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.

An implementation of the Martini coarse-grained force field in OpenMM.

We describe a complete implementation of Martini 2 and Martini 3 in the OpenMM molecular dynamics software package. Martini is a widely used coarse-grained force field with applications in biomolecular simulation, materials, and broader areas of chemistry. It is implemented as a force field but makes extensive use of facilities unique to the GROMACS software, including virtual sites and bonded terms that are not commonly used in standard atomistic force fields. OpenMM is a flexible molecular dynamics package widely used for methods development and is competitive in speed on GPUs with other commonly used packages. OpenMM has facilities to easily implement new force field terms, external forces and fields, and other nonstandard features, which we use to implement all force field terms used in Martini 2 and Martini 3. This allows Martini simulations, starting with GROMACS topology files that are processed by custom scripts, with all the added flexibility of OpenMM. We provide a GitHub repository with test cases, compare accuracy and performance between GROMACS and OpenMM, and discuss the limitations of our implementation in terms of direct comparison with GROMACS. We describe a use case that implements the Modeling Employing Limited Data method to apply experimental constraints in a Martini simulation to efficiently determine the structure of a protein complex. We also discuss issues and a potential solution with the Martini 2 topology for cholesterol.

Multiscale simulations reveal TDP-43 molecular-level interactions driving condensation.

The RNA-binding protein TDP-43 is associated with mRNA processing and transport from the nucleus to the cytoplasm. TDP-43 localizes in the nucleus as well as accumulating in cytoplasmic condensates such as stress granules. Aggregation and formation of amyloid-like fibrils of cytoplasmic TDP-43 are hallmarks of numerous neurodegenerative diseases, most strikingly present in >90% of amyotrophic lateral sclerosis (ALS) patients. If excessive accumulation of cytoplasmic TDP-43 causes, or is caused by, neurodegeneration is presently not known. In this work, we use molecular dynamics simulations at multiple resolutions to explore TDP-43 self- and cross-interaction dynamics. A full-length molecular model of TDP-43, all 414 amino acids, was constructed from select structures of the protein functional domains (N-terminal domain, and two RNA recognition motifs, RRM1 and RRM2) and modeling of disordered connecting loops and the low complexity glycine-rich C-terminus domain. All-atom CHARMM36m simulations of single TDP-43 proteins served as guides to construct a coarse-grained Martini 3 model of TDP-43. The Martini model and a coarser implicit solvent C⍺ model, optimized for disordered proteins, were subsequently used to probe TDP-43 interactions; self-interactions from single-chain full-length TDP-43 simulations, cross-interactions from simulations with two proteins and simulations with assemblies of dozens to hundreds of proteins. Our findings illustrate the utility of different modeling scales for accessing TDP-43 molecular-level interactions and suggest that TDP-43 has numerous interaction preferences or patterns, exhibiting an overall strong, but dynamic, association and driving the formation of biomolecular condensates.

Rational Prediction of PROTAC-Compatible Protein-Protein Interfaces by Molecular Docking.

Proteolysis targeting chimeras (PROTACs) are heterobifunctional ligands that mediate the interaction between a protein target and an E3 ligase, resulting in a ternary complex, whose interaction with the ubiquitination machinery leads to target degradation. This technology is emerging as an exciting new avenue for therapeutic development, with several PROTACs currently undergoing clinical trials targeting cancer. Here, we describe a general and computationally efficient methodology combining restraint-based docking, energy-based rescoring, and a filter based on the minimal solvent-accessible surface distance to produce PROTAC-compatible PPIs suitable for when there is no a priori known PROTAC ligand. In a benchmark employing a manually curated data set of 13 ternary complex crystals, we achieved an accuracy of 92% when starting from bound structures and 77% when starting from unbound structures, respectively. Our method only requires that the ligand-bound structures of the monomeric forms of the E3 ligase and target proteins be given to run, making it general, accurate, and highly efficient, with the ability to impact early-stage PROTAC-based drug design campaigns where no structural information about the ternary complex structure is available.

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.

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.

Computational Modeling of Realistic Cell Membranes.

Cell membranes contain a large variety of lipid types and are crowded with proteins, endowing them with the plasticity needed to fulfill their key roles in cell functioning. The compositional complexity of cellular membranes gives rise to a heterogeneous lateral organization, which is still poorly understood. Computational models, in particular molecular dynamics simulations and related techniques, have provided important insight into the organizational principles of cell membranes over the past decades. Now, we are witnessing a transition from simulations of simpler membrane models to multicomponent systems, culminating in realistic models of an increasing variety of cell types and organelles. Here, we review the state of the art in the field of realistic membrane simulations and discuss the current limitations and challenges ahead.

Titratable Martini model for constant pH simulations.

