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

Regulate PD-L1's membrane orientation thermodynamics with hydrophobic nanoparticles

Tumor cells can escape from immune killing by binding their programmed death ligand-1 (PD-L1) to the programmed cell death protein 1 (PD-1) of T cells. These immune checkpoint proteins (PD-L1/PD-1) have become very important drug targets, since blocking PD-L1 or PD-1 can recover the killing capability of T cells against tumor cells. Instead of targeting the binding interface between PD-L1 and PD-1, we explored the possibility of regulating the membrane orientation thermodynamics of PD-L1 with ligand-modified ultra-small hydrophobic nanoparticles (NPs) using μs-scale coarse-grained molecular dynamics (MD) simulations in this work. Our MD results indicate that embedded hydrophobic NPs can significantly change the membrane orientation thermodynamics of the extracellular domain of PD-L1, enhancing the probability in the "stand up" state for better binding to PD-1. Meanwhile, embedded hydrophobic NPs promote the tilt of the transmembrane domain of PD-L1. Besides, effects on both extracellular and transmembrane domains are determined by the ligand length and NP concentration. Our study may provide an alternative strategy to achieve PD-L1-related immunotherapy with nanomedicine.

A Neural-Network-Based Mapping and Optimization Framework for High-Precision Coarse-Grained Simulation

The accuracy and efficiency of a coarse-grained (CG) force field are pivotal for high-precision molecular simulations of large systems with complex molecules. We present an automated mapping and optimization framework for molecular simulation (AMOFMS), which is designed to streamline and improve the force field optimization process. It features a neural-network-based mapping function, DSGPM-TP (deep supervised graph partitioning model with type prediction). This model can accurately and efficiently convert atomistic structures to CG mappings, reducing the need for manual intervention. By integrating bottom-up and top-down methodologies, AMOFMS allows users to freely combine these approaches or use them independently as optimization targets. Moreover, users can select and combine different optimizers to meet their specific mission. With its parallel optimizer, AMOFMS significantly accelerates the optimization process, reducing the time required to achieve optimal results. Successful applications of AMOFMS include parameter optimizations for systems such as POPC and PEO, demonstrating its robustness and effectiveness. Overall, AMOFMS provides a general and flexible framework for the automated development of high-precision CG force fields.

The Role of Cholesterol in M2 Clustering and Viral Budding Explained

The influenza A M2 homotetrameric channel consists of four transmembrane (TM) and four amphipathic helices (AHs). This viral proton channel is suggested to form clusters in the catenoid budding neck areas in raft-like domains of the plasma membrane, resulting in cell membrane scission and viral release. The channel clustering environment is rich in cholesterol. Previous experiments have shown that cholesterol significantly contributes to lipid bilayer undulations in viral buds. However, a clear explanation of membrane curvature from the distribution of cholesterol around the M2TM-AH clusters is lacking. Using coarse-grained molecular dynamics simulations of M2TM-AH in bilayers, we observed that M2 channels form specific, C2-symmetric, clusters with conical shapes driven by the attraction of their AHs. We showed that cholesterol stabilized the formation of M2 channel clusters by filling and bridging the conical gap between M2 channels at specific sites in the N-termini of adjacent channels or via the C-terminal region of TM and AHs, with the latter sites displaying a longer interaction time and higher stability. The potential of mean force calculations showed that when cholesterols occupy the identified interfacial binding sites between two M2 channels, the dimer is stabilized by 11 kJ/mol. This translates to the cholesterol-bound dimer being populated by almost 2 orders of magnitude compared to a dimer lacking cholesterol. We demonstrated that the cholesterol-bridged M2 channels can exert a lateral force on the surrounding membrane to induce the necessary negative Gaussian curvature profile, which permits spontaneous scission of the catenoid membrane neck and leads to viral buds and scission.

How well do empirical molecular mechanics force fields model the cholesterol condensing effect?

Membrane properties are determined in part by lipid composition, and cholesterol plays a large role in determining these properties. Cellular membranes show a diverse range of cholesterol compositions, the effects of which include alterations to cellular biomechanics, lipid raft formation, membrane fusion, signaling pathways, metabolism, pharmaceutical therapeutic efficacy, and disease onset. In addition, cholesterol plays an important role in non-cellular membranes, with its concentration in the skin lipid matrix being implicated in several skin diseases. In phospholipid membranes, cholesterol increases the tail ordering of neighboring lipids, decreasing the membrane lateral area and increasing the thickness. This reduction in the lateral area, known as the cholesterol condensing effect, results from cholesterol-lipid mixtures deviating from ideal mixing. Capturing the cholesterol condensing effect is crucial for molecular dynamics simulations as it directly affects the accuracy of predicted membrane properties, which are essential for understanding membrane function. We present a comparative analysis of cholesterol models across several popular force fields: CHARMM36, Slipids, Lipid17, GROMOS 53A6L, GROMOS-CKP, MARTINI 2, MARTINI 3, and ELBA. The simulations of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) membranes with varying cholesterol concentrations were conducted to calculate the partial-molecular areas of cholesterol and other condensing parameters, which are compared to the experimental data for validation. While all tested force fields predict small negative deviations from ideal mixing in cholesterol-DOPC membranes, only all-atom force fields capture the larger deviations expected in DMPC membranes. United-atom and coarse-grained models under-predict this effect, condensing fewer neighboring lipids by smaller magnitudes, resulting in too small deviations from ideal mixing. These results suggest that all-atom force fields, particularly CHARMM36 or Slipids, should be used for accurate simulations of cholesterol-containing membranes.

Free Energy of Membrane Pore Formation and Stability from Molecular Dynamics Simulations

Understanding the molecular mechanisms of pore formation is crucial for elucidating fundamental biological processes and developing therapeutic strategies, such as the design of drug delivery systems and antimicrobial agents. Although experimental methods can provide valuable information, they often lack the temporal and spatial resolution necessary to fully capture the dynamic stages of pore formation. In this study, we present two novel collective variables (CVs) designed to characterize membrane pore behavior, particularly its energetics, through molecular dynamics (MD) simulations. The first CV─termed Full-Path─effectively tracks both the nucleation and expansion phases of pore formation. The second CV─called Rapid─is tailored to accurately assess pore expansion in the limit of large pores, providing quick and reliable method for evaluating membrane line tension under various conditions. Our results clearly demonstrate that the line tension predictions from both our CVs are in excellent agreement. Moreover, these predictions align qualitatively with available experimental data. Specifically, they reflect higher line tension of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) membranes containing 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-l-serine (POPS) lipids compared to pure POPC, the decrease in line tension of POPC vesicles as the 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoglycerol (POPG) content increases, and higher line tension when ionic concentration is increased. Notably, these experimental trends are accurately captured only by the all-atom CHARMM36 and prosECCo75 force fields. In contrast, the all-atom Slipids force field, along with the coarse-grained Martini 2.2, Martini 2.2 polarizable, and Martini 3 models, show varying degrees of agreement with experiments. Our developed CVs can be adapted to various MD simulation engines for studying pore formation, with potential implications in membrane biophysics. They are also applicable to simulations involving external agents, offering an efficient alternative to existing methodologies.

FlowBack: A Generalized Flow-Matching Approach for Biomolecular Backmapping

Coarse-grained models have become ubiquitous in biomolecular modeling tasks aimed at studying slow dynamical processes such as protein folding and DNA hybridization. These models can considerably accelerate sampling but it remains challenging to accurately and efficiently restore all-atom detail to the coarse-grained trajectory, which can be vital for detailed understanding of molecular mechanisms and calculation of observables contingent on all-atom coordinates. In this work, we introduce FlowBack as a deep generative model employing a flow-matching objective to map samples from a coarse-grained prior distribution to an all-atom data distribution. We construct our prior distribution to be agnostic to the coarse-grained map and molecular type. A protein-specific model trained on ∼65k structures from the Protein Data Bank achieves state-of-the-art performance on structural metrics compared to previous generative and rules-based approaches in applications to static PDB structures, all-atom simulations of fast-folding proteins, and coarse-grained trajectories generated by a machine-learned force field. A DNA-protein model trained on ∼1.5k DNA-protein complexes achieves excellent reconstruction and generative capabilities on static DNA-protein complexes from the Protein Data Bank as well as on out-of-distribution coarse-grained dynamical simulations of DNA-protein complexation. FlowBack offers an accurate, efficient, and easy-to-use tool to recover all-atom structures from coarse-grained molecular simulations with higher robustness and fewer steric clashes than previous approaches. We make FlowBack freely available to the community as an open source Python package.

Molecular-Scale Simulation of Wetting of Actin Filaments by Protein Droplets

Liquid phase-separating proteins can form condensates that play an important role in spatial and temporal organization of biological cells. The understanding of the mechanisms that lead to the formation of protein condensates and their interactions with other biomolecules may lead to processing routes for soft materials with tailored geometry and function. Fused in sarcoma (FUS) is an example of a nuclear protein that forms stable complexes, and recent studies have highlighted its ability to wet actin filaments and bundle them into networks. We perform coarse-grained molecular dynamics simulations to investigate the wetting and spreading of FUS droplets on actin filaments. We employ the Martini model and rescale the protein-protein and protein-actin interactions to tune the interfacial and wetting properties of FUS droplets. By measuring the molecular displacements in the three-phase region, we are able to relate contact angle, contact line velocity, and contact line friction in terms of a linear approximation of molecular kinetic theory. The results show that the rescaled Martini model can be used to study the molecular mechanisms of dynamic wetting at the nanoscale and to obtain quantitative predictions of the contact line friction and contact angles during dynamic wetting.

Elasticity of Swollen and Folded Polyacrylamide Hydrogel Using the MARTINI Coarse-Grained Model

One of the key advantages of using a hydrogel is its superb control over elasticity obtained through variations of constituent polymer and water. The underlying molecular nature of a hydrogel is a fundamental origin of hydrogel mechanics. In this article, we report a Polyacrylamide (PAAm)-based hydrogel model using the MARTINI coarse-grained (CG) force field. The MARTINI hydrogel is molecularly developed through Iterative Boltzmann inversion (IBI) using all-atom molecular dynamics (AAMD), and its quality is evaluated through the experimental realization of the target hydrogel. The developed model offers a mechanically high-fidelity CG hydrogel that can access large-scale water-containing hydrogel behavior, which is difficult to explore through AAMD in practical time. With the modeled hydrogel, we reveal that the polymer conformation modulates the elasticity of the hydrogel from a folded state to a swollen state, confirmed by the Panyukov model. The results provide a robust bridge for linking the polymer conformations and alignment to their bulk deformation, enabling the multifaceted and material-specific predictions required for hydrogel applications.

From experimental studies to computational approaches: recent trends in designing novel therapeutics for amyloidogenesis

Amyloidosis is a condition marked by misfolded proteins that build up in tissues and eventually destroy organs. It has been connected to a number of fatal illnesses, including non-neuropathic and neurodegenerative conditions, which in turn have a significant influence on the worldwide health sector. The inability to identify the underlying etiology of amyloidosis has hampered efforts to find a treatment for the condition. Despite the identification of a multitude of putative pathogenic variables that may operate independently or in combination, the molecular mechanisms responsible for the development and progression of the disease remain unclear. A thorough investigation into protein aggregation and the impacts of toxic aggregated species will help to clarify the cytotoxicity of aggregation-mediated cellular apoptosis and lay the groundwork for future studies aimed at creating effective treatments and medications. This review article provides a thorough summary of the combination of various experimental and computational approaches to modulate amyloid aggregation. Further, an overview of the latest developments of novel therapeutic agents is given, along with a discussion of the possible obstacles and viewpoints on this developing field. We believe that the information provided by this review will help scientists create innovative treatment strategies that affect the way proteins aggregate.

goChem: A Composable Library for Multi-Scale Computational Chemistry Data Analysis

Data analysis is a major task for Computational Chemists. The diversity of modeling tools currently available in Computational Chemistry requires the development of flexible analysis tools that can adapt to different systems and output formats. As a contribution to this need, we report the implementation of goChem, a versatile open-source library for multiscale analysis of computational chemistry data. The library, written in and for the Go programming language, allows for easy integration of different levels of theory, in an easy-to-use API, allowing the development of both one-use and complex analysis programs in Go. We describe the library and detail some selected applications that illustrate the capabilities and potential of this tool. The library is available at http://gochem.org.

RNA Translocation through Protein Nanopores: Interlude of the Molten RNA Globule

Direct translocation of RNA with secondary structures using single-molecule electrophoresis through protein nanopores shows significant fluctuations in the measured ionic current, in contrast to unstructured single-stranded RNA or DNA. We developed a multiscale model combining the oxRNA model for RNA with the 3-dimensional Poisson-Nernst-Planck formalism for electric fields within protein pores, aiming to map RNA conformations to ionic currents as RNA translocates through three protein nanopores: α-hemolysin, CsgG, and MspA. Our findings reveal three distinct stages of translocation (pseudoknot, melting, and molten globule) based on contact maps and current values. Two translocation modes emerge: fast and slow. In the fast mode, the speed is determined by the electric field, independent of pore geometry. In the slow mode, the molten globule stage is the rate-determining factor in slowing the translocation, instead of the previous paradigm of melting of the base pairs. Using these insights, we propose a neural network framework to identify and reconstruct RNA secondary structures from ionic current windows. We find that the electric field distribution, not the nanopore geometry, drives the molten globule stage. Our results explain the large current fluctuations. These results provide a fundamental understanding of the role of secondary and tertiary structures in the translocation of RNA in direct RNA translocation platforms based on single-molecule electrophoresis. This work offers design rules for new protein pores and real-time imaging of the secondary structures of RNA.

XPB: an Extendable Polymer Builder for High-Throughput and High-Quality Generation of Complex Polymer Structures

The efficient generation of complex initial structures for polymers remains a critical challenge in the field of molecular simulation. This necessitates the development of high-quality and highly efficient modeling algorithms. Inspired by fundamental polymerization reactions, we propose a general algorithm for an efficient de novo polymer model building, resulting in the development of the eXtendable Polymer Builder (XPB) package. We show that XPB is well-suited for constructing a wide range of polymer models, including linear, dendritic, and cross-linked structures. It offers a precise control over polymer morphology through adjustable, physically meaningful parameters such as residue types, connection preferences, and cross-linking distances. As a showcase, XPB can construct well-defined dendrimers up to the 10th generation and hyperbranched polymers with tens of thousands of residues within mere minutes, while effectively minimizing structural overlaps. This versatility facilitates the construction of more complex polymer architectures than before, providing a general and robust framework for the high-throughput and high-quality generation of diverse polymer structures.

Validation of a Coarse-Grained Martini 3 Model for Molecular Oxygen

Molecular oxygen (O2) is essential for life, and continuous effort has been made to understand its pathways in cellular respiration with all-atom (AA) molecular dynamics (MD) simulations of, e.g., membrane permeation or binding to proteins. To reach larger length scales with models, such as curved membranes in mitochondria or caveolae, coarse-grained (CG) simulations could be used at much lower computational cost than AA simulations. Yet a CG model for O2 is lacking. In this work, a CG model for O2 is therefore carefully selected from the Martini 3 force field based on criteria including size, zero charge, nonpolarity, solubility in nonpolar organic solvents, and partitioning in a phospholipid membrane. This chosen CG model for O2 (TC3 bead) is then further evaluated through the calculation of its diffusion constant in water and hexadecane, its permeability rate across pure phospholipid- and cholesterol-containing membranes, and its binding to the T4 lysozyme L99A protein. Our CG model shows semiquantitative agreement between CG diffusivity and permeation rates with the corresponding AA values and available experimental data. Additionally, it captures the binding to hydrophobic cavities of the protein, aligning well with the AA simulation of the same system. Thus, the results show that our O2 model approximates the behavior observed in the AA simulations. The CG O2 model is compatible with the widely used multifunctional Martini 3 force field for biological simulations, which will allow for the simulation of large biomolecular systems involved in O2's transport in the body.

Open-boundary molecular dynamics of ultrasound using supramolecular water models

Ultrasound can be used to manipulate protein function and activity, as well as for targeted drug delivery, making it a powerful diagnostic and therapeutic modality with wide applications in sonochemistry, nanotechnology, and engineering. However, a general particle-based approach to ultrasound modeling remains challenging due to the significant disparity between characteristic time scales governing ultrasound propagation. In this study, we use open-boundary molecular dynamics to simulate ultrasound waves in liquid water under ambient conditions by employing supramolecular water models, i.e., the Martini 3, dissipative particle dynamics, and many-body dissipative particle dynamics models. We demonstrate that our approach successfully reproduces the solution of the traveling wave equation and captures the velocity dispersion characteristic of high-frequency ultrasound waves.

Modeling Diffusive Motion of Ferredoxin and Plastocyanin on the PSI Domain of Procholorococcus marinus MIT9313

Diffusion of mobile charge carriers, such as ferredoxin and plastocyanin, often constitutes a rate-determining step in photosynthetic energy conversion. The diffusion time scales typically exceed that of other primary bioenergetic processes and remain beyond the reach of direct simulation at the molecular level. We characterize the diffusive kinetics of ferredoxin and plastocyanin upon the photosystem I-rich domain of Prochlorococcus, the most abundant phototroph on Earth by mass. A modeling approach for ferredoxin and plastocyanin diffusion is presented that uses ensembles of coarse-grained molecular dynamics simulations in Martini 2.2P with GROMACS 2021.2. The simulation ensembles are used to construct the diffusion coefficient and drift for ferredoxin and plastocyanin as spatial functions in the photosystem I domain of the MIT9313 ecotype. Four separate models are constructed, corresponding to ferredoxin and plastocyanin in reduced and oxidized states. A single scaling constant of 0.7 is found to be sufficient to adjust the diffusion coefficient obtained from the Martini simulation ensemble to match the in vitro values for both ferredoxin and plastocyanin. A comparison of Martini versions (2.2P, 2.2, 3) is presented with respect to diffusion scaling. The diffusion coefficient and drift together quantify the inhomogeneity of diffusive behavior. Notably, a funnel-like convergence toward the corresponding putative binding positions is observed for both ferredoxin and plastocyanin, even without such a priori foreknowledge supplied in the simulation protocol. The approach presented here is of relevance for studying diffusion kinetics in photosynthetic and other bioenergetic processes.

Light Harvesting Complex II Resists Non-bilayer Lipid-Induced Polymorphism in Plant Thylakoid Membranes via Lipid Redistribution

The plant thylakoid membrane hosting the light-harvesting complex (LHCII) is the site of oxygenic photosynthesis. Contrary to the earlier consensus of a protein-driven single lamellar phase of the thylakoid, despite containing 40% non-bilayer-forming lipids, recent experiments confirm the polymorphic state of the functional thylakoid. What, then, is the origin of this polymorphism and what factors control it? The current Letter addresses the question using a total of 617.8 μs long coarse-grained simulations of thylakoids with and without LHCII and varying concentrations of non-bilayer lipids using Martini-2.2 and -3.0 at 323 K. The LHCII redistributes the non-bilayer lipids into its annular region, increases the bending modulus and the stalk formation free energy, reduces the nonzero mean curvature propensity, and resists the polymorphism these lipids promote. The thermodynamic trade-off between non-bilayer lipids and LHCII dictates the degree of nanoscopic curvature leading to the polymorphism crucial for non-photochemical quenching under excess light conditions.

Impact of Guanidinium Hydrochloride on the Shapes of Prothymosin-α and α-Synuclein Is Dramatically Different

The effects of guanidinium hydrochloride (GdmCl) on two intrinsically disordered proteins (IDPs) are investigated using simulations of the self-organized polymer-IDP (SOP-IDP) model. The impact of GdmCl is taken into account using the molecular transfer model (MTM). We show that due to the dramatic reduction in the stiffness of the highly charged Prothymosin-α (ProTα) with increasing concentration of GdmCl ([GdmCl]), the radius of gyration (Rg) decreases sharply until about 1.0 M. Above 1.0 M, ProTα expands, caused by the swelling effect of GdmCl. In contrast, Rg of α-Synuclein (αSyn) swells as continuously as [GdmCl] increases, with most of the expansion occurring at concentrations less than 0.2 M. Strikingly, the amplitude of the small-angle X-ray scattering (SAXS) profiles for ProTα increases until [GdmCl] ≈ 1.0 M and decreases beyond 1.0 M. The [GdmCl]-dependent SAXS profiles for αSyn, which has a pronounced bump at small wave vector (q ∼ 0.5 nm-1) at low [GdmCl] (≤0.2 M), monotonically decrease at all values of [GdmCl]. The contrasting behavior predicted by the combination of MTM and SOP-IDP simulations may be qualitatively understood by modeling ProTα as a strongly charged polyelectrolyte with nearly uniform density of charges along the chain contour and αSyn as a nearly neutral polymer, except near the C-terminus, where the uncompensated negatively charged residues are located. The precise predictions for the SAXS profiles as a function of [GdmCl] can be readily tested.

Secondary Structure in Free and Assisted Modeling of Proteins with the Coarse-Grained UNRES Force Field

Secondary structure is a solid scaffold on which the three-dimensional structure of a protein is built. Therefore, care must be taken to reproduce the secondary structure as accurately as possible in the simulations of protein systems. In this chapter, we summarize the physics-based energy terms that govern secondary-structure formation, the auxiliary restraints on secondary structure derived from bioinformatics and from the experimental data, and the role of those in the modeling of protein structures, dynamics, and thermodynamics with the physics-based coarse-grained UNRES force field. Examples illustrating the methodology discussed and further directions of development are presented.

G-Protein-Coupled Receptor Surface Creates a Favorable Pathway for Membrane Permeation of Drug Molecules

G-protein-coupled receptors (GPCRs) play a crucial role in modulating physiological responses and serve as the main drug target. Specifically, salmeterol and salbutamol, which are used for the treatment of pulmonary diseases, exert their effects by activating the GPCR β2-adrenergic receptor (β2AR). In our study, we employed coarse-grained molecular dynamics simulations with the Martini 3 force field to investigate the dynamics of drug molecules in membranes in the presence and absence of β2AR. Our simulations reveal that, in more than 50% of the flip-flop events, the drug molecules use the β2AR surface to permeate the membrane. The pathway along the GPCR surface is significantly more energetically favorable for the drug molecules, which was revealed by umbrella sampling simulations along spontaneous flip-flop pathways. Furthermore, we assessed the behavior of drugs with intracellular targets, such as kinase inhibitors, whose therapeutic efficacy could benefit from this observation. In summary, our results show that β2AR surface interactions can significantly enhance the membrane permeation of drugs, emphasizing their potential for consideration in future drug development strategies.

