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

Simulations of Functional Motions of Super Large Biomolecules with a Mixed-Resolution Model

Many large protein machines function through an interplay between large-scale movements and intricate conformational changes. Understanding functional motions of these proteins through simulations becomes challenging for both all-atom and coarse-grained (CG) modeling techniques because neither approach alone can readily capture the full details of these motions. In this study, we develop a multiscale model by employing the popular MARTINI CG model to represent a heterogeneous environment and structurally stable proteins and using the united-atom (UA) model PACE to describe proteins undergoing subtle conformational changes. PACE was previously developed to be compatible with the MARTINI solvent and membrane. Here, we couple the protein descriptions of the two models by directly mixing UA and CG interaction parameters to greatly simplify parameter determination. Through extensive validations with diverse protein systems in solution or membrane, we demonstrate that only additional parameter rescaling is needed to enable the resulting model to recover the stability of native structures of proteins under mixed representation. Moreover, we identify the optimal scaling factors that can be applied to various protein systems, rendering the model potentially transferable. To further demonstrate its applicability for realistic systems, we apply the model to a mechanosensitive ion channel Piezo1 that has peripheral arms for sensing membrane tension and a central pore for ion conductance. The model can reproduce the coupling between Piezo1's large-scale arm movement and subtle pore opening in response to membrane stress while consuming much less computational costs than all-atom models. Therefore, our model shows promise for studying functional motions of large protein machines.

Molecular Dynamics Study of Protein Aggregation at Moving Interfaces

Repeated compression and dilation of a protein film adsorbed to an interface lead to aggregation and entry of film fragments into the bulk. This is a major mechanism for protein aggregate formation in drug products upon mechanical stress, such as shaking or pumping. To gain a better understanding of these events, we developed a molecular dynamics (MD) setup, which would, in a later stage, allow for in silico formulation optimization. In contrast to previous approaches, the molecules of our model protein human growth hormone displayed realistic shapes, surfaces, and interactions with each other and the interface. This enabled quantitative assessment of protein cluster formation. Simulation outcomes aligned with experimental data on subvisible particles and turbidity, thereby validating the model. Computational and experimental results indicated that compression speed does not affect the aggregation behavior of preformed protein films but rather their regeneration. Protein clusters that formed during compression disassembled upon relaxation, suggesting that the particles originate from a partly compressed state. Desorption studies via steered MD revealed that proteins from compressed systems are more likely to detach as clusters, implying that compression effects at the interface translate into aggregates present in the bulk solution. With the possibility of studying the impact of different variables upon compression and dilation at the interface on a molecular level, our model contributes to the understanding of the mechanisms of protein aggregation at moving interfaces. It also enables further studies to change formulation parameters, interfaces, or proteins.

Discovery of lipid binding sites in a ligand-gated ion channel by integrating simulations and cryo-EM

Ligand-gated ion channels transduce electrochemical signals in neurons and other excitable cells. Aside from canonical ligands, phospholipids are thought to bind specifically to the transmembrane domain of several ion channels. However, structural details of such lipid contacts remain elusive, partly due to limited resolution of these regions in experimental structures. Here, we discovered multiple lipid interactions in the channel GLIC by integrating cryo-electron microscopy and large-scale molecular simulations. We identified 25 bound lipids in the GLIC closed state, a conformation where none, to our knowledge, were previously known. Three lipids were associated with each subunit in the inner leaflet, including a buried interaction disrupted in mutant simulations. In the outer leaflet, two intrasubunit sites were evident in both closed and open states, while a putative intersubunit site was preferred in open-state simulations. This work offers molecular details of GLIC-lipid contacts particularly in the ill-characterized closed state, testable hypotheses for state-dependent binding, and a multidisciplinary strategy for modeling protein-lipid interactions.

