SPICA Force Field for Lipid Membranes: Domain Formation Induced by Cholesterol

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

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

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

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

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery

Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble in water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design of LNCs, a detailed understanding of the composition-structure-function relationships is missing. This review presents currently available computational methods for LNC investigation, screening, and design. The state-of-the-art physics-based approaches are described, with the focus on molecular dynamics simulations in all-atom and coarse-grained resolution. Their strengths and weaknesses are discussed, highlighting the aspects necessary for obtaining reliable results in the simulations. Furthermore, a machine learning, i.e., data-based learning, approach to the design of lipid-mediated API delivery is introduced. The data produced by the experimental and theoretical approaches provide valuable insights. Processing these data can help optimize the design of LNCs for better performance. In the final section of this Review, state-of-the-art of computer simulations of LNCs are reviewed, specifically addressing the compatibility of experimental and computational insights.

Non-ideal mixing of lipids: A molecular dynamics perspective

Lipid membranes have complex compositions, and modeling the thermodynamic properties of multi-component lipid systems remains a remote goal. In this work, we attempt to describe the thermodynamics of binary lipid mixtures by mapping coarse-grained molecular dynamics systems to two-dimensional simple fluid mixtures. By computing and analyzing the density fluctuations of this model lipid bilayer, we determine the numerical value of the quadratic coupling term appearing in a model of regular solutions for the dipalmitoylphosphatidylcholine-dilinoleoylphosphatidylcholine pair of lipids at three different compositions. Our methodology is general and discussed in detail.

Tracking Cholesterol Flip-Flop in Mammalian Plasma Membrane through Coarse-Grained Molecular Dynamics Simulations

Plasma membrane (PM) simulations at longer length and time scales at nearly atomistic resolution can provide invaluable insights into cell signaling, apoptosis, lipid trafficking, and lipid raft formation. We propose a coarse-grained (CG) model of a mammalian PM considering major lipid head groups distributed asymmetrically across the membrane bilayer and validate the model against bilayer structural properties from atomistic simulation. Using the proposed CG model, we identify a recurring pattern in the passive collective cholesterol transbilayer motion and study the individual cholesterol flip-flop events and associated pathways along with lateral ordering in the bilayer during a flip-flop event. We identify two discrete cholesterol flip-flop pathways: (i) a systematic rototranslational pathway and (ii) intraleaflet inversion followed by interleaflet translation (or reverse). We observe a periodic cholesterol enrichment in the exoplasmic leaflet of the PM bilayer and examine the underlying cholesterol-lipid affinities. We observe closer association between cholesterol and palmitoylsphingomyelin (PSM) lipid, relative to other lipids, and conclude that the cholesterol enrichment in the exoplasmic leaflet can be attributed to higher PSM content in that leaflet, together leading to formation of short-lived PSM-cholesterol-rich domains.

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.

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.

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.

A Coarse-Grained SPICA Makeover for Solvated and Bare Sodium and Chloride Ions

Aqueous ionic solutions are pivotal in various scientific domains due to their natural prevalence and vital roles in biological and chemical processes. Molecular dynamics has emerged as an effective methodology for studying the dynamic behavior of these systems. While all-atomistic models have made significant strides in accurately representing and simulating these ions, the challenge persists in achieving precise models for coarse-grained (CG) simulations. Our study introduces two optimized models for sodium and chloride ions within the nonpolarizable surface property fitting coarse-grained force field (SPICA-FF) framework. The two models represent solvated ions, such as the original FF model, and unsolvated or bare ions. The nonbonded Lennard-Jones interactions were reparameterized to faithfully reproduce bulk properties, including density and surface tension, in sodium chloride solutions at varying concentrations. Notably, these optimized models replicate experimental surface tensions at high ionic strengths, a property not well-captured by the ions of the original model in the SPICA-FF. The optimized unsolvated model also proved successful in reproducing experimental osmotic pressure. Additionally, the newly reparameterized ion models capture hydrophobic interactions within sodium chloride solutions and show qualitative agreement when modeling structural changes in phospholipid bilayers, aligning with experimental observations. For aqueous solutions, these optimized models promise a more precise representation of the ion behavior.

Exploring the Properties of Curved Lipid Membranes: Comparative Analysis of Atomistic and Coarse-Grained Force Fields

Curvature emerges as a fundamental membrane characteristic crucial for diverse biological processes, including vesicle formation, cell signaling, and membrane trafficking. Increasingly valuable insights into atomistic details governing curvature-dependent membrane properties are provided by computer simulations. Nevertheless, the underlying force field models are conventionally calibrated and tested in relation to experimentally derived parameters of planar bilayers, thereby leaving uncertainties concerning their consistency in reproducing curved lipid systems. In this study we compare the depiction of buckled phosphatidylcholine (POPC) and POPC-cholesterol membranes by four popular force field models. Aside from agreement with respect to general trends in curvature dependence of a number of parameters, we observe a few qualitative differences. Among the most prominent ones is the difference between atomistic and coarse grained force fields in their representation of relative compressibility of the polar headgroup region and hydrophobic lipid core. Through a number of downstream effects, this discrepancy can influence the way in which curvature modulates the behavior of membrane bound proteins depending on the adopted simulation model.

GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models

GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.

Lipids and proteins: Insights into the dynamics of assembly, recognition, condensate formation. What is still missing?

Lipid membranes and proteins, which are part of us throughout our lives, have been studied for decades. However, every year, new discoveries show how little we know about them. In a reader-friendly manner for people not involved in the field, this paper tries to serve as a bridge between physicists and biologists and new young researchers diving into the field to show its relevance, pointing out just some of the plethora of lines of research yet to be unraveled. It illustrates how new ways, from experimental to theoretical approaches, are needed in order to understand the structures and interactions that take place in a single lipid, protein, or multicomponent system, as we are still only scratching the surface.

