Swarm-CG: Automatic Parametrization of Bonded Terms in MARTINI-Based Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization

A millisecond coarse-grained simulation approach to decipher allosteric cannabinoid binding at the glycine receptor α1

Glycine receptors (GlyR) are regulated by small-molecule binding at several allosteric sites. Cannabinoids like tetrahydrocannabinol (THC) and N-arachidonyl-ethanol-amide (AEA) potentiate the GlyR response but their mechanism of action is not fully established. By combining millisecond coarse-grained (CG) MD simulations powered by Martini 3 with backmapping to all-atom representations, we have characterized the cannabinoid-binding site(s) at the zebrafish GlyR-α1 active state with atomic resolution. Based on hundreds of thousand ligand-binding events, we find that cannabinoids bind to the transmembrane domain of the receptor at both intrasubunit and intersubunit sites. For THC, the intrasubunit binding mode predicted in simulation is in excellent agreement with recent cryo-EM structures, while intersubunit binding recapitulates in full previous mutagenesis experiments. Intriguingly, AEA is predicted to bind at the same intersubunit site despite the strikingly different chemistry. Statistical analyses of the ligand-receptor interactions highlight potentially relevant residues for GlyR potentiation, offering experimentally testable predictions. The predictions for AEA have been validated by electrophysiology recordings of rationally designed mutants. The results highlight the existence of multiple cannabinoid-binding sites for the allosteric regulation of GlyR and put forward an effective strategy for the identification and structural characterization of allosteric binding sites.

Coarse-Grained Molecular Dynamics Simulation of Heterogeneous Polysorbate 80 Surfactants and their Interactions with Small Molecules and Proteins

Polysorbate 80 (PS80) is widely used in pharmaceutical formulations, and its commercial grades exhibit certain levels of structural heterogeneity. The objective of this study was to apply coarse-grained molecular dynamics simulations to better understand the effect of PS80 heterogeneity on micelle self-assembly, the loading of hydrophobic small molecules into the micelle core, and the interactions between PS80 and a protein, bovine serum albumin (BSA). Four representative PS80 variants with different head and tail structures were studied. Our simulations found that PS80 structural heterogeneity could affect blank micelle properties such as solvent-accessible surface area, aggregation number, and micelle aspect ratio. It was also found that hydrophobic small molecules such as ethinyl estradiol preferentially partitioned into the PS80 micelle core and PS80 dioleates formed a more hydrophobic core compared to PS80 monooleates. Furthermore, multiple PS80 molecules could bind to BSA, and PS80 heterogeneity profoundly changed the binding ratio as well as the surfactant-protein contact area.

Leveraging Machine Learning-Guided Molecular Simulations Coupled with Experimental Data to Decipher Membrane Binding Mechanisms of Aminosterols

Understanding the molecular mechanisms of the interactions between specific compounds and cellular membranes is essential for numerous biotechnological applications, including targeted drug delivery, elucidation of the drug mechanism of action, pathogen identification, and novel antibiotic development. However, estimation of the free energy landscape associated with solute binding to realistic biological systems is still a challenging task. In this work, we leverage the Time-lagged Independent Component Analysis (TICA) in combination with neural networks (NN) through the Deep-TICA approach for determining the free energy associated with the membrane insertion processes of two natural aminosterol compounds, trodusquemine (TRO), and squalamine (SQ). These compounds are particularly noteworthy because they interact with the outer layer of neuron membranes, protecting them from the toxic action of misfolded proteins involved in neurodegenerative disorders, in both their monomeric and oligomeric forms. We demonstrate how this strategy could be used to generate an effective collective variable for describing solute absorption in the membrane and for estimating free energy landscape of translocation via on-the-fly probability enhanced sampling (OPES) method. In this context, the computational protocol allowed an exhaustive characterization of the aminosterol entry pathway into a neuron-like lipid bilayer. Furthermore, it provided accurate prediction of membrane binding affinities, in close agreement with the experimental binding data obtained by using fluorescently labeled aminosterols and large unilamellar vesicles (LUVs). The findings contribute significantly to our understanding of aminosterol entry pathways and aminosterol-lipid membrane interactions. Finally, the computational methods deployed in this study further demonstrate considerable potential for investigating membrane binding processes.