A Perspective on the Prospective Use of AI in Protein Structure Prediction

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

Assessing the interaction between hemoglobin and the receptor binding domain of SARS-CoV-2 spike protein through MARTINI coarse-grained molecular dynamics

The emergence of different coronavirus-related diseases in the 2000's (SARS, MERS, and Covid-19) warrants the need of a complete understanding of the pathological, biological, and biochemical behavior of this class of pathogens. Great attention has been paid to the SARS-CoV-2 Spike protein, and its interaction with the human ACE2 has been thoroughly investigated. Recent findings suggested that the SARS-CoV-2 components may interact with different human proteins, and hemoglobin has very recently been demonstrated as a potential target for the Spike protein. Here we have investigated the interaction between either adult or fetal hemoglobin and the receptor binding domain of the Spike protein at molecular level through advanced molecular dynamics techniques and proposed rational binding modes and energy estimations. Our results agree with biochemical data previously reported in literature. We also demonstrated that co-incubation of pulmonary epithelial cells with hemoglobin strongly reduces the pro-inflammatory effects exerted by the concomitant administration of Spike protein.

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.

Improved Protein Model in SPICA Force Field

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

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.

Structural basis of dimerization of chemokine receptors CCR5 and CXCR4

G protein-coupled receptors (GPCRs) are prominent drug targets responsible for extracellular-to-intracellular signal transduction. GPCRs can form functional dimers that have been poorly characterized so far. Here, we show the dimerization mechanism of the chemokine receptors CCR5 and CXCR4 by means of an advanced free-energy technique named coarse-grained metadynamics. Our results reproduce binding events between the GPCRs occurring in the minute timescale, revealing a symmetric and an asymmetric dimeric structure for each of the three investigated systems, CCR5/CCR5, CXCR4/CXCR4, and CCR5/CXCR4. The transmembrane helices TM4-TM5 and TM6-TM7 are the preferred binding interfaces for CCR5 and CXCR4, respectively. The identified dimeric states differ in the access to the binding sites of the ligand and G protein, indicating that dimerization may represent a fine allosteric mechanism to regulate receptor activity. Our study offers structural basis for the design of ligands able to modulate the formation of CCR5 and CXCR4 dimers and in turn their activity, with therapeutic potential against HIV, cancer, and immune-inflammatory diseases.

Effects of Sulforaphane on SARS‑CoV‑2 infection and NF‑κB dependent expression of genes involved in the COVID‑19 'cytokine storm'

Since its spread at the beginning of 2020, the coronavirus disease 2019 (COVID‑19) pandemic represents one of the major health problems. Despite the approval, testing, and worldwide distribution of anti‑severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) vaccines, the development of specific antiviral agents targeting the SARS‑CoV‑2 life cycle with high efficiency, and/or interfering with the associated 'cytokine storm', is highly required. A recent study, conducted by the authors' group indicated that sulforaphane (SFN) inhibits the expression of IL‑6 and IL‑8 genes induced by the treatment of IB3‑1 bronchial cells with a recombinant spike protein of SARS‑CoV‑2. In the present study, the ability of SFN to inhibit SARS‑CoV‑2 replication and the expression of pro‑inflammatory genes encoding proteins of the COVID‑19 'cytokine storm' was evaluated. SARS‑CoV‑2 replication was assessed in bronchial epithelial Calu‑3 cells. Moreover, SARS‑CoV‑2 replication and expression of pro‑inflammatory genes was evaluated by reverse transcription quantitative droplet digital PCR. The effects on the expression levels of NF‑κB were assessed by western blotting. Molecular dynamics simulations of NF‑kB/SFN interactions were conducted with Gromacs 2021.1 software under the Martini 2 CG force field. Computational studies indicated that i) SFN was stably bound with the NF‑κB monomer; ii) a ternary NF‑kB/SFN/DNA complex was formed; iii) the SFN interacted with both the protein and the nucleic acid molecules modifying the binding mode of the latter, and impairing the full interaction between the NF‑κB protein and the DNA molecule. This finally stabilized the inactive complex. Molecular studies demonstrated that SFN i) inhibits the SARS‑CoV‑2 replication in infected Calu‑3 cells, decreasing the production of the N‑protein coding RNA sequences, ii) decreased NF‑κB content in SARS‑CoV‑2 infected cells and inhibited the expression of NF‑kB‑dependent IL‑1β and IL‑8 gene expression. The data obtained in the present study demonstrated inhibitory effects of SFN on the SARS‑CoV‑2 life cycle and on the expression levels of the pro‑inflammatory genes, sustaining the possible use of SFN in the management of patients with COVID‑19.

Predicting Biomolecular Binding Kinetics: A Review

Biomolecular binding kinetics including the association (k_on) and dissociation (k_off) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.

Scaling Protein-Water Interactions in the Martini 3 Coarse-Grained Force Field to Simulate Transmembrane Helix Dimers in Different Lipid Environments

Martini 3, the latest version of the widely used Martini force field for coarse-grained molecular dynamics simulations, is a promising tool to investigate proteins in phospholipid bilayers. However, simulating other lipid environments, such as detergent micelles, presents challenges due to the absence of validated parameters for their constituent molecules. Here, we propose parameters for the micelle-forming surfactant, dodecylphosphocholine (DPC). These result in micelle assembly with aggregation numbers in agreement with the experimental values. However, we identified a lack of hydrophobic interactions between transmembrane helix protein dimers and the tails of DPC molecules, preventing insertion and stabilization of the protein in the micelles. This problem was also observed for protein insertion by self-assembling 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) or dipalmitoylphosphatidylcholine (DPPC) bilayers. We propose the reduction of the nonbonded interactions between protein and water beads by 10% as a simple and effective solution to this problem that enables protein encapsulation in phospholipid micelles and bilayers without altering protein dimerization or the bilayer structure.

Molecular architecture and dynamics of SARS-CoV-2 envelope by integrative modeling

Despite tremendous efforts, the exact structure of SARS-CoV-2 and related betacoronaviruses remains elusive. SARS-CoV-2 envelope is a key structural component of the virion that encapsulates viral RNA. It is composed of three structural proteins, spike, membrane (M), and envelope, which interact with each other and with the lipids acquired from the host membranes. Here, we developed and applied an integrative multi-scale computational approach to model the envelope structure of SARS-CoV-2 with near atomistic detail, focusing on studying the dynamic nature and molecular interactions of its most abundant, but largely understudied, M protein. The molecular dynamics simulations allowed us to test the envelope stability under different configurations and revealed that the M dimers agglomerated into large, filament-like, macromolecular assemblies with distinct molecular patterns. These results are in good agreement with current experimental data, demonstrating a generic and versatile approach to model the structure of a virus de novo.

pH-Dependent Conformational Switch Impacts Stability of the PsbS Dimer

The photosystem II PsbS protein triggers the photoprotective mechanism of plants by sensing the acidification of the thylakoid lumen. Despite the mechanism of action of PsbS would require a pH-dependent monomerization of the dimeric form, a clear connection between the pH-induced structural changes and the dimer stability is missing. Here, by applying constant pH coarse-grained and all-atom molecular dynamics simulations, we investigate the pH-dependent structural response of the PsbS dimer. We find that the pH variation leads to structural changes in the lumen-exposed helices, located at the dimeric interface, providing an effective switch between PsbS inactive and active form. Moreover, the monomerization free energies reveal that in the neutral pH conformation, where the network of H-bond interactions at the dimeric interface is destroyed, the protein-protein interaction is weaker. Our results show how the pH-dependent conformations of PsbS affect their dimerization propensity, which is at the basis of the photoprotective mechanism.

The impact of bilayer composition on the dimerization properties of the Slg1 stress sensor TMD from a multiscale analysis

The impact of mutual interactions between the transmembrane domains of membrane proteins and lipids on bilayer properties has gained major attraction. Most simulation studies of membranes rely on the Martini force field, which has proven extremely helpful in providing molecular insights into realistic systems. Accordingly, an evaluation of the accuracy of the Martini force field is crucial to be able to correctly interpret the reported data. In this study, we combine atomistic and coarse-grained Martini simulations to investigate the properties of transmembrane domains (TMDs) in a model yeast membrane. The results show that the TMD binding state (monomeric and dimeric with positive or negative crossing angle) and the membrane composition significantly influence the properties around the TMDs and change TMD-TMD and TMD-lipid affinities. Furthermore, ergosterol (ERG) exhibits a strong affinity to TMD dimers. Importantly, the right-handed TMD dimer configuration is stabilized via TMD-TMD contacts by the addition of asymmetric anionic phosphatidylserine (PS). The coarse-grained simulations corroborate many of these findings, with two notable exceptions: a systematic overestimation of TMD-ERG interaction and lack of stabilization of the right-handed TMD dimers with the addition of PS.

Molecular modeling of lactoferrin for food and nutraceutical applications: insights from in silