Martini 3: a general purpose force field for coarse-grained molecular dynamics

Anti-GPC3 antibody and cell-penetrating peptide CPP44 dual-ligand modified liposomes for targeted delivery of arsenic trioxide in the treatment of hepatocellular carcinoma

Arsenic trioxide (ATO), the active ingredient in Chinese arsenic, effectively inhibits hepatocellular carcinoma (HCC) cell growth, but its clinical application is limited by the lack of a targeted delivery system. Phosphatidylinositol proteoglycan 3 (GPC3) is specifically expressed in HCC, and CPP44 is a cell-penetrating peptide that targets HCC cells. Here, we developed a liposome incorporating ATO with dual surface modifications of anti-GPC3 antibody and CPP44. The system was firstly enriched and localised at the liver tumour site through passive targeting by EPR and active targeting by specific binding of anti-GPC3 antibody to GPC3 protein. CPP44 then facilitated ATO penetration into HCC cells. Specifically, we first employed computational modelling to demonstrate that the covalently-coupled antibody maintained its binding ability to the GPC3 antigen. Subsequent experimental assays revealed that Dl-ATO-Lp exhibited higher cell uptake rate and stronger tumour cell killing effect. In an HCC mouse model, Dl-ATO-Lp achieved effective tumour targeting, with a tumour inhibition rate of 63.43%. This dual-ligand liposome system enhances the targeted delivery and therapeutic efficacy of ATO, offering a promising direction for solid tumour therapy and advancing the clinical application of ATO.

Enhancing Martini 3 for protein self-interaction simulations

Coarse-grained molecular dynamics simulations are highly valuable for studying protein-protein interactions. Unfortunately, commonly used force fields often overestimate these interactions. Here, we investigate the performance of the Martini 3 force field in predicting the self-interaction behavior of lysozyme and subtilisin using Metadynamics. The original Martini 3, despite improvements over its predecessor, overestimates interaction strength. Through reparameterization of bead interactions, we achieve good agreement with experimental data of the second virial coefficient and the diffusion coefficient. The new, refined force field enables more accurate CG-MD simulations, with potential applications in understanding and prediction of protein stability, aggregation tendencies, and solubility, with the possibility to aid in the development of protein-based drugs.

Genetic Variants and Clinical Phenotyping in 39 Pediatric Patients with Neuropathic Pain

Pathogenic variants in voltage-gated sodium channels (VGSCs) may cause disturbed sensory function, including small fiber neuropathy (SFN) in adults, but little is known about their role in children and adolescents.A total of 39 prospectively enrolled children (age 12.03 ± 4.61 years) with abnormal pain sensation underwent detailed diagnostics including quantitative sensory testing (QST, if >5 years old), quality of life assessment, and genetic studies for VGSC variants and further etiologies.QST results were consistent with Aẟ- und C-fiber damage, including increased cold, warmth, and mechanical detection thresholds, higher thermal sensory limen, and allodynia. Intraepidermal nerve fiber densities were low in 9/18 children. This resulted in a great impact on physical quality of life and pain scales but not on social life. Five children showed heterozygous variants of unknown significance (VUS) in genes encoding VGSC (SCN9A, n = 2; SCN10A, n = 3) with maternal or paternal inheritance in two and one patients, respectively. Three further patients showed likely disease-associated variants in the HUWE1, TRIO, and PYGM genes.Despite a high disease burden and small fiber damage indicated by QST and skin histology, only VUS in VGSC and additional monogenic causes of pain symptoms outside of VGSC genes were identified. Genetic studies in affected children should therefore be comprehensive, not restricted to VGSC variants and be supplemented by a detailed clinical workup. In silico modeling and future functional studies might help to identify VUS that play a role in altered pain perception.

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.

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.

The tubular cavity of tobacco mosaic virus shields mechanical stress and regulates disassembly

Here we probe Tobacco mosaic virus (TMV) particles immobilized on a solid surface under transversal mechanical stress. We use atomic force microscopy to implement punctual deformation with high force (∼nN) that induces immediate virus rupture (single indentation assay), and continuous cycles of low force (∼100 pN) that generate a gradual disassembly of the virus particle (mechanical fatigue assay). These experiments are interpreted with the help of TMV coarse-grained and finite elements simulations, which indicate that the tubular cavity screens the transmission of mechanical stress from the top to the bottom half of the virion structure. Likewise, mechanical fatigue experiments reveal how TMV disassembles following growing transversal rifts with different dynamics that depend on a combination of the applied force and the tubular geometry of the virus. Our results indicate how the cylindrical cavity of TMV cushions the lower half of the virus structure from mechanical stress and regulates mechanical disassembly. STATEMENT OF SIGNIFICANCE: The inability of plant viruses like tobacco mosaic virus (TMV) to infect mammals makes them ideal for technological applications. While TMV is known for it's durability, it's unclear if this is due solely to its capsid proteins or its tubular structure. Using Atomic Force Microscopy, coarse-grained and finite elements models, we found that the tubular hole screens the transmission of mechanical stress from the top to the bottom half of the virion structure. This characteristic induces a stepwise disassembly process from intact to half virus, finishing in the virion disruption. Since the energies between proteins are comparable to those of other viruses, there is a protective effect of the tubular cavity that transcends the size down to the nanoscale.