GENESIS CGDYN: large-scale coarse-grained MD simulation with dynamic load balancing for heterogeneous biomolecular systems

Residue-level coarse-grained (CG) molecular dynamics (MD) simulation is widely used to investigate slow biological processes that involve multiple proteins, nucleic acids, and their complexes. Biomolecules in a large simulation system are distributed non-uniformly, limiting computational efficiency with conventional methods. Here, we develop a hierarchical domain decomposition scheme with dynamic load balancing for heterogeneous biomolecular systems to keep computational efficiency even after drastic changes in particle distribution. These schemes are applied to the dynamics of intrinsically disordered protein (IDP) droplets. During the fusion of two droplets, we find that the changes in droplet shape correlate with the mixing of IDP chains. Additionally, we simulate large systems with multiple IDP droplets, achieving simulation sizes comparable to those observed in microscopy. In our MD simulations, we directly observe Ostwald ripening, a phenomenon where small droplets dissolve and their molecules redeposit into larger droplets. These methods have been implemented in CGDYN of the GENESIS software, offering a tool for investigating mesoscopic biological processes using the residue-level CG models.

Understanding the effects of ethanol on the liposome bilayer structure using microfluidic-based time-resolved small-angle X-ray scattering and molecular dynamics simulations

Lipid nanoparticles (LNPs) are essential carrier particles in drug delivery systems, particularly in ribonucleic acid delivery. In preparing lipid-based nanoparticles, microfluidic-based ethanol injection may produce precisely size-controlled nanoparticles. Ethanol is critical in LNP formation and post-treatment processes and affects liposome size, structure, lamellarity, and drug-loading efficiency. However, the effects of time-dependent changes in the ethanol concentration on the structural dynamics of liposomes are not clearly understood. Herein, we investigated ethanol-induced lipid bilayer changes in liposomes on a time scale from microseconds to tens of seconds using a microfluidic-based small-angle X-ray scattering (SAXS) measurement system coupled with molecular dynamics (MD) simulations. The time-resolved SAXS measurement system revealed that single unilamellar liposomes were converted to multilamellar liposomes within 0.8 s of contact with ethanol, and the d-spacing was decreased from 6.1 (w/o ethanol) to 4.4 nm (80% ethanol) with increasing ethanol concentration. We conducted 1 μs MD simulations to understand the molecular-level structural changes in the liposomes. The MD simulations revealed that the changes in the lamellar structure caused by ethanol at the molecular level could explain the structural changes in the liposomes observed via time-resolved SAXS. Therefore, the post-treatment process to remove residual ethanol is critical in liposome formation.

When Data Are Lacking: Physics-Based Inverse Design of Biopolymers Interacting with Complex, Fluid Phases

Biomolecular research traditionally revolves around comprehending the mechanisms through which peptides or proteins facilitate specific functions, often driven by their relevance to clinical ailments. This conventional approach assumes that unraveling mechanisms is a prerequisite for wielding control over functionality, which stands as the ultimate research goal. However, an alternative perspective emerges from physics-based inverse design, shifting the focus from mechanisms to the direct acquisition of functional control strategies. By embracing this methodology, we can uncover solutions that might not have direct parallels in natural systems, yet yield crucial insights into the isolated molecular elements dictating functionality. This provides a distinctive comprehension of the underlying mechanisms.In this context, we elucidate how physics-based inverse design, facilitated by evolutionary algorithms and coarse-grained molecular simulations, charts a promising course for innovating the reverse engineering of biopolymers interacting with intricate fluid phases such as lipid membranes and liquid protein phases. We introduce evolutionary molecular dynamics (Evo-MD) simulations, an approach that merges evolutionary algorithms with the Martini coarse-grained force field. This method directs the evolutionary process from random amino acid sequences toward peptides interacting with complex fluid phases such as biological lipid membranes, offering significant promises in the development of peptide-based sensors and drugs. This approach can be tailored to recognize or selectively target specific attributes such as membrane curvature, lipid composition, membrane phase (e.g., lipid rafts), and protein fluid phases. Although the resulting optimal solutions may not perfectly align with biological norms, physics-based inverse design excels at isolating relevant physicochemical principles and thermodynamic driving forces governing optimal biopolymer interaction within complex fluidic environments. In addition, we expound upon how physics-based evolution using the Evo-MD approach can be harnessed to extract the evolutionary optimization fingerprints of protein-lipid interactions from native proteins. Finally, we outline how such an approach is uniquely able to generate strategic training data for predictive neural network models that cover the whole relevant physicochemical domain. Exploring challenges, we address key considerations such as choosing a fitting fitness function to delineate the desired functionality. Additionally, we scrutinize assumptions tied to system setup, the targeted protein structure, and limitations posed by the utilized (coarse-grained) force fields and explore potential strategies for guiding evolution with limited experimental data. This discourse encapsulates the potential and remaining obstacles of physics-based inverse design, paving the way for an exciting frontier in biomolecular research.

Force matching and iterative Boltzmann inversion coarse grained force fields for ZIF-8

Despite the intense activity at electronic and atomistic resolutions, coarse grained (CG) modeling of metal-organic frameworks remains largely unexplored. One of the main reasons for this is the lack of adequate CG force fields. In this work, we present iterative Boltzmann inversion and force matching (FM) force fields for modeling ZIF-8 at three different coarse grained resolutions. Their ability to reproduce structure, elastic tensor, and thermal expansion is evaluated and compared with that of MARTINI force fields considered in previous work [Alvares et al., J. Chem. Phys. 158, 194107 (2023)]. Moreover, MARTINI and FM are evaluated for their ability to depict the swing effect, a subtle phase transition ZIF-8 undergoes when loaded with guest molecules. Overall, we found that all our force fields reproduce structure reasonably well. Elastic constants and volume expansion results are analyzed, and the technical and conceptual challenges of reproducing them are explained. Force matching exhibits promising results for capturing the swing effect. This is the first time these CG methods, widely applied in polymer and biomolecule communities, are deployed to model porous solids. We highlight the challenges of fitting CG force fields for these materials.

Multiscale computational simulation of pollutant behavior at water interfaces

The investigation of pollutant behavior at water interfaces is critical to understand pollution in aquatic systems. Computational methods allow us to overcome the limitations of experimental analysis, delivering valuable insights into the chemical mechanisms and structural characteristics of pollutant behavior at interfaces across a range of scales, from microscopic to mesoscopic. Quantum mechanics, all-atom molecular dynamics simulations, coarse-grained molecular dynamics simulations, and dissipative particle dynamics simulations represent diverse molecular interaction calculation methods that can effectively model pollutant behavior at environmental interfaces from atomic to mesoscopic scales. These methods provide a rich variety of information on pollutant interactions with water surfaces. This review synthesizes the advancements in applying typical computational methods to the formation, adsorption, binding, and catalytic conversion of pollutants at water interfaces. By drawing on recent advancements, we critically examine the current challenges and offer our perspective on future directions. This review seeks to advance our understanding of computational techniques for elucidating pollutant behavior at water interfaces, a critical aspect of water research.

Stability of Cytoplasmic Membrane of Escherichia coli Bacteria in Aqueous and Ethanolic Environment

The mechanism of action of any antibacterial agent or disinfectant depends largely on their interaction with the bacterial membrane. Herein, we use the SPICA (surface property fitting coarse graining) force-field and develop a coarse-grained (CG) model for the structure of the cytoplasmic membrane of Escherichia coli (E. coli) and its interaction with water and ethanol. We elucidate the impact of different concentrations of ethanol on the cytoplasmic membrane bilayers and vesicles of E. coli using the CG molecular dynamics (CG MD) simulations. Our modeling approach first focuses on the parametrization of the required force-field for POPG lipid and its interaction with water, ethanol, and POPE lipid. Subsequently, the structural stability of the E. coli bacterial membrane in the presence of high and low concentrations of ethanol is delineated. Both flat bilayers as well as vesicles of E. coli membrane were considered for the CG MD. Our results reveal that, at low ethanol concentrations (<30 mol %), the size of the E. coli vesicles increases with discernible deformations in their shapes. Because of ethanol-induced interdigitation, thinning of the E. coli vesicular membrane is also observed. However, at higher ethanol concentrations (>30 mol %), the integrity of the vesicles is lost because of deteriorating invasion of ethanol molecules into the vesicle bilayer and significant weakening of lipid-lipid interactions. At higher ethanol concentrations (40 and 70 mol %), both the multivesicle and single-vesicle bacterial membranes exhibit a similar rupturing pattern wherein the extraction of lipids from the membrane and formation of aggregates of the component lipids are observed. These aggregates consist of polar head groups of 3-5 POPE/POPG lipids with intertwined nonpolar tails.

pSPICA Force Field Extended for Proteins and Peptides

Many coarse-grained (CG) molecular dynamics (MD) studies have been performed to investigate biological processes involving proteins and lipids. CG force fields (FFs) in these MD studies often use implicit or nonpolar water models to reduce computational costs. CG-MD using water models cannot properly describe electrostatic screening effects owing to the hydration of ionic segments and thus cannot appropriately describe molecular events involving water channels and pores through lipid membranes. To overcome this issue, we developed a protein model in the pSPICA FF, in which a polar CG water model showing the proper dielectric response was adopted. The developed CG model greatly improved the transfer free energy profiles of charged side chain analogues across the lipid membrane. Application studies on melittin-induced membrane pores and mechanosensitive channels in lipid membranes demonstrated that CG-MDs using the pSPICA FF correctly reproduced the structure and stability of the pores and channels. Furthermore, the adsorption behavior of the highly charged nona-arginine peptides on lipid membranes changed with salt concentration, indicating the pSPICA FF is also useful for simulating protein adsorption on membrane surfaces.

Improved Protein Model in SPICA Force Field

The previous version of the SPICA coarse-grained (CG) force field (FF) protein model focused primarily on membrane proteins and successfully reproduced the dimerization free energies of several transmembrane helices and the stable structures of various membrane protein assemblies. However, that model had limited accuracy when applied to other proteins, such as intrinsically disordered proteins (IDPs) and peripheral proteins, because the dimensions of the IDPs in an aqueous solution were too compact, and protein binding on the lipid membrane surface was overstabilized. To improve the accuracy of the SPICA FF model for the simulation of such systems, in this study, we introduce protein secondary structure-dependent nonbonded interaction parameters to the backbone segments and reoptimize almost all nonbonded parameters for amino acids. The improved FF proposed here successfully reproduces the radii of gyration of various IDPs, the binding sensitivity of several peripheral membrane proteins, and the dimerization free energies of several transmembrane helices. The new model also shows improved agreement with experiments on the free energy of peptide association in water. In addition, an extensive library of nonbonded interactions between proteins and lipids, including various glycerophospholipids, sphingolipids, and cholesterol, allows the study of specific interactions between lipids and peripheral and transmembrane proteins. Hence, the new SPICA FF (version 2) proposed herein is applicable with high accuracy for simulating a wide range of protein systems.

DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces

Coarse-grained molecular models of proteins permit access to length and time scales unattainable by all-atom models and the simulation of processes that occur on long time scales, such as aggregation and folding. The reduced resolution realizes computational accelerations, but an atomistic representation can be vital for a complete understanding of mechanistic details. Backmapping is the process of restoring all-atom resolution to coarse-grained molecular models. In this work, we report DiAMoNDBack (Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping) as an autoregressive denoising diffusion probability model to restore all-atom details to coarse-grained protein representations retaining only Cα coordinates. The autoregressive generation process proceeds from the protein N-terminus to C-terminus in a residue-by-residue fashion conditioned on the Cα trace and previously backmapped backbone and side-chain atoms within the local neighborhood. The local and autoregressive nature of our model makes it transferable between proteins. The stochastic nature of the denoising diffusion process means that the model generates a realistic ensemble of backbone and side-chain all-atom configurations consistent with the coarse-grained Cα trace. We train DiAMoNDBack over 65k+ structures from the Protein Data Bank (PDB) and validate it in applications to a hold-out PDB test set, intrinsically disordered protein structures from the Protein Ensemble Database (PED), molecular dynamics simulations of fast-folding mini-proteins from DE Shaw Research, and coarse-grained simulation data. We achieve state-of-the-art reconstruction performance in terms of correct bond formation, avoidance of side-chain clashes, and the diversity of the generated side-chain configurational states. We make the DiAMoNDBack model publicly available as a free and open-source Python package.

Pragmatic Coarse-Graining of Proteins: Models and Applications

The molecular details involved in the folding, dynamics, organization, and interaction of proteins with other molecules are often difficult to assess by experimental techniques. Consequently, computational models play an ever-increasing role in the field. However, biological processes involving large-scale protein assemblies or long time scale dynamics are still computationally expensive to study in atomistic detail. For these applications, employing coarse-grained (CG) modeling approaches has become a key strategy. In this Review, we provide an overview of what we call pragmatic CG protein models, which are strategies combining, at least in part, a physics-based implementation and a top-down experimental approach to their parametrization. In particular, we focus on CG models in which most protein residues are represented by at least two beads, allowing these models to retain some degree of chemical specificity. A description of the main modern pragmatic protein CG models is provided, including a review of the most recent applications and an outlook on future perspectives in the field.

The SIRAH force field: A suite for simulations of complex biological systems at the coarse-grained and multiscale levels

The different combinations of molecular dynamics simulations with coarse-grained representations have acquired considerable popularity among the scientific community. Especially in biocomputing, the significant speedup granted by simplified molecular models opened the possibility of increasing the diversity and complexity of macromolecular systems, providing realistic insights on large assemblies for more extended time windows. However, a holistic view of biological ensembles' structural and dynamic features requires a self-consistent force field, namely, a set of equations and parameters that describe the intra and intermolecular interactions among moieties of diverse chemical nature (i.e., nucleic and amino acids, lipids, solvent, ions, etc.). Nevertheless, examples of such force fields are scarce in the literature at the fully atomistic and coarse-grained levels. Moreover, the number of force fields capable of handling simultaneously different scales is restricted to a handful. Among those, the SIRAH force field, developed in our group, furnishes a set of topologies and tools that facilitate the setting up and running of molecular dynamics simulations at the coarse-grained and multiscale levels. SIRAH uses the same classical pairwise Hamiltonian function implemented in the most popular molecular dynamics software. In particular, it runs natively in AMBER and Gromacs engines, and porting it to other simulation packages is straightforward. This review describes the underlying philosophy behind the development of SIRAH over the years and across families of biological molecules, discussing current limitations and future implementations.

Extension of the iSoLF implicit-solvent coarse-grained model for multicomponent lipid bilayers

iSoLF is a coarse-grained (CG) model for lipid molecules with the implicit-solvent approximation used in molecular dynamics (MD) simulations of biological membranes. Using the original iSoLF (iSoLFv1), MD simulations of lipid bilayers consisting of either POPC or DPPC and these bilayers, including membrane proteins, can be performed. Here, we improve the original model, explicitly treating the electrostatic interactions between different lipid molecules and adding CG particle types. As a result, the available lipid types increase to 30. To parameterize the potential functions of the new model, we performed all-atom MD simulations of each lipid at three different temperatures using the CHARMM36 force field and the modified TIP3P model. Then, we parameterized both the bonded and non-bonded interactions to fit the area per lipid and the membrane thickness of each lipid bilayer by using the multistate Boltzmann Inversion method. The final model reproduces the area per lipid and the membrane thickness of each lipid bilayer at the three temperatures. We also examined the applicability of the new model, iSoLFv2, to simulate the phase behaviors of mixtures of DOPC and DPPC at different concentrations. The simulation results with iSoLFv2 are consistent with those using Dry Martini and Martini 3, although iSoLFv2 requires much fewer computations. iSoLFv2 has been implemented in the GENESIS MD software and is publicly available.

Computational modeling of membrane trafficking processes: From large molecular assemblies to chemical specificity

In the last decade, molecular dynamics (MD) simulations have become an essential tool to investigate the molecular properties of membrane trafficking processes, often in conjunction with experimental approaches. The combination of MD simulations with recent developments in structural biology, such as cryo-electron microscopy and artificial intelligence-based structure determination, opens new, exciting possibilities for future investigations. However, the full potential of MD simulations to provide a molecular view of the complex and dynamic processes involving membrane trafficking can only be realized if certain limitations are addressed, and especially those concerning the quality of coarse-grain models, which, despite recent successes in describing large-scale systems, still suffer from far-from-ideal chemical accuracy. In this review, we will highlight recent success stories of MD simulations in the investigation of membrane trafficking processes, their implications for future research, and the challenges that lie ahead in this specific research domain.

Bacterial lipids drive compartmentalization on the nanoscale

The design of cellular functions in synthetic systems, inspired by the internal partitioning of living cells, is a constantly growing research field that is paving the way to a large number of new remarkable applications. Several hierarchies of internal compartments like polymersomes, liposomes, and membranes are used to control the transport, release, and chemistry of encapsulated species. However, the experimental characterization and the comprehension of glycolipid mesostructures are far from being fully addressed. Lipid A is indeed a glycolipid and the endotoxic part of Gram-negative bacterial lipopolysaccharide; it is the moiety that is recognized by the eukaryotic receptors giving rise to the modulation of innate immunity. Herein we propose, for the first time, a combined approach based on hybrid Particle-Field (hPF) Molecular Dynamics (MD) simulations and Small Angle X-Ray Scattering (SAXS) experiments to gain a molecular picture of the complex supramolecular structures of lipopolysaccharide (LPS) and lipid A at low hydration levels. The mutual support of data from simulations and experiments allowed the unprecedented discovery of the presence of a nano-compartmentalized phase composed of liposomes of variable size and shape which can be used in synthetic biological applications.

Development of a coarse-grained model for surface-functionalized gold nanoparticles: towards an accurate description of their aggregation behavior

Understanding the dispersion stability and aggregation propensity of self-assembled monolayer gold NPs at a molecular level is crucial to guide their rational design and to inform about the optimal surface functionalization for specific applications. To reach this goal, in silico modeling via coarse-grained (CG) molecular dynamics (MD) simulations is a fundamental tool to complement the information acquired from experimental studies since CG modeling allows to get a deep knowledge of the molecular interactions that take place at the nanoscale in this kind of systems. Unfortunately, current CG models of monolayer-protected AuNPs present several drawbacks that limit their accuracy in certain scenarios. We here develop a CG model that is fully compatible and extends the SPICA/SDK (Shinoda-DeVane-Klein) force field. Our model allows reproducing the behavior of AuNPs functionalized with hydrophobic as well as charged and more hydrophilic ligands. This model improves upon results obtained with previously derived CG force fields and successfully describes NPs aggregation and self-assembly in aqueous solution.

Self-Assembly of Glycerol-Amphiphilic Janus Dendrimers Amplifies and Indicates Principles for the Selection of Stereochemistry by Biological Membranes

The principles for the selection of the stereochemistry of phospholipids of biological membranes remain unclear and continue to be debated. Therefore, any new experiments on this topic may help progress in this field. To address this question, three libraries of constitutional isomeric glycerol-amphiphilic Janus dendrimers (JDs) with nonsymmetric homochiral, racemic, and symmetric achiral branching points were synthesized by an orthogonal-modular-convergent methodology. These JDs amplify self-assembly, and therefore, monodisperse vesicles known as dendrimersomes (DSs) with predictable dimensions programmed by JD concentration were assembled by rapid injection of their ethanol solution into water. DSs of homochiral JD enantiomers, racemic, including mixtures of different enantiomers, and achiral exhibited similar DS size-concentration dependence. However, the number of bilayers of DSs assembled from homochiral, achiral, and racemic JDs determined by cryo-TEM were different. Statistical analysis of the number of bilayers and coarse-grained molecular dynamics simulations demonstrated that homochiral JDs formed predominantly unilamellar DSs. Symmetric achiral JDs assembled only unilamellar DSs while racemic JDs favored multilamellar DSs. Since cell membranes are unilamellar, these results indicate a new rationale for nonsymmetric homochiral vs racemic selection. Simultaneously, these experiments imply that the symmetric achiral lipids forming more stable membrane, probably had been the preferable assemblies of prebiotic cell membranes.

Towards de novo design of transmembrane α-helical assemblies using structural modelling and molecular dynamics simulation

Computational de novo protein design involves iterative processes consisting of amino acid sequence design, structural modelling and scoring, and design validation by synthesis and experimental characterisation. Recent advances in protein structure prediction and modelling methods have enabled the highly efficient and accurate design of water-soluble proteins. However, the design of membrane proteins remains a major challenge. To advance membrane protein design, considering the higher complexity of membrane protein folding, stability, and dynamic interactions between water, ions, lipids, and proteins is an important task. For introducing explicit solvents and membranes to these design methods, all-atom molecular dynamics (MD) simulations of designed proteins provide useful information that cannot be obtained experimentally. In this review, we first describe two major approaches to designing transmembrane α-helical assemblies, consensus and de novo design. We further illustrate recent MD studies of membrane protein folding related to protein design, as well as advanced treatments in molecular models and conformational sampling techniques in the simulations. Finally, we discuss the possibility to introduce MD simulations after the existing static modelling and screening of design decoys as an additional step for refinement of the design, which considers membrane protein folding dynamics and interactions with explicit membranes.

Triglyceride lipolysis triggers liquid crystalline phases in lipid droplets and alters the LD proteome

Lipid droplets (LDs) are reservoirs for triglycerides (TGs) and sterol-esters (SEs), but how these lipids are organized within LDs and influence their proteome remain unclear. Using in situ cryo-electron tomography, we show that glucose restriction triggers lipid phase transitions within LDs generating liquid crystalline lattices inside them. Mechanistically this requires TG lipolysis, which decreases the LD's TG:SE ratio, promoting SE transition to a liquid crystalline phase. Molecular dynamics simulations reveal TG depletion promotes spontaneous TG and SE demixing in LDs, additionally altering the lipid packing of the PL monolayer surface. Fluorescence imaging and proteomics further reveal that liquid crystalline phases are associated with selective remodeling of the LD proteome. Some canonical LD proteins, including Erg6, relocalize to the ER network, whereas others remain LD-associated. Model peptide LiveDrop also redistributes from LDs to the ER, suggesting liquid crystalline phases influence ER-LD interorganelle transport. Our data suggests glucose restriction drives TG mobilization, which alters the phase properties of LD lipids and selectively remodels the LD proteome.

LnDOTA-d8 , a versatile chemical-shift thermometer for 2 H solid-state NMR

² H solid-state nuclear magnetic resonance (NMR) is a method for examining the mobility and orientation of molecules in the field of biophysics. In studies on lipid bilayer membranes, ² H NMR is often adopted to detect a phase transition from the gel to the liquid-crystal phase, which is observed as a change in spectral shape, and to evaluate the ordering of lipid alkyl chains using quadrupole coupling values. Because the mobility of membrane lipids is highly temperature dependent, precise temperature control is a prerequisite for evaluating the physical properties of membranes. Generally, NMR instruments monitor the temperature of the variable temperature (VT) gas. The temperature inside the sample tube and the VT gas match only when the heat generated by the radio frequency (rf) pulse emitted from the coil or magic angle spinning is significantly lower than the cooling capacity of the VT gas. In other words, the sample temperature inside the tube depends on the measurement method. Therefore, in this study, we took advantage of temperature-dependent changes in the chemical shift of a paramagnetic metal-ligand complex. We designed and synthesized a deuterated ligand complex and evaluated its temperature dependence as a thermometer for ² H solid-state NMR spectroscopy. We chose Tb, Dy, Ho, and Er as the paramagnetic central metals. We then measured the ² H NMR spectrum of each metal complex and confirmed the ² H chemical shift to be temperature dependent. Furthermore, with the use of the thermometer molecule with Er, we succeeded in accurately evaluating the segmental melting of an alkyl chain in lipid bilayers with 0.1°C accuracy.

Cooperative antimicrobial action of melittin on lipid membranes: A coarse-grained molecular dynamics study

We conducted a series of coarse-grained molecular dynamics (CG-MD) simulations to investigate the complicated actions of melittin, which is an antimicrobial peptide (AMP) derived from honey bee venom, on a lipid membrane. To accurately simulate the AMP action, we developed and used a protein CG model as an extension of the pSPICA force field (FF), which was designed to reproduce several thermodynamic quantities and structural properties. At a low peptide-to-lipid (P/L) ratio (1/102), no defect was detected. At P/L = 1/51, toroidal pore formation was observed due to collective insertion of multiple melittin peptides from the N-termini. The pore formation was initiated by a local increase in membrane curvature in the vicinity of the peptide aggregate. At a higher P/L ratio (1/26), two more modes were detected, seemingly not controlled by the P/L ratio but by a local arrangement of melittin peptides: 1. Pore formation accompanied by lipid extraction by melittin peptides:a detergent-like mechanism. 2. A rapidly formed large pore in a significantly curved membrane: bursting. Thus, we observed three pore formation modes (toroidal pore formation, lipid extraction, and bursting) depending on the peptide concentration and local arrangement. These observations were consistent with experimental observations and hypothesized melittin modes. Through this study, we found that the local arrangements and population of melittin peptides and the area expansion rate by membrane deformation were key to the initiation of and competition among the multiple pore formation mechanisms.

SPICA Force Field for Proteins and Peptides

A coarse-grained (CG) model for peptides and proteins was developed as an extension of the Surface Property fItting Coarse grAined (SPICA) force field (FF). The model was designed to examine membrane proteins that are fully compatible with the lipid membranes of the SPICA FF. A preliminary version of this protein model was created using thermodynamic properties, including the surface tension and density in the SPICA (formerly called SDK) FF. In this study, we improved the CG protein model to facilitate molecular dynamics (MD) simulations with a reproduction of multiple properties from both experiments and all-atom (AA) simulations. An elastic network model was adopted to maintain the secondary structure within a single chain. The side-chain analogues reproduced the transfer free energy profiles across the lipid membrane and demonstrated reasonable association free energy (potential of mean force) in water compared to those from AA MD. A series of peptides/proteins adsorbed onto or penetrated into the membrane simulated by the CG MD correctly predicted the penetration depths and tilt angles of peripheral and transmembrane peptides/proteins as comparable to those in the orientations of proteins in membranes (OPM) database. In addition, the dimerization free energies of several transmembrane helices within a lipid bilayer were comparable to those from experimental estimation. Application studies on a series of membrane protein assemblies, scramblases, and poliovirus capsids demonstrated the good performance of the SPICA FF.

Implementation of residue-level coarse-grained models in GENESIS for large-scale molecular dynamics simulations

Residue-level coarse-grained (CG) models have become one of the most popular tools in biomolecular simulations in the trade-off between modeling accuracy and computational efficiency. To investigate large-scale biological phenomena in molecular dynamics (MD) simulations with CG models, unified treatments of proteins and nucleic acids, as well as efficient parallel computations, are indispensable. In the GENESIS MD software, we implement several residue-level CG models, covering structure-based and context-based potentials for both well-folded biomolecules and intrinsically disordered regions. An amino acid residue in protein is represented as a single CG particle centered at the Cα atom position, while a nucleotide in RNA or DNA is modeled with three beads. Then, a single CG particle represents around ten heavy atoms in both proteins and nucleic acids. The input data in CG MD simulations are treated as GROMACS-style input files generated from a newly developed toolbox, GENESIS-CG-tool. To optimize the performance in CG MD simulations, we utilize multiple neighbor lists, each of which is attached to a different nonbonded interaction potential in the cell-linked list method. We found that random number generations for Gaussian distributions in the Langevin thermostat are one of the bottlenecks in CG MD simulations. Therefore, we parallelize the computations with message-passing-interface (MPI) to improve the performance on PC clusters or supercomputers. We simulate Herpes simplex virus (HSV) type 2 B-capsid and chromatin models containing more than 1,000 nucleosomes in GENESIS as examples of large-scale biomolecular simulations with residue-level CG models. This framework extends accessible spatial and temporal scales by multi-scale simulations to study biologically relevant phenomena, such as genome-scale chromatin folding or phase-separated membrane-less condensations.

Deciphering Ethanol-Driven Swelling, Rupturing, Aggregation, and Fusion of Lipid Vesicles Using Coarse-Grained Molecular Dynamics Simulations

Traditionally, liquid ethanol is known to enhance the permeability of lipid membranes and causes vesicle aggregation and fusion. However, how the amphiphilic ethanol molecules perturb the lipid vesicles to facilitate their aggregation or fusion has not been addressed at any level of molecular simulations. Herein, not only have we developed a coarse-grained (CG) model for liquid ethanol, its aqueous mixture, and hydrated lipid membranes for molecular dynamics (MD) simulations, but also utilized it to delineate the aggregation and fusion of lipid vesicles using CG-MD simulations with multimillion particles. We have systematically parametrized the force-field for pure ethanol and its interactions with hydrated POPC and POPE model lipid membranes. In this process, we have successfully reproduced the bulk ethanol structure and concentration-dependent density of aqueous ethanol. To quantify the interaction of ethanol with lipid membranes, we have reproduced the transfer free energy of the ethanol molecule across the hydrated bilayers, and the concentration-dependent distribution of ethanol molecules across the lipid bilayers. After having acceptable force-field parameters for ethanol-membrane interactions, we have checked the effect of ethanol toward the vesicles comprising POPC lipids. We observe a rapid increase in the size of the POPC lipid vesicles with increasing amounts of ethanol up to 30 mol %. We unambiguously observe swelling and decrease in the thickness of the POPC vesicles with increasing amounts of ethanol up to 30 mol %, beyond which the vesicles begin to lose their integrity and rupture at higher mol % of ethanol. The fusion study of two vesicles demonstrates that fused vesicles can be obtained from 20 to 30 mol % of ethanol provided that they are brought closer than a critical distance at a particular mol %. The multivesicle simulations show that along with the increase in the sizes of vesicles the propensity of vesicle aggregation increases as the mol % of ethanol increases.

Rheology of sliding leaflets in coarse-grained DSPC lipid bilayers

Amphiphilic lipid bilayers modify the friction properties of the surfaces on top of which they are deposited. In particular, the measured sliding friction coefficient can be significantly reduced compared with the native surface. We investigate in this work the friction properties of a numerical coarse-grained model of DSPC (1,2-distearoyl-sn-glycero-3-phosphocholine) lipid bilayer subject to longitudinal shear. The interleaflet friction coefficient is obtained from out-of-equilibrium pulling or from relaxation simulations. In particular, we gain access to the transient viscoelastic response of a sheared bilayer. The bilayer mechanical response is found to depend significantly on the membrane physical state, with evidence in favor of a linear response regime in the fluid but not in the gel region. The linear response validity domain is established, and the timescales appearing in the membrane response discussed.

Modeling lipid-protein interactions for coarse-grained lipid and Cα protein models

Biological membranes that play major roles in diverse functions are composed of numerous lipids and proteins, making them an important target for coarse-grained (CG) molecular dynamics (MD) simulations. Recently, we have developed the CG implicit solvent lipid force field (iSoLF) that has a resolution compatible with the widely used Cα protein representation [D. Ugarte La Torre and S. Takada, J. Chem. Phys. 153, 205101 (2020)]. In this study, we extended it and developed a lipid-protein interaction model that allows the combination of the iSoLF and the Cα protein force field, AICG2+. The hydrophobic-hydrophilic interaction is modeled as a modified Lennard-Jones potential in which parameters were tuned partly to reproduce the experimental transfer free energy and partly based on the free energy profile normal to the membrane surface from previous all-atom MD simulations. Then, the obtained lipid-protein interaction is tested for the configuration and placement of transmembrane proteins, water-soluble proteins, and peripheral proteins, showing good agreement with prior knowledge. The interaction is generally applicable and is implemented in the publicly available software, CafeMol.

Weight average approaches for predicting dynamical properties of biomolecules

Recent advances in atomistic molecular dynamics (MD) simulations of biomolecules allow us to explore their conformational spaces widely, observing large-scale conformational fluctuations or transitions between distinct structures. To reproduce or refine experimental data using MD simulations, structure ensembles, which are characterized by multiple structures and their statistical weights on the rugged free-energy landscapes, are often used. Here, we summarize weight average approaches for various experimental measurements. Weight average approaches are now applied to hybrid quantum mechanics/molecular mechanics MD simulations to predict fast vibrational motions in a protein with a high accuracy for better understanding of molecular functions from atomic structures.

Identifying crucial E-protein residues responsible for unusual stability of Zika virus envelope

An outbreak of Zika virus (ZIKV) infections in 2015-16 that caused microcephaly and other congenital abnormalities in newborns prompted intense research across the globe. These studies have suggested that ZIKV can survive high temperatures and harsh physiological conditions, unlike the other flaviviruses such as dengue virus (DENV). In contrast, recent cryo-electron microscopy studies have shown very similar architecture of the ZIKV and DENV envelopes that constitute the primary level of viral protection. Encouraged by these findings, here we attempt to identify the crucial protein residues that make the ZIKV envelope so robust by employing coarse-grained and all-atomic molecular dynamics simulations and computational mutagenesis studies. In accordance with more recent cryo-electron microscopy findings, our simulation results exhibited stable ZIKV envelope protein shell both at 29^oC and 40°C, whereas the DENV2 shell loosened up significantly at 40°C. Subsequently, we simulated a series of ZIKV variants to identify the specific domain and residues involved in maintaining the structural integrity of the viral protein shell at high temperatures. Our results suggest that the DIII domain-more specifically, the CD- and FG-loop residues of the ZIKV protein shell-play a crucial role in making the virus envelope thermostable by inducing strong raft-raft interactions. These findings can accelerate the rational design of ZIKV therapeutics.

Exploring the Minimum-Energy Pathways and Free-Energy Profiles of Enzymatic Reactions with QM/MM Calculations

Understanding molecular mechanisms of enzymatic reactions is of vital importance in biochemistry and biophysics. Here, we introduce new functions of hybrid quantum mechanical/molecular mechanical (QM/MM) calculations in the GENESIS program to compute the minimum-energy pathways (MEPs) and free-energy profiles of enzymatic reactions. For this purpose, an interface in GENESIS is developed to utilize a highly parallel electronic structure program, QSimulate-QM (https://qsimulate.com), calling it as a shared library from GENESIS. Second, algorithms to search the MEP are implemented, combining the string method (E et al. J. Chem. Phys. 2007, 126, 164103) with the energy minimization of the buffer MM region. The method implemented in GENESIS is applied to an enzyme, triosephosphate isomerase, which converts dihyroxyacetone phosphate to glyceraldehyde 3-phosphate in four proton-transfer processes. QM/MM-molecular dynamics simulations show performances of greater than 1 ns/day with the density functional tight binding (DFTB), and 10-30 ps/day with the hybrid density functional theory, B3LYP-D3. These performances allow us to compute not only MEP but also the potential of mean force (PMF) of the enzymatic reactions using the QM/MM calculations. The barrier height obtained as 13 kcal mol-1 with B3LYP-D3 in the QM/MM calculation is in agreement with the experimental results. The impact of conformational sampling in PMF calculations and the level of electronic structure calculations (DFTB vs B3LYP-D3) suggests reliable computational protocols for enzymatic reactions without high computational costs.