Insertion and Anchoring of the HIV-1 Fusion Peptide into a Complex Membrane Mimicking the Human T-Cell

A fundamental understanding of how the HIV-1 envelope (Env) protein facilitates fusion is still lacking. The HIV-1 fusion peptide, consisting of 15 to 22 residues, is the N-terminus of the gp41 subunit of the Env protein. Further, this peptide, a promising vaccine candidate, initiates viral entry into target cells by inserting and anchoring into human immune cells. The influence of membrane lipid reorganization and the conformational changes of the fusion peptide during the membrane insertion and anchoring processes, which can significantly affect HIV-1 cell entry, remains largely unexplored due to the limitations of experimental measurements. In this work, we investigate the insertion of the fusion peptide into an immune cell membrane mimic through multiscale molecular dynamics simulations. We mimic the native T-cell by constructing a nine-lipid asymmetric membrane, along with geometrical restraints accounting for insertion in the context of gp41. To account for the slow time scale of lipid mixing while enabling conformational changes, we implement a protocol to go back and forth between atomistic and coarse-grained simulations. Our study provides a molecular understanding of the interactions between the HIV-1 fusion peptide and the T-cell membrane, highlighting the importance of the conformational flexibility of fusion peptides and local lipid reorganization in stabilizing the anchoring of gp41 into the targeted host membrane during the early events of HIV-1 cell entry. Importantly, we identify a motif within the fusion peptide critical for fusion that can be further manipulated in future immunological studies.

Recent Progress in Modeling and Simulation of Biomolecular Crowding and Condensation Inside Cells

Macromolecular crowding in the cellular cytoplasm can potentially impact diffusion rates of proteins, their intrinsic structural stability, binding of proteins to their corresponding partners as well as biomolecular organization and phase separation. While such intracellular crowding can have a large impact on biomolecular structure and function, the molecular mechanisms and driving forces that determine the effect of crowding on dynamics and conformations of macromolecules are so far not well understood. At a molecular level, computational methods can provide a unique lens to investigate the effect of macromolecular crowding on biomolecular behavior, providing us with a resolution that is challenging to reach with experimental techniques alone. In this review, we focus on the various physics-based and data-driven computational methods developed in the past few years to investigate macromolecular crowding and intracellular protein condensation. We review recent progress in modeling and simulation of biomolecular systems of varying sizes, ranging from single protein molecules to the entire cellular cytoplasm. We further discuss the effects of macromolecular crowding on different phenomena, such as diffusion, protein-ligand binding, and mechanical and viscoelastic properties, such as surface tension of condensates. Finally, we discuss some of the outstanding challenges that we anticipate the community addressing in the next few years in order to investigate biological phenomena in model cellular environments by reproducing in vivo conditions as accurately as possible.

Martignac: Computational Workflows for Reproducible, Traceable, and Composable Coarse-Grained Martini Simulations

Despite their wide use and far-reaching implications, molecular dynamics (MD) simulations suffer from a lack of both traceability and reproducibility. We introduce Martignac: computational workflows for the coarse-grained (CG) Martini force field. Martignac describes Martini CG MD simulations as an acyclic directed graph, providing the entire history of a simulation─from system preparation to property calculations. Martignac connects to NOMAD, such that all simulation data generated are automatically normalized and stored according to the FAIR principles. We present several prototypical Martini workflows, including system generation of simple liquids and bilayers, as well as free-energy calculations for solute solvation in homogeneous liquids and drug permeation in lipid bilayers. By connecting to the NOMAD database to automatically pull existing simulations and push any new simulation generated, Martignac contributes to improving the sustainability and reproducibility of molecular simulations.

Coarse-Grained Simulation Study of the Association of Selected Dipeptides

The association of 55 dipeptides extracted from aggregation-prone regions of selected proteins was studied by means of multiplexed replica-exchange molecular dynamics simulations with the coarse-grained UNRES model of polypeptide chains. Each simulation was carried out with 320 dipeptide molecules in a periodic box at 0.24 mol/dm3 concentration, in the 260-370 K temperature range. The temperature profiles of the degree of association, distributions of dipeptide cluster size, and structures of clusters were examined. It has been found that the dipeptides composed of strongly nonpolar (aromatic or aliphatic) residues associate nearly completely at all temperatures to form tight clusters, while those composed of charged or polar residues exhibited no or residual association. The dipeptides composed of nonpolar and small polar residues and those composed of less hydrophobic residues formed single clusters, gradually dissolving with increasing temperature, while those composed of phenylalanine or tryptophan and polar or charged residues formed multiple irregular clusters with room to accommodate water inside, suggesting the formation of liquid droplets or gels. The logarithms of the average degree of association and the free energy of aggregation per monomer were found to correlate with the dipeptide hydrophobicity.

Molecular Dynamics Simulations Elucidate the Molecular Organization of Poly(beta-amino ester) Based Polyplexes for siRNA Delivery

Cationic polymers are known to efficiently deliver nucleic acids to target cells by encapsulating the cargo into nanoparticles. However, the molecular organization of these nanoparticles is often not fully explored. Yet, this information is crucial to understand complex particle systems and the role influencing factors play at later stages of drug development. Coarse-grained molecular dynamics (CG-MD) enables modeling of systems that are the size of real nanoparticles, providing meaningful insights into molecular interactions between polymers and nucleic acids. Herein, the particle assembly of variations of an amphiphilic poly(beta-amino ester) (PBAE) with siRNA was simulated to investigate the influence of factors such as polymer lipophilicity and buffer conditions on the nanoparticle structure. Simulations were validated by wet lab methods including nuclear magnetic resonance (NMR) and align well with experimental findings. Therefore, this work emphasizes that CG-MD simulations can provide underlying explanations of experimentally observed nanoparticle properties by visualizing the nanoscale structure of polyplexes.

Automated Parametrization Approach for Coarse-Graining Soil Organic Matter Molecules

Investigating the molecular structure of soil organic matter (SOM), along with its intramolecular interactions and interactions with other soil components and xenobiotics, is essential due to its ecological importance. However, the complexity and heterogeneity of SOM present significant challenges for systematic studies. While experimental methods are commonly employed, atomistic simulations provide a complementary approach to exploring molecular-level processes. The Vienna Soil Organic Matter Modeler 2 (VSOMM2) facilitates the construction of molecular models of SOM systems with various compositions at the atomistic scale, which can then be examined through molecular dynamics (MD) simulations. This study introduces a parametrization strategy that enables the conversion of VSOMM2-generated structures into a coarse-grained representation, thus allowing larger time and length scales to be explored. By employing a conformer search technique, direct construction and analysis of coarse-grained SOM models with diverse compositions were made possible, eliminating the need for atomistic MD simulations. To demonstrate this approach, coarse-grained SOM models were created based on selected samples from the International Humic Substances Society, considering different water content levels for each model. Comprehensive analyses, including density and potential energy profile calculations, revealed a partial correlation with the SOM compositions and demonstrated that electrostatic interactions govern the structural packing. Moreover, a local phase separation process, particularly the formation of SOM voids, was observed over several microseconds, underscoring the advantages of the coarse-graining technique.