Bartender: Martini 3 Bonded Terms via Quantum Mechanics-Based Molecular Dynamics

Coarse-grained (CG) molecular dynamics (MD) simulations have grown in applicability over the years. The recently released version of the Martini CG force field (Martini 3) has been successfully applied to simulate many processes, including protein-ligand binding. However, the current ligand parametrization scheme is manual and requires an a priori reference all-atom (AA) simulation for benchmarking. For systems with suboptimal AA parameters, which are often unknown, this translates into a CG model that does not reproduce the true dynamical behavior of the underlying molecule. Here, we present Bartender, a quantum mechanics (QM)/MD-based parametrization tool written in Go. Bartender harnesses the power of QM simulations and produces reasonable bonded terms for Martini 3 CG models of small molecules in an efficient and user-friendly manner. For small, ring-like molecules, Bartender generates models whose properties are indistinguishable from the human-made models. For more complex, drug-like ligands, it is able to fit functional forms beyond simple harmonic dihedrals and thus better captures their dynamical behavior. Bartender has the power to both increase the efficiency and the accuracy of Martini 3-based high-throughput applications by producing numerically stable and physically realistic CG models.

Martini-Based Coarse-Grained Soil Organic Matter Model Derived from Atomistic Simulations

The significance of soil organic matter (SOM) in environmental contexts, particularly its role in pollutant adsorption, has prompted an increased utilization of molecular simulations to understand microscopic interactions. This study introduces a coarse-grained SOM model, parametrized within the framework of the versatile Martini 3 force field. Utilizing models generated by the Vienna Soil Organic Matter Modeler 2, which constructs humic substance systems from a fragment database, we employed Swarm-CG to parametrize the fragments and subsequently assembled them into macromolecules. Direct Boltzmann inversion (DBI) facilitated the determination of bonded parameters between fragments. The parametrization yielded favorable agreement in the radius of gyration and solvent-accessible surface area. Transfer free energies exhibited a strong correlation with hexadecane-water and chloroform-water values, albeit deviations were noted for octanol-water values. Comparing densities of modeled Leonardite humic acid systems at coarse-grained and atomistic levels revealed promising agreement, particularly at higher water concentrations. The DBI approach effectively reproduced average values of bonded interactions between fragments. Radial distribution functions between carboxylate groups and calcium ions showed partial agreement, however, reproducing certain peaks was challenging due to fixed bead sizes. Detailed analysis of atomistic systems revealed different configurations between the groups, explaining discrepancies. The present contribution provides a comprehensive insight into the properties, strengths, and weaknesses of the coarse-grained SOM model, serving as a foundation for future investigations encompassing pollutant interactions and varied SOM compositions.

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.

Martinoid: the peptoid martini force field

Many exciting innovations have been made in the development of assembling peptoid materials. Typically, these have utilised large oligomeric sequences, though elsewhere the study of peptide self-assembly has yielded numerous examples of assemblers below 6-8 residues in length, evidencing that minimal peptoid assemblers are not only feasible but expected. A productive means of discovering such materials is through the application of in silico screening methods, which often benefit from the use of coarse-grained molecular dynamics (CG-MD) simulations. At the current level of development, CG models for peptoids are insufficient and we have been motivated to develop a Martini forcefield compatible peptoid model. A dual bottom-up and top-down parameterisation approach has been adopted, in keeping with the Martini parameterisation methodology, targeting the reproduction of atomistic MD dynamics and trends in experimentally obtained log D7.4 partition coefficients, respectively. This work has yielded valuable insights into the practicalities of parameterising peptoid monomers. Additionally, we demonstrate that our model can reproduce the experimental observations of two very different peptoid assembly systems, namely peptoid nanosheets and minimal tripeptoid assembly. Further we can simulate the peptoid helix secondary structure relevant for antimicrobial sequences. To be of maximum usefulness to the peptoid research community, we have developed freely available code to generate all requisite simulation files for the application of this model with Gromacs MD software.

Lessons Learned from Multiobjective Automatic Optimizations of Classical Three-Site Rigid Water Models Using Microscopic and Macroscopic Target Experimental Observables

The development of accurate water models is of primary importance for molecular simulations. Despite their intrinsic approximations, three-site rigid water models are still ubiquitously used to simulate a variety of molecular systems. Automatic optimization approaches have been recently used to iteratively refine three-site water models to fit macroscopic (average) thermodynamic properties, providing state-of-the-art three-site models that still present some deviations from the liquid water properties. Here, we show the results obtained by automatically optimizing three-site rigid water models to fit a combination of microscopic and macroscopic experimental observables. We use Swarm-CG, a multiobjective particle-swarm-optimization algorithm, for training the models to reproduce the experimental radial distribution functions of liquid water at various temperatures (rich in microscopic-level information on, e.g., the local orientation and interactions of the water molecules). We systematically analyze the agreement of these models with experimental observables and the effect of adding macroscopic information to the training set. Our results demonstrate how adding microscopic-rich information in the training of water models allows one to achieve state-of-the-art accuracy in an efficient way. Limitations in the approach and in the approximated description of water in these three-site models are also discussed, providing a demonstrative case useful for the optimization of approximated molecular models, in general.

Self-assembly of cyclic peptide monolayers by hydrophobic supramolecular hinges

Supramolecular polymerisation of two-dimensional (2D) materials requires monomers with non-covalent binding motifs that can control the directionality of both dimensions of growth. A tug of war between these propagation forces can bias polymerisation in either direction, ultimately determining the structure and properties of the final 2D ensemble. Deconvolution of the assembly dynamics of 2D supramolecular systems has been widely overlooked, making monomer design largely empirical. It is thus key to define new design principles for suitable monomers that allow the control of the direction and the dynamics of two-dimensional self-assembled architectures. Here, we investigate the sequential assembly mechanism of new monolayer architectures of cyclic peptide nanotubes by computational simulations and synthesised peptide sequences with selected mutations. Rationally designed cyclic peptide scaffolds are shown to undergo hierarchical self-assembly and afford monolayers of supramolecular nanotubes. The particular geometry, the rigidity and the planar conformation of cyclic peptides of alternating chirality allow the orthogonal orientation of hydrophobic domains that define lateral supramolecular contacts, and ultimately direct the propagation of the monolayers of peptide nanotubes. A flexible 'tryptophan hinge' at the hydrophobic interface was found to allow lateral dynamic interactions between cyclic peptides and thus maintain the stability of the tubular monolayer structure. These results unfold the potential of cyclic peptide scaffolds for the rational design of supramolecular polymerisation processes and hierarchical self-assembly across the different dimensions of space.

Photodynamic inactivation of bacteria: Why it is not enough to excite a photosensitizer

BACKGROUND

The development of multidrug resistance (MDR) in infectious agents is one of the most serious global problems facing humanity. Antimicrobial photodynamic therapy (APDT) shows encouraging results in the fight against MDR pathogens, including those in biofilms.

METHODS

Photosensitizers (PS), monocationic methylene blue, polycationic and polyanionic derivatives of phthalocyanines, electroneutral and polycationic derivatives of bacteriochlorin were used to study photodynamic inactivation of Gram-positive and Gram-negative planktonic bacteria and biofilms under LED irradiation. Zeta potential measurements, confocal fluorescence imaging, and coarse-grained modeling were used to evaluate the interactions of PS with bacteria. PS aggregation and photobleaching were studied using absorption and fluorescence spectroscopy.

RESULTS

The main approaches to ensure high efficiency of bacteria photosensitization are analyzed.

CONCLUSIONS

PS must maintain a delicate balance between binding to exocellular and external structures of bacterial cells and penetration through the cell wall so as not to get stuck on the way to photooxidation-sensitive structures of the bacterial cell.

CGCompiler: Automated Coarse-Grained Molecule Parametrization via Noise-Resistant Mixed-Variable Optimization

Coarse-grained force fields (CG FFs) such as the Martini model entail a predefined, fixed set of Lennard-Jones parameters (building blocks) to model virtually all possible nonbonded interactions between chemically relevant molecules. Owing to its universality and transferability, the building-block coarse-grained approach has gained tremendous popularity over the past decade. The parametrization of molecules can be highly complex and often involves the selection and fine-tuning of a large number of parameters (e.g., bead types and bond lengths) to optimally match multiple relevant targets simultaneously. The parametrization of a molecule within the building-block CG approach is a mixed-variable optimization problem: the nonbonded interactions are discrete variables, whereas the bonded interactions are continuous variables. Here, we pioneer the utility of mixed-variable particle swarm optimization in automatically parametrizing molecules within the Martini 3 coarse-grained force field by matching both structural (e.g., RDFs) as well as thermodynamic data (phase-transition temperatures). For the sake of demonstration, we parametrize the linker of the lipid sphingomyelin. The important advantage of our approach is that both bonded and nonbonded interactions are simultaneously optimized while conserving the search efficiency of vector guided particle swarm optimization (PSO) methods over other metaheuristic search methods such as genetic algorithms. In addition, we explore noise-mitigation strategies in matching the phase-transition temperatures of lipid membranes, where nucleation and concomitant hysteresis introduce a dominant noise term within the objective function. We propose that noise-resistant mixed-variable PSO methods can both improve and automate parametrization of molecules within building-block CG FFs, such as Martini.

Systematic exploration of accessible topologies of cage molecules via minimalistic models

Cages are macrocyclic structures with an intrinsic internal cavity that support applications in separations, sensing and catalysis. These materials can be synthesised via self-assembly of organic or metal-organic building blocks. Their bottom-up synthesis and the diversity in building block chemistry allows for fine-tuning of their shape and properties towards a target property. However, it is not straightforward to predict the outcome of self-assembly, and, thus, the structures that are practically accessible during synthesis. Indeed, such a prediction becomes more difficult as problems related to the flexibility of the building blocks or increased combinatorics lead to a higher level of complexity and increased computational costs. Molecular models, and their coarse-graining into simplified representations, may be very useful to this end. Here, we develop a minimalistic toy model of cage-like molecules to explore the stable space of different cage topologies based on a few fundamental geometric building block parameters. Our results capture, despite the simplifications of the model, known geometrical design rules in synthetic cage molecules and uncover the role of building block coordination number and flexibility on the stability of cage topologies. This leads to a large-scale and systematic exploration of design principles, generating data that we expect could be analysed through expandable approaches towards the rational design of self-assembled porous architectures.

Interaction of Methylene Blue with Severe Acute Respiratory Syndrome Coronavirus 2 Envelope Revealed by Molecular Modeling

Methylene blue has multiple antiviral properties against Severe Acute Respiratory Syndrome-related Coronavirus 2 (SARS-CoV-2). The ability of methylene blue to inhibit different stages of the virus life cycle, both in light-independent and photodynamic processes, is used in clinical practice. At the same time, the molecular aspects of the interactions of methylene blue with molecular components of coronaviruses are not fully understood. Here, we use Brownian dynamics to identify methylene blue binding sites on the SARS-CoV-2 envelope. The local lipid and protein composition of the coronavirus envelope plays a crucial role in the binding of this cationic dye. Viral structures targeted by methylene blue include the S and E proteins and negatively charged lipids. We compare the obtained results with known experimental data on the antiviral effects of methylene blue to elucidate the molecular basis of its activity against coronaviruses.

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.

Extension of the SUGRES-1P Coarse-Grained Model of Polysaccharides to Heparin

Heparin is an unbranched periodic polysaccharide composed of negatively charged monomers and involved in key biological processes, including anticoagulation, angiogenesis, and inflammation. Its structure and dynamics have been studied extensively using experimental as well as theoretical approaches. The conventional approach of computational chemistry applied to the analysis of biomolecules is all-atom molecular dynamics, which captures the interactions of individual atoms by solving Newton's equation of motion. An alternative is molecular dynamics simulations using coarse-grained models of biomacromolecules, which offer a reduction of the representation and consequently enable us to extend the time and size scale of simulations by orders of magnitude. In this work, we extend the UNIfied COarse-gRaiNed (UNICORN) model of biological macromolecules developed in our laboratory to heparin. We carried out extensive tests to estimate the optimal weights of energy terms of the effective energy function as well as the optimal Debye-Hückel screening factor for electrostatic interactions. We applied the model to study unbound heparin molecules of polymerization degree ranging from 6 to 68 residues. We compare the obtained coarse-grained heparin conformations with models obtained from X-ray diffraction studies of heparin. The SUGRES-1P force field was able to accurately predict the general shape and global characteristics of heparin molecules.

Neural potentials of proteins extrapolate beyond training data

We evaluate neural network (NN) coarse-grained (CG) force fields compared to traditional CG molecular mechanics force fields. We conclude that NN force fields are able to extrapolate and sample from unseen regions of the free energy surface when trained with limited data. Our results come from 88 NN force fields trained on different combinations of clustered free energy surfaces from four protein mapped trajectories. We used a statistical measure named total variation similarity to assess the agreement between reference free energy surfaces from mapped atomistic simulations and CG simulations from trained NN force fields. Our conclusions support the hypothesis that NN CG force fields trained with samples from one region of the proteins' free energy surface can, indeed, extrapolate to unseen regions. Additionally, the force matching error was found to only be weakly correlated with a force field's ability to reconstruct the correct free energy surface.

Tidying up the conformational ensemble of a disordered peptide by computational prediction of spectroscopic fingerprints

The most advanced structure prediction methods are powerless in exploring the conformational ensemble of disordered peptides and proteins and for this reason the "protein folding problem" remains unsolved. We present a novel methodology that enables the accurate prediction of spectroscopic fingerprints (circular dichroism, infrared, Raman, and Raman optical activity), and by this allows for "tidying up" the conformational ensembles of disordered peptides and disordered regions in proteins. This concept is elaborated for and applied to a dodecapeptide, whose spectroscopic fingerprint is measured and theoretically predicted by means of enhanced-sampling molecular dynamics coupled with quantum mechanical calculations. Following this approach, we demonstrate that peptides lacking a clear propensity for ordered secondary-structure motifs are not randomly, but only conditionally disordered. This means that their conformational landscape, or phase-space, can be well represented by a basis-set of conformers including about 10 to 100 structures. The implications of this finding have profound consequences both for the interpretation of experimental electronic and vibrational spectral features of peptides in solution and for the theoretical prediction of these features using accurate and computationally expensive techniques. The here-derived methods and conclusions are expected to fundamentally impact the rationalization of so-far elusive structure-spectra relationships for disordered peptides and proteins, towards improved and versatile structure prediction methods.

Automated Parameterization of Coarse-Grained Polyethylenimine under a Martini Framework

As a versatile polymer in many applications, synthesized polyethylenimine (PEI) is polydisperse with diverse branched structures that attain pH-dependent protonation states. Understanding the structure-function relationship of PEI is necessary for enhancing its efficacy in various applications. Coarse-grained (CG) simulations can be performed at length and time scales directly comparable with experimental data while maintaining the molecular perspective. However, manually developing CG forcefields for complex PEI structures is time-consuming and prone to human errors. This article presents a fully automated algorithm that can coarse-grain any branched architecture of PEI from its all-atom (AA) simulation trajectories and topology. The algorithm is demonstrated by coarse-graining a branched 2 kDa PEI, which can replicate the AA diffusion coefficient, radius of gyration, and end-to-end distance of the longest linear chain. Commercially available 25 and 2 kDa Millipore-Sigma PEIs are used for experimental validation. Specifically, branched PEI architectures are proposed, coarse-grained using the automated algorithm, and then simulated at different mass concentrations. The CG PEIs can reproduce existing experimental data on PEI's diffusion coefficient and Stokes-Einstein radius at infinite dilution as well as its intrinsic viscosity. This suggests a strategy where probable chemical structures of synthetic PEIs can be inferred computationally using the developed algorithm. The coarse-graining methodology presented here can also be extended to other polymers.

Automatic Optimization of Lipid Models in the Martini Force Field Using SwarmCG

After two decades of continued development of the Martini coarse-grained force field (CG FF), further refinment of the already rather accurate Martini lipid models has become a demanding task that could benefit from integrative data-driven methods. Automatic approaches are increasingly used in the development of accurate molecular models, but they typically make use of specifically designed interaction potentials that transfer poorly to molecular systems or conditions different than those used for model calibration. As a proof of concept, here, we employ SwarmCG, an automatic multiobjective optimization approach facilitating the development of lipid force fields, to refine specifically the bonded interaction parameters in building blocks of lipid models within the framework of the general Martini CG FF. As targets of the optimization procedure, we employ both experimental observables (top-down references: area per lipid and bilayer thickness) and all-atom molecular dynamics simulations (bottom-up reference), which respectively inform on the supra-molecular structure of the lipid bilayer systems and on their submolecular dynamics. In our training sets, we simulate at different temperatures in the liquid and gel phases up to 11 homogeneous lamellar bilayers composed of phosphatidylcholine lipids spanning various tail lengths and degrees of (un)saturation. We explore different CG representations of the molecules and evaluate improvements a posteriori using additional simulation temperatures and a portion of the phase diagram of a DOPC/DPPC mixture. Successfully optimizing up to ∼80 model parameters within still limited computational budgets, we show that this protocol allows the obtainment of improved transferable Martini lipid models. In particular, the results of this study demonstrate how a fine-tuning of the representation and parameters of the models may improve their accuracy and how automatic approaches, such as SwarmCG, may be very useful to this end.

Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles

The reshuffling mobility of molecular building blocks in self-assembled micelles is a key determinant of many their interesting properties, from emerging morphologies and surface compartmentalization, to dynamic reconfigurability and stimuli-responsiveness. However, the microscopic details of such complex structural dynamics are typically nontrivial to elucidate, especially in multicomponent assemblies. Here we show a machine-learning approach that allows us to reconstruct the structural and dynamic complexity of mono- and bicomponent surfactant micelles from high-dimensional data extracted from equilibrium molecular dynamics simulations. Unsupervised clustering of smooth overlap of atomic position (SOAP) data enables us to identify, in a set of multicomponent surfactant micelles, the dominant local molecular environments that emerge within them and to retrace their dynamics, in terms of exchange probabilities and transition pathways of the constituent building blocks. Tested on a variety of micelles differing in size and in the chemical nature of the constitutive self-assembling units, this approach effectively recognizes the molecular motifs populating them in an exquisitely agnostic and unsupervised way, and allows correlating them to their composition in terms of constitutive surfactant species.

Tween-80 on Water/Oil Interface: Structure and Interfacial Tension by Molecular Dynamics Simulations

Surfactants are used in many fields of the chemical industry in a wide range of applications. Generally, surfactants on water/oil interfaces reduce interfacial tension, enabling the formation of emulsions or providing greater stability to the emulsion formed. Although molecular dynamics has been extensively used and has achieved remarkable success in describing thermodynamic and molecular properties of systems with surface-active compounds, the traditional molecular simulation force fields considerably constrain the system size and the time scale of simulations. Here, we propose a coarse-grained model of polysorbate 80, a nonionic surfactant commercially known as Tween-80. Based on the proposed coarse-grained model, we evaluate the influence of the more internal ethylene oxide chain on the properties of the water/Tween-80/decane system. We verify with the simulation results that the model can reproduce the expected decrease in interfacial tension as the surfactant quantity increases in the simulations. Furthermore, we observe changes in the surfactant orientation as their quantity in the interface increases, indicating a preferential orientation for these molecules in the adsorption layer. We also assessed the partition coefficient of the surfactant between the two bulk phases by performing free energy calculations, which showed a higher affinity of Tween-80 surfactants with the water phase.

Facilitating CG Simulations with MAD: The MArtini Database Server

The MArtini Database (MAD - https://mad.ibcp.fr) is a web server designed for the sharing of structures and topologies of molecules parametrized with the Martini coarse-grained (CG) force field. MAD can also convert atomistic structures into CG structures and prepare complex systems (including proteins, lipids, etc.) for molecular dynamics (MD) simulations at the CG level. It is dedicated to the generation of input files for Martini 3, the most recent version of this popular CG force field. Specifically, the MAD server currently includes tools to submit or retrieve CG models of a wide range of molecules (lipids, carbohydrates, nanoparticles, etc.), transform atomistic protein structures into CG structures and topologies, with fine control on the process and assemble biomolecules into large systems, and deliver all files necessary to start simulations in the GROMACS MD engine.

Differential interactions of resting, activated, and desensitized states of the α7 nicotinic acetylcholine receptor with lipidic modulators

The α7 nicotinic acetylcholine receptor is a pentameric ligand-gated ion channel that modulates neuronal excitability, largely by allowing Ca2+ permeation. Agonist binding promotes transition from a resting state to an activated state, and then rapidly to a desensitized state. Recently, cryogenic electron microscopy (cryo-EM) structures of the human α7 receptor in nanodiscs were reported in multiple conformations. These were selectively stabilized by inhibitory, activating, or potentiating compounds. However, the functional annotation of these structures and their differential interactions with unresolved lipids and ligands remain incomplete. Here, we characterized their ion permeation, membrane interactions, and ligand binding using computational electrophysiology, free-energy calculations, and coarse-grained molecular dynamics. In contrast to nonconductive structures in apparent resting and desensitized states, the structure determined in the presence of the potentiator PNU-120596 was consistent with an activated state permeable to Ca2+. Transition to this state was associated with compression and rearrangement of the membrane, particularly in the vicinity of the peripheral MX helix. An intersubunit transmembrane site was implicated in selective binding of either PNU-120596 in the activated state or cholesterol in the desensitized state. This substantiates functional assignment of all three lipid-embedded α7-receptor structures with ion-permeation simulations. It also proposes testable models of their state-dependent interactions with lipophilic ligands, including a mechanism for allosteric modulation at the transmembrane subunit interface.

Bottom-up Coarse-Graining: Principles and Perspectives

Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.

Shaping and Dilating the Fitness Landscape for Parameter Estimation in Stochastic Biochemical Models

The parameter estimation (PE) of biochemical reactions is one of the most challenging tasks in systems biology given the pivotal role of these kinetic constants in driving the behavior of biochemical systems. PE is a non-convex, multi-modal, and non-separable optimization problem with an unknown fitness landscape; moreover, the quantities of the biochemical species appearing in the system can be low, making biological noise a non-negligible phenomenon and mandating the use of stochastic simulation. Finally, the values of the kinetic parameters typically follow a log-uniform distribution; thus, the optimal solutions are situated in the lowest orders of magnitude of the search space. In this work, we further elaborate on a novel approach to address the PE problem based on a combination of adaptive swarm intelligence and dilation functions (DFs). DFs require prior knowledge of the characteristics of the fitness landscape; therefore, we leverage an alternative solution to evolve optimal DFs. On top of this approach, we introduce surrogate Fourier modeling to simplify the PE, by producing a smoother version of the fitness landscape that excludes the high frequency components of the fitness function. Our results show that the PE exploiting evolved DFs has a performance comparable with that of the PE run with a custom DF. Moreover, surrogate Fourier modeling allows for improving the convergence speed. Finally, we discuss some open problems related to the scalability of our methodology.

Modulating albumin-mediated transport of peptide-drug conjugates for antigen-specific Treg induction

The therapeutic potential of antigen-specific regulatory T cells (Treg) has been extensively explored, leading to the development of several tolerogenic vaccines. Dexamethasone-antigen conjugates represent a prominent class of tolerogenic vaccines that enable coordinated delivery of antigen and dexamethasone to target immune cells. The importance of nonspecific albumin association towards the biodistribution of antigen-adjuvant conjugates has gained increasing attention, by which hydrophobic and electrostatic interactions govern the association capacity. Using an ensemble of computational and experimental techniques, we evaluate the impact of charged residues adjacent to the drug conjugation site in dexamethasone-antigen conjugates (Dex-K/E4-OVA323, K: lysine, E: glutamate) towards their albumin association capacity and induction of antigen-specific Treg. We find that Dex-K4-OVA323 possesses a higher albumin association capacity than Dex-E4-OVA323, leading to enhanced liver distribution and antigen-presenting cell uptake. Furthermore, using an OVA323-specific adoptive-transfer mouse model, we show that Dex-K4-OVA323 selectively upregulated OVA323-specific Treg cells, whereas Dex-E4-OVA323 exerted no significant effect on Treg cells. Our findings serve as a guide to optimize the functionality of dexamethasone-antigen conjugate amid switching vaccine epitope sequences. Moreover, our study demonstrates that moderating the residues adjacent to the conjugation sites can serve as an engineering approach for future peptide-drug conjugate development.

Martini 3 coarse-grained force field for poly(para-phenylene ethynylene)s

Poly(para-phenylene ethynylene)s, or short PPEs, are a class of conjugated and semi-flexible polymers with a strongly delocalized π electron system and increased chain stiffness. Due to this, PPEs have a wide range of technological applications. Although the material properties of single-chains or mixtures of few PPE chains have been studied in detail, the properties of large assemblies remain to be fully explored. Here, we developed a coarse-grained model for PPEs with the Martini 3 force field to enable computational studies of PPEs in large-scale assembly. We used an optimization geometrical approach to take the shape of the π conjugated backbone into account and also applied an additional angular potential to tune the mechanical bending stiffness of the polymer. Our Martini 3 model reproduces key structural and thermodynamic observables of single PPE chains and mixtures, such as persistence length, density, packing and stacking. We show that chain entanglement increases with the expense of nematic ordering with growing PPE chain length. With the Martini 3 PPE model at hand, we are now able to cover large spatio-temporal scales and thereby to uncover key aspects for the structural organization of PPE bulk systems. The model is also predicted to be of high applicability to investigate out-of-equilibrium behavior of PPEs under mechanical force.

Porous covalent organic nanotubes and their assembly in loops and toroids

Carbon nanotubes, and synthetic organic nanotubes more generally, have in recent decades been widely explored for application in electronic devices, energy storage, catalysis and biosensors. Despite noteworthy progress made in the synthesis of nanotubular architectures with well-defined lengths and diameters, purely covalently bonded organic nanotubes have remained somewhat challenging to prepare. Here we report the synthesis of covalently bonded porous organic nanotubes (CONTs) by Schiff base reaction between a tetratopic amine-functionalized triptycene and a linear dialdehyde. The spatial orientation of the functional groups promotes the growth of the framework in one dimension, and the strong covalent bonds between carbon, nitrogen and oxygen impart the resulting CONTs with high thermal and chemical stability. Upon ultrasonication, the CONTs form intertwined structures that go on to coil and form toroidal superstructures. Computational studies give some insight into the effect of the solvent in this assembly process.

Automatic multi-objective optimization of coarse-grained lipid force fields using SwarmCG

The development of coarse-grained (CG) molecular models typically requires a time-consuming iterative tuning of parameters in order to have the approximated CG models behave correctly and consistently with, e.g., available higher-resolution simulation data and/or experimental observables. Automatic data-driven approaches are increasingly used to develop accurate models for molecular dynamics simulations. However, the parameters obtained via such automatic methods often make use of specifically designed interaction potentials and are typically poorly transferable to molecular systems or conditions other than those used for training them. Using a multi-objective approach in combination with an automatic optimization engine (SwarmCG), here, we show that it is possible to optimize CG models that are also transferable, obtaining optimized CG force fields (FFs). As a proof of concept, here, we use lipids for which we can avail reference experimental data (area per lipid and bilayer thickness) and reliable atomistic simulations to guide the optimization. Once the resolution of the CG models (mapping) is set as an input, SwarmCG optimizes the parameters of the CG lipid models iteratively and simultaneously against higher-resolution simulations (bottom-up) and experimental data (top-down references). Including different types of lipid bilayers in the training set in a parallel optimization guarantees the transferability of the optimized lipid FF parameters. We demonstrate that SwarmCG can reach satisfactory agreement with experimental data for different resolution CG FFs. We also obtain stimulating insights into the precision-resolution balance of the FFs. The approach is general and can be effectively used to develop new FFs and to improve the existing ones.

Toward Chemotactic Supramolecular Nanoparticles: From Autonomous Surface Motion Following Specific Chemical Gradients to Multivalency-Controlled Disassembly

Nature designs chemotactic supramolecular structures that can selectively bind specific groups present on surfaces, autonomously scan them moving along density gradients, and react once a critical concentration is encountered. Since such properties are key in many biological functions, these also offer inspirations for designing artificial systems capable of similar bioinspired autonomous behaviors. One approach is to use soft molecular units that self-assemble in an aqueous solution generating nanoparticles (NPs) that display specific chemical groups on their surface, enabling multivalent interactions with complementarily functionalized surfaces. However, a first challenge is to explore the behavior of these assemblies at sufficiently high-resolution to gain insights on the molecular factors controlling their behaviors. Here, by coupling coarse-grained molecular models and advanced simulation approaches, we show that it is possible to study the (autonomous or driven) motion of self-assembled NPs on a receptor-grafted surface at submolecular resolution. As an example, we focus on self-assembled NPs composed of facially amphiphilic oligomers. We observe how tuning the multivalent interactions between the NP and the surface allows to control of the NP binding, its diffusion along chemical surface gradients, and ultimately, the NP reactivity at determined surface group densities. In silico experiments provide physical-chemical insights on key molecular features in the self-assembling units which determine the dynamic behavior and fate of the NPs on the surface: from adhesion, to diffusion, and disassembly. This offers a privileged point of view into the chemotactic properties of supramolecular assemblies, improving our knowledge on how to design new types of materials with bioinspired autonomous behaviors.

Development of Martini 2.2 parameters for N-glycans: a case study of the HIV-1 Env glycoprotein dynamics

N-linked glycans are ubiquitous in nature and play key roles in biology. For example, glycosylation of pathogenic proteins is a common immune evasive mechanism, hampering the development of successful vaccines. Due to their chemical variability and complex dynamics, an accurate molecular understanding of glycans is still limited by the lack of effective resolution of current experimental approaches. Here, we have developed and implemented a reductive model based on the popular Martini 2.2 coarse-grained force field for the computational study of N-glycosylation. We used the HIV-1 Env as a direct applied example of a highly glycosylated protein. Our results indicate that the model not only reproduces many observables in very good agreement with a fully atomistic force field but also can be extended to study large amount of glycosylation variants, a fundamental property that can aid in the development of drugs and vaccines.

Understanding functional group and assembly dynamics in temperature responsive systems leads to design principles for enzyme responsive assemblies

Understanding the molecular rules behind the dynamics of supramolecular assemblies is fundamentally important for the rational design of responsive assemblies with tunable properties. Herein, we report that the dynamics of temperature-sensitive supramolecular assemblies is not only affected by the dehydration of oligoethylene glycol (OEG) motifs, but also by the thermally-promoted molecular motions. These counteracting features set up a dynamics transition point (DTP) that can be modulated with subtle variations in a small hydrophobic patch on the hydrophilic face of the amphiphilic assembly. Understanding the structural factors that control the dynamics of the assemblies leads to rational design of enzyme-responsive assemblies with tunable temperature responsive profiles.

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.

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.