In this work, we deliver a proof of concept for a fast method that introduces pH effects into classical coarse-grained (CG) molecular dynamics simulations. Our approach is based upon the latest version of the popular Martini CG model to which explicit proton mimicking particles are added. We verify our approach against experimental data involving several different molecules and different environmental conditions. In particular, we compute titration curves, pH dependent free energies of transfer, and lipid bilayer membrane affinities as a function of pH. Using oleic acid as an example compound, we further illustrate that our method can be used to study passive translocation in lipid bilayers via protonation. Finally, our model reproduces qualitatively the expansion of the macromolecule dendrimer poly(propylene imine) as well as the associated pKa shift of its different generations. This example demonstrates that our model is able to pick up collective interactions between titratable sites in large molecules comprising many titratable functional groups.

High performance collision cross section calculation-HPCCS.

Since the commercial introduction of Ion Mobility coupled with Mass Spectrometry (IM-MS) devices in 2003, a large number of research laboratories have embraced the technique. IM-MS is a fairly rapid experiment used as a molecular separation tool and to obtain structural information. The interpretation of IM-MS data is still challenging and relies heavily on theoretical calculations of the molecule's collision cross section (CCS) against a buffer gas. Here, a new software (HPCCS) is presented, which performs CCS calculations using high perfomance computing techniques. Based on the trajectory method, HPCCS can accurately calculate CCS for a great variety of molecules, ranging from small organic molecules to large protein complexes, using helium or nitrogen as buffer gas with considerable gains in computer time compared to publicly available codes under the same level of theory. HPCCS is available as free software under the Academic Use License at https://github.com/cepid-cces/hpccs. © 2018 Wiley Periodicals, Inc.

CHARMM Force Field Parameterization of Peroxisome Proliferator-Activated Receptor γ Ligands.

The peroxisome proliferator-activated receptor γ (PPARγ) ligands are important therapeutic drugs for the treatment of type 2 diabetes, obesity and cardiovascular diseases. In particular, partial agonists and non-agonists are interesting targets to reduce glucose levels, presenting few side effects in comparison to full agonists. In this work, we present a set of CHARMM-based parameters of a molecular mechanics force field for two PPARγ ligands, GQ16 and SR1664. GQ16 belongs to the thiazolidinedione class of drugs and it is a PPARγ partial agonist that has been shown to promote the "browning" of white adipose tissue. SR1664 is the precursor of the PPARγ non-agonist class of ligands that activates PPARγ in a non-classical manner. Here, we use quantum chemical calculations consistent with the CHARMM protocol to obtain bonded and non-bonded parameters, including partial atomic charges and effective torsion potentials for both molecules. The newly parameterized models were evaluated by examining the behavior of GQ16 and SR1664 free in water and bound to the ligand binding pocket of PPARγ using molecular dynamics simulations. The potential parameters derived here are readily transferable to a variety of pharmaceutical compounds and similar PPARγ ligands.

Assessing the Martini 3 protein model: A review of its path and potential.

Coarse-grained (CG) protein models have become indispensable tools for studying many biological protein details, from conformational dynamics to the organization of protein macro-complexes, and even the interaction of proteins with other molecules. The Martini force field is one of the most widely used CG models for bio-molecular simulations, partly because of the enormous success of its protein model. With the recent release of a new and improved version of the Martini force field - Martini 3 - a new iteration of its protein model was also made available. The Martini 3 protein force field is an evolution of its Martini 2 counterpart, aimed at improving many of the shortcomings that had been previously identified. In this mini-review, we first provide a general overview of the model and then focus on the successful advances made in the short time since its release, many of which would not have been possible before. Furthermore, we discuss reported limitations, potential directions for model improvement and comment on what the likely future development and application avenues are.

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.

OLIVES: A GoÌ -like Model for Stabilizing Protein Structure via Hydrogen Bonding Native Contacts in the Martini 3 Coarse-Grained Force Field.

Coarse-grained molecular dynamics simulations enable the modeling of increasingly complex systems at millisecond timescales. The transferable coarse-grained force field Martini 3 has shown great promise in modeling a wide range of biochemical processes, yet folded proteins in Martini 3 are not stable without the application of external bias potentials, such as elastic networks or Go̅-like models. We herein develop an algorithm, called OLIVES, which identifies native contacts with hydrogen bond capabilities in coarse-grained proteins and use it to implement a novel Go̅-like model for Martini 3. We show that the protein structure instability originates in part from the lack of hydrogen bond energy in the coarse-grained force field representation. By using realistic hydrogen bond energies obtained from literature ab initio calculations, it is demonstrated that protein stability can be recovered by the reintroduction of a coarse-grained hydrogen bond network and that OLIVES removes the need for secondary structure restraints. OLIVES is validated against known protein complexes and at the same time addresses the open question of whether there is a need for protein quaternary structure bias in Martini 3 simulations. It is shown that OLIVES can reduce the number of bias terms, hereby speeding up Martini 3 simulations of proteins by up to ≈30% on a GPU architecture compared to the established Go̅MARTINI Go̅-like model.

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.

GQ-16, a novel peroxisome proliferator-activated receptor γ (PPARγ) ligand, promotes insulin sensitization without weight gain.