Kinetics and Mechanism of Phase Separation in Ternary Lipid Mixtures Containing APP C99: Atomistic vs Coarse-Grained MD Simulations

The phase separation of lipid bilayers, composed of mixtures of saturated and unsaturated lipids and cholesterol, is a topic of fundamental importance in membrane biophysics and cell biology. The formation of lipid domains, including liquid-disordered domains enriched in unsaturated lipids and liquid-ordered domains enriched in saturated lipids and cholesterol, is believed to be essential to the function of many membrane proteins. Experiment, theory, and simulation have been used to develop a general understanding of the thermodynamic driving forces underlying phase separation in ternary and quaternary lipid mixtures. However, the kinetics of early events in lipid phase separation in the presence of transmembrane proteins remain relatively understudied. Using large-scale all-atom and coarse-grained simulations, we explore the kinetics and phase separation of ternary lipid mixtures of saturated lipid, unsaturated lipid, and cholesterol in the presence of transmembrane proteins. Order parameters employed in the Cahn-Hilliard theory provide insight into the kinetics and mechanism of lipid phase separation. We observe three distinct time regimes in the phase separation process: a shorter exponential time phase, followed by a power-law phase, and then a longer plateau phase. Comparison of lipid, protein, and lipid-protein dynamics between all-atom and coarse-grained models identifies both quantitative and qualitative differences and similarities in the phase separation kinetics. Moreover, timescaling of the dynamics of the AA and CG simulations yields a similar kinetic mechanism of phase separation. The findings of this study elucidate fundamental aspects of membrane biophysics and contribute to ongoing efforts to define the role of lipid rafts in the structure and function of the cellular membrane.

Toward Molecular Simulation Guided Design of Next-Generation Membranes: Challenges and Opportunities

Membranes provide energy-efficient solutions for separating ions from water, ion-ion separation, neutral or charged molecules, and mixed gases. Understanding the fundamental mechanisms and design principles for these separation challenges has significant applications in the food and agriculture, energy, pharmaceutical, and electronics industries and environmental remediation. In situ experimental probes to explore Angstrom-nanometer length-scale and pico-nanosecond time-scale phenomena remain limited. Currently, molecular simulations such as density functional theory, ab initio molecular dynamics (MD), all-atom MD, and coarse-grained MD provide physics-based predictive models to study these phenomena. The status of molecular simulations to study transport mechanisms and state-of-the-art membrane separation is discussed. Furthermore, limitations and open challenges in molecular simulations are discussed. Finally, the importance of molecular simulations in generating data sets for machine learning and exploration of membrane design space is addressed.

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.

Determining the Role of Electrostatics in the Making and Breaking of the Caprin1-ATP Nanocondensate

We employ a multiscale computational approach to investigate the condensation process of the C-terminal low-complexity region of the Caprin1 protein as a function of increasing ATP concentration for three states: the initial mixed state, nanocondensate formation, and dissolution of the droplet as it reenters the mixed state. We show that upon condensation, ATP assembles via pi-pi interactions, resulting in the formation of a large cluster of stacked ATP molecules stabilized by sodium counterions. The surface of the ATP assembly interacts with the arginine-rich regions of the Caprin1 protein, particularly with its N-terminus, to promote the complete phase-separated droplet on a length scale of tens of nanometers. In order to understand droplet stability, we analyzed the near-surface electrostatic potential (NS-ESP) of Caprin1 and estimated the zeta potential of the Caprin1-ATP assemblies. We predict a positive NS-ESP at the Caprin1 surface for low ATP concentrations that defines the early mixed state, in excellent agreement with the NS-ESP obtained from NMR experiments using paramagnetic resonance enhancement. By contrast, the NS-ESP of Caprin1 at the surface of the nanocondensate at moderate levels of ATP is highly negative compared to that at the mixed state, and estimates of a large zeta potential outside the highly dense region of charge further explain the remarkable stability of this phase-separated droplet assembly. As ATP concentrations rise further, the strong electrostatic forces needed for nanocondensate stability are replaced by weaker Caprin1-ATP interactions that drive the re-entry into the mixed state that exhibits a much lower zeta potential.

Lipid Scrambling Pathways in the Sec61 Translocon Complex

Cellular homeostasis depends on the rapid, ATP-independent translocation of newly synthesized lipids across the endoplasmic reticulum (ER) membrane. Lipid translocation is facilitated by membrane proteins known as scramblases, a few of which have recently been identified in the ER. Our previous structure of the translocon-associated protein (TRAP) bound to the Sec61 translocation channel revealed local membrane thinning, suggesting that the Sec61/TRAP complex might be involved in lipid scrambling. Using complementary fluorescence spectroscopy assays, we detected nonselective scrambling by reconstituted translocon complexes. This activity was unaffected by Sec61 inhibitors that block its lateral gate, suggesting a second lipid scrambling pathway within the complex. Molecular dynamics simulations indicate that the trimeric TRAP subunit forms this alternative route, facilitating lipid translocation via a "credit card" mechanism, using a crevice lined with polar residues to shield lipid head groups from the hydrophobic membrane interior. Kinetic and thermodynamic analyses confirmed that local membrane thinning enhances scrambling efficiency and that both Sec61 and TRAP scramble phosphatidylcholine faster than phosphatidylethanolamine and phosphatidylserine, reflecting the intrinsic lipid flip-flop tendencies of these lipid species. As the Sec61 scrambling site lies in the lateral gate region, it is likely inaccessible during protein translocation, in line with our experiments on Sec61-inhibited samples. Hence, our findings suggest that the metazoan-specific trimeric TRAP bundle is a viable candidate for lipid scrambling activity that is insensitive to the functional state of the translocon.

Ensemble Adaptive Sampling Scheme: Identifying an Optimal Sampling Strategy via Policy Ranking

Efficient sampling in biomolecular simulations is critical for accurately capturing the complex dynamic behaviors of biological systems. Adaptive sampling techniques aim to improve efficiency by focusing computational resources on the most relevant regions of the phase space. In this work, we present a framework for identifying the optimal sampling policy through metric-driven ranking. Our approach systematically evaluates the policy ensemble and ranks the policies based on their ability to explore the conformational space effectively. Through a series of biomolecular simulation case studies, we demonstrate that the choice of a different adaptive sampling policy at each round significantly outperforms single policy sampling, leading to faster convergence and improved sampling performance. This approach takes an ensemble of adaptive sampling policies and identifies the optimal policy for the next round based on current data. Beyond presenting this ensemble view of adaptive sampling, we also propose two sampling algorithms that approximate this ranking framework on the fly. The modularity of this framework allows incorporation of any adaptive sampling policy, making it versatile and suitable as a comprehensive adaptive sampling scheme.

