold great promise to expand the druggable space, offering new therapeutic opportunities for previously untreatable diseases.

Global Optimization of Large Molecular Systems Using Rigid-Body Chain Stochastic Surface Walking

The global potential energy surface (PES) search of large molecular systems remains a significant challenge in chemistry due to "the curse of dimensionality". To address this, here we develop a rigid-body chain method in the framework of a stochastic surface walking (SSW) global optimization method, termed rigid-body chain SSW (RC-SSW). Based on the angle-axis representation for a single rigid body, our algorithm realizes the cooperative motion of connected rigid bodies and achieves the coupling between rigid-body chain movement and lattice variation in the generalized coordinate. By exploiting the numerical energy second derivative information on rigid bodies, RC-SSW can optimize the global PES of large molecular systems with an unprecedentedly high efficiency. We show that RC-SSW is more than 10 times faster in locating the model protein global minimum while revealing many more low energy conformations than molecular dynamics and can identify low energy phases of molecular crystals up to 172 atoms missed in the sixth CCDC blind test.

Molecular Mechanism of Polyphosphate-Mediated Nanosheet Self-Assembly

Polyphosphate (polyP), a biocompatible and biodegradable polymer, holds significant promise for drug delivery applications. Recent studies reveal that polyP and Mn2+ ions can self-assemble into nanosheets, with cetrimonium (CTA) acting as a templating agent. However, the underlying molecular mechanism remains poorly understood. Using coarse-grained molecular dynamics simulations, we reveal that polyP and CTA form a stable, sandwich-like nanostructure, with polyP positioned at the center. Self-assembly is driven by hydrophobic interactions, with curvature controlled by surface tension, which is determined by the interplay of electrostatic and hydrophobic interactions at the polyP/CTA interface. The addition of Mn2+ and oleate ions into the solution flattens the structure, while higher polyP-to-CTA ratios promote Mn2+ penetration. These findings highlight the power of simulations in uncovering self-assembly mechanisms and advancing applications in drug delivery.

Numerical Investigations of Translocation Characteristics of Paired Nonspherical Silica Nanoparticles across Pulmonary Surfactant Monolayer

To understand and prevent the toxic effects of lunar dust on astronauts' health in future manned lunar exploration missions, the translocation characteristics of paired nonspherical silica nanoparticles (P-NS-SiNPs) across a pulmonary surfactant (PS) monolayer are studied using a coarse-grained molecular dynamics method considering both ellipsoidal and cubic SiNPs with/without bugles. The key findings are as follows: (1) Compared with an individual SiNP, the translocation times for 4 and 6 nm ellipsoidal P-SiNPs decrease by 25-50% and 7.7-30.7% respectively, while those for 4 and 6 nm cubic P-SiNPs increase by 428.6% and 44.4% respectively, due to cooperative effects. (2) As initial minimum distance increases, the crossing times for ellipsoidal P-SiNPs first decrease and then increase, while embedding times for cubic P-SiNPs first increase and then decrease, due to different local curvature. (3) In the combinations of ellipsoidal-coupled cubic P-SiNPs, the translocation times for the 4 nm combination decrease by 25% and 14.3% respectively, while those for the 6 nm combination increase by 30.8% and 88.9%, respectively. (4) As the number of bulges on P-SiNPs increases, the average crossing times for 4 and 6 nm ellipsoidal-type P-SiNPs increase by 40%, 20%, as well as 14.3% and 219.1% respectively, while the embedding times for 4 nm cubic-type P-SiNPs decrease by 60.8% and 68.9% respectively, and those for 6 nm cubic-type P-SiNPs increase by 80.8% and 46.2%, respectively. In conclusion, due to the differences in contact area and local curvature, the translocation characteristics of ellipsoidal and cubic P-SiNPs exhibit two opposite trends under varying situations.

PolyConstruct: Adapting Biomolecular Simulation Pipelines for Polymers with PolyBuild, PolyConf, and PolyTop

Molecular dynamics simulations are invaluable tools that provide both a molecular understanding and a means for the rational design of polymers. A key bottleneck in current polymer molecular dynamics simulations is the lack of a comprehensive and generalizable method that streamlines the preparation of simulations for novel polymer architectures and chemistries. Here, we present PolyConstruct, a generalizable computational framework that leverages the GROMACS biomolecular simulation package for force field agnostic atomistic simulations of biocompatible and stimuli-responsive polymers. PolyConstruct contains three workflows, PolyBuild, PolyTop, and PolyConf, for generating chemically accurate topology parameters from monomer parameters and structural coordinates for complex polymer architectures and chemistries. We highlight the utility and robustness of PolyBuild, PolyTop, and PolyConf with examples of linear, branched, star, and dendritic polymers.

Enhanced Exploration of Protein Conformational Space through Integration of Ultra-Coarse-Grained Models to Multiscale Workflows

Computational techniques such as all-atom (AA) molecular dynamics (MD) simulations and coarse-grained (CG) models have been essential to study various biological problems over a wide range of scales. While AA simulations provide detailed insights, they are computationally expensive for capturing dynamics over longer length and time scales. CG approaches, particularly ultra-coarse-grained (UCG) models as considered in this study, have addressed this limitation by simplifying molecular representations, enabling the study of larger systems and longer time scales. This work focuses on the development of UCG models of proteins and their integration into the Multiscale Machine-Learned Modeling Infrastructure (MuMMI) to efficiently sample protein conformations, exemplified by the RAS-RBDCRD protein complex. By employing a combination of essential dynamics coarse graining (EDCG) and heterogeneous elastic network modeling (hENM) with anharmonic modifications, we developed UCG models based on the fluctuations observed in the higher resolution Martini CG simulations. These models allow the accurate sampling of protein configurations and long-range conformational changes. The incorporation of an implicit membrane model further enhanced the exploration of protein-membrane dynamics. Additionally, a novel machine-learning-based backmapping approach was developed to convert UCG structures to Martini CG representations, resulting in improved prediction accuracy. Finally, the integration of UCG models into MuMMI significantly enhances the exploration of protein configurations, offering critical insights into the role of protein dynamics in biological processes.

Unravelling the role of key amino acid residues of the parainfluenza fusion peptide in membrane fusion

Parainfluenza viruses enter host cells by fusing their envelope with the cell membrane. In this process mediated by the fusion glycoprotein, the fusion peptide plays an essential role in membrane binding and triggering fusion. Previously, we demonstrated that the parainfluenza fusion peptide (PIFP) oligomerizes into porelike structures within the membrane, leading to membrane perturbations, fusion, and leakage. Additionally, we identified two key amino acid residues in the PIFP, F103 and Q120, which are important in inducing lipid tail protrusion and maintaining peptide-peptide interactions, respectively. Here, we seek to elucidate the role of these two residues in the PIFP function by studying the impact of F103A and Q120A substitutions on peptide activity. We compared the substituted peptides with the native peptide using biophysical experiments and molecular dynamics (MD) simulations. Our results show that the F103A substitution significantly impairs PIFP's interaction with the membrane and its ability to induce lipid mixing and membrane leakage in experimental assays. Moreover, a decrease in lipid perturbation and water flux through the membrane was observed in the MD simulations. In contrast, the Q120A substitution appears to have minimal impact on membrane interaction and PIFP-induced membrane leakage. Interestingly, a pronounced change in the interpeptide interactions within the membrane of the substituted peptides was observed in the MD simulations. These findings provide crucial insights into the potential role of F103 and Q120 in PIFP activity: the N-terminal phenylalanine (F103) is pivotal for membrane insertion and fusion, while the Q120 is crucial for regulating peptide oligomerization and pore formation.

