Surface ligand rigidity modulates lipid raft affinity of ultra-small hydrophobic nanoparticles: insights from molecular dynamics simulations

Differential preferences between lipids and proteins drive the formation of dynamical nanoscale membrane domains (lipid rafts), which play key roles in the proper functioning of cells. On the other hand, due to the potent physicochemical properties of nanoparticles (NPs), they have been widely used in drug delivery, bio-imaging and regulating various essential biological processes of the cells. Hence, in this work, we aim to design ultra-small hydrophobic NPs with tunable raft affinity, which is supposed to partition into the hydrophobic region of lipid membranes and be able to regulate the dynamics of the lipid raft domains. A series of μs-scale coarse-grained molecular dynamics simulations and umbrella sampling free energy calculations were performed to investigate the role of surface ligand rigidity of ultra-small hydrophobicNPs in their raft affinity. Our results indicated that the preferred localization of NPs can be tuned by adjusting their surface ligand rigidity. Generally, rigid NPs tended to target the raft domain, while soft NPs preferred the interface of the raft and non-raft domains. The free energy analysis further indicated that the surface ligand rigidity of NPs can enhance their targeting to lipid raft domains. Besides, we found that these ultra-small NPs had no significant effects on the phase separation of the lipid membrane although they might cause some local interference to surrounding lipids. These results indicate that the targeting to the lipid raft domain can be achieved by the surface ligand rigidity of NPs, which provides helpful insights for further regulations of lipid raft-mediated biological processes.

Evaluating Coarse-Grained MARTINI Force-Fields for Capturing the Ripple Phase of Lipid Membranes

Phospholipids, which are an integral component of cell membranes, exhibit a rich variety of lamellar phases modulated by temperature and composition. Molecular dynamics (MD) simulations have greatly enhanced our understanding of phospholipid membranes by capturing experimentally observed phases and phase transitions at molecular resolution. However, the ripple (P_β') membrane phase, observed as an intermediate phase below the main gel-to-liquid crystalline transition with some lipids, has been challenging to capture with MD simulations, both at all-atom and coarse-grained (CG) resolutions. Here, with an aggregate ∼2.5 μs all-atom and ∼122 μs CGMD simulations, we systematically assess the ability of six CG MARTINI 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) lipid and water force-field (FF) variants, parametrized to capture the DPPC gel and fluid phases, for their ability to capture the P_β' phase, and compared observations with those from an all-atom FF. Upon cooling from the fluid phase to below the phase transition temperature with smaller (380-lipid) and larger (>2200-lipid) MARTINI and all-atom (CHARMM36 FF) DPPC lipid bilayers, we observed that smaller bilayers with both all-atom and MARTINI FFs sampled interdigitated P_β' and ripple-like states, respectively. However, while all-atom simulations of the larger DPPC membranes exhibited the formation of the P_β' phase, MARTINI membranes did not sample interdigitated ripple-like states at larger system sizes. We then demonstrated that the ripple-like states in smaller MARTINI membranes were kinetically trapped structures caused by finite size effects rather than being representative of true P_β' phases. We showed that a MARTINI FF variant that could capture the tilted L_β' gel phase, a prerequisite for stabilizing the P_β' phase, was unable to capture the rippled phase upon cooling. Our study reveals that the current MARTINI FFs (including MARTINI3) may require specific reparametrization of the interaction potentials to stabilize lipid interdigitation, a characteristic of the ripple phase.

Investigating the structural properties of hydrophobic solvent-rich lipid bilayers

In vitro reconstitutions of lipid membranes have proven to be an indispensable tool to rationalize their molecular complexity and to understand their role in countless cellular processes. However, amongst the various techniques used to reconstitute lipid bilayers in vitro, several approaches are not solvent-free, but rather contain residual hydrophobic solvents in between the two bilayer leaflets, generally as a consequence of the procedure used to generate the bilayer. To what extent the presence of these hydrophobic solvents modifies bilayer properties with respect to native, solvent-free, conditions remains an open question that has important implications for the appropriate interpretation of numerous experimental observations. Here, we thorouhgly characterize hydrophobic solvent-rich lipid bilayers using atomistic molecular dynamics simulations. Our data indicate that while the presence of hydrophobic solvents at high concentrations, such as hexadecane, has a significant effect on membrane thickness, their effects on surface properties, membrane order and lateral stress are quite moderate. Our results corroborate the validity of in vitro approaches as model systems for the investigations of biological membranes but raise a few cautionary aspects that must be considered when investigating specific membrane properties.

The Martini Model in Materials Science

The Martini model, a coarse-grained force field initially developed with biomolecular simulations in mind, has found an increasing number of applications in the field of soft materials science. The model's underlying building block principle does not pose restrictions on its application beyond biomolecular systems. Here, the main applications to date of the Martini model in materials science are highlighted, and a perspective for the future developments in this field is given, particularly in light of recent developments such as the new version of the model, Martini 3.

Computational methods for exploring protein conformations

Proteins are dynamic molecules that can transition between a potentially wide range of structures comprising their conformational ensemble. The nature of these conformations and their relative probabilities are described by a high-dimensional free energy landscape. While computer simulation techniques such as molecular dynamics simulations allow characterisation of the metastable conformational states and the transitions between them, and thus free energy landscapes, to be characterised, the barriers between states can be high, precluding efficient sampling without substantial computational resources. Over the past decades, a dizzying array of methods have emerged for enhancing conformational sampling, and for projecting the free energy landscape onto a reduced set of dimensions that allow conformational states to be distinguished, known as collective variables (CVs), along which sampling may be directed. Here, a brief description of what biomolecular simulation entails is followed by a more detailed exposition of the nature of CVs and methods for determining these, and, lastly, an overview of the myriad different approaches for enhancing conformational sampling, most of which rely upon CVs, including new advances in both CV determination and conformational sampling due to machine learning.