Temperature-Dependent Coarse-Grained Model for Simulations of Intrinsically Disordered Protein LCST and UCST Liquid-Liquid Phase Separations

Many intrinsically disordered proteins (IDPs) can undergo a liquid-liquid phase separation (LLPS) in water, depending on solution conditions (temperature, pH, and ionic strength). There are two types of LLPS that are controlled by temperature: those occurring above a lower critical solution temperature (LCST) and those occurring below an upper critical solution temperature (UCST). IDP coarse-grained (CG) models are particularly appropriate for investigating the physical and chemical factors that govern their LLPS and supramolecular organization. However, the development of CG models allowing simulations of both LCST and UCST behavior of temperature-sensitive IDPs is still in its infancy. In this context, we present here a novel temperature-dependent (TD) CG model for IDP simulations based on the MARTINI 3 force field. The model was developed by modifying the Lennard-Jones potentials between apolar or charged solute beads and water with a TD rescaling factor. It was parametrized to fit the TD potentials of mean force (PMF) between two apolar or two charged molecules computed using all-atom (AA) simulations. We show that the TD CG model is able to reproduce the experimentally known LLPS of both LCST and UCST low-complexity sequences and to estimate phase transition temperatures comparable to experimental measurements.

Multiple-Basin Go̅-Martini for Investigating Conformational Transitions and Environmental Interactions of Proteins

Proteins are inherently dynamic molecules, and their conformational transitions among various states are essential for numerous biological processes, which are often modulated by their interactions with surrounding environments. Although molecular dynamics (MD) simulations are widely used to investigate these transitions, all-atom (AA) methods are often limited by short time scales and high computational costs, and coarse-grained (CG) implicit-solvent Go̅-like models are usually incapable of studying the interactions between proteins and their environments. Here, we present an approach called Multiple-basin Go̅-Martini, which combines the recent Go̅-Martini model with an exponential mixing scheme to facilitate the simulation of spontaneous protein conformational transitions in explicit environments. We demonstrate the versatility of our method through five diverse case studies: GlnBP, Arc, Hinge, SemiSWEET, and TRAAK, representing ligand-binding proteins, fold-switching proteins, de novo designed proteins, transporters, and mechanosensitive ion channels, respectively. Multiple-basin Go̅-Martini offers a new computational tool for investigating protein conformational transitions, identifying key intermediate states, and elucidating essential interactions between proteins and their environments, particularly protein-membrane interactions. In addition, this approach can efficiently generate thermodynamically meaningful data sets of protein conformational space, which may enhance deep learning-based models for predicting protein conformation distributions.

Unveiling G-Protein-Coupled Receptor Conformational Dynamics via Metadynamics Simulations and Markov State Models

The dynamic character of G-protein-coupled receptors (GPCRs) is essential in their functionality as signal transducers. However, the molecular details of how ligands affect this conformational repertoire to steer intracellular signaling pathways remain elusive. To address this question, we present a generally applicable protocol that combines metadynamics simulations and Markov state modeling to compute the free energy landscape of the growth hormone secretagogue receptor (GHSR-1a), a prototypical class A GPCR, in its apo state and bound to pharmacologically distinct ligands. Consistent with the current multistate model of GPCR activity, we found that GHSR-1a populates multiple metastable states whose energies and transition probabilities change depending on the bound ligand. We identified intermediate states that have not yet been described by experimental structures and shed light on the molecular differences between basal and agonist-induced GHSR-1a activation. Our results are not only compatible with previously reported experimental data, but they capture the equilibria governing GHSR-1a activation in unprecedented detail. Due to its applicability to all class A GPCRs, our protocol is a valuable tool for the development of pharmaceuticals targeting this protein family.

Lipid Shape as a Membrane Activity Modulator of a Fusogenic Antimicrobial Peptide

An intriguing feature of many bacterial membranes is their prevalence of non-bilayer-forming lipids, such as the cone-shaped phosphatidylethanolamines and cardiolipins. Many membrane-active antimicrobial peptides lower the bilayer-to-hexagonal phase transition energy barrier in membranes containing such types of cone-shaped lipids. Here, we systematically studied how the molecular shape of lipids affects the activity of antimicrobial peptide EcDBS1R4, which is known to be an efficient fusogenic peptide. Using coarse-grained molecular dynamics simulations, we show the ability of EcDBS1R4 to form "hourglass-shaped" pores, which is inhibited by cone-shaped lipids. The abundance of cone-shaped lipids further correlates with the propensity of this peptide to oligomerize preferentially in antiparallel dimers. We also observe that EcDBS1R4 promotes the segregation of the anionic lipids. When coupled to dimerization, this charge segregation leads to regions in the bilayer that are devoid of peptides and rich in zwitterionic lipids. Our results indicate a protective role of cone-shaped lipids in bacterial membranes against pore-mediated permeabilization by EcDBS1R4.

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.

Viral fusion proteins of classes II and III recognize and reorganize complex biological membranes

Viral infection requires stable binding of viral fusion proteins to host membranes, which contain hundreds of lipid species. The mechanisms by which fusion proteins utilize specific host lipids to drive virus-host membrane fusion remains elusive. We conducted molecular simulations of classes I, II, and III fusion proteins interacting with membranes of diverse lipid compositions. Free energy calculations reveal that class I fusion proteins generally exhibit stronger membrane binding compared to classes II and III - a trend consistent across 74 fusion proteins from 13 viral families as suggested by sequence analysis. Class II fusion proteins utilize a lipid binding pocket formed by fusion protein monomers, stabilizing the initial binding of monomers to the host membrane prior to assembling into fusogenic trimers. In contrast, class III fusion proteins form a lipid binding pocket at the monomer-monomer interface through a unique fusion loop crossover. The distinct lipid binding modes correlate with the differing maturation pathways of classes II and III proteins. Binding affinity was predominantly controlled by cholesterol and gangliosides as well as via local enrichment of polyunsaturated lipids, thereby locally enhancing membrane disorder. Our study reveals energetics and atomic details underlying lipid recognition and reorganization by different viral fusion protein classes, offering insights into their specialized membrane fusion pathways.