Effects of All-Atom and Coarse-Grained Molecular Mechanics Force Fields on Amyloid Peptide Assembly: The Case of a Tau K18 Monomer

To propose new mechanism-based therapeutics for Alzheimer's disease (AD), it is crucial to study the kinetics and oligomerization/aggregation mechanisms of the hallmark tau proteins, which have various isoforms and are intrinsically disordered. In this study, multiple all-atom (AA) and coarse-grained (CG) force fields (FFs) have been benchmarked on molecular dynamics (MD) simulations of K18 tau (M243-E372), which is a truncated form (130 residues) of full-length tau (441 residues). FF19SB is first excluded because the dynamics are too slow, and the conformations are too stable. All other benchmarked AAFFs (Charmm36m, FF14SB, Gromos54A7, and OPLS-AA) and CGFFs (Martini3 and Sirah2.0) exhibit a trend of shrinking K18 tau into compact structures with the radius of gyration (ROG) around 2.0 nm, which is much smaller than the experimental value of 3.8 nm, within 200 ns of AA-MD or 2000 ns of CG-MD. Gromos54A7, OPLS-AA, and Martini3 shrink much faster than the other FFs. To perform meaningful postanalysis of various properties, we propose a strategy of selecting snapshots with 2.5 < ROG < 4.5 nm, instead of using all sampled snapshots. The calculated chemical shifts of all C, CA, and CB atoms have very good and close root-mean-square error (RMSE) values, while Charmm36m and Sirah2.0 exhibit better chemical shifts of N than other FFs. Comparing the calculated distributions of the distance between the CA atoms of CYS291 and CYS322 with the results of the FRET experiment demonstrates that Charmm36m is a perfect match with the experiment while other FFs exhibit limitations. In summary, Charmm36m is recommended as the best AAFF, and Sirah2.0 is recommended as an excellent CGFF for simulating tau K18.

Dynamics and Activation of Membrane-Bound B Cell Receptor Assembly

B-cell receptor complexes (BCR) are expressed on the surface of a B-cell and are critical in antigen recognition and modulating the adaptive immune response. Even though the relevance of antibodies has been known for almost a hundred years, the antigen-dependent activation mechanism of B-cells has remained elusive. Several models have been proposed for BCR activation, including cross-linking, conformation-induced oligomerization, and dissociation activation models. Recently, the first cryo-EM structures of the human B-cell antigen receptor of the IgM and IgG isotypes were published that validated the asymmetric organization of the BCR complex. Here, we have carried out extensive molecular dynamics simulations to probe the conformational changes upon antigen binding and the influence of the membrane lipids. We identified two critical dynamical events that could be associated with antigen-dependent activation of BCR. First, antigen binding caused increased flexibility in regions distal to the antigen binding site. Second, antigen binding altered the rearrangement of IgM transmembrane helices, including the relative interaction of Igα/Igβ that mediates intracellular signaling. Furthermore, these transmembrane rearrangements led to changes in localized lipid composition.

CYCLOPEp Builder: Facilitating cyclic peptide and nanotube research through a user-friendly web platform

The study of cyclic peptides (CPs) and self-assembling cyclic peptide nanotubes (SCPNs) is pivotal in advancing applications in diverse fields such as biomedicine, nanoelectronics, and catalysis. Recognizing the limitations in the experimental study of these molecules, this article introduces CYCLOPEp Builder, a comprehensive web-based application designed to facilitate the design, simulation, and visualization of CPs and SCPNs. The tool is engineered to generate molecular topologies, essential for conducting Molecular Dynamics simulations that span All-Atom to Coarse-Grain resolutions. CYCLOPEp Builder's user-friendly interface simplifies the complex process of molecular modeling, providing researchers with the ability to readily construct CPs and SCPNs. The platform is versatile, equipped with various force fields, and capable of producing structures ranging from individual CPs to complex SCPNs with different sequences, offering parallel and antiparallel orientations among them. By enhancing the capacity for detailed visualization of molecular assemblies, CYCLOPEp Builder improves the understanding of CP and SCPN molecular interactions. This tool is a step forward in democratizing access to sophisticated simulations, offering an invaluable resource to the scientific community engaged in the exploration of supramolecular structures. CYCLOPEp is accessible at http://cyclopep.com/.

Characterization of DsrD and its interaction with the DsrAB dissimilatory sulfite reductase

Microbial dissimilatory sulfate reduction is a key process in the global sulfur and carbon cycles in anoxic ecosystems. In this anaerobic respiration, sulfate is phosphorylated and reduced to sulfite, which is further reduced to a DsrC-trisulfide by the dissimilatory sulfite reductase DsrAB. DsrD is a small protein that acts as an allosteric activator of DsrAB, increasing the efficiency of sulfite reduction. Here, we report a detailed study of DsrD and its interaction with DsrAB. Sequence similarity analyses show that there are three groups of DsrD in organisms with a reductive-type DsrAB. The protein regions involved in the DsrD-DsrAB interaction and activity-promoting effect were investigated through in vitro and in silico studies, including mutations of conserved DsrD residues. The results reveal that the conserved β-loop of DsrD is involved in the interaction, contributing to a better understanding of its mechanism of action.

Structure of the aminoterminal domain of the birnaviral multifunctional VP3 protein and its unexplored critical role

To overcome their limited genetic capacity, numerous viruses encode multifunctional proteins. The birnavirus VP3 protein plays key roles during infection, including scaffolding of the viral capsid during morphogenesis, recruitment, and regulation of the viral RNA polymerase, shielding of the double-stranded RNA genome and targeting of host endosomes for genome replication, and immune evasion. The dimeric form of VP3 is critical for these functions. In previous work, we determined the X-ray structure of the central domains (D2-D3) of VP3 from the infectious bursal disease virus (IBDV). However, the structure and function of the IBDV VP3 N-terminal domain (D1) could not be determined at that time. Using integrated structural biology approaches and functional cell assays, here we characterize the IBDV VP3 D1 domain, unveiling its unexplored roles in virion stability and infection. The X-ray structure of D1 shows that this domain folds in four α-helices arranged in parallel dimers, which are essential for maintaining the dimeric arrangement of the full-length protein. Combining small-angle X-ray scattering analyses with molecular dynamics simulations allowed us to build a structural model for the D1-D3 domains. This model consists of an elongated structure with high flexibility in the D2-D3 connection, keeping D1 as the only driver of VP3 dimerization. Using reverse genetics tools, we show that the obliteration of D1 domain prevents the VP3 scaffold function during capsid assembly and severely impacts IBDV infection. Altogether, our study elucidates the structure of the VP3 D1 domain and reveals its role in VP3 protein dimerization and IBDV infection.

CryoSTAR: leveraging structural priors and constraints for cryo-EM heterogeneous reconstruction

Resolving conformational heterogeneity in cryogenic electron microscopy datasets remains an important challenge in structural biology. Previous methods have often been restricted to working exclusively on volumetric densities, neglecting the potential of incorporating any preexisting structural knowledge as prior or constraints. Here we present cryoSTAR, which harnesses atomic model information as structural regularization to elucidate such heterogeneity. Our method uniquely outputs both coarse-grained models and density maps, showcasing the molecular conformational changes at different levels. Validated against four diverse experimental datasets, spanning large complexes, a membrane protein and a small single-chain protein, our results consistently demonstrate an efficient and effective solution to conformational heterogeneity with minimal human bias. By integrating atomic model insights with cryogenic electron microscopy data, cryoSTAR represents a meaningful step forward, paving the way for a deeper understanding of dynamic biological processes.

Phase Coexistence in Hamiltonian Hybrid Particle-Field Theory Using a Multi-Gaussian Approach

This study introduces an implementation of multiple Gaussian filters within the Hamiltonian hybrid particle-field (HhPF) theory, aimed at capturing phase coexistence phenomena in mesoscopic molecular simulations. By employing a linear combination of two Gaussians, we demonstrate that HhPF can generate potentials with attractive and steric components analogous to Lennard-Jones (LJ) potentials, which are crucial for modeling phase coexistence. We compare the performance of this method with the multi-Gaussian core model (MGCM) in simulating liquid-gas coexistence for a single-component system across various densities and temperatures. Our results show that HhPF effectively captures detailed information on phase coexistence and interfacial phenomena, including microconfiguration transitions and increased interfacial fluctuations at higher temperatures. Notably, the phase boundaries obtained from HhPF simulations align more closely with those of LJ systems compared to the MGCM results. This work advances the hybrid particle-field methodology to address phase coexistence without requiring modifications to the equation of state or introducing additional interaction energy functional terms, offering a promising approach for mesoscale molecular simulations of complex systems.

Evaluation of All-Atom and Martini 3 Coarse-Grained Force Fields from the Structural Investigation of Nitroxide Spin Probes and Their Confinement in Beta-Cyclodextrin

Nitroxide radicals have found wide applications as spin labels or probes, and their guest-host interactions with cyclodextrins exhibit enhanced applications in electron spin resonance (ESR) spectroscopy and imaging due to improved biostability toward reducing agents. Although the computational prediction of the guest-host binding has become increasingly common for small ligands, molecular simulations regarding the conformational preferences of hosted spin probes have not been conducted. Here we present molecular dynamics simulations at an atomistic level for a set of four TEMPO (2,2,6,6-tetramethylpiperidine 1-oxyl) spin probes and thereafter develop coarse-grained models compatible with the recent version of the Martini force field (v 3.0) to tackle their encapsulation in the cavity of β-cyclodextrin (βCD) for which experimental ESR data are available. The results indicate that the atomistic descriptions perform well in relation to the structural parameters derived from X-ray diffraction as well as hydrogen bonding and hydrogen patterns and predict that the guest-host complexation is hydrophobically driven by the presence of a methyl group pair of the spin probe at the cavity center of βCD. The spin probe mobility at the binding site reveals the nitroxide group orientation toward the secondary rim of the cyclodextrin and the alternating presence of the two methyl group pairs inside the cavity, features in agreement with the experimental behavior of the ESR parameters. The coarse-grained parameterizations of TEMPO probes and βCD rely on optimizing the bonded and nonbonded parameters with references to the atomistic simulation results, and they are capable of recovering the orientation and location of the spin probe inside the cyclodextrin cavity predicted by the atomistic guest-host complexes. The results suggest the cyclodextrin host-guest system as a powerful validation suite to evaluate new coarse-grained parameterizations of small ligands and future extensions to functionalized cyclodextrins in inclusion complexes.

Coordination of transporter, cargo, and membrane properties during non-vesicular lipid transport

Homeostasis of cellular membranes is maintained by fine-tuning their lipid composition. Yeast lipid transporter Osh6, belonging to the oxysterol-binding protein-related proteins family, was found to participate in the transport of phosphatidylserine (PS). PS synthesized in the endoplasmic reticulum is delivered to the plasma membrane, where it is exchanged for phosphatidylinositol 4-phosphate (PI4P). PI4P provides the driving force for the directed PS transport against its concentration gradient. In this study, we employed an in vitro approach to reconstitute the transport process into the minimalistic system of large unilamellar vesicles to reveal its fundamental biophysical determinants. Our study draws a comprehensive portrait of the interplay between the structure and dynamics of Osh6, the carried cargo lipid, and the physical properties of the involved membranes, with particular attention to the presence of charged lipids and to membrane fluidity. Specifically, we address the role of the cargo lipid, which, by occupying the transporter, imposes changes in its dynamics and, consequently, predisposes the cargo to disembark in the correct target membrane.

Refined Protein-Sugar Interactions in the Martini Force Field

Sugar molecules play important roles as mediators of biomolecular interactions in cellular functions, disease, and infections. Molecular dynamics simulations are an indispensable tool to explore these interactions at the molecular level. The large time and length scales involved frequently necessitate the use of coarse-grained representations, which heavily depend on the parametrization of sugar-protein interactions. Here, we adjust the sugar-protein interactions in the widely used Martini 2.2 force field to reproduce the experimental osmotic second virial coefficients between sugars and proteins. In simulations of two model proteins in glucose solutions with adjusted force field parameters, we observe weak protein-sugar interactions. The sugar molecules are thus acting mainly as crowding agents, in agreement with experimental measurements. The procedure to fine-tune sugar-protein interactions is generally applicable and could also prove useful for atomistic force fields.

Nonspecific Yet Selective Interactions Contribute to Small Molecule Condensate Binding

Biomolecular condensates are essential in various cellular processes, and their misregulation has been demonstrated to underlie disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated all-atom simulations of various condensate-ligand systems. Systematic characterization of the ligand binding poses reveals that condensates can form diverse and heterogeneous chemical environments with one or multiple chains to bind small molecules. Unlike traditional protein-ligand interactions, these chemical environments are dominated by nonspecific hydrophobic interactions. Nevertheless, the chemical environments feature unique amino acid compositions and physicochemical properties that favor certain small molecules over others, resulting in varied ligand partitioning coefficients within condensates. Notably, different condensates share similar sets of chemical environments but at different populations. This population shift drives ligand selectivity toward specific condensates. Our approach can enhance the interpretation of experimental screening data and may assist in the rational design of small molecules targeting specific condensates.

SOP-MULTI: A Self-Organized Polymer-Based Coarse-Grained Model for Multidomain and Intrinsically Disordered Proteins with Conformation Ensemble Consistent with Experimental Scattering Data

Multidomain proteins with long flexible linkers and full-length intrinsically disordered proteins (IDPs) are best defined as an ensemble of conformations rather than a single structure. Determining high-resolution ensemble structures of such proteins poses various challenges by using tools from experimental structural biophysics. Integrative approaches combining available low-resolution ensemble-averaged experimental data and in silico biomolecular reconstructions are now often used for the purpose. However, extensive Boltzmann weighted conformation sampling for large proteins, especially for ones where both the folded and disordered domains exist in the same polypeptide chain, remains a challenge. In this work, we present a 2-site per amino-acid resolution SOP-MULTI force field for simulating coarse-grained models of multidomain proteins. SOP-MULTI combines two well-established self-organized polymer models─: (i) SOP-SC models for folded systems and (ii) SOP-IDP for IDPs. For the SOP-MULTI, we introduce cross-interaction terms between the beads belonging to the folded and disordered regions to generate conformation ensembles for full-length multidomain proteins such as hnRNP A1, TDP-43, G3BP1, hGHR-ECD, TIA1, HIV-1 Gag, polyubiquitin, and FUS. When back-mapped to all-atom resolution, SOP-MULTI trajectories faithfully recapitulate the scattering data over the range of the reciprocal space. We also show that individual folded domains preserve native contacts with respect to solved folded structures, and root-mean-square fluctuations of residues in folded domains match those obtained from all-atom molecular dynamics simulation trajectories of the same folded systems. SOP-MULTI force field is made available as a LAMMPS-compatible user package along with setup codes for generating the required files for any full-length protein with folded and disordered regions.