Binding of Ca2+-independent C2 domains to lipid membranes: A multi-scale molecular dynamics study

C2 domains facilitate protein interactions with lipid bilayers in either a Ca²⁺-dependent or -independent manner. We used molecular dynamics (MD) simulations to explore six Ca²⁺-independent C2 domains, from KIBRA, PI3KC2α, RIM2, PTEN, SHIP2, and Smurf2. In coarse-grained MD simulations these C2 domains formed transient interactions with zwitterionic bilayers, compared with longer-lived interactions with anionic bilayers containing phosphatidylinositol bisphosphate (PIP₂). Type I C2 domains bound non-canonically via the front, back, or side of the β sandwich, whereas type II C2 domains bound canonically, via the top loops. C2 domains interacted strongly with membranes containing PIP₂, causing bound anionic lipids to cluster around the protein. Binding modes were refined via atomistic simulations. For PTEN and SHIP2, CG simulations of their phosphatase plus C2 domains with PIP₂-containing bilayers were also performed, and the roles of the two domains in membrane localization compared. These studies establish a simulation protocol for membrane-recognition proteins.

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Binding of Ca²⁺-independent C2 domains to membranes was explored by MD simulation

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C2 domains from KIBRA, PI3KC2α, RIM2, PTEN, SHIP2, and Smurf2 were compared

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C2 domains formed longer-lived interactions with lipid bilayers containing PIP₂

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For PTEN and SHIP2, simulations of their phosphatase plus C2 domains were performed

Molecular Dynamics Scoring of Protein-Peptide Models Derived from Coarse-Grained Docking

One of the major challenges in the computational prediction of protein-peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein-peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor-ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein-peptide complexes show that the proposed MD-based scoring approach can be used to identify protein-peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures.

Synergistic Effect of Chemical Penetration Enhancers on Lidocaine Permeability Revealed by Coarse-Grained Molecular Dynamics Simulations

The search for new formulations for transdermal drug delivery (TDD) is an important field in medicine and cosmetology. Molecules with specific physicochemical properties which can increase the permeability of active ingredients across the stratum corneum (SC) are called chemical penetration enhancers (CPEs), and it was shown that some CPEs can act synergistically. In this study, we performed coarse-grained (CG) molecular dynamics (MD) simulations of the lidocaine delivery facilitated by two CPEs-linoleic acid (LA) and ethanol-through the SC model membrane containing cholesterol, N-Stearoylsphingosine (DCPE), and behenic acid. In our simulations, we probed the effects of individual CPEs as well as their combination on various properties of the SC membrane and the lidocaine penetration across it. We demonstrated that the addition of both CPEs decreases the membrane thickness and the order parameters of the DPCE hydrocarbon chains. Moreover, LA also enhances diffusion of the SC membrane components, especially cholesterol. The estimated potential of mean force (PMF) profiles for the lidocaine translocation across SC in the presence/absence of two individual CPEs and their combination demonstrated that while ethanol lowers the free energy barrier for lidocaine to enter SC, LA decreases the depth of the free energy minima for lidocaine inside SC. These two effects supposedly result in synergistic penetration enhancement of drugs. Altogether, the present simulations provide a detailed molecular picture of CPEs' action and their synergistic effect on the penetration of small molecular weight therapeutics that can be beneficial for the design of novel drug and cosmetics formulations.

Conformational Reorganization of Apolipoprotein E Triggered by Phospholipid Assembly

Apolipoprotein E (apoE), a major determinant protein for lipid metabolism, actively participates in lipid transport in the central nervous system via high-affinity interaction with the low-density lipoprotein receptor (LDLR). Prior evidences indicate that the phospholipids first need to assemble around apoE before the protein can recognize its receptor. However, despite multiple attempts via spectroscopic and biochemical investigations, it is unclear what are the impacts of lipid assembly on the globular structure of apoE. Here, using a combination of all-atom and coarse-grained molecular dynamics simulations, we demonstrate that an otherwise compact tertiary fold of monomeric apoE3 spontaneously unwraps in an aqueous phospholipid solution in two distinct stages. Interestingly, these structural reorganizations are triggered by an initial localized binding of lipid molecules to the C-terminal domain of the protein, which induce a rapid separation of the C-terminal domain of apoE3 from the rest of its tertiary fold. This is followed by a slow lipid-induced interhelix separation event within the N-terminal domain of the protein, as seen in an extensively long coarse-grained simulation. Remarkably, the resultant complex takes the shape of an "open conformation" of the lipid-stabilized unwrapped protein, which intriguingly coincides with an earlier proposal by a small-angle X-ray scattering (SAXS) experiment. The lipid-binding activity and the lipid-induced protein conformation are found to be robust across a monomeric mutant and wild-type sequence of apoE3. The "open" complex derived in coarse-grained simulation retains its structural morphology after reverse-mapping to the all-atom representation. Collectively, the investigation puts forward a plausible structure of currently elusive conformationally activated state of apoE3, which is primed for recognition by the lipoprotein receptor and can be exploited for eventual lipid transport.

Coarse grained simulation of the aggregation and structure control of polyethylene nanocrystals

Polyethylene (PE) telechelics with carboxylate functional groups at both ends have been shown to assemble into hexagonal nanocrystal platelets with a height defined by their chain length in basic CsOH-solution. In this coarse grained (CG) simulation study we show how properties of the functional groups alter the aggregation and crystallization behavior of those telechelics. Systematic variation of the parameters of the CG model showed that important factors which control nanoparticle stability and structure are the PE chain length and the hydrophilicity and the steric demand of the head groups. To characterize the aggregation process we analyzed the number and size of the obtained aggregates as well as intramolecular order and intermolecular alignment of the polymer chains. By comparison of CG and atomistic simulation data, it could be shown that atomistic simulations representing different chemical systems can be emulated with specific, different CG parameter sets. Thus, the results from the (generic) CG simulation models can be used to explain the effect of different head groups and different counterions on the aggregation of PE telechelics and the order of the obtained nanocrystals.