Saddle curvature association of nsP1 facilitates the replication complex assembly of Chikungunya virus in cells

Positive-sense RNA viruses, including SARS-CoV-1 and -2, DENV, and CHIKV, replicate in curved membrane compartments within host cells. Non-structural proteins (nsPs) critically regulate these nanoscale membrane structures, yet their curvature-dependent assembly remains elusive due to the challenges of imaging nanoscale interaction on curved surfaces. Using vertically aligned nanostructures to generate pre-defined membrane curvatures, we here investigate the impact of curvature on nsPs assembly. Taking CHIKV as a model, we reveal that nsP1 preferentially binds and stabilizes on positively curved membranes, with stronger accumulation at radii ≤150 nm. This is driven by hydrophobic residues in the membrane association (MA) loops of individual nsP1. Molecular dynamics simulations further confirm the improved binding stability of nsP1 on curved membranes, particularly when it forms a dodecamer ring. Together, nsP1 supports a strong saddle curvature association, with flexible MA loops sensing a range of positive curvatures in the x-z plane while the rigid dodecamer stabilizing fixed negative curvature in the x-y plane - crucial for constraining the membrane spherule neck during replication progression. Moreover, CHIKV replication enriches on patterned nanoring structures, underscoring the curvature-guided assembly of the viral replication complex. Our findings highlight membrane curvature as a key regulator of viral nsPs organization, opening new avenues for studying membrane remodeling in viral replication.

Interplay of Hydrophobicity, Charge, and Sequence Length in Oligopeptide Coassembly

Peptide coassembly offers novel opportunities for designing advanced nanomaterials. This study used coarse-grained molecular dynamics simulations to examine the coassembly of charge-complementary peptides, assessing various ratios and the role of charge and hydrophobicity in their aggregation. We discovered that peptide length, charge, and hydrophobicity significantly influence coassembly behavior, with more hydrophobic peptides exhibiting greater aggregation despite electrostatic repulsion. Beyond the coassembly of two peptides, we also observed that the coassembly of more than two peptides will likely lead to new assembly structures and properties. Our findings underscore the importance of peptide composition and length in tuning the coassembly and the resulting properties, thus facilitating the design of complex peptide nanoparticles for biomedical and biotechnological applications.

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.

Architectural control of rod-coil block polypeptide thermoresponsive self-assembly via de novo design of coiled-coil orientation

The architectural control of the self-assembly of a series of block polypeptides comprising a concatenation of an elastin-like peptide and a coiled-coil, bundle-forming peptide (ELP-BFPs), has been demonstrated. Assembly of the polypeptides is controlled by coacervation of the hydrophobic ELP domain, while the type of coiled-coil assembly of the BFP and the specific placement of short histidine tags significantly tunes assembly behavior. Spectrophotometric analysis of self-assembly demonstrated that the transition temperature of assembly can be controlled by the design of the BFP domain and positioning of the His-tags in the constructs. Cryogenic transmission electron microscopy of assembled polypeptides confirmed distinct morphologies including core-shell particles and multilayer vesicles, depending on the parallel or antiparallel bundle architecture of the block polypeptide. The results have applications in materials design and highlight the potential for controlling multi-stimuli responsiveness and morphologies through fine control of the architectural features of the component polypeptide domains.

Increased burden of rare risk variants across gene expression networks predisposes to sporadic Parkinson's disease

Alpha-synuclein (αSyn) is an intrinsically disordered protein that accumulates in the brains of patients with Parkinson's disease (PD). Through a high-throughput screen, we recently identified 38 genes whose knockdown modulates αSyn propagation. Here, we show that, among those, TAX1BP1 regulates how αSyn interacts with lipids, and ADAMTS19 modulates how αSyn phase separates into inclusions, adding to the growing body of evidence implicating those processes in PD. Through RNA sequencing, we identify several genes that are differentially expressed after knockdown of TAX1BP1 or ADAMTS19 and carry an increased frequency of rare risk variants in patients with PD versus healthy controls. Those differentially expressed genes cluster within modules in regions of the brain that develop high degrees of αSyn pathology. We propose a model for the genetic architecture of sporadic PD: increased burden of risk variants across genetic networks dysregulates pathways underlying αSyn homeostasis and leads to pathology and neurodegeneration.

Cobalt-Based ZIF Composite Membranes: In Situ Defect Engineering for Enhanced Water Stability and Gas Separation

Porous coordination polymers with excellent molecular sieving ability, high dispersibility, and good compatibility with engineered polymer matrices hold promise for various industrial applications, such as gas separation and battery separators. Here, an in situ defect engineering approach is proposed for highly processable cobalt (Co)-based zeolitic imidazolate frameworks (ZIFs) with enhanced molecular sieving ability and water stability. By varying alkylamine (AA) modulators, the pore structures and textural properties of ZIFs can be fine-tuned. The resulting high-loading composite membrane exhibits excellent C3H6/C3H8 separation performance and mechanical properties. This in situ defect engineering approach enables efficient interfacial engineering for high-performance composite membranes.