Enhancing the Assembly Properties of Bottom-Up Coarse-Grained Phospholipids

A plethora of key biological events occur at the cellular membrane where the large spatiotemporal scales necessitate dimensionality reduction or coarse-graining approaches over conventional all-atom molecular dynamics simulation. Constructing coarse-grained descriptions of membranes systematically from statistical mechanical principles has largely remained challenging due to the necessity of capturing amphipathic self-assembling behavior in coarse-grained models. We show that bottom-up coarse-grained lipid models can possess metastable morphological behavior and that this potential metastability has ramifications for accurate development and training. We in turn develop a training algorithm which evades metastability issues by linking model training to self-assembling behavior, and demonstrate its robustness via construction of solvent-free coarse-grained models of various phospholipid membranes, including lipid species such as phosphatidylcholines, phosphatidylserines, sphingolipids, and cholesterol. The resulting coarse-grained lipid models are orders of magnitude faster than their atomistic counterparts while retaining structural fidelity and constitute a promising direction for the development of coarse-grained models of realistic cell membranes.

Dipteran-specific Daedalus controls Zucchini endonucleolysis in piRNA biogenesis independent of exonucleases

PIWI-interacting RNAs (piRNAs) protect germline genomes and maintain fertility by repressing transposons. Daedalus and Gasz act together as a mitochondrial scaffold for Armitage, a necessary factor for Zucchini-dependent piRNA processing. However, the mechanism underlying this function remains unclear. Here, we find that the roles of Daedalus and Gasz in this process are distinct, although both are necessary: Daedalus physically interacts with Armitage, whereas Gasz supports Daedalus to maintain its function. Daedalus binds to Armitage through two distinct regions, an extended coiled coil identified in this study and a sterile α motif (SAM). The former tethers Armitage to mitochondria, while the latter controls Zucchini endonucleolysis to define the length of piRNAs in an exonuclease-independent manner. piRNAs produced in the absence of the Daedalus SAM do not exhibit full transposon silencing functionality. Daedalus is Dipteran specific. Unlike Drosophila and mosquitoes, other species, such as mice, rely on exonucleases after Zucchini processing to specify the length of piRNAs.

Ebola Virus Matrix Protein VP40 Single Mutations G198R and G201R Significantly Enhance Plasma Membrane Localization

Viral proteins frequently undergo single or multiple amino acid mutations during replication, which can significantly alter their functionality. The Ebola virus matrix protein VP40 is multifunctional but primarily responsible for creating the viral envelope by binding to the inner leaflet of the host cell plasma membrane (PM). Changes to the VP40 surface cationic charge via mutations can influence PM interactions, resulting in altered viral assembly and budding. A recent mutagenesis study evaluated the effects of several mutations and found that mutations G198R and G201R enhanced VP40 assembly at the PM and virus-like particle budding. These two mutations lie in the loop region of the C-terminal domain (CTD), which directly interacts with the PM. To understand the role of these mutations in PM localization at the molecular level, we performed both all-atom and coarse-grained molecular dynamics simulations using a dimer-dimer configuration of VP40, which contains the CTD-CTD interface. Our studies indicate that the location of mutations on the outer surface of the CTD regions can lead to changes in membrane binding orientation and degree of membrane penetration. Direct PI(4,5)P2 interactions with the mutated residues seem to further stabilize and pull VP40 into the PM, thereby enhancing interactions with numerous amino acids that were otherwise infrequently or completely inaccessible. These multiscale computational studies provide new insights at the atomic and molecular level as to how VP40-PM interactions are altered through single amino acid mutations. Given the high case fatality rates associated with Ebola virus disease in humans, it is essential to explore the mechanisms of viral assembly in the presence of mutations to mitigate the severity of the disease and understand the potential of future outbreaks.

MARTINI Coarse-Grained Force Field for Thermoplastic Starch Nanocomposites

Thermoplastic starch (TPS) is an excellent film-forming material, and the addition of fillers, such as tetramethylammonium-montmorillonite (TMA-MMT) clay, has significantly expanded its use in packaging applications. We first used an all-atom (AA) simulation to predict several macroscopic (Young's modulus, glass transition temperature, density) and microscopic (conformation along 1-4 and 1-6 glycosidic linkages, composite morphology) properties of TPS melt and TPS-TMA-MMT composite. The interplay of polymer-surface (weakly repulsive), plasticizer-surface (attractive), and polymer-plasticizer (weakly attractive) interactions leads to conformational and dynamics properties distinct from those in systems with either attractive or repulsive polymer-surface interactions. A subset of AA properties was used to parametrize the MARTINI-2 coarse-grained (CG) force field (FF) for the melt and composite systems. The missing bonded parameters of amylose and amylopectin and the bead types for 1-4 and 1-6 linked α-D glucose were determined using two-body excess entropy, density, and bond and angle distributions in the AA TPS melt. This new MARTINI-2 CG model was also compared with the MARTINI-3 model for the TPS melt. However, the requirement of a polarizable water model necessitates the use of MARTINI-2 FF for the composite system. This liquid-liquid partitioning-based FF shows freezing and compaction of polymer chains near the clay surface, further accentuated by lowering of dispersive interactions between pairs of high-covalent-coordination ring units of TPS polymers and the montmorillonite sheet. A rescaling of the effective dispersive component of TPS-MMT cross interactions was used to optimize the MARTINI-2 FF for the composite system with structural (chain size distribution), thermodynamic (chain conformational entropy and density), and dynamic (self-diffusion coefficient) properties obtained from long AA simulations forming the constraints for optimization. The obtained CG FF parameters provided excellent estimates for several other properties of the melt and composite systems not used in parameter estimation, thus establishing the robustness of the developed model.

How Salt and Temperature Drive Reentrant Condensation of Aβ40

Within the framework of liquid-liquid phase separation (LLPS), biomolecular condensation orchestrates vital cellular processes, and its dysregulation is implicated in severe pathological conditions. Recent studies highlight the role of intrinsically disordered proteins (IDPs) in LLPS, yet the influence of microenvironmental factors has remained a puzzling factor. Here, via computational simulation of the impact of solution conditions on LLPS behavior of neurologically pathogenic IDP Aβ40, we chanced upon a salt-driven reentrant condensation phenomenon, wherein Aβ40 aggregation increases with low salt concentrations (25-50 mM), followed by a decline with further salt increments. An exploration of the thermodynamic and kinetic signatures of reentrant condensation unveils a nuanced interplay between protein electrostatics and ionic strength as potential drivers. Notably, the charged residues of the N-terminus exhibit a nonmonotonic response to salt screening, intricately linked to the recurrence of reentrant behavior in hydrophobic core-induced condensation. Intriguingly, our findings also unveil the reappearance of similar reentrant condensation phenomena under varying temperature conditions. Collectively, our study illuminates the profoundly context-dependent nature of Aβ40s liquid-liquid phase separation behavior, extending beyond its intrinsic molecular framework, where microenvironmental cues wield significant influence over its aberrant functionality.

Recalibration of MARTINI-3 Parameters for Improved Interactions between Peripheral Proteins and Lipid Bilayers

The MARTINI force field is one of the most used coarse-grained models for biomolecular simulations. Many limitations of the model including the protein-protein overaggregation have been improved in its latest version, MARTINI-3. In this study, we investigate the efficacy of the MARTINI-3 parameters for capturing the interactions of peripheral proteins with model plasma membranes. Particularly, we consider two classes of proteins, namely, annexin and epsin, which are known to generate negative and positive membrane curvatures, respectively. We find that current MARTINI-3 parameters are not able to correctly describe the protein-membrane interface and the protein-induced membrane curvatures for any of these proteins. The problem arises due to the lack of proper hydrophobic interactions between the protein residues and lipid tails. Making systematic adjustments, we show that a combination of reduction in the protein-water interactions and enhancement of protein-lipid hydrophobic interactions is essential for accurate prediction of the interfacial structure including the protein-induced membrane curvature. Next, we apply our model to a couple of other peripheral proteins, namely, Snf7, a core component of the ESCRT-III complex, and the PH domain of evectin-2. We find that our model captures the protein-membrane interfacial structure much more accurately than the MARTINI-3 model for all of the peripheral proteins considered in this study. However, the strategy described in this study may not be suitable for oligomeric transmembrane proteins where protein-protein hydrophobic interactions should be increased instead of protein-lipid hydrophobic interactions.