Coacervate formation studied by explicit solvent coarse-grain molecular dynamics with the Martini model

Complex coacervates are liquid-liquid phase separated systems, typically containing oppositely charged polyelectrolytes. They are widely studied for their functional properties as well as their potential involvement in cellular compartmentalization as biomolecular condensates. Diffusion and partitioning of solutes into a coacervate phase are important to address because their highly dynamic nature is one of their most important functional characteristics in real-world systems, but are difficult to study experimentally or even theoretically without an explicit representation of every molecule in the system. Here, we present an explicit-solvent, molecular dynamics coarse-grain model of complex coacervates, based on the Martini 3.0 force field. We demonstrate the accuracy of the model by reproducing the salt dependent coacervation of poly-lysine and poly-glutamate systems, and show the potential of the model by simulating the partitioning of ions and small nucleotides between the condensate and surrounding solvent phase. Our model paves the way for simulating coacervates and biomolecular condensates in a wide range of conditions, with near-atomic resolution.

"Dividing and Conquering" and "Caching" in Molecular Modeling

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.

Assessing the Role of Calmodulin's Linker Flexibility in Target Binding

Calmodulin (CaM) is a highly-expressed Ca2+ binding protein known to bind hundreds of protein targets. Its binding selectivity to many of these targets is partially attributed to the protein’s flexible alpha helical linker that connects its N- and C-domains. It is not well established how its linker mediates CaM’s binding to regulatory targets yet. Insights into this would be invaluable to understanding its regulation of diverse cellular signaling pathways. Therefore, we utilized Martini coarse-grained (CG) molecular dynamics simulations to probe CaM/target assembly for a model system: CaM binding to the calcineurin (CaN) regulatory domain. The simulations were conducted assuming a ‘wild-type’ calmodulin with normal flexibility of its linker, as well as a labile, highly-flexible linker variant to emulate structural changes that could be induced, for instance, by post-translational modifications. For the wild-type model, 98% of the 600 simulations across three ionic strengths adopted a bound complex within 2 μs of simulation time; of these, 1.7% sampled the fully-bound state observed in the experimentally-determined crystallographic structure. By calculating the mean-first-passage-time for these simulations, we estimated the association rate to be ka= 8.7 × 108 M−1 s−1, which is similar to the diffusion-limited, experimentally-determined rate of 2.2 × 108 M−1 s−1. Furthermore, our simulations recapitulated its well-known inverse relationship between the association rate and the solution ionic strength. In contrast, although over 97% of the labile linker simulations formed tightly-bound complexes, only 0.3% achieved the fully-bound configuration. This effect appears to stem from a difference in the ensembles of extended and collapsed states which are controlled by the linker flexibility. Therefore, our simulations suggest that variations in the CaM linker’s propensity for alpha helical secondary structure can modulate the kinetics of target binding.

Biomolecular modeling thrives in the age of technology

The biomolecular modeling field has flourished since its early days in the 1970s due to the rapid adaptation and tailoring of state-of-the-art technology. The resulting dramatic increase in size and timespan of biomolecular simulations has outpaced Moore's law. Here, we discuss the role of knowledge-based versus physics-based methods and hardware versus software advances in propelling the field forward. This rapid adaptation and outreach suggests a bright future for modeling, where theory, experimentation and simulation define three pillars needed to address future scientific and biomedical challenges.

Evaluating the Efficiency of the Martini Force Field to Study Protein Dimerization in Aqueous and Membrane Environments

Protein-protein complex assembly is one of the major drivers of biological response. Understanding the mechanisms of protein oligomerization/dimerization would allow one to elucidate how these complexes participate in biological activities and could ultimately lead to new approaches in designing novel therapeutic agents. However, determining the exact association pathways and structures of such complexes remains a challenge. Here, we use parallel tempering metadynamics simulations in the well-tempered ensemble to evaluate the performance of Martini 2.2P and Martini open-beta 3 (Martini 3) force fields in reproducing the structure and energetics of the dimerization process of membrane proteins and proteins in an aqueous solution in reasonable accuracy and throughput. We find that Martini 2.2P systematically overestimates the free energy of association by estimating large barriers in distinct areas, which likely leads to overaggregation when multiple monomers are present. In comparison, the less viscous Martini 3 results in a systematic underestimation of the free energy of association for proteins in solution, while it performs well in describing the association of membrane proteins. In all cases, the near-native dimer complexes are identified as minima in the free energy surface albeit not always as the lowest minima. In the case of Martini 3, we find that the spurious supramolecular protein aggregation present in Martini 2.2P multimer simulations is alleviated and thus this force field may be more suitable for the study of protein oligomerization. We propose that the use of enhanced sampling simulations with a refined coarse-grained force field and appropriately defined collective variables is a robust approach for studying the protein dimerization process, although one should be cautious of the ranking of energy minima.

Biomolecular interactions of ultrasmall metallic nanoparticles and nanoclusters

The use of nanoparticles (NPs) in biomedicine has made a gradual transition from proof-of-concept to clinical applications, with several NP types meeting regulatory approval or undergoing clinical trials. A new type of metallic nanostructures called ultrasmall nanoparticles (usNPs) and nanoclusters (NCs), while retaining essential properties of the larger (classical) NPs, have features common to bioactive proteins. This combination expands the potential use of usNPs and NCs to areas of diagnosis and therapy traditionally reserved for small-molecule medicine. Their distinctive physicochemical properties can lead to unique in vivo behaviors, including improved renal clearance and tumor distribution. Both the beneficial and potentially deleterious outcomes (cytotoxicity, inflammation) can, in principle, be controlled through a judicious choice of the nanocore shape and size, as well as the chemical ligands attached to the surface. At present, the ability to control the behavior of usNPs is limited, partly because advances are still needed in nanoengineering and chemical synthesis to manufacture and characterize ultrasmall nanostructures and partly because our understanding of their interactions in biological environments is incomplete. This review addresses the second limitation. We review experimental and computational methods currently available to understand molecular mechanisms, with particular attention to usNP-protein complexation, and highlight areas where further progress is needed. We discuss approaches that we find most promising to provide relevant molecular-level insight for designing usNPs with specific behaviors and pave the way to translational applications.

Asymmetric CorA Gating Mechanism as Observed by Molecular Dynamics Simulations

The CorA family of proteins plays a housekeeping role in the homeostasis of divalent metal ions in many bacteria and archaea as well as in mitochondria of eukaryotes, rendering it an important target to study the mechanisms of divalent transport and regulation across different life domains. Despite numerous studies, the mechanistic details of the channel gating and the transport of the metal ions are still not entirely understood. Here, we use all-atom and coarse-grained molecular dynamics simulations combined with in vitro experiments to investigate the influence of divalent cations on the function of CorA. Simulations reveal pronounced asymmetric movements of monomers that enable the rotation of the α7 helix and the cytoplasmic subdomain with the subsequent formation of new interactions and the opening of the channel. These computational results are functionally validated using site-directed mutagenesis of the intracellular cytoplasmic domain residues and biochemical assays. The obtained results infer a complex network of interactions altering the structure of CorA to allow gating. Furthermore, we attempt to reconcile the existing gating hypotheses for CorA to conclude the mechanism of transport of divalent cations via these proteins.

Kirkwood-Buff-Derived Force Field for Peptides and Proteins: Philosophy and Development of KBFF20

A new classical nonpolarizable force field, KBFF20, for the simulation of peptides and proteins is presented. The force field relies heavily on the use of Kirkwood-Buff theory to provide a comparison of simulated and experimental Kirkwood-Buff integrals for solutes containing the functional groups common in proteins, thus ensuring intermolecular interactions that provide a good balance between the peptide-peptide, peptide-solvent, and solvent-solvent distributions observed in solution mixtures. In this way, it differs significantly from other biomolecular force fields. Further development and testing of the intermolecular potentials are presented here. Subsequently, rotational potentials for the ϕ/ψ and χ dihedral degrees of freedom are obtained by analysis of the Protein Data Bank, followed by small modifications to provide a reasonable balance between simulated and observed α and β percentages for small peptides. This, the first of two articles, describes in detail the philosophy and development behind KBFF20.

Fast bilayer-micelle fusion mediated by hydrophobic dipeptides

To understand the transition from inanimate matter to life, we studied a process that directly couples simple metabolism to evolution via natural selection, demonstrated experimentally by Adamala and Szostak. In this process, dipeptides synthesized inside precursors of cells promote absorption of fatty acid micelles to vesicles, inducing their preferential growth and division at the expense of other vesicles. The process is explained on the basis of coarse-grained molecular dynamics simulations, each extending for tens of microseconds, carried out to model fusion between a micelle and a membrane, both made of fatty acids in the absence and presence of hydrophobic dipeptides. In all systems with dipeptides, but not in their absence, fusion events were observed. They involve the formation of a stalk made by hydrophobic chains from the micelle and the membrane, similar to that postulated for vesicle-vesicle fusion. The emergence of a stalk is facilitated by transient clusters of dipeptides, side chains of which form hydrophobic patches at the membrane surface. Committor probability calculations indicate that the size of a patch is a suitable reaction coordinate and allows for identifying the transition state for fusion. Free-energy barrier to fusion is greatly reduced in the presence of dipeptides to only 4-5 kcal/mol, depending on the hydrophobicity of side chains. The mechanism of mediated fusion, which is expected to apply to other small peptides and hydrophobic molecules, provides a robust means by which a nascent metabolism can confer evolutionary advantage to precursors of cells.

TorchMD: A Deep Learning Framework for Molecular Simulations

Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All force computations including bond, angle, dihedral, Lennard-Jones, and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab initio potential, performing an end-to-end training, and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool set to support molecular simulations of machine learning potentials. Code and data are freely available at github.com/torchmd.

Thermodynamics and Free Energy Landscape of BAR-Domain Dimerization from Molecular Simulations

Proteins with BAR domains function to bind to and remodel biological membranes, where the dimerization of BAR domains is a key step in this function. These domains can dimerize in solution or after localizing to the membrane surface. Here, we characterize the binding thermodynamics of homodimerization between the LSP1 BAR domain proteins in solution, using molecular dynamics (MD) simulations. By combining the MARTINI coarse-grained protein models with enhanced sampling through metadynamics, we construct a two-dimensional free energy surface quantifying the bound versus unbound ensembles as a function of two distance variables. With this methodology, our simulations can simultaneously characterize the structures and relative stabilities of a range of sampled dimers, portraying a heterogeneous and extraordinarily stable bound ensemble, where the proper crystal structure dimer is the most stable in a 100 mM NaCl solution. Nonspecific dimers that are sampled involve contacts that are consistent with experimental structures of higher-order oligomers formed by the LSP1 BAR domain. Because the BAR dimers and oligomers can assemble on membranes, we characterize the relative alignment of the known membrane binding patches, finding that only the specific dimer is aligned to form strong interactions with the membrane. Hence, we would predict a strong selection of the specific dimer in binding to or assembling when on the membrane. Establishing the pairwise stabilities of homodimer contacts is difficult experimentally when the proteins form stable oligomers, but through the method used here, we can isolate these contacts, providing a foundation to study the same interactions on the membrane.

Multiscale polarizable coarse-graining water models on cluster-level electrostatic dipoles

The development of a coarse-grained (CG) water model is increasingly important in CG studies of biological processes. In this work, we developed a generic CG force field of liquid water on cluster-level electrostatic dipoles. An exponential term is introduced in the non-bonded potential to adjust the well depth. The whole force field is parametrized on the AMOEBA simulation and then refined on the experimental density, dielectric permittivity and isothermal compressibility. The new CG water force field is suitable for the construction of multi-resolution water models and here the N_C = 4/5/10 systems are taken as examples. The results show that the N_C = 4/5/10 models can correctly reproduce the density and relative dielectric permittivity. The models can well predict the pressure-density/density-temperature relationships close to the all-atom or experiment results. However, the new models behave differently from other CG models in several water properties such as the air-water surface tension. Through dipole distributions, two representative polarizable configurations are captured after the N_C = 4/5/10 systems are dynamically equilibrated. Besides, the N_C = 4 model is coupled with the Martini Na⁺/Cl^- models to predict ion-relevant radial distribution functions in comparison to the Martini result. Lastly, CPU tests suggest that the new CG models can enhance simulation efficiency by factors of 20-42, compared to the TIP3P force field. The newly proposed polarizable water force field is practical and transferable and can be flexibly extended to higher coarse-graining of liquid water.

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

The coarse-grained Martini force field is widely used in biomolecular simulations. Here we present the refined model, Martini 3 ( http://cgmartini.nl ), with an improved interaction balance, new bead types and expanded ability to include specific interactions representing, for example, hydrogen bonding and electronic polarizability. The updated model allows more accurate predictions of molecular packing and interactions in general, which is exemplified with a vast and diverse set of applications, ranging from oil/water partitioning and miscibility data to complex molecular systems, involving protein-protein and protein-lipid interactions and material science applications as ionic liquids and aedamers.

Biomolecular response to hour-long ultralow field microwave radiation: An effective coarse-grained model simulation

Various electronic devices, which we commonly use, radiate microwaves. Such external perturbation influences the functionality of biomolecules. In an ultralow field, the cumulative response of a molecule is expected only over a time scale of hours. To study the structural dynamics of biomolecules over hours, we adopt a simple methodology for constructing the coarse-grained structure of the protein molecule and solve the Langevin equation under different working potentials. In this approach, each amino acid residue of a biomolecule is mapped onto a number of beads, a few for the backbone, and few for the side chain, depending on the complexity of its chemical structure. We choose the force field in such a way that the dynamics of the protein molecule in the presence of ultralow radiation field of microvolt/nm could be followed over the time frame of 2 h. We apply the model to describe a biomolecule, hen egg white lysozyme, and simulate its structural evolution under ultralow strength electromagnetic radiation. The simulation revealed the finer structural details, like the extent of exposure of bioactive residues and the state of the secondary structures of the molecule, further confirmed from spectroscopic measurements [details are available in Phys. Rev. E 97, 052416 (2018)10.1103/PhysRevE.97.052416 and briefly described here]. Though tested for a specific system, the model is quite general. We believe that it harnesses the potential in studying the structural dynamics of any biopolymer under external perturbation over an extended time scale.

Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations

Molecular docking is central to rational drug design. Current docking techniques suffer, however, from limitations in protein flexibility and solvation models and by the use of simplified scoring functions. All-atom molecular dynamics simulations, on the other hand, feature a realistic representation of protein flexibility and solvent, but require knowledge of the binding site. Recently we showed that coarse-grained molecular dynamics simulations, based on the most recent version of the Martini force field, can be used to predict protein/ligand binding sites and pathways, without requiring any a priori information, and offer a level of accuracy approaching all-atom simulations. Given the excellent computational efficiency of Martini, this opens the way to high-throughput drug screening based on dynamic docking pipelines. In this opinion article, we sketch the roadmap to achieve this goal.

Understanding the Synergistic Correlation between the Spatial Distribution of Drug-Loaded Mixed Micellar Systems and In Vitro Behavior via Experimental and Computational Approaches

To better promote the application of polymeric mixed micelles (PMMs), a coarse-grained molecular dynamics simulation (CGMD) has been employed to investigate the factors controlling the spatial distribution within the PMMs and predict their drug-loading properties, meanwhile, combined with experimental methods to validate and examine it. In this study, the snapshots obtained from CGMD and the results of proton nuclear magnetic resonance (¹H NMR) and transmission electron microscopy (TEM) provide new insights into the distribution principle that the spatial distribution depends on the hydrophobic compatibility of drugs with the regions within PMMs. Docetaxel (DTX) is located within the interior or near the core-corona interface of the HS15 hydrophobic core inside FS/PMMs (PMMs fabricated from a nonionic triblock copolymer (F127)) and a nonionic surfactant (HS15), and therefore, the system with a high HS15 ratio, such as system I, is more suitable for loading DTX. In contrast, the more water-soluble puerarin (PUE) is more likely to be solubilized in the "secondary hydrophobic area," mainly formed by the hydrophobic part of F127 within FS/PMMs. However, when the initial feeding concentration of the drug is increased or the FS mixing ratios are changed, an inappropriate distribution would occur and hence influence the drug-loading stability. Also, this impact was further elucidated by the calculated parameters (solvent-accessible surface area (SASA), the radius of gyration (R_g), and energy landscape), and the analysis of the drug leakage, concluding that inappropriate distribution of the drug would lower the stability of the drug in the PMMs. These results combined together provide new insights into the distribution principle that the spatial distribution of drugs within PMMs depends on the hydrophobic compatibility of drugs with the regions formed by micellar materials. Additionally, in vitro drug release yielded a consistent picture with the above conclusions and provides evidence that both the location of the drug within the systems and the stability of the drug-loading system have a great influence on the drug release behavior. Accordingly, this work demonstrates that we can tune the drug-loading stability and drug release behavior via the drug-PMM interaction and drug location study, and CGMD technology would be a step forward in the search for suitable drug-delivery PMMs.

Development of accurate coarse-grained force fields for weakly polar groups by an indirect parameterization strategy

Coarse-grained (CG) molecular dynamics simulations are widely used to predict morphological structures and interpret mechanisms of mesoscopic behavior between the scope of traditional experiments and all-atom simulations. However, most current CG force fields (FFs) are not precise enough, especially for polar molecules or functional groups. A main obstacle in developing accurate CG FFs for polar molecules is the freezing problem met at room temperature. In this work, we introduce an indirect parametrization strategy for weakly polar groups by considering their short-chain homologs to avoid freezing. Here, a polar group containing three to four heavy atoms is mapped into one CG bead that is connected to one alkyl bead composed of three or four carbons. The CG beads interact via 4-parameter nonbonded Morse potentials and harmonic bonded potentials. An efficient meta-multilinear interpolation parameterization algorithm, as recently developed by us, is used to rigorously optimize the force parameters. Satisfactory accuracy is witnessed in terms of the density, heat of vaporization, surface tension, and solvation free energy of the homologs of twelve polar molecules, all deviating from the experiment by less than 5%. The transferability of the current FF is indicated by the predicted density, heat of vaporization, and end-to-end distance distributions of fatty acid methyl esters composed of multiple functional groups parameterized in this work.

Coarse-grained simulation of the self-assembly of lipid vesicles concomitantly with novel block copolymers with multiple tails

We carried out a series of coarse-grained molecular dynamics liposome-copolymer simulations with varying extent of copolymer concentration in an attempt to understand the effect of copolymer structure and concentration on vesicle self-assembly and stability. For one particular case molecular dynamics simulation data was successfully verified against experimental NMR results enhancing the credulity in the simulation methodology. The study focused on a new class of promising copolymers based on ethylene oxide (EO) chains and short blocks of aliphatic double chains that mimic lipid tails. The lipid mimetic units are based on 1,3-didodecyloxy-2-glycidylglycerol (DDGG) and 1,3-didodecyloxy-propane-2-ol (DDP). The conducted simulations indicate that multiple lipid anchor-bearing copolymers lead to stable hybrid liposome formations. Single lipid bearing might incur liposome-stabilizing potential for relatively small ethylene oxide chains but fails dramatically in combination with a longer EO-based moiety. The consistency of the theoretical evidence with experimental NMR observation for certain cases provides confidence for the relevance of the methodology in eliciting the factors governing liposome-polymer stability which is of fundamental and practical significance.

Development of the computational antibiotic screening platform (CLASP) to aid in the discovery of new antibiotics

Bacterial colonization of biotic and abiotic surfaces and antibiotic resistance are grand challenges with paramount societal impacts. However, in the face of increasing bacterial resistance to all known antibiotics, efforts to discover new classes of antibiotics have languished, creating an urgent need to accelerate the antibiotic discovery pipeline. A major deterrent in the discovering of new antibiotics is the limited permeability of molecules across the bacterial envelope. Notably, the Gram-negative bacteria have nutrient specific protein channels (or porins) that restrict the permeability of non-essential molecules, including antibiotics. Here, we have developed the Computational Antibiotic Screening Platform (CLASP) for screening of potential drug molecules through the porins. The CLASP takes advantage of coarse grain (CG) resolution, advanced sampling techniques, and a parallel computing environment to maximize its performance. The CLASP yields comprehensive thermodynamic and kinetic output data of a potential drug molecule within a few hours of wall-clock time. Its output includes the potential of mean force profile, energy barrier, the rate constant, and contact analysis of the molecule with the pore-lining residues, and the orientational analysis of the molecule in the porin channel. In our first CLASP application, we report the transport properties of six carbapenem antibiotics-biapenem, doripenem, ertapenem, imipenem, meropenem, and panipenem-through OccD3, a major channel for carbapenem uptake in Pseudomonas aeruginosa. The CLASP is designed to screen small molecule libraries with a fast turnaround time to yield structure-property relationships to discover antibiotics with high permeability. The CLASP will be freely distributed to enable accelerated antibiotic drug discovery.

Rotational dynamics of proteins in nanochannels: role of solvent's local viscosity

Viscosity variation of solvent in local regions near a solid surface, be it a biological surface of a protein or an engineered surface of a nanoconfinement, is a direct consequence of intermolecular interactions between the solid body and the solvent. The current coarse-grained molecular dynamics study takes advantage of this phenomenon to investigate the anomaly in a solvated protein's rotational dynamics confined using a representative solid matrix. The concept of persistence time, the characteristic time of structural reordering in liquids, is used to compute the solvent's local viscosity. With an increase in the degree of confinement, the confining matrix significantly influences the solvent molecule's local viscosity present in the protein hydration layer through intermolecular interactions. This effect contributes to the enhanced drag force on protein motion, causing a reduction in the rotational diffusion coefficient. Simulation results suggest that the direct matrix-protein non-bonded interaction is responsible for the occasional jump and discontinuity in orientational motion when the protein is in very tight confinement.

Not only in silico drug discovery: Molecular modeling towards in silico drug delivery formulations

The use of methods at molecular scale for the discovery of new potential active ligands, as well as previously unknown binding sites for target proteins, is now an established reality. Literature offers many successful stories of active compounds developed starting from insights obtained in silico and approved by Food and Drug Administration (FDA). One of the most famous examples is raltegravir, a HIV integrase inhibitor, which was developed after the discovery of a previously unknown transient binding area thanks to molecular dynamics simulations. Molecular simulations have the potential to also improve the design and engineering of drug delivery devices, which are still largely based on fundamental conservation equations. Although they can highlight the dominant release mechanism and quantitatively link the release rate to design parameters (size, drug loading, et cetera), their spatial resolution does not allow to fully capture how phenomena at molecular scale influence system behavior. In this scenario, the "computational microscope" offered by simulations at atomic scale can shed light on the impact of molecular interactions on crucial parameters such as release rate and the response of the drug delivery device to external stimuli, providing insights that are difficult or impossible to obtain experimentally. Moreover, the new paradigm brought by nanomedicine further underlined the importance of such computational microscope to study the interactions between nanoparticles and biological components with an unprecedented level of detail. Such knowledge is a fundamental pillar to perform device engineering and to achieve efficient and safe formulations. After a brief theoretical background, this review aims at discussing the potential of molecular simulations for the rational design of drug delivery systems.

Using multiscale molecular dynamics simulations to obtain insights into pore forming toxin mechanisms

Pore forming toxins (PFTs) are virulent proteins released by several species, including many strains of bacteria, to attack and kill host cells. In this article, we focus on the utility of molecular dynamics (MD) simulations and the molecular insights gleaned from these techniques on the pore forming pathways of PFTs. In addition to all-atom simulations which are widely used, coarse-grained MARTINI models and structure-based models have also been used to study PFTs. Here, the emphasis is on methods and techniques involved while setting up, monitoring, and evaluating properties from MD simulations of PFTs in a membrane environment. We draw from several case studies to illustrate how MD simulations have provided molecular insights into protein-protein and protein-lipid interactions, lipid dynamics, conformational transitions and structures of both the oligomeric intermediates and assembled pore structures.

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.

Molecular simulations of lipid membrane partitioning and translocation by bacterial quorum sensing modulators

Quorum sensing (QS) is a bacterial communication process mediated by both native and non-native small-molecule quorum sensing modulators (QSMs), many of which have been synthesized to disrupt QS pathways. While structure-activity relationships have been developed to relate QSM structure to the activation or inhibition of QS receptors, less is known about the transport mechanisms that enable QSMs to cross the lipid membrane and access intracellular receptors. In this study, we used atomistic MD simulations and an implicit solvent model, called COSMOmic, to analyze the partitioning and translocation of QSMs across lipid bilayers. We performed umbrella sampling at atomistic resolution to calculate partitioning and translocation free energies for a set of naturally occurring QSMs, then used COSMOmic to screen the water-membrane partition and translocation free energies for 50 native and non-native QSMs that target LasR, one of the LuxR family of quorum-sensing receptors. This screening procedure revealed the influence of systematic changes to head and tail group structures on membrane partitioning and translocation free energies at a significantly reduced computational cost compared to atomistic MD simulations. Comparisons with previously determined QSM activities suggest that QSMs that are least likely to partition into the bilayer are also less active. This work thus demonstrates the ability of the computational protocol to interrogate QSM-bilayer interactions which may help guide the design of new QSMs with engineered membrane interactions.

A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes

Study of the permeability of small organic molecules across lipid membranes plays a significant role in designing potential drugs in the field of drug discovery. Approaches to design promising drug molecules have gone through many stages, from experiment-based trail-and-error approaches, to the well-established avenue of the quantitative structure-activity relationship, and currently to the stage guided by machine learning (ML) and artificial intelligence techniques. In this work, we present a study of the permeability of small drug-like molecules across lipid membranes by two types of ML models, namely the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models. Molecular descriptors and fingerprints are used for featurization of organic molecules. Using molecular descriptors, the LASSO model uncovers that the electro-topological, electrostatic, polarizability, and hydrophobicity/hydrophilicity properties are the most important physical properties to determine the membrane permeability of small drug-like molecules. Additionally, with molecular fingerprints, the LASSO model suggests that certain chemical substructures can significantly affect the permeability of organic molecules, which closely connects to the identified main physical properties. Moreover, the DNN model using molecular fingerprints can help develop a more accurate mapping between molecular structures and their membrane permeability than LASSO models. Our results provide deep understanding of drug-membrane interactions and useful guidance for the inverse molecular design of drug-like molecules. Last but not least, while the current focus is on the permeability of drug-like molecules, the methodology of this work is general and can be applied for other complex physical chemistry problems to gain molecular insights.

Comment on "Martini force field for protonated polyethyleneimine"

We comment on the recently published Martini forcefield for linear polyethylenimine (Beu et al., J. Comput. Chem., 2020, 41, 349). With supporting evidence, we demonstrate that the new coarse-graining methodology presented in that work is inconsistent with the Martini methodology and lacks important validation steps which is usually required by Martini forcefields.

The micromechanical model to computationally investigate cooperative and correlated phenomena in metal-organic frameworks

Computational insight into the impact of cooperative phenomena and correlated spatial disorder on the macroscopic behaviour of metal-organic frameworks (MOFs) is essential in order to consciously engineer these phenomena for targeted applications. However, the spatial extent of these effects, ranging over hundreds of nanometres, limits the applicability of current state-of-the-art computational tools in this field. To obtain a fundamental understanding of these long-range effects, the micromechanical model is introduced here. This model overcomes the challenges associated with conventional coarse-graining techniques by exploiting the natural partitioning of a MOF material into unit cells. By adopting the elastic deformation energy as the central quantity, the micromechanical model hierarchically builds on experimentally accessible input parameters that are obtained from atomistic quantum mechanical or force field simulations. As a result, the here derived micromechanical equations of motion can be adopted to shed light on the effect of long-range cooperative phenomena and correlated spatial disorder on the performance of mesoscale MOF materials.

Relative Resolution: A Computationally Efficient Implementation in LAMMPS

Recently, a novel type of multiscale simulation, called Relative Resolution (RelRes), was introduced. In a single system, molecules switch their resolution in terms of their relative separation, with near neighbors interacting via fine-grained potentials yet far neighbors interacting via coarse-grained potentials; notably, these two potentials are analytically parametrized by a multipole approximation. This multiscale approach is consequently able to correctly retrieve across state space the structural and thermal, as well as static and dynamic, behavior of various nonpolar mixtures. Our current work focuses on the practical implementation of RelRes in LAMMPS, specifically for the commonly used Lennard-Jones potential. By examining various correlations and properties of several alkane liquids, including complex solutions of alternate cooligomers and block copolymers, we confirm the validity of this automated LAMMPS algorithm. Most importantly, we demonstrate that this RelRes implementation gains almost an order of magnitude in computational efficiency, as compared with conventional simulations. We thus recommend this novel LAMMPS algorithm for anyone studying systems governed by Lennard-Jones interactions.

Predicting the aptamer SYL3C-EpCAM complex's structure with the Martini-based simulation protocol

Aptamers (small single strand DNA/RNAs) such as SYL3C are considered as ideal alternatives to antibodies in cancer related research studies. However, 3D structure predictions for aptamers and aptamer-protein complexes are scarce due to the high cost of experimental measurements and unreliable computer-based methods. Thus aptamers' diagnostic and therapeutic applications are severely restricted. To meet the challenge, we proposed a Martini-based aptamer-protein complex prediction protocol. By combining the base-base contact map from simulation and secondary structure prediction from various tools, improved secondary structure predictions can be obtained. This method reduced the risk of providing incorrect or incomplete base pairs in secondary structure prediction. Thus 3D structure modeling based on the secondary structure can be more reliable. We introduced the soft elastic network to the hairpin folded regions of the Martini ssDNAs to preserve their canonical structure. Using our protocol, we predicted the first 3D structure of the aptamer SYL3C and the SYL3C-EpCAM complex. We believe that our work could contribute to the future aptamer-related research studies and medical implications.

Coarse-Grained Force Fields from the Perspective of Statistical Mechanics: Better Understanding of the Origins of a MARTINI Hangover

The popular MARTINI coarse-grained model is used as a test case to analyze the adherence of top-down coarse-grained molecular dynamics models (i.e., models primarily parametrized to match experimental results) to the known features of statistical mechanics for the underlying all-atom representations. Specifically, the temperature dependence of various pair distribution functions, and hence their underlying potentials of mean force via the reversible work theorem, are compared between MARTINI 2.0, Dry MARTINI, and all-atom simulations mapped onto equivalent coarse-grained sites for certain lipid bilayers. It is found that the MARTINI models do not completely capture the lipid structure seen in atomistic simulations as projected onto the coarse-grained mappings and that issues of accuracy and temperature transferability arise due to an incorrect enthalpy-entropy decomposition of these potentials of mean force. The potential of mean force for the association of two amphipathic helices in a lipid bilayer is also calculated, and especially at shorter ranges, the MARTINI and all-atom projection results differ substantially. The former is much less repulsive and hence will lead to a higher probability of MARTINI helix association in the MARTINI bilayer than occurs in the actual all-atom case. Additionally, the bilayer height fluctuation spectra are calculated for the MARTINI model, and compared to the all-atom results, it is found that the magnitude of thermally averaged amplitudes at intermediate length scales are quite different, pointing to a number of possible consequences for realistic modeling of membrane processes. Taken as a whole, the results presented here show disagreement in the enthalpic and entropic driving forces driving lateral structure in lipid bilayers as well as quantitative differences in association of embedded amphipathic helices, which can help direct future efforts to parametrize CG models with better agreement to the all-atom systems they aspire to represent.

Modification of lipid membrane compressibility induced by an electric field

Changes in membrane deformation and compressibility, induced by an external electric field, are investigated using coarse-grained martini force field simulations in a salt-free environment. We observe changes in the area of the membrane above a critical electric field. Below this value, the membrane compressibility modulus is found to decrease monotonically. For higher electric fields, the membrane projected area remains constant while the net interfacial area increases, with the corresponding compressibility moduli, show the opposite behavior. We find that the mechanical parameters, surface tension and bending modulus, of a freely floating membrane in the absence of explicit ions, are unaffected by the presence of the electric field. We believe these results have a bearing on our understanding of the electroformation of uncharged lipids in a salt-free environment.

Wrapping Up Viruses at Multiscale Resolution: Optimizing PACKMOL and SIRAH Execution for Simulating the Zika Virus

Simulating huge biomolecular complexes of million atoms at relevant biological time scales is becoming accessible to the broad scientific community. That proves to be crucial for urgent responses against emergent diseases in real time. Yet, there are still issues to sort regarding the system setup so that molecular dynamics (MD) simulations can be run in a simple and standard way. Here, we introduce an optimized pipeline for building and simulating enveloped virus-like particles (VLP). First, the membrane packing problem is tackled with new features and optimized options in PACKMOL. This allows preparing accurate membrane models of thousands of lipids in the context of a VLP within a few hours using a single CPU. Then, the assembly of the VLP system is done within the multiscale framework of the coarse-grained SIRAH force field. Finally, the equilibration protocol provides a system ready for production MD simulations within a few days on broadly accessible GPU resources. The pipeline is applied to study the Zika virus as a test case for large biomolecular systems. The VLP stabilizes at approximately 0.5 μs of MD simulation, reproducing correlations greater than 0.90 against experimental density maps from cryo-electron microscopy. Detailed structural analysis of the protein envelope also shows very good agreement in root-mean-square deviations and B-factors with the experimental data. The level of details attained shows for the first time a possible role for anionic phospholipids in stabilizing the envelope. Combining an efficient and reliable setup procedure with an accurate coarse-grained force field provides a valuable pipeline for simulating arbitrary viral systems or subcellular compartments, paving the way toward whole-cell simulations.

Estimating the accuracy of the MARTINI model towards the investigation of peripheral protein–membrane interactions

In this article, we investigate the ability of the MARTINI CG force field, specifically the 3 open-beta version, to reproduce known experimental observations regarding the membrane binding behavior of 12 peripheral membrane proteins and peptides.

Computational and Experimental Approaches to Investigate Lipid Nanoparticles as Drug and Gene Delivery Systems

Lipid nanoparticles (LNPs) have been widely applied in drug and gene delivery. More than twenty years ago, DoxilTM was the first LNPs-based drug approved by the US Food and Drug Administration (FDA). Since then, with decades of research and development, more and more LNP-based therapeutics have been used to treat diverse diseases, which often offer the benefits of reduced toxicity and/or enhanced efficacy compared to the active ingredients alone. Here, we provide a review of recent advances in the development of efficient and robust LNPs for drug/gene delivery. We emphasize the importance of rationally combining experimental and computational approaches, especially those providing multiscale structural and functional information of LNPs, to the design of novel and powerful LNP-based delivery systems.

From adaptive resolution to molecular dynamics of open systems

ABSTRACT

We provide an overview of the Adaptive Resolution Simulation method (AdResS) based on discussing its basic principles and presenting its current numerical and theoretical developments. Examples of applications to systems of interest to soft matter, chemical physics, and condensed matter illustrate the method's advantages and limitations in its practical use and thus settle the challenge for further future numerical and theoretical developments.

Computational modelling of flavivirus dynamics: The ins and outs

Enveloped viruses such as the flaviviruses represent a significant burden to human health around the world, with hundreds of millions of people each year affected by dengue alone. In an effort to improve our understanding of the molecular basis for the infective mechanisms of these viruses, extensive computational modelling approaches have been applied to elucidate their conformational dynamics. Multiscale protocols have been developed to simulate flavivirus envelopes in close accordance with biophysical data, in particular derived from cryo-electron microscopy, enabling high-resolution refinement of their structures and elucidation of the conformational changes associated with adaptation both to host environments and to immunological factors such as antibodies. Likewise, integrative modelling efforts combining data from biophysical experiments and from genome sequencing with chemical modification are providing unparalleled insights into the architecture of the previously unresolved nucleocapsid complex. Collectively, this work provides the basis for the future rational design of new antiviral therapeutics and vaccine development strategies targeting enveloped viruses.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.