Transmembrane clustering of short amyloid peptide fragments: A coarse grained molecular dynamics study

Toxicity of amyloid peptides has been linked to peptide aggregation and interactions with lipid bilayers. In this work we use coarse-grained molecular dynamics simulations to study aggregation and transmembrane clustering of short amyloid peptide fragments, Aβ(25-35) and Aβ(29-42), in the presence of dipalmitoylphosphatidylcholine (DPPC) and palmitoylolyoilphosphatidylcholine (POPC) bilayers. First, we explored peptide aggregation starting from free monomers placed at the interface of preformed lipid membranes. At low peptide concentrations, no transmembrane clusters were formed in DPPC or POPC membranes. At high peptide concentration, the longer fragment, Aβ(29-42), showed strong peptide-peptide interactions that led to spontaneous formation of transmembrane clusters in POPC and DPPC. However, the shorter fragment, Aβ(25-35), did not form transmembrane clusters within the simulation time in either bilayer. To overcome the free-energy barriers to transmembrane clustering, we changed the simulation protocol and started simulations from random mixtures of peptides, lipids, and solvent. Using this system self-assembly approach, we found that both Aβ(25-35) and Aβ(29-42) can form stable transmembrane clusters in DPPC and POPC bilayers. Our study suggests that the cooperative effects induced by a localized increase in peptide density may be a mechanism of membrane disruption by short amyloid peptide fragments.

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.

Engineering cardiolipin binding to an artificial membrane protein reveals determinants for lipid-mediated stabilization

Integral membrane proteins carry out essential functions in the cell, and their activities are often modulated by specific protein-lipid interactions in the membrane. Here, we elucidate the intricate role of cardiolipin (CDL), a regulatory lipid, as a stabilizer of membrane proteins and their complexes. Using the in silico-designed model protein TMHC4_R (ROCKET) as a scaffold, we employ a combination of molecular dynamics simulations and native mass spectrometry to explore the protein features that facilitate preferential lipid interactions and mediate stabilization. We find that the spatial arrangement of positively charged residues as well as local conformational flexibility are factors that distinguish stabilizing from non-stabilizing CDL interactions. However, we also find that even in this controlled, artificial system, a clear-cut distinction between binding and stabilization is difficult to attain, revealing that overlapping lipid contacts can partially compensate for the effects of binding site mutations. Extending our insights to naturally occurring proteins, we identify a stabilizing CDL site within the E. coli rhomboid intramembrane protease GlpG and uncover its regulatory influence on enzyme substrate preference. In this work, we establish a framework for engineering functional lipid interactions, paving the way for the design of proteins with membrane-specific properties or functions.

Structural insights into terminal arabinosylation of mycobacterial cell wall arabinan

The global challenge of tuberculosis, caused by Mycobacterium tuberculosis (Mtb), is compounded by the emergence of drug-resistant strains. A critical factor in Mtb's pathogenicity is its intricate cell envelope, which acts as a formidable barrier against immune defences and pharmacological interventions. Central to this envelope are arabinogalactan (AG) and lipoarabinomannan (LAM), two complex polysaccharides containing arabinan domains essential for maintaining cell wall structure and function. The arabinofuranosyltransferase AftB plays a pivotal role in the biosynthesis of these arabinan domains by catalyzing the addition of β-(1 → 2)-linked terminal arabinofuranose residues. Here, we present the cryo-EM structures of Mycobacterium chubuense AftB in both its apo form and bound to a donor substrate analog, resolved at 2.9 Å and 3.4 Å resolution, respectively. These structures reveal that AftB has a GT-C fold, with a transmembrane (TM) domain comprised of eleven TM helices and a periplasmic cap domain. AftB has a distinctive irregular, tube-shaped cavity that connects two proposed substrate binding sites. Through an integrated approach combining structural analysis, biochemical assays, and molecular dynamics simulations, we delineate the molecular basis of AftB's reaction mechanism and propose a model for its catalytic function.

Mechanistic studies of mycobacterial glycolipid biosynthesis by the mannosyltransferase PimE

Tuberculosis (TB), a leading cause of death among infectious diseases globally, is caused by Mycobacterium tuberculosis (Mtb). The pathogenicity of Mtb is largely attributed to its complex cell envelope, which includes a class of glycolipids called phosphatidyl-myo-inositol mannosides (PIMs). These glycolipids maintain the integrity of the cell envelope, regulate permeability, and mediate host-pathogen interactions. PIMs comprise a phosphatidyl-myo-inositol core decorated with one to six mannose residues and up to four acyl chains. The mannosyltransferase PimE catalyzes the transfer of the fifth PIM mannose residue from a polyprenyl phosphate-mannose (PPM) donor. This step contributes to the proper assembly and function of the mycobacterial cell envelope; however, the structural basis for substrate recognition and the catalytic mechanism of PimE remain poorly understood. Here, we present the cryo-electron microscopy (cryo-EM) structures of PimE from Mycobacterium abscessus in its apo and product-bound form. The structures reveal a distinctive binding cavity that accommodates both donor and acceptor substrates/products. Key residues involved in substrate coordination and catalysis were identified and validated via in vitro assays and in vivo complementation, while molecular dynamics simulations delineated access pathways and binding dynamics. Our integrated approach provides comprehensive insights into PimE function and informs potential strategies for anti-TB therapeutics.

Molecular mapping and functional validation of GLP-1R cholesterol binding sites in pancreatic beta cells

G protein-coupled receptors (GPCRs) are integral membrane proteins which closely interact with their plasma membrane lipid microenvironment. Cholesterol is a lipid enriched at the plasma membrane with pivotal roles in the control of membrane fluidity and maintenance of membrane microarchitecture, directly impacting on GPCR stability, dynamics, and function. Cholesterol extraction from pancreatic beta cells has previously been shown to disrupt the internalisation, clustering, and cAMP responses of the glucagon-like peptide-1 receptor (GLP-1R), a class B1 GPCR with key roles in the control of blood glucose levels via the potentiation of insulin secretion in beta cells and weight reduction via the modulation of brain appetite control centres. Here, we unveil the detrimental effect of a high cholesterol diet on GLP-1R-dependent glucoregulation in vivo, and the improvement in GLP-1R function that a reduction in cholesterol synthesis using simvastatin exerts in pancreatic islets. We next identify and map sites of cholesterol high occupancy and residence time on active vs inactive GLP-1Rs using coarse-grained molecular dynamics (cgMD) simulations, followed by a screen of key residues selected from these sites and detailed analyses of the effects of mutating one of these, Val229, to alanine on GLP-1R-cholesterol interactions, plasma membrane behaviours, clustering, trafficking and signalling in INS-1 832/3 rat pancreatic beta cells and primary mouse islets, unveiling an improved insulin secretion profile for the V229A mutant receptor. This study (1) highlights the role of cholesterol in regulating GLP-1R responses in vivo; (2) provides a detailed map of GLP-1R - cholesterol binding sites in model membranes; (3) validates their functional relevance in beta cells; and (4) highlights their potential as locations for the rational design of novel allosteric modulators with the capacity to fine-tune GLP-1R responses.