The recent discovery that peroxisome proliferator-activated receptor γ (PPARγ) targeted anti-diabetic drugs function by inhibiting Cdk5-mediated phosphorylation of the receptor has provided a new viewpoint to evaluate and perhaps develop improved insulin-sensitizing agents. Herein we report the development of a novel thiazolidinedione that retains similar anti-diabetic efficacy as rosiglitazone in mice yet does not elicit weight gain or edema, common side effects associated with full PPARγ activation. Further characterization of this compound shows GQ-16 to be an effective inhibitor of Cdk5-mediated phosphorylation of PPARγ. The structure of GQ-16 bound to PPARγ demonstrates that the compound utilizes a binding mode distinct from other reported PPARγ ligands, although it does share some structural features with other partial agonists, such as MRL-24 and PA-082, that have similarly been reported to dissociate insulin sensitization from weight gain. Hydrogen/deuterium exchange studies reveal that GQ-16 strongly stabilizes the ß-sheet region of the receptor, presumably explaining the compound's efficacy in inhibiting Cdk5-mediated phosphorylation of Ser-273. Molecular dynamics simulations suggest that the partial agonist activity of GQ-16 results from the compound's weak ability to stabilize helix 12 in its active conformation. Our results suggest that the emerging model, whereby "ideal" PPARγ-based therapeutics stabilize the ß-sheet/Ser-273 region and inhibit Cdk5-mediated phosphorylation while minimally invoking adipogenesis and classical agonism, is indeed a valid framework to develop improved PPARγ modulators that retain antidiabetic actions while minimizing untoward effects.

Transmembrane dimers of type 1 receptors sample alternate configurations: MD simulations using coarse grain Martini 3 versus AlphaFold2 Multimer.

Structures and dynamics of transmembrane (TM) receptor regions are key to understanding their signaling mechanism across membranes. Here we examine configurations of TM region dimers, assembled using the recent Martini 3 force field for coarse-grain (CG) molecular dynamics simulations. At first glance, our results show only a reasonable agreement with ab initio predictions using PREDDIMER and AlphaFold2 Multimer and with nuclear magnetic resonance (NMR)-derived structures. 5 of 11 CG TM structures are similar to the NMR structures (within <3.5 Å root-mean-square deviation [RMSD]) compared with 10 and 9 using PREDDIMER and AlphaFold2, respectively (with 8 structures of the later within 1.5 Å). Surprisingly, AlphaFold2 predictions are closer to NMR structures when the 2001 instead of 2020 database is used for training. The CG simulations reveal that alternative configurations of TM dimers readily interconvert with a predominant population. The implications for transmembrane signaling are discussed, including for the development of peptide-based pharmaceuticals.

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.

Martini 3 Force Field Parameters for Protein Lipidation Post-Translational Modifications.

Protein lipidations are vital co/post-translational modifications that tether lipid tails to specific protein amino acids, allowing them to anchor to biological membranes, switch their subcellular localization, and modulate association with other proteins. Such lipidations are thus crucial for multiple biological processes including signal transduction, protein trafficking, and membrane localization and are implicated in various diseases as well. Examples of lipid-anchored proteins include the Ras family of proteins that undergo farnesylation; actin and gelsolin that are myristoylated; phospholipase D that is palmitoylated; glycosylphosphatidylinositol-anchored proteins; and others. Here, we develop parameters for cysteine-targeting farnesylation, geranylgeranylation, and palmitoylation, as well as glycine-targeting myristoylation for the latest version of the Martini 3 coarse-grained force field. The parameters are developed using the CHARMM36m all-atom force field parameters as reference. The behavior of the coarse-grained models is consistent with that of the all-atom force field for all lipidations and reproduces key dynamical and structural features of lipid-anchored peptides, such as the solvent-accessible surface area, bilayer penetration depth, and representative conformations of the anchors. The parameters are also validated in simulations of the lipid-anchored peripheral membrane proteins Rheb and Arf1, after comparison with independent all-atom simulations. The parameters, along with mapping schemes for the popular martinize2 tool, are available for download at 10.5281/zenodo.7849262 and also as supporting information.

Expulsion mechanism of the substrate-translocating subunit in ECF transporters.

Energy-coupling factor (ECF)-type transporters mediate the uptake of micronutrients in many bacteria. They consist of a substrate-translocating subunit (S-component) and an ATP-hydrolysing motor (ECF module) Previous data indicate that the S-component topples within the membrane to alternately expose the binding site to either side of the membrane. In many ECF transporters, the substrate-free S-component can be expelled from the ECF module. Here we study this enigmatic expulsion step by cryogenic electron microscopy and reveal that ATP induces a concave-to-convex shape change of two long helices in the motor, thereby destroying the S-component's docking site and allowing for its dissociation. We show that adaptation of the membrane morphology to the conformational state of the motor may favour expulsion of the substrate-free S-component when ATP is bound and docking of the substrate-loaded S-component after hydrolysis. Our work provides a picture of bilayer-assisted chemo-mechanical coupling in the transport cycle of ECF transporters.

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.