Lateral Organization and Dynamics of the Realistic Plasma Membrane

Large-scale simulations of realistic crowded cell membranes can bridge the gap between the simulations and experiments. However, the compositional complexity and structural asymmetry of cell membranes continue to pose significant challenges in computational biology. Recent advances in understanding native membranes, including their composition and protein structures, enable us to construct a highly realistic model of the mammalian plasma membrane. Using this model, we explore the organization and dynamics of biological cell membranes at the molecular level. We found that the interaction preferences of protein-lipid mediate the formation of dynamic clusters of nonrandomly distributed proteins, accompanied by heterogeneous structural properties and anomalous diffusion. These evolving dynamic clusters intertwine to form a highly complex and continuously changing protein network. Our study provides significant insights into the intricate lateral dynamic organization of cell membranes.

New Coarse-Grained Models to Describe the Self-Assembly of Aqueous Aerosol-OT

Aerosol-OT (AOT) is a very versatile surfactant that exhibits a plethora of self-assembly behaviors. In particular, due to its double-tail structure, it is capable of forming vesicles in water. However, the size of these structures, and the time scales over which they form, make them difficult to study using traditional all-atomistic molecular dynamics simulations. Here, three coarse-grained models are developed for AOT with different levels of detail. The models take advantage of the Martini 3 force field, which enables 2:1 mappings to be employed for the tail groups. It is shown that these models are able to reproduce the self-assembly behavior of AOT in water at three concentrations: below the critical vesicle concentration (CVC), above the CVC, and in the lamellar phase. The results also demonstrate the formation of vesicles from bicelles above the critical vesicle concentration, which is an important milestone for the continued study of vesicle behavior.

A skin-mimicking multifunctional hydrogel via hierarchical, reversible noncovalent interactions

Artificial skin is essential for bionic robotics, facilitating human skin-like functions such as sensation, communication, and protection. However, replicating a skin-matched all-in-one material with excellent mechanical properties, self-healing, adhesion, and multimodal sensing remains a challenge. Herein, we developed a multifunctional hydrogel by establishing a consolidated organic/metal bismuth ion architecture (COMBIA). Benefiting from hierarchical reversible noncovalent interactions, the COMBIA hydrogel exhibits an optimal combination of mechanical and functional properties, particularly its integrated mechanical properties, including unprecedented stretchability, fracture toughness, and resilience. Furthermore, these hydrogels demonstrate superior conductivity, optical transparency, freezing tolerance, adhesion capability, and spontaneous mechanical and electrical self-healing. These unified functions render our hydrogel exceptional properties such as shape adaptability, skin-like perception, and energy harvesting capabilities. To demonstrate its potential applications, an artificial skin using our COMBIA hydrogel was configured for stimulus signal recording, which, as a promising soft electronics platform, could be used for next-generation human-machine interfaces.

Fast-Rising Electric Pulses by Reducing Membrane Tension for Efficient Membrane Electroporation

Membrane electroporation is an emerging minimally invasive ablation technique being rapidly applied in the ablation treatment of tumors and heart conditions. Different rise times of electric fields lead to variations in the distribution and duration of electric field strength on the cell membrane. This study investigated the effect of the electric field's rise time on membrane electroporation characteristics using molecular dynamics simulations. The results showed that fast-rising electrical pulses can significantly reduce the membrane tension induced by the Coulomb force within a short period of time and lead to a trend of the electric field angle distribution towards smaller values below 45°, thereby effectively promoting the pore formation process. Optimizing the electric field's rise time is an effective electroporation ablation strategy, potentially improving the efficacy of clinical cancer treatment.

Polyimide Polymer Simulations through Coarse-Grained Modeling: Prediction of Structure, Physical Properties, and Gas Separation Properties

In this study, we introduce a set of coarse-grained (CG) force field parameters for simulating a series of 6FDA-based polyimides. Utilizing atomistic descriptors, we developed CG models that accurately predict the specific volume of the polymers under investigation. Our findings suggest that certain parameters, particularly those associated with specific diamines, can be employed to predict properties such as density using a multiple linear regression. Our study further explores the halogenation of diamines and proposes methods for estimating intermolecular interaction parameters. Our calculations refer to various structural properties, including the radius of gyration, end-to-end distance, glass transition temperature, and diffusion coefficients. Utilizing the newly developed CG force field parameters, we conducted gas separation simulations for 6FDA-DAM polyimide, particularly to predict both sorption- and diffusion-separation mechanisms within the polymer. These simulations provided excellent agreement with experimental data on solubility, diffusion, and permeability selectivity for CO2/CH4, O2/N2, and propylene/propane. The results contribute significantly to our understanding of polyimide behavior, and the parameters proposed here offer a promising tool for the development of new materials with tailored properties for targeted applications.

Water and Collagen: A Mystery Yet to Unfold

Collagen is the most abundant protein in the human body and plays an essential role in determining the mechanical properties of the tissues. Both as a monomeric protein and in fibrous assemblies, collagen interacts with its surrounding molecules, in particular with water. Interestingly, while it is well established that the interaction with water strongly influences the molecular and mechanical properties of collagen and its assemblies, the underlying mechanisms remain largely unknown. Here, we review the research conducted over the past 30 years on the interplay between water and collagen and its relevance for tissue properties. We discuss the water-collagen interaction on relevant time- and length scales, ranging from the vital role of water in stabilizing the characteristic triple helix structure to the negative impact of dehydration on the mechanical properties of tissues. A better understanding of the water-collagen interaction will help to unravel the effect of mutations and defective collagen production in collagen-related diseases and to pinpoint the key design features required to synthesize collagen-based biomimetic tissues with tailored mechanical properties.

How Are Plastoglobules Formed in Green Algae?

Plastoglobules are droplet-like organelles with a hydrophobic core of neutral lipids surrounded by a lipid monolayer, usually found in the chloroplasts of most plants and green algae. They not only serve as lipid storage units in the thylakoid membranes but are also involved in many cellular processes, including photoprotection, metabolite synthesis, protein recruitment, and chloroplast differentiation. Unlike lipid droplets, which nucleate, grow, and subsequently detach from the endoplasmic reticulum (ER) membrane, plastoglobules remain permanently coupled to the stromal side of the thylakoid membrane. In this study, we employ molecular dynamics simulations to investigate the growth mechanism of plastoglobules in a model thylakoid membrane of Dunaliella algae. Our findings suggest that significant membrane remodeling, likely driven by the thylakoid membrane proteins, is essential for the directional growth and stability of the plastoglobules.

Dynamics and Machine Learning Reveal the Link between Tripeptide Sequences and Evaporation-Driven Material Properties

Previous research showed that a peptide composed of three tyrosines (YYY) can turn into organic glass and cause strong adhesion between substrates via evaporation. However, the mechanisms of these processes remain unclear, and the exploration of applications of other peptide sequences is necessary. In this study, an optimized evaporation method was employed in molecular dynamics. It was found that YYY evaporation products possess abundant internal hydrogen bonds, which may facilitate the amorphous glass state formation. Moderate hydrophilicity of a peptide enhances molecular mobility and self-healing ability, while excessive hydrophilicity causes a water plasticizing effect. Stronger hydrophilicity also brings a larger curvature of evaporation products on polydimethylsiloxane (PDMS) substrate. A machine learning model was developed to predict the evaporation contact angle of peptide evaporation products and agrees well with the experiment. This research aims to improve understanding of peptide structure-function relationships and aid in designing custom organic optical devices based on peptide sequences.