Accurate coarse grained models for protein association and recognition

Protein-protein interactions are fundamental to the cell function, but some of them are slow processes happening in time scales in the microsecond to millisecond range, therefore inaccessible for standard atomistic molecular dynamics (MD) simulations. A way to reduce the computational cost demanded by the simulation of long timescale phenomena is to use coarse-grained (CG) models to reduce the number of particles included in the simulation. In this Review we provide an overview of CG models for the study of protein dynamics and interactions. The majority of protein CG models have been designed to describe accurately the structure of folded, stable proteins, but recently new CG models and force fields have been designed to study disordered proteins. The difficulty of finding a force field fully transferable between stable and disordered proteins hinders the computational study of the intracellular environment in its most complex case, where protein-protein interactions occur in multiprotein systems constituted by both stable and disordered proteins. In this Review we overview several existing CG protein models, focusing on its applicability to the study of multiprotein systems including both stable and disordered proteins. We also discuss the utility of implicit solvent models, which accelerate the conformational sampling of protein solutions, to explore a broader configurational space of the system in shorter simulation times, and analyze the inaccuracies inherent to this approximation.

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.

Oligolysine Enhances and Inhibits DNA Condensate Formation

The formation of biomolecular condensates via phase separation relates to various cellular functions. Reconstituting these condensates with designed molecules facilitates the exploration of their mechanisms and potential applications. Sequence-designed DNA nanostructures enable the investigation of how structural design influences condensate formation and the construction of functional artificial condensates. Despite the high designability of DNA-based condensates, free nanostructures that do not assemble into condensates remain a challenge. Combining DNA nanostructures with other molecules, such as peptides, represents a promising approach to overcoming the limitations of DNA condensates and gaining a deeper understanding of molecular condensates. Herein, we report the effects of cationic oligolysines with several residues on DNA condensate formation assembled from Y-shaped DNA nanostructures. DNA condensate formation was enhanced by oligolysines at an appropriate L/P ratio, which refers to the ratio of positively charged amine groups in lysine (L) to negatively charged nucleic acid phosphate groups (P). Oligolysines with five residues enhanced condensate formation while maintaining the sequence-specific interaction of DNA. In contrast, oligolysines inhibited condensate formation depending on the L/P ratio and residue number. This was attributed to nanostructure deformation caused by oligolysines. These results suggest that the amount and length of cationic peptides significantly affect the self-assembly of branched DNA nanostructures. This study offers important insights into biomolecular condensates that can guide further development of DNA/peptide hybrid condensates to enhance the functions of artificial condensates for use in artificial cells and molecular robots.

Cholesterol-dependent dimerization and conformational dynamics of EphA2 receptors from coarse-grained and all-atom simulations

The EphA2 transmembrane receptor regulates cellular growth, differentiation, and motility, and its overexpression in various cancers makes it a potential biomarker for clinical cancer management. EphA2 signaling occurs through ligand-induced dimerization where the transmembrane (TM) and juxtamembrane (JM) domains play crucial roles in stabilizing the dimer conformations, thereby facilitating signal transduction. Electrostatic interactions between basic JM residues and signaling lipids (PIP2 and PIP3) regulate phosphorylation while cholesterol's potential role in modulating EphA2 activation remains unclear. To investigate this, we modeled the TM-full JM peptide of EphA2 and employed coarse-grain and all-atom simulations to investigate its dimerization in cholesterol-rich and cholesterol-deficient membranes. Our findings reveal that cholesterol stabilizes specific TM dimers and TM-JM interactions with PIP2, highlighting the importance of membrane composition in EphA2 dimerization, oligomerization, and clustering. These insights enhance our understanding of lipid-mediated regulation of EphA2 and its implications in receptor signaling and cancer progression.

Advancing Molecular Simulations: Merging Physical Models, Experiments, and AI to Tackle Multiscale Complexity

Proteins and protein complexes form adaptable networks that regulate essential biochemical pathways and define cell phenotypes through dynamic mechanisms and interactions. Advances in structural biology and molecular simulations have revealed how protein systems respond to changes in their environments, such as ligand binding, stress conditions, or perturbations like mutations and post-translational modifications, influencing signal transduction and cellular phenotypes. Here, we discuss how computational approaches, ranging from molecular dynamics (MD) simulations to AI-driven methods, are instrumental in studying protein dynamics from isolated molecules to large assemblies. These techniques elucidate conformational landscapes, ligand-binding mechanisms, and protein-protein interactions and are starting to support the construction of multiscale realistic representations of highly complex systems, ranging up to whole cell models. With cryo-electron microscopy, cryo-electron tomography, and AlphaFold accelerating the structural characterization of protein networks, we suggest that integrating AI and Machine Learning with multiscale MD methods will enhance fundamental understating for systems of ever-increasing complexity, usher in exciting possibilities for predictive modeling of the behavior of cell compartments or even whole cells. These advances are indeed transforming biophysics and chemical biology, offering new opportunities to study biomolecular mechanisms at atomic resolution.