Lys716 in the transmembrane domain of yeast mitofusin Fzo1 modulates anchoring and fusion

Outer mitochondrial membrane fusion, a vital cellular process, is mediated by mitofusins. However, the underlying molecular mechanism remains elusive. We have performed extensive multiscale molecular dynamics simulations to predict a model of the transmembrane (TM) domain of the yeast mitofusin Fzo1. Coarse-grained simulations of the two TM domain helices, TM1 and TM2, reveal a stable interface, which is controlled by the charge status of residue Lys716. Atomistic replica-exchange simulations further tune our model, which is confirmed by a remarkable agreement with an independent AlphaFold2 (AF2) prediction of Fzo1 in complex with its fusion partner Ugo1. Furthermore, the presence of the TM domain destabilizes the membrane, even more if Lys716 is charged, which can be an asset for initiating fusion. The functional role of Lys716 was confirmed with yeast experiments, which show that mutating Lys716 to a hydrophobic residue prevents mitochondrial fusion.

Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction

Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. Here, we introduce MCGLPPI, a geometric representation learning framework that combines graph neural networks (GNNs) with MARTINI molecular coarse-grained (CG) models to predict PPI overall properties accurately and efficiently. Extensive experiments on three types of downstream PPI property prediction tasks demonstrate that at the CG-scale, MCGLPPI achieves competitive performance compared with the counterparts at the atom- and residue-scale, but with only a third of computational resource consumption. Furthermore, CG-scale pre-training on protein domain-domain interaction structures enhances its predictive capabilities for PPI tasks. MCGLPPI offers an effective and efficient solution for PPI overall property predictions, serving as a promising tool for the large-scale analysis of biomolecular interactions.

Coarse-grained polarizable soft solvent models, with applications in dissipative particle dynamics

We critically examine a broad class of explicitly polarizable soft solvent models aimed at applications in dissipative particle dynamics. We obtain the dielectric permittivity using the fluctuating box dipole method in linear response theory and verify the models in relation to several test cases, including demonstrating ion desorption from an oil-water interface due to image charge effects. We additionally compute the Kirkwood factor and find that it uniformly lies in the range gK≃0.7-0.8, indicating that dipole-dipole correlations are not negligible in these models. This is supported by the measurements of dipole-dipole correlation functions. As a consequence, Onsager theory over-predicts the dielectric permittivity by 20%-30%. The mean square molecular dipole moment can be accurately estimated with a first-order Wertheim perturbation theory.

Neural network-assisted model of interfacial fluids with explicit coarse-grained molecular structures

Interfacial fluids are ubiquitous in systems ranging from biological membranes to chemical droplets and exhibit a complex behavior due to their nonlinear, multiphase, and multicomponent nature. The development of accurate coarse-grained (CG) models for such systems poses significant challenges, as these models must effectively capture the intricate many-body interactions, both inter- and intramolecular, arising from atomic-level phenomena, and account for the diverse density distributions and fluctuations at the interface. In this study, we use advanced machine learning techniques incorporating force matching and diffusion probabilistic models to construct a robust CG model of interfacial fluids. We evaluate our model through simulations in various settings, including the water-air interface, bulk decane, and dipalmitoylphosphatidylcholine monolayer membranes. Our results show that our CG model accurately reproduces the essential many-body and interfacial properties of interfacial fluids and proves effective across different CG mapping strategies. This work not only validates the utility of our model for multiscale simulations, but also lays the groundwork for future improvements in the simulation of complex interfacial systems.

OXA β-lactamases from Acinetobacter spp. are membrane-bound and secreted into outer membrane vesicles

β-lactamases from Gram-negative bacteria are generally regarded as soluble, periplasmic enzymes. NDMs have been exceptionally characterized as lipoproteins anchored to the outer membrane. A bioinformatics study on all sequenced β-lactamases was performed that revealed a predominance of putative lipidated enzymes in the class D OXAs. Namely, 60% of the OXA class D enzymes contain a lipobox sequence in their signal peptide, that is expected to trigger lipidation and membrane anchoring. This contrasts with β-lactamases from other classes, which are predicted to be mostly soluble proteins. Almost all (> 99%) putative lipidated OXAs are present in Acinetobacter spp. Importantly, we further demonstrate that OXA-23 and OXA-24/40 are lipidated, membrane-bound proteins in Acinetobacter baumannii. In contrast, OXA-48 (commonly produced by Enterobacterales) lacks a lipobox and is a soluble protein. Outer membrane vesicles (OMVs) from Acinetobacter baumannii cells expressing OXA-23 and OXA-24/40 contain these enzymes in their active form. Moreover, OXA-loaded OMVs were able to protect A. baumannii, Escherichia coli and Pseudomonas aeruginosa cells susceptible to piperacillin and imipenem. These results permit us to conclude that membrane binding is a bacterial host-specific phenomenon in OXA enzymes. These findings reveal that membrane-bound β-lactamases are more common than expected and support the hypothesis that OMVs loaded with lipidated β-lactamases are vehicles for antimicrobial resistance and its dissemination. This advantage could be crucial in polymicrobial infections, in which Acinetobacter spp. are usually involved, and underscore the relevance of identifying the cellular localization of lactamases to better understand their physiology and target them.

Lipid organization by the Caveolin-1 complex

Caveolins are lipid-binding proteins that can organize membrane remodeling and oligomerize into the 8S complex. The CAV1-8S complex comprises a disk-like structure, about 15 nm in diameter, with a central beta barrel. Further oligomerization of 8S complexes remodels the membrane into caveolae vessels, with a dependence on cholesterol concentration. However, the molecular mechanisms behind membrane remodeling and cholesterol filtering are still not understood. Performing atomistic molecular dynamics simulations in combination with advanced sampling techniques, we describe how the CAV1-8S complex bends the membrane and accumulates cholesterol. Here, our simulations show an enhancing effect by the palmitoylations of CAV1, and we predict that the CAV1-8S complex can extract cholesterol molecules from the lipid bilayer and accommodate them in its beta barrel. Through backmapping to the all-atom level, we also conclude that the Martini v.2 coarse-grained force field overestimates membrane bending, as the atomistic simulations exhibit only very localized bending.

Toward understanding lipid reorganization in RNA lipid nanoparticles in acidic environments

The use of lipid nanoparticles (LNPs) for therapeutic RNA delivery has gained significant interest, particularly highlighted by recent milestones such as the approval of Onpattro and two mRNA-based SARS-CoV-2 vaccines. However, despite substantial advancements in this field, our understanding of the structure and internal organization of RNA-LNPs -and their relationship to efficacy, both in vitro and in vivo- remains limited. In this study, we present a coarse-grained molecular dynamics (MD) approach that allows for the simulations of full-size LNPs. By analyzing MD-derived structural characteristics in conjunction with cellular experiments, we investigate the effect of critical parameters, such as pH and composition, on LNP structure and potency. Additionally, we examine the mobility and chemical environment within LNPs at a molecular level. Our findings highlight the significant impact that LNP composition and internal molecular mobility can have on key stages of LNP-based intracellular RNA delivery.