Neutral Imidazole Lipid Analogues Exhibit Improved Properties for Artificial Model Biomembranes

In recent years, a variety of lipid-mimetic imidazolium salts have been developed and applied to investigate biological membranes and related processes. Despite their overall similar properties to natural lipids, there are potential drawbacks including cytotoxicity attributed to the cationic charge. Herein, we report the investigation of a novel class of electronically neutral imidazole-based lipids. In comparison to their positively charged congeners, they show improved biophysical properties and higher similarity to native lipids. By employing calorimetry, fluorescence spectroscopies, and fluorescence and atomic force microscopy, we examined changes in the thermotropic phase behavior, lipid order parameter, fluidity, and lateral membrane organization upon incorporation of the lipid mimetics. Depending on the characteristic of the lipid chains, charge of the headgroup, and substitution pattern, we observed changes in lipid order and fluidity, thus allowing modulation and fine-tuning of the physicochemical properties of the modified membrane. Notably, a newly synthesized imidazole-based cholesterol showed membrane properties very similar to natural cholesterol. Extensive computational studies indicate effective mimicking of cholesterol and reveal its capability to participate in raft formation. This new class of neutral imidazole lipid analogues is expected to lead to better molecular probes and tools.

Enhanced transdermal permeation of caffeine through a skin model using electric field-induced lipid vesicles: a novel approach for drug transport

Caffeine is a highly beneficial compound for human health, known for its anticancer, anti-inflammatory, and antioxidant properties, particularly in protecting the skin from UVB radiation damage. Although caffeine shows excellent potential for transdermal delivery, its hydrophilic nature often requires a chemical enhancer for effective transport. Traditional methods like iontophoresis and electroporation utilize external electric fields to create micro-pores in the skin, enhancing the delivery of hydrophilic molecules. While electroporation is well understood, the molecular mechanisms of iontophoresis are unclear. This investigation presents an innovative mechanism for caffeine transport from an aqueous solution without chemical enhancers using lipid vesicles generated by external electric fields. To investigate the caffeine transdermal transport process, we combined our iontophoresis methodology with molecular dynamics simulations using Gromacs and the Martini force field alongside a practical custom experiment. Our approach employed a constant electric field of 22-25 mV nm-1, successfully generating lipid vesicles that transport caffeine molecules. Notably, alternating electric fields at 306 K (physiological skin temperature) increased caffeine transport by 20%, and at 323 K, we achieved an impressive 300% increase compared to scenarios without electric fields. Our homemade Franz cell setup showed a permeation rate dependent on the electric field correlated with vesicle formation. Additionally, hyperspectral Raman mapping identified unsaturated carbon and C-N groups as key contributors to vesicle and pore instability. This groundbreaking approach offers significant potential for enhancing transdermal drug delivery systems.

GōMartini 3: From large conformational changes in proteins to environmental bias corrections

Coarse-grained modeling has become an important tool to supplement experimental measurements, allowing access to spatio-temporal scales beyond all-atom based approaches. The GōMartini model combines structure- and physics-based coarse-grained approaches, balancing computational efficiency and accurate representation of protein dynamics with the capabilities of studying proteins in different biological environments. This paper introduces an enhanced GōMartini model, which combines a virtual-site implementation of Gō models with Martini 3. The implementation has been extensively tested by the community since the release of the reparametrized version of Martini. This work demonstrates the capabilities of the model in diverse case studies, ranging from protein-membrane binding to protein-ligand interactions and AFM force profile calculations. The model is also versatile, as it can address recent inaccuracies reported in the Martini protein model. Lastly, the paper discusses the advantages, limitations, and future perspectives of the Martini 3 protein model and its combination with Gō models.

Self-Assembled Multilayered Concentric Supraparticle Architecture

Supraparticles (SPs) with unique properties are emerging as versatile platforms for applications in catalysis, photonics, and medicine. However, the synthesis of novel SPs with complex internal structures remains a challenge. Self-Assembled Multilayered Supraparticles (SAMS) presented here are composed of concentric lamellar spherical structures made from metallic nanoparticles, formed from a synergistic three-way interaction phenomenon between gold nanoparticles, lipidoid, and gelatin, exhibiting interlayer spacing of 3.5  ± 0.2 nm within a self-limited 156.8  ± 56.6 nm diameter. The formation is critically influenced by both physical (including nanoparticle size, lipidoid chain length) and chemical factors (including elemental composition, nanoparticle cap, and organic material), which collectively modulate the surface chemistry and hydrophobicity, affecting interparticle interactions. SAMS can efficiently deliver labile payloads such as siRNA, achieving dose-dependent silencing in vivo, while also showing potential for complex payloads such as mRNA. This work not only advances the field of SP design by introducing a new structure and interaction phenomenon but also demonstrates its potential in nanomedicine.

Local mapping of the nanoscale viscoelastic properties of fluid membranes by AFM nanorheology

Biological membranes are intrinsically dynamic entities that continually adapt their biophysical properties and molecular organisation to support cellular function. Current microscopy techniques can derive high-resolution structural information of labelled molecules but quantifying the associated viscoelastic behaviour with nanometre precision remains challenging. Here, we develop an approach based on atomic force microscopy in conjunction with fast nano-actuators to map the viscoelastic response of unlabelled supported membranes with nanometre spatial resolution. On fluid membranes, we show that the method can quantify local variations in the molecular mobility of the lipids and derive a diffusion coefficient. We confirm our experimental approach with molecular dynamics simulations, also highlighting the role played by the water at the interface with the membrane on the measurement. Probing ternary model bilayers reveals spatial correlations in the local diffusion over distances of ≈20 nm within liquid disordered domains. This lateral correlation is enhanced in native bovine lens membranes, where the inclusion of protein-rich domains induces four-fold variations in the diffusion coefficient across < 100 nm of the fluid regions, consistent with biological function. Our findings suggest that diffusion is highly localised in fluid biomembranes.

ART-SM: Boosting Fragment-Based Backmapping by Machine Learning

In sequential multiscale molecular dynamics simulations, which advantageously combine the increased sampling and dynamics at coarse-grained resolution with the higher accuracy of atomistic simulations, the resolution is altered over time. While coarse-graining is straightforward once the mapping between atomistic and coarse-grained resolution is defined, reintroducing the atomistic details is still a nontrivial process called backmapping. Here, we present ART-SM, a fragment-based backmapping framework that learns from atomistic simulation data to seamlessly switch from coarse-grained to atomistic resolution. ART-SM requires minimal user input and goes beyond state-of-the-art fragment-based approaches by selecting from multiple conformations per fragment via machine learning to simultaneously reflect the coarse-grained structure and the Boltzmann distribution. Additionally, we introduce a novel refinement step to connect individual fragments by optimizing specific bonds, angles, and dihedral angles in the backmapping process. We demonstrate that our algorithm accurately restores the atomistic bond length, angle, and dihedral angle distributions for various small and linear molecules from Martini coarse-grained beads and that the resulting high-resolution structures are representative of the input coarse-grained conformations. Moreover, the reconstruction of the TIP3P water model is fast and robust, and we demonstrate that ART-SM can be applied to larger linear molecules as well. To illustrate the efficiency of the local and autoregressive approach of ART-SM, we simulated a large realistic system containing the surfactants TAPB and SDS in solution using the Martini3 force field. The self-assembled micelles of various shapes were backmapped with ART-SM after training on only short atomistic simulations of a single water-solvated SDS or TAPB molecule. Together, these results indicate the potential for the method to be extended to more complex molecules such as lipids, proteins, macromolecules, and materials in the future.

Bayesian Nonparametric Analysis of Residence Times for Protein-Lipid Interactions in Molecular Dynamics Simulations

Molecular Dynamics (MD) simulations are a versatile tool to investigate the interactions of proteins within their environments, in particular, of membrane proteins with the surrounding lipids. However, quantitative analysis of lipid-protein binding kinetics has remained challenging due to considerable noise and low frequency of long binding events, even in hundreds of microseconds of simulation data. Here, we apply Bayesian nonparametrics to compute residue-resolved residence time distributions from MD trajectories. Such an analysis characterizes binding processes at different time scales (quantified by their kinetic off-rate) and assigns to each trajectory frame a probability of belonging to a specific process. In this way, we classify trajectory frames in an unsupervised manner and obtain, for example, different binding poses or molecular densities based on the time scale of the process. We demonstrate our approach by characterizing interactions of cholesterol with six different G-protein-coupled receptors (A2AAR, β2AR, CB1R, CB2R, CCK1R, and CCK2R) simulated with coarse-grained MD simulations with the MARTINI model. The nonparametric Bayesian analysis allows us to connect the coarse binding time series data to the underlying molecular picture and thus not only infers accurate binding kinetics with error distributions from MD simulations but also describes molecular events responsible for the broad range of kinetic rates.

Weakening Solvent-Solute Interactions for High-Efficiency Screen-Printed Perovskite Solar Cells

Screen printing has emerged as a leading candidate for industrial-scale fabrication of perovskite photovoltaics. However, strong solvent-solute interaction in conventional formulations accelerates the preferential crystallization of perovskites at points, hindering the progressive phase evolution from point to line to plane. In this work, we introduced halogen ions to weaken solvent-solute interactions, achieving the reduced Pb⋅⋅⋅O coordination strength counterbalanced by enhanced Pb-I bonding interactions. This weakened interaction delays formamidinium iodide participation in rapid phase transitions to α-formamidinium lead iodide, enabling controlled crystallization kinetics. The optimized screen-printed perovskite solar cells demonstrate remarkable power conversion efficiencies (PCE) of 21.8 % for 0.05 cm2 devices and 18.95 % for 5 cm×5 cm mini-modules (active area: 12.60 cm2). Furthermore, this strategy exhibits broad process compatibility, achieving 23-24 % PCEs for both blade-coating and spin-coating devices fabricated under ambient conditions (25-30 °C, 35-50 % relative humidity). These breakthroughs highlight the universal potential of coordination engineering for scalable perovskite photovoltaics.

Predicting aquatic toxicity of anionic hydrocarbon and perfluorinated surfactants using membrane-water partition coefficients from coarse-grained simulations

Anionic surfactants are widely used in commercial and industrial applications. For assessment of their environmental fate and effects, it is highly desirable to quantify the membrane-water partition/distribution coefficient (Kmw/Dmw). Here, we further develop a computational route to Dmw for anionic surfactants based on coarse-grained molecular dynamics simulations, validating it against new and existing experimental measurements. Having parameterised molecular fragments for the coarse-grained models, the simulations are used to predict Dmw for molecules where no experimental values are available. This expanded set of simulated Dmw values is then used to derive QSARs for acute toxicity of mono-constituent anionic surfactants in daphnids and fish, allowing for extrapolation to similar compounds without experimental Dmw values. For this study, we have selected hydrocarbon-based (HC) surfactants because of their widespread use, and perfluorinated (FC) surfactants as a challenging case study. Separate daphnid and fish QSARs demonstrate good fits, robustness and predictivity, and highlight differing toxicity relationships for HC and FC surfactants in daphnids. Overall, the combined use of simulated Dmw and derived QSARs is a promising approach for ecotoxicity screening of surfactants.

Coarse-Grained Molecular Dynamics Simulations Reveal Potential Role of Cardiolipin in Lateral Organization of Proteorhodopsin

Proteorhodopsin (PR) is a microbial light-harvesting proton pump protein that is ubiquitous in marine ecosystems and is critical for biological solar energy conversion. A unique characteristic of PR is that its function can be directly affected by changes in the surrounding cellular membrane environment. Cardiolipin (CL) is a commonly found lipid in mitochondria and bacterial cell membranes and plays a prominent role in the function of numerous integral membrane proteins, due to its bulky conical shape and ionizable nature of its headgroup. CL can directly interact with other microbial rhodopsins and modulate their function; however, the potential role of CL in the function of PR is unclear. In this study, we used the MARTINI coarse-grained force field to characterize the interactions of CL with PR in a model bilayer via coarse-grained molecular dynamics (MD) simulations. Our simulations show that both electrostatic and nonpolar forces drive residue-specific interactions of CL with proteorhodopsin, especially for the asymmetrical -1 charge state of CL. Several CL binding sites were identified, with lipid-protein interactions occurring on the μs time scale. These binding sites are proximal to key functional areas and regions of oligomerization on PR, suggesting that CL could play a role in modulating proton pumping of proteorhodopsin.

Coarse-grained simulation of water: A comparative study and overview

In spite of the tremendous increase in computational power over the last few decades, the problem of simulating atomistic systems containing large amounts of water molecules over longer lengths and time scales still remains. In this respect, the coarse-grained (CG) force field reduces the computational cost and, therefore, allows simulations of larger systems for longer times. However, the specific scope of the different CG water models is more limited compared to their atomistic counterparts. In this context, we conducted a comparative study on the molecular physical structure, thermodynamic, and dynamic properties of bulk water systems using six distinct CG water models and all-atom (AA) simulations. The six CG simulation procedures involved modeling with three variants of the water model coming from the MARTINI force field, one from the SPICA force field, and the two Iterative Boltzmann Inversion (IBI) derived potentials from the AA simulations. The AA simulations have been performed using the SPC/E and TIP4P force fields. The IBI models, namely SPC/E-IBI and TIP4P-IBI, depict the structural features in close agreement with the atomistic samples. The explicit number of water molecules in the first coordination shell for the three MARTINI models and the SPICA force field is in excellent agreement with the SPC/E and TIP4P values. The ensuing simulated densities for the various water models align significantly with the literature data, indicating the reliability of our approach. The SPC/E and SPICA models stand out in predicting the enthalpy of vaporization among the all-atom and CG force fields, respectively. The two all-atom models and their IBI equivalents are better at representing the isobaric specific heat capacity compared to the other models. The isothermal compressibility is reproduced comprehensively by the SPC/E force field followed by TIP4P, while SPICA is the better choice within the CG models. With respect to the dynamics of the system, the diffusion coefficient of the SPICA force field is in perfect agreement with the experimental data, even better than the atomistic samples. The overall scores of the different models, indicative of their relative performances compared to the other models, have also been computed.