Dissecting Large-Scale Structural Transitions in Membrane Transporters Using Advanced Simulation Technologies

Membrane transporters are integral membrane proteins that act as gatekeepers of the cell, controlling fundamental processes such as recruitment of nutrients and expulsion of waste material. At a basic level, transporters operate using the "alternating access model," in which transported substances are accessible from only one side of the membrane at a time. This model usually involves large-scale structural changes in the transporter, which often cannot be captured using unbiased, conventional molecular simulation techniques. In this article, we provide an overview of some of the major simulation techniques that have been applied to characterize the structural dynamics and energetics involved in the transition of membrane transporters between their functional states. After briefly introducing each technique, we discuss some of their advantages and limitations and provide some recent examples of their application to membrane transporters.

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.

Optimized Protein-Excipient Interactions in the Martini 3 Force Field

The high doses of drugs required for biotherapeutics, such as monoclonal antibodies (mAbs), and the small volumes that can be administered to patients by subcutaneous injections pose challenges due to high-concentration formulations. The addition of excipients, such as arginine and glutamate, to high-concentration protein formulations can increase solubility and reduce the tendency of protein particle formation. Molecular dynamics (MD) simulations can provide microscopic insights into the mode of action of excipients in mAb formulations but require large system sizes and long time scales that are currently beyond reach at the fully atomistic level. Computationally efficient coarse-grained models such as the Martini 3 force field can tackle this challenge but require careful parametrization, testing, and validation. This study extends the popular Martini 3 force field toward realistic protein-excipient interactions of arginine and glutamate excipients, using the Fab domains of the therapeutic mAbs trastuzumab and omalizumab as model systems. A novel all-atom to coarse-grained mapping of the amino acid excipients is introduced, which explicitly captures the zwitterionic character of the backbone. The Fab-excipient interactions of arginine and glutamate are characterized concerning molecular contacts with the Fabs at the single-residue level. The Martini 3 simulations are compared with results from all-atom simulations as a reference. Our findings reveal an overestimation of Fab-excipient contacts with the default interaction parameters of Martini 3, suggesting a too strong attraction between protein residues and excipients. Therefore, we reparametrized the protein-excipient interaction parameters in Martini 3 against all-atom simulations. The excipient interactions obtained with the new Martini 3 mapping and Lennard-Jones (LJ) interaction parameters, coined Martini 3-exc, agree closely with the all-atom reference data. This work presents an improved parameter set for mAb-arginine and mAb-glutamate interactions in the Martini 3 coarse-grained force field, a key step toward large-scale coarse-grained MD simulations of high-concentration mAb formulations and the stabilizing effects of excipients.

CGsmiles: A Versatile Line Notation for Molecular Representations across Multiple Resolutions

Coarse-grained (CG) models simplify molecular representations by grouping multiple atoms into effective particles, enabling faster simulations and reducing the chemical compound space compared to atomistic methods. Additionally, models with chemical specificity, such as Martini, may extrapolate to cases where experimental data is scarce, making CG methods highly promising for high-throughput (HT) screenings and chemical space exploration. Yet no rigorous data formats exist for the crucial aspect of describing how the atoms are grouped (i.e., the mapping). As CG models advance toward true HT capabilities, the lack of mappings and indexing capabilities for the growing number of CG molecules poses a significant barrier. To address this, we introduce CGsmiles, a versatile line notation inspired by the popular Simplified Molecular Input Line Entry System (SMILES) and BigSMILES. CGsmiles encodes the molecular graph and particle (atom) properties independent of their resolution and incorporates a framework that allows seamless conversion between coarse- and fine-grained resolutions. By specifying fragments that describe how each particle is represented at the next finer resolution (e.g., CG particles to atoms), CGsmiles can represent multiple resolutions and their hierarchical relationships in a single string. In this paper, we present the CGSmiles syntax and analyze a benchmark set of 407 molecules from the Martini force field. We highlight key features missing in existing notations that are essential for accurately describing CG models. To demonstrate the utility of CGsmiles beyond simulations, we construct two simple machine-learning models for predicting partition coefficients, both trained on CGsmiles-indexed data and leveraging information from both CG and atomistic resolutions. Finally, we briefly discuss the applicability of CGsmiles to polymers, which particularly benefit from the multiresolution nature of the notation.

MartiniGlass: a Tool for Enabling Visualization of Coarse-Grained Martini Topologies

As molecular modeling gains ever more prominence in understanding cellular processes, high quality visualization of models and dynamics has never been more important. Naturally, much molecular visualization software is written to enable the visualization of atomic level details in structures. While necessary, this means that visualization of increasingly popular coarse-grained (CG) models remains a challenge. Here, we present a Python package, MartiniGlass, that facilitates the visualization of systems simulated with the widely used CG Martini force field using the popular visualization package VMD. MartiniGlass rapidly processes molecular topologies and accounts for important topological features at CG resolution, such as secondary structure restraints, preparing them for easy visualization of simulated trajectories.

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.

Characterizing the Sequence Landscape of Peptide Fibrillization with a Bottom-Up Coarse-Grained Model

Molecular insight into amyloid aggregation is crucial for understanding the details of protein fibril nucleation and growth, which play a significant role in a wide range of proteinopathies. The length and time scales for fibrillization make its computational study an intrinsically multiscale problem, necessitating the use of coarse-grained modeling. A wide variety of coarse-grained models for peptides have been proposed, often parametrized with a combination of top-down and bottom-up approaches. Here, we present a predictive, sequence-transferable bottom-up coarse-grained model, systematically developed using only information from atomistic simulations by applying an extended-ensemble relative entropy minimization technique. The resulting model is capable of accurately recovering conformational properties of peptides constructed from a reduced alphabet of amino acids, of predicting secondary structures of isolated and interacting peptides from their sequences alone, and of simulating aggregation of peptides that have been experimentally characterized as amyloidogenic. Finally, we couple such coarse-grained simulations with a genetic algorithm to characterize the sequence space of the reduced alphabet and identify features of sequences for which ordered fibrillar states are both thermodynamically favorable and kinetically accessible.