Surface science of cosmetic substrates, cleansing actives and formulations

The development of shampoo and cleansing formulations in cosmetics is at a crossroads due to consumer demands for better performing, more natural products and also the strong commitment of cosmetic companies to improve the sustainability of cosmetic products. In order to go beyond traditional formulations, it is of great importance to clearly establish the science behind cleansing technologies and appreciate the specificity of cleansing biological surfaces such as hair and skin. In this review, we present recent advances in our knowledge of the physicochemical properties of the hair surface from both an experimental and a theoretical point of view. We discuss the opportunities and challenges that newer, sustainable formulations bring compared to petroleum-based ingredients. The inevitable evolution towards more bio-based, eco-friendly ingredients and sustainable formulations requires a complete rethink of many well-known physicochemical principles. The pivotal role of digital sciences and modelling in the understanding and conception of new ingredients and formulations is discussed. We describe recent numerical approaches that take into account the specificities of the hair surface in terms of structuration, different methods that study the adsorption of formulation ingredients and finally the success of new data-driven approaches. We conclude with practical examples on current formulation efforts incorporating bio-surfactants, controlling foaming and searching for new rheological properties.

Diosgenin-induced physicochemical effects on phospholipid bilayers in comparison with cholesterol

Diosgenin (DGN), which is a sterol occurring in plants of the Dioscorea family, has attracted increasing attention for its various pharmacological activities. DGN has a structural similarity to cholesterol (Cho). In this study we investigated the effects of the common tetracyclic cores and the different side chains on the physicochemical properties of lipid bilayer membranes. Differential scanning calorimetry showed that DGN and Cho reduce the phase transition enthalpy to a similar extent. In ²H NMR, deuterated-DGN/Cho and POPC showed similar ordering in POPC bilayers, which revealed that DGN is oriented parallel to the membrane normal like Cho. It was suggested that the affinity of DGN-Cho in membrane is stronger than that of DGN-DGN or Cho-Cho interaction. ³¹P NMR of POPC in bilayers revealed that, unlike Cho, DGN altered the interactions of POPC headgroups at 30 mol%. These results suggest that DGN below 30 mol% has similar effects with Cho on basic biomembrane properties.

Seipin accumulates and traps diacylglycerols and triglycerides in its ring-like structure

Lipid droplets (LDs) are intracellular organelles responsible for lipid storage, and they emerge from the endoplasmic reticulum (ER) upon the accumulation of neutral lipids, mostly triglycerides (TG), between the two leaflets of the ER membrane. LD biogenesis takes place at ER sites that are marked by the protein seipin, which subsequently recruits additional proteins to catalyze LD formation. Deletion of seipin, however, does not abolish LD biogenesis, and its precise role in controlling LD assembly remains unclear. Here, we use molecular dynamics simulations to investigate the molecular mechanism through which seipin promotes LD formation. We find that seipin clusters TG, as well as its precursor diacylglycerol, inside its unconventional ring-like oligomeric structure and that both its luminal and transmembrane regions contribute to this process. This mechanism is abolished upon mutations of polar residues involved in protein-TG interactions into hydrophobic residues. Our results suggest that seipin remodels the membrane of specific ER sites to prime them for LD biogenesis.

Optimizing Gō-MARTINI Coarse-Grained Model for F-BAR Protein on Lipid Membrane

Coarse-grained (CG) molecular dynamics (MD) simulations allow us to access much larger length and time scales than atomistic MD simulations, providing an attractive alternative to the conventional simulations. Based on the well-known MARTINI CG force field, the recently developed Gō-MARTINI model for proteins describes large-amplitude structural dynamics, which has not been possible with the commonly used elastic network model. Using the Gō-MARTINI model, we conduct MD simulations of the F-BAR Pacsin1 protein on lipid membrane. We observe that structural changes of the non-globular protein are largely dependent on the definition of the native contacts in the Gō model. To address this issue, we introduced a simple cutoff scheme and tuned the cutoff distance of the native contacts and the interaction strength of the Lennard-Jones potentials in the Gō-MARTINI model. With the optimized Gō-MARTINI model, we show that it reproduces structural fluctuations of the Pacsin1 dimer from atomistic simulations. We also show that two Pacsin1 dimers properly assemble through lateral interaction on the lipid membrane. Our work presents a first step towards describing membrane remodeling processes in the Gō-MARTINI CG framework by simulating a crucial step of protein assembly on the membrane.

Adsorption of Organic Molecules and Surfactants on Graphene: A Coarse-Grained Study

The research studies on the adsorption of surfactants on graphene help us to know how to use surfactants to exfoliate graphene from graphite or functionalize the graphene surface. Among them, molecular dynamics (MD) simulation has been widely used to investigate the adsorption of organic molecules and surfactants on graphene. In particular, coarse-grained (CG) MD simulation greatly improves the computational efficiency by simplifying the complexity of the studied systems, allowing us to explore the structure and dynamics of complex systems on larger spatial scales and longer time scales. However, an accurate prediction of the adsorption of surfactants on graphene is required by optimizing the interaction between surfactants and graphene, which is often overlooked by some CG models. In this work, we found that an accurate prediction of the adsorption enthalpies of organic molecules on graphene can be achieved by optimizing the interactions between organic molecules and benzene. Meanwhile, we simulated the adsorption of a surfactant on single-layer and double-layer graphene nanosheets, respectively. Our results revealed that increasing the temperature would favor the interactions between hydrophilic groups of surfactants. In addition, we discovered that the surfactant prefers to be adsorbed on the inner surfaces of double-layer graphene compared with the outer surfaces, and this is owing to the dehydration in the middle of double-layer graphene, which is beneficial to the hydrophilic interactions between surfactant molecules inside the double-layer graphene.