The Role of Polyisobutylene-Bis-Succinimide (PIBSI) Dispersants in Lubricant Oils on the Deposit Control Mechanism

Molecular modeling calculations for the design and improvement of next-generation additives for motor oils have reached a level that can support and improve experimental results. The regulation of insoluble sludge nanoparticle aggregations within oil and on engine pistons is a critical performance metric for lubricant oil additives. There is a general agreement regarding the mechanism of deposit formation which is attributed to the self-aggregation of nano-sized carbon rich insoluble structures. Dispersants are a primary category of additives employed to inhibit aggregation in lubricant formulations. Along with the base oil, they are crucial in dispersing and stabilizing insoluble particles to manage the formation of deposits. In this study, multiscale modeling methods were used to elucidate molecular mechanism of deposit control via polyisobutylene-bis-succinimide (PIBSI) dispersants by using density functional theory (DFT), molecular dynamics (MD) simulations of cells constructed by statistical sampling of molecular configurations, and coarse-grained (CG) simulations. The aim of this study was to understand the role of different groups such as succinimide, amine center, and two polyisobutylene (PIB) tails in PIBSI dispersants. It was demonstrated that the mechanism of deposit control by the polymer-based PIBSI dispersant can be elucidated through the interactions among various constituents, including hydrogen bonding and hydrophilic-hydrophobic interactions. We showed that sludge type nanoparticle aggregation is mitigated by intercalation of polar amine central groups of dispersant between the nanoparticles followed by the extension of two hydrophobic PIB chains into the oil phase that decreases coalesce further by forming a hydrophobic repulsive layer.

Martini 3 coarse-grained model of enzymes: Framework with validation by all-atom simulations and x-ray diffraction measurements

Recent experiments have shown that complexation with a stabilizing compound can preserve enzyme activity in harsh environments. Such complexation is believed to be driven by noncovalent interactions at the enzyme surface, including hydrophobicity and electrostatics. Molecular modeling of these interactions is costly at the all-atom scale due to the long time scales and large particle counts needed to characterize binding. Protein structure at the scale of amino acid residues is parsimoniously represented by a coarse-grained model in which one particle represents several atoms, significantly reducing the cost of simulation. Coarse-grained models may then be used to generate reduced surface descriptions to underlie detailed theories of surface adhesion. In this study, we present two coarse-grained enzyme models-lipase and dehalogenase-that have been prepared using the Martini 3 top-down modeling framework. We simulate each enzyme in aqueous solution and calculate the statistics of protein surface features and shape descriptors. The values from the coarse-grained data are compared with the same calculations performed on all-atom reference systems, revealing key similarities of surface chemistry at the two scales. Structural measures are calculated from the all-atom reference systems and compared with estimates from small-angle x-ray scattering experiments, with good agreement between the two. The described procedures of modeling and analysis comprise a framework for the development of coarse-grained models of protein surfaces with validation to experiment.

Highly tail-asymmetric lipids interdigitate and cause bidirectional ordering

Phospholipids form structurally and compositionally diverse membranes. A less studied type of compositional diversity involves phospholipid tail variety. Some phospholipids contain two acyl tails which differ in length. These tail-asymmetric lipids are shown to contribute to temperature sensitivity, oxygen adaptability, and membrane fluidity. Membranes of a highly virulent intracellular bacterium, Francisella tularensis, contain highly tail-asymmetric 1-lignoceroyl-2-decanoyl-sn-glycero-3-phosphatidylethanolamine (XJPE) lipids which were previously shown to inhibit inflammatory responses in host cells. XJPE tails have unusually high asymmetry, and how they contribute to membrane properties on a molecular level is unknown. Here, we use small angle X-ray scattering and molecular dynamics simulations to investigate how varying XJPE ratios alters properties of simple membranes. Our results demonstrate that at high concentration they promote liquid-to-gel transition in otherwise liquid membranes, while at low concentration they are tolerated well, minimally altering membrane properties. In liquid membranes, XJPE lipids dynamically adopt two main conformations; with the long tail extended into the opposing leaflet or bent-back residing in its own leaflet. When added to both leaflets XJPE primarily adopts an extended confirmation, while asymmetric addition results in more bent-back orientations. The former increases tail ordering and the latter decreases it. XJPE tails adopt different conformations that induce composition- and leaflet-dependent bidirectional effect on membrane fluidity and this suggests that Francisella tularensis could use tail asymmetry to facilitate vesicle fusion and destabilize host cells. The effect of tail-asymmetric lipids on complex membranes should be further investigated to reveal the regulatory roles of high tail asymmetry.

Effects of Nanoplastics on Lipid Membranes and Vice Versa: Insights from All-Atom Molecular Dynamics Simulations

We compute the potential of mean force (PMF) between semicrystalline polyethylene (PE) nanoplastics (NPLs) and model POPC and DPPC bilayers, which approximate in vivo membranes, using atomistic simulations. Our work shows that atomistic resolution is required to characterize the NPL and lipid interactions. By analyzing the PMF, we demonstrate that the mechanical properties of membranes, rather than NPL semicrystalline morphologies, govern NPL-membrane interactions. Resistance to NPL penetration arises from the elastic energy of the membrane deformation. The flexible POPC membranes resist NPL translocation, and the brittle DPPC membranes fracture under stress. Using an elastic free energy model, we approximate effective repulsions between lipid membranes and NPLs of various sizes. Our mean first-passage time analysis shows that even small, bare NPLs cannot easily penetrate brittle lipid membranes via passive diffusion, even at high concentrations. However, eco-coronas or other mechanisms, such as endocytosis, may still facilitate the cellular uptake of NPLs and MPLs. While semicrystalline morphologies do not directly impact NPL translocation, they do influence NPL behavior within lipid membranes upon translocation. Semicrystalline NPLs remain intact within lipid membranes, whereas amorphous NPLs can dissolve into the hydrophobic core and alter the elastic properties of the membrane.

Physical determinants of nanoparticle-mediated lipid membrane fusion

A wide range of fundamental cellular activities rely on lipid membrane fusion. Membrane fusion processes can be mimicked by synthetic approaches to understand fusion mechanisms and develop novel drug delivery systems and therapeutic agents. Recently, membrane-embedded amphiphilic gold nanoparticles (AuNPs) have been employed as artificial fusogens to induce finely tuned membrane fusion in vitro. However, the physical determinants driving and regulating the fusion process mediated by AuNPs remain largely unexplored, thus limiting the application potential of this synthetic fusion system. Herein, we focus on unraveling the effect of the interplay between the curvature of the lipid membrane and the size of amphiphilic AuNPs during fusion events. We employed AuNPs with the same surface chemistry but different core diameters (∼2 nm and ∼4 nm) interacting with phosphatidylcholine unilamellar vesicles of different membrane curvatures containing a biologically relevant percentage of cholesterol. Based on a combination of fluorescence spectroscopy assays, dissipative quartz microbalance, and molecular dynamics simulations, our findings reveal that small AuNPs promote vesicle fusion regardless of the membrane curvature. In contrast, large AuNPs do not exhibit fusogenic properties with low curvature membranes and can induce fusion events only with significantly curved membranes. Large NPs impede the progression from the stalk state to the hemifused state via steric hindrance, an effect that is only partially compensated by the membrane curvature. These results offer novel insights into the role of AuNP core size and membrane curvature in mediating the interaction between the vesicles during fusion and highlight how understanding these physical determinants has broad implications in fully exploiting the application potential of novel synthetic fusion approaches.