Methods for Theoretical Treatment of Local Fields in Proteins and Enzymes

Electric fields generated by protein scaffolds are crucial in enzymatic catalysis. This review surveys theoretical approaches for detecting, analyzing, and comparing electric fields, electrostatic potentials, and their effects on the charge density within enzyme active sites. Pioneering methods like the empirical valence bond approach rely on evaluating ionic and covalent resonance forms influenced by the field. Strategies employing polarizable force fields also facilitate field detection. The vibrational Stark effect connects computational simulations to experimental Stark spectroscopy, enabling direct comparisons. We highlight how protein dynamics induce fluctuations in local fields, influencing enzyme activity. Recent techniques assess electric fields throughout the active site volume rather than only at specific bonds, and machine learning helps relate these global fields to reactivity. Quantum theory of atoms in molecules captures the entire electron density landscape, providing a chemically intuitive perspective on field-driven catalysis. Overall, these methodologies show protein-generated fields are highly dynamic and heterogeneous, and understanding both aspects is critical for elucidating enzyme mechanisms. This holistic view empowers rational enzyme engineering by tuning electric fields, promising new avenues in drug design, biocatalysis, and industrial applications. Future directions include incorporating electric fields as explicit design targets to enhance catalytic performance and biochemical functionalities.

Can Coarse-Grained Molecular Dynamics Simulations Predict Pharmaceutical Crystal Growth?

To investigate the ability of coarse-grained molecular dynamics simulations to predict the relative growth rates of crystal facets of pharmaceutical molecules, we apply two coarse-graining strategies to two drug molecules, phenytoin and carbamazepine. In the first method, we map an atomistic model to a MARTINI-level coarse-grained (CG) force field that uses 2 or 3 heavy atoms per bead. This is followed by applying Particle Swarm Optimization (PSO), a global optimum searching algorithm, to the CG Lennard-Jones intermolecular potentials to fit the radial distribution functions of both the crystalline and melt structures. In the second, a coarser-grained method, we map 5 or more heavy atoms into one bead with the help of the Iterative Boltzmann Inversion (IBI) method to derive a tabulated longer-range force field (FF). Simulations using the FF's derived from both strategies were able to stabilize the crystal in the correct structure and to predict crystal growth from the melt with modest computational resources. We evaluate the advantages and limitations of both methods and compare the relative growth rates of various facets of both drug crystals with those predicted by the Bravais-Friedel-Donnay-Harker (BFDH) and attachment energy (AE) theories. While all methods, except for the simulations conducted with the coarser-grained IBI-generated model, produced similarly good results for phenytoin, the finer-grained PSO-generated FF using MARTINI mapping rules outperformed the other methods in its prediction of the facet growth rates and resulting crystalline morphology for carbamazepine.

Protein-membrane interactions with a twist

Within a framework of elasticity theory and geometry, the twister mechanism has been proposed some years ago for describing the interaction between a biofilament containing a twisted hydrophobic strip and a lipid membrane: this mechanism is capable of inducing deformations of the membrane, which can lead to its opening. The present work intends to extend this model to the interactions between a membrane and protein regions conserving their folds using coarse-grained molecular dynamics simulations. The protein region is modeled as a cylinder stabilized by a tensegrity scheme, leading to an elasticity similar to that observed in real proteins. Recording molecular dynamics trajectories of this cylinder in the presence of a fluid lipid bilayer membrane allows investigation of the effect of the positions of the hydrophobic parts on the interaction with the membrane. The entire configuration space is explored by systematically varying the hydrophobic strip width, the twisting of the strip as well as the range of hydrophobic interactions between the cylinder and the membrane. Three different states are observed: no interaction between the cylinder and membrane, the cylinder in contact with the membrane surface and the cylinder inserted into the membrane with a variable tilt angle. The variations of the tilt angle are explained using a qualitative model based on the total hydrophobic moment of the cylinder. A deformation pattern of the membrane, previously predicted for the filament-membrane interaction by the twister model, is observed for the state when the cylinder is in contact with the membrane surface, which allows estimation of the applied torques.

An S-acylated N-terminus and a conserved loop regulate the activity of the ABHD17 deacylase

The dynamic addition and removal of long-chain fatty acids modulate protein function and localization. The α/β hydrolase domain-containing (ABHD) 17 enzymes remove acyl chains from membrane-localized proteins such as the oncoprotein NRas, but how the ABHD17 proteins are regulated is unknown. Here, we used cell-based studies and molecular dynamics simulations to show that ABHD17 activity is controlled by two mobile elements-an S-acylated N-terminal helix and a loop-that flank the putative substrate-binding pocket. Multiple S-acylation events anchor the N-terminal helix in the membrane, enabling hydrophobic residues in the loop to engage with the bilayer. This stabilizes the conformation of both helix and loop, alters the conformation of the binding pocket, and optimally positions the enzyme for substrate engagement. S-acylation may be a general feature of acyl-protein thioesterases. By providing a mechanistic understanding of how the lipid modification of a lipid-removing enzyme promotes its enzymatic activity, this work contributes to our understanding of cellular S-acylation cycles.

Any1 is a phospholipid scramblase involved in endosome biogenesis

Endosomes are central organelles in the recycling and degradation of receptors and membrane proteins. Once endocytosed, such proteins are sorted at endosomes into intraluminal vesicles (ILVs). The resulting multivesicular bodies (MVBs) then fuse with the lysosomes, leading to the degradation of ILVs and recycling of the resulting monomers. However, the biogenesis of MVBs requires a constant lipid supply for efficient ILV formation. An ER-endosome membrane contact site has been suggested to play a critical role in MVB biogenesis. Here, we identify Any1 as a novel phospholipid scramblase, which functions with the lipid transfer protein Vps13 in MVB biogenesis. We uncover that Any1 cycles between the early endosomes and the Golgi and colocalizes with Vps13, possibly at a here-discovered potential contact site between lipid droplets (LDs) and endosomes. Strikingly, both Any1 and Vps13 are required for MVB formation, presumably to couple lipid flux with membrane homeostasis during ILV formation and endosome maturation.

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.