Multiscale Simulations and Profiling of Human Thymidine Phosphorylase Mutations: Insights into Structural, Dynamics, and Functional Impacts in Mitochondrial Neurogastrointestinal Encephalopathy

Mitochondrial neurogastrointestinal encephalopathy (MNGIE) is a rare metabolic disorder caused by missense mutations in the TYMP gene, leading to the loss of human thymidine phosphorylase (HTP) activity and subsequent mitochondrial dysfunction. Despite its well-characterized biochemical basis, the molecular mechanisms by which MNGIE-associated mutations alter HTP's structural stability, dynamics, and substrate (thymidine) binding remain unclear. In this study, we employ a multiscale computational approach, integrating AlphaFold2-based structural modeling, all-atom and coarse-grained molecular dynamics (MD) simulations, protein-ligand (HTP-thymidine) docking, HTP-thymidine complex simulations, binding free-energy landscape analysis, and systematic mutational profiling to investigate the impact of key MNGIE-associated mutations (R44Q, G145R, G153S, K222S, and E289A) on HTP function. Analyses of our long-duration multiscale simulations (comprising 9 μs coarse-grained, 1.2 μs all-atom apo HTP, and 1.2 μs HTP-thymidine complex MD simulations) and physicochemical properties reveal that while wild-type HTP maintains structural integrity and strong thymidine-binding affinity, MNGIE-associated mutations induce substantial destabilization, increased flexibility, and reduced enzymatic efficiency. Free-energy landscape analysis highlights a shift toward less stable conformational states in mutant HTPs, further supporting their functional impairment. Additionally, the G145R mutation introduces steric hindrance at the active site, preventing thymidine binding and causing off-site interactions. These findings not only provide fundamental insights into the physicochemical and dynamic alterations underlying HTP dysfunction in MNGIE but also establish a computational framework for guiding future experimental studies and the rational design of therapeutic interventions aimed at restoring HTP function.

Unveiling Interactions of a Peptide-Bound Monolayer-Protected Metal Nanocluster with a Lipid Bilayer

Monolayer-protected atomically precise nanoclusters (MPCs) are potential candidates for drug delivery because of their unique, versatile, and tunable physiochemical properties. The rational design of nanosized drug carriers relies on a deep understanding of their molecular-level interactions with cell membranes and other biological entities. In this work, we applied coarse-grained molecular dynamics and umbrella sampling simulations to investigate the interactions between the magainin 2 (MG2)-loaded Au144(MPA)60 (MPA = 5-mercaptopentanoic acid) nanocluster (MG2-MPC) and a model anionic tumor cell membrane. Electrostatic interactions between MPC ligands and MG2's positively charged residues with the polar headgroups of lipids play a crucial role in the adhesion of the MG2-MPC complex to the membrane surface. Furthermore, MG2-MPCs self-assemble in the linear trimeric supramolecular aggregate on the bilayer surface, indicating a possible mechanism of MPC's action in peptide delivery to the membrane.

GPCR oligomerization across classes: A2AR-mediated regulation of mGlu5R activation

The adenosine A2A receptor (A2AR), a class A GPCR, is a known player in neurological diseases, including Parkinson's disease and Alzheimer's disease, and is also implicated in SARS-CoV-2 infection. Recent studies have revealed its oligomerization with metabotropic glutamate receptor type 5 (mGlu5R), a class C G protein coupled receptor (GPCR) that exists in the homodimeric form. Simultaneous activation of both receptors synergistically enhances mGlu5R-mediated effects in the hippocampus. Despite their importance, the molecular mechanisms governing these interactions remain unclear. In this study, we used molecular modelling techniques, including molecular docking, extensive molecular dynamics (MD) simulations, and detailed analysis, to elucidate the interactions between mGlu5R and A2AR in the inactive and active states. Our findings provide molecular-level insights into the permissive role of A2AR in mGlu5R activation, demonstrating that the inactive A2AR interface within the oligomer blocks the mGlu5R transmembrane helix 6 (TM6), which is crucial for activation. Upon A2AR activation, the oligomer interface undergoes conformational rearrangement, exposing mGlu5R-TM6 and allowing for mGlu5R activation. Furthermore, we identified a pivotal role of the mGlu5R-TM4:A2AR-TM4 interface in facilitating mGlu5R activation. These results highlight the intricate architecture of the mGlu5R:A2AR oligomer, advancing our understanding of GPCR oligomerization and its regulatory mechanisms on receptor activity.

Molecular dynamics study of stiffness and rupture of axonal membranes

Diffuse axonal injury (DAI), characterized by widespread damage to axons throughout the brain, represents one of the most devastating and difficult-to-treat forms of traumatic brain injury. Different theories exist about the mechanism of DAI, among which one hypothesis states that membrane poration of the axons initiates DAI. To investigate the hypothesis, molecular models of axonal membranes, incorporating 25 different lipids distributed asymmetrically in the leaflets, were developed using a coarse-grain description and simulated using molecular dynamics techniques. Different protein concentrations were embedded inside the lipid bilayer to describe the different sub-cellular parts in myelinated and unmyelinated axons. The models were investigated in equilibration and under deformation to characterize the structural and mechanical properties of the membranes, and comparisons were made with other subcellular parts, particularly myelin. Employing a bottom-top approach, the results were coupled with a finite element model representing the axon at the cell level. The results indicate that pore formation in the node-of-Ranvier occurs at a lower rupture strain compared to other axolemma parts, whereas myelin poration exhibits the highest rupture strains among the investigated models. The observed rupture strain for the node-of-Ranvier aligns with experimental studies, indicating a threshold for injury at axonal strains exceeding 10-13 % depending on the strain rate. The results indicate that the hypothesis suggesting mechanoporation triggers axonal injury cannot be dismissed, as this phenomenon occurs within the threshold of axonal injury.

Coarse-Grained Simulations of Phosphorylation Regulation of p53 Autoinhibition

Intrinsically disordered proteins (IDPs) are key components of cellular signaling and regulatory networks. They frequently remain dynamic even in complexes and thus rely on potentially subtle shifts in the disordered conformational ensemble for function. Understanding the molecular basis of these fascinating mechanisms of IDP function and regulation requires a detailed characterization of dynamic ensembles in various biologically relevant states. Here, we study the phosphorylation dependence of the dynamic interaction between the N-terminal transactivation domain (NTAD) and DNA-binding domain (DBD) of tumor suppressor p53, which plays a key role in the autoinhibition and regulation of p53 activation or termination during various stages of stress response. By extending the hybrid-resolution (HyRes) coarse-grained (CG) protein force field to model phosphorylated side chains, we show that HyRes simulations accurately recapitulate the effects of phosphorylation on the p53 NTAD/DBD interactions. The simulated ensembles show that phosphorylation of Thr55 as well as Ser46 enhances dynamic NTAD/DBD interactions and further induces conformational shifts that promote trans interactions between two p53 dimers to drive dissociation from DNA. These CG simulations thus provide a strong molecular basis in support of previous experimental studies suggesting the central role of dynamic interactions of disordered domains and phosphorylation in the function of p53. The success of this study also suggests that HyRes provides an efficient and viable tool for studying dynamic interactions and post-translational modifications in IDP function and regulation.

Challenges in simulating whole virus particles and how to fix them with the SIRAH force field

Current developments in specialized software and computer power make the simulation of large molecular assemblies a technical possibility despite their computational cost. Coarse-grained (CG) approaches simplify molecular complexity and reduce computational costs while preserving intermolecular physical/chemical interactions. These methods enable virus simulations, making them more accessible to research groups with limited supercomputing resources. However, setting up and running molecular dynamics simulations of multimillion systems requires specialized molecular modeling, editing, and visualization skills. Moreover, many issues related to the computational setup, the choice of simulation engines, and the force fields that rule the intermolecular interactions require particular attention and are key to attaining a realistic description of viral systems at the fully atomistic or CG levels. Here, we provide an overview of the current challenges in simulating entire virus particles and the potential of the SIRAH force field to address these challenges through its implementations for CG and multiscale simulations.

Understanding dynamics in coarse-grained models. V. Extension of coarse-grained dynamics theory to non-hard sphere systems

Coarse-grained (CG) modeling has gained significant attention in recent years due to its wide applicability in enhancing the spatiotemporal scales of molecular simulations. While CG simulations, often performed with Hamiltonian mechanics, faithfully recapitulate structural correlations at equilibrium, they lead to ambiguously accelerated dynamics. In Paper I [J. Jin, K. S. Schweizer, and G. A. Voth, J. Chem. Phys. 158(3), 034103 (2023)], we proposed the excess entropy scaling relationship to understand the CG dynamics. Then, in Paper II [J. Jin, K. S. Schweizer, and G. A. Voth, J. Chem. Phys. 158(3), 034104 (2023)], we developed a theory to map the CG system into a dynamically consistent hard sphere system to analytically derive an expression for fast CG dynamics. However, many chemical and physical systems do not exhibit hard sphere-like behavior, limiting the extensibility of the developed theory. In this paper, we aim to generalize the theory to the non-hard sphere system based on the Weeks-Chandler-Andersen perturbation theory. Since non-hard sphere-like CG interactions affect the excess entropy term as it deviates from the hard sphere description, we explicitly account for the extra entropy to correct the non-hard sphere nature of the system. This approach is demonstrated for two different types of interactions seen in liquids, and we further provide a generalized description for any CG models using the generalized Gaussian CG models using Gaussian basis sets. Altogether, this work allows for extending the range and applicability of the hard sphere CG dynamics theory to a myriad of CG liquids.

Probing nanopores: molecular dynamics insights into the mechanisms of DNA and protein translocation through solid-state and biological nanopores

Nanopore sequencing technology has revolutionized single-molecule analysis through its unique capability to detect and characterize individual biomolecules with unprecedented precision. This perspective provides a comprehensive analysis of molecular dynamics (MD) simulations in nanopore research, with particular emphasis on comparing molecular transport mechanisms between biological and solid-state platforms. We first examine how MD simulations at atomic resolution reveal distinct characteristics: biological nanopores exhibit sophisticated molecular recognition through specific amino acid interactions, while solid-state nanopores demonstrate advantages in structural stability and geometric control. Through detailed analysis of simulation methodologies and their applications, we show how computational approaches have advanced our understanding of critical phenomena such as ion selectivity, conformational dynamics, and surface effects in both nanopore types. Despite computational challenges including limited simulation timescales and force field accuracy constraints, recent advances in high-performance computing and artificial intelligence integration have significantly improved simulation capabilities. By synthesizing perspectives from physics, chemistry, biology, and computational science, this perspective provides both theoretical insights and practical guidelines for developing next-generation nanopore platforms. The integration of computational and experimental approaches discussed here offers promising directions for advancing nanopore technology in applications ranging from DNA/RNA sequencing and protein post-translational modification analysis to disease diagnosis and drug screening.

Microphase separation produces interfacial environment within diblock biomolecular condensates

The phase separation of intrinsically disordered proteins is emerging as an important mechanism for cellular organization. However, efforts to connect protein sequences to the physical properties of condensates, that is, the molecular grammar, are hampered by a lack of effective approaches for probing high-resolution structural details. Using a combination of multiscale simulations and fluorescence lifetime imaging microscopy experiments, we systematically explored a series of systems consisting of diblock elastin-like polypeptides (ELPs). The simulations succeeded in reproducing the variation of condensate stability upon amino acid substitution and revealed different microenvironments within a single condensate, which we verified with environmentally sensitive fluorophores. The interspersion of hydrophilic and hydrophobic residues and a lack of secondary structure formation result in an interfacial environment, which explains both the strong correlation between ELP condensate stability and interfacial hydrophobicity scales, as well as the prevalence of protein-water hydrogen bonds. Our study uncovers new mechanisms for condensate stability and organization that may be broadly applicable.

Mechanistic study of enhanced drug release in mixed pH-responsive peptide-loaded liposomes

Liposomes serving as nanocarriers offer significant advantages in drug delivery for tumor treatment. There still exists challenges in controlling drug release by disintegrating the liposome membrane for the improvement of therapeutic efficiency. In this paper, a novel method involving the mixture of short peptides with pH-responsive characteristics into the cargo has been introduced. This approach facilitates the release of doxorubicin (DOX) in the acidic tumor tissue environment. The efficacy of this improvement was elucidated through molecular dynamics simulations and experiments. Liposomes incorporating a 1:1 ratio of peptides-DOX exhibited pronounced pH sensitivity and an enhanced drug release profile. The underlying mechanism is attributed to the peptides entering tumor tissues and undergoing protonation in acidic conditions, which increases the hydrophilicity of the peptide-DOX clusters and the internal surface tension of the liposomes. This alteration disrupts the balance between the inner and outer surface tensions of the nanocarrier, causing the liposomes to structurally disintegrate and thus enhancing drug release. The results from both thermodynamic analysis results and experimental data confirm the augmented drug release efficiency of this method, offering valuable theoretical insights for nanoparticle design and determining the optimal mixing ratio for therapeutic applications.

A bilinear model for the elastic response of hydrated lipid bilayers under normal pressure difference

The elasticity of phospholipid membranes as a function of hydration was investigated using coarse-grained molecular simulations. Multilamellar membranes consist of two or more lipid bilayers separated by a thin layer of water, a system commonly found in cell membranes that provides surface tension in the alveoli of the lungs and on cartilaginous surfaces of synovial joints. The objective was to quantify the response of such systems to compression in the direction perpendicular to the membranes as a function of the amount of water between the bilayers or hydration of the system. The present study investigated a variety of phospholipids with six levels of hydration found in multilamellar bilayers in biological systems. Our simulations support the existence of a universal behavior of the increase in surface area per lipid as a function of the normal pressure difference, the difference between the pressure applied in the direction normal to the membrane and the pressure applied in the directions parallel to the membrane. Normalizing the surface area per lipid and the pressure difference by their respective values at rupture yields a composite function of two linear regimes for all the hydration levels under investigation. Where possible, a physics-based interpretation of the normalization scales was provided. Although some parameters of the model are determined empirically, the model represents a promising step in continuum modeling of the response of multilamellar lipid membranes as a function of mechanical stress and hydration.

A dynamic structural unit of phase-separated heterochromatin protein 1α as revealed by integrative structural analyses

The heterochromatin protein HP1α consists of an N-terminal disordered tail (N-tail), chromodomain (CD), hinge region (HR), and C-terminal chromo shadow domain (CSD). While CD binds to the lysine9-trimethylated histone H3 (H3K9me3) tail in nucleosomes, CSD forms a dimer bridging two nucleosomes with H3K9me3. Phosphorylation of serine residues in the N-tail enhances both H3K9me3 binding and liquid-liquid phase separation (LLPS) by HP1α. We have used integrative structural methods, including nuclear magnetic resonance, small-angle X-ray scattering (SAXS), and multi-angle-light scattering combined with size-exclusion chromatography, and coarse-grained molecular dynamics simulation with SAXS, to probe the HP1α dimer and its CSD deletion monomer. We show that dynamic intra- and intermolecular interactions between the N-tails and basic segments in CD and HR depend on N-tail phosphorylation. While the phosphorylated HP1α dimer undergoes LLPS via the formation of aggregated multimers, the N-tail phosphorylated mutant without CSD still undergoes LLPS, but its structural unit is a dynamic intermolecular dimer formed via the phosphorylated N-tail and a basic segment at the CD end. Furthermore, we reveal that mutation of this basic segment in HP1α affects the size of heterochromatin foci in cultured mammalian cells, suggesting that this interaction plays an important role in heterochromatin formation in vivo.

Full conversion of lignocellulose using polyoxometalate catalysts with redox sites and antagonistic acidity/basicity

The full utilization of lignocellulose involves two distinct catalytic routes: i) oxidative depolymerization of lignin and ii) acid/alkaline hydrolysis of hemicellulose and cellulose. To improve efficiency and reduce costs, constructing a single-cluster catalyst represents a desirable yet challenging strategy. Herein, triple-functional molecular polyoxometalates (POMs), NLLnH6-nV2Mo18O62 (n = 1-6) were fabricated using N-lauroyl-l-lysine (NLL) and H6V2Mo18O62 as precursors. Besides its amphiphilicity to form nano-micelles with polyanion uniformly dispersed outside and NLL inside, NLL also provided basic sites to H+/redox POMs to compensate the loss of acidity and enabled spatial separation of antagonistic acid/base sites within a single POM molecule. Density Functional Theory, Molecular Dynamics simulations and experiments were employed to analyze these processes. The adsorption of -OH in 2-phenoxy-1-phenylethanol (pp-ol) was achieved by interacting with polyanion and extra with NH and C = O groups in NLL. These synergistic effects resulted in concentrating and confining pp-ol and reactive oxygen species around polyanion, which turnover frequency increased by 0.066 h-1 compared to homogeneous H6V2Mo18O62. Full conversion of various soft and hard lignocellulose was achieved using NLLH5V2Mo18O62 catalyst under gradually increasing temperature. During the conversion process, the lignin was oxidized mainly through β-O-4 bond cleavage without addition of NaOH, and the degradations of hemicellulose and cellulose were realized through acidic hydrolysis. The characteristics of this triple POMs allowed it to show higher activity than homogeneous H6V2Mo18O62 and previous BetH5V2Mo18O62 (Bet, i.e. betaine), which provided an alternative to developing new surfactant-type POMs in biomass conversion. The temperature-controlled properties in NLLH5V2Mo18O62 allowed easy separation and regeneration.

Mechanistic Insight on Ethanol Driven Swelling and Disruption of Cholesterol Containing Biomimetic Vesicles From Coarse-Grained Molecular Dynamics

We have performed coarse-grained (CG) molecular dynamics (MD) simulations to delineate the impact of ethanol (EtOH) on cholesterol (CHOL) containing biomimetic bilayer and vesicle composed of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipids. We have first deduced the missing interaction parameters for the POPC-CHOL-EtOH-water system within the SPICA/SDK CG force-field (CG-FF). By monitoring the electron density profiles, the orientational order parameter, and reproducing the all-atom MD-derived free energy for the insertion of ethanol from the bulk aqueous phase to the hydrophobic core of the POPC-CHOL lipid bilayer, we successfully determined all the missing non-bonding interaction parameters for the POPC-CHOL-EtOH-water system. The proposed force field was applied to investigate the effect of ethanol at various concentrations on unilamellar vesicles composed of POPC and cholesterol. It was found that 40 mol% or more concentration of ethanol is required to disintegrate or rupture the POPC-CHOL vesicle membranes. While cholesterol offers some resilience against the detrimental effects of ethanol, we still observe an increase in vesicle size (swelling) and a contraction in the bilayer thickness (thinning) as ethanol concentration rises from 0 to 30 mol%. At ethanol concentrations exceeding 30 mol%, the vesicles become increasingly susceptible to disintegration due to enhanced penetration of ethanol and water molecules into the hydrophobic core of the membranes.

Functional Design of Peptide Materials Based on Supramolecular Cohesion

Peptide materials offer a broad platform to design biomimetic soft matter, and filamentous networks that emulate those in extracellular matrices and the cytoskeleton are among the important targets. Given the vast sequence space, a combination of computational approaches and readily accessible experimental techniques is required to design peptide materials efficiently. We report here on a strategy that utilizes this combination to predict supramolecular cohesion within filaments of peptide amphiphiles, a property recently linked to supramolecular dynamics and consequently bioactivity. Using established coarse-grained simulations on 10,000 randomly generated peptide sequences, we identified 3500 likely to self-assemble in water into nanoscale filaments. Atomistic simulations of small clusters were used to further analyze this subset of sequences and identify mathematical descriptors that are predictive of intermolecular cohesion, which was the main purpose of this work. We arbitrarily selected a small cohort of these sequences for chemical synthesis and verified their fiber morphology. With further characterization, we were able to link the latent heat associated with fiber to micelle transitions, an indicator of cohesion and potential supramolecular dynamicity within the filaments, to calculated hydrogen bond densities in the simulation clusters. Based on validation from in situ synchrotron X-ray scattering and differential scanning calorimetry, we conclude that the phase transitions can be easily observed by very simple polarized light microscopy experiments. We are encouraged by the methodology explored here as a relatively low-cost and fast way to design potential functions of peptide materials.

Benchmark of Coacervate Formation and Mechanism Exploration Using the Martini Force Field

Peptide-based coacervates are crucial for drug delivery due to their biocompatibility, versatility, high drug loading capacity, and cell penetration rates; however, their stability mechanism and phase behavior are not fully understood. Additionally, although Martini is one of the most famous force fields capable of describing coacervate formation with molecular details, a comprehensive benchmark of its accuracy has not been conducted. This research utilized the Martini 3.0 force field and machine learning algorithms to explore representative peptide-based coacervates, including those composed of polyaspartate (PAsp)/polyarginine (PArg), rmfp-1, sticker-and-spacer small molecules, and HBpep molecules. We identified key coacervate formation driving forces such as Coulomb, cation-π, and π-π interactions and established three criteria for determining coacervate formation in simulations. The results also indicate that while Martini 3.0 accurately captures coacervate formation trends, it tends to underestimate Coulomb interactions and overestimate π-π interactions. What is more, our study on drug encapsulation of HBpep and its derivative coacervates suggested that most loaded drugs were distributed on surfaces of HBpep clusters, awaiting experimental validation. This study employs simulation to enhance understanding of peptide-based coacervate phase behavior and stability mechanisms while also benchmarking Martini 3.0, thereby providing fundamental insights for future experimental and simulation investigations.

Computational Design and In Vitro and In Vivo Characterization of an ApoE-Based Synthetic High-Density Lipoprotein for Sepsis Therapy

Introduction: Septic patients have low levels of high-density lipoproteins (HDLs), which is a risk factor. Replenishing HDLs with synthetic HDLs (sHDLs) has shown promise as a therapy for sepsis. This study aimed to develop a computational approach to design and test new types of sHDLs for sepsis treatment. Methods: We used a three-step computational approach to design sHDL nanoparticles based on the structure of HDLs and their binding to endotoxins. We tested the efficacy of these sHDLs in two sepsis mouse models-cecal ligation and puncture (CLP)-induced and P. aeruginosa-induced sepsis models-and assessed their impact on inflammatory signaling in cells. Results: We designed four sHDL nanoparticles: two based on the ApoA-I sequence (YGZL1 and YGZL2) and two based on the ApoE sequence (YGZL3 and YGZL4). We demonstrated that an ApoE-based sHDL nanoparticle, YGZL3, provides effective protection against CLP- and P. aeruginosa-induced sepsis. The sHDLs effectively suppressed inflammatory signaling in HEK-blue or RAW264 cells. Conclusions: Unlike earlier approaches, we developed a new approach that employs computational simulations to design a new type of sHDL based on HDL's structure and function. We found that YGZL3, an ApoE sequence-based sHDL, provides effective protection against sepsis in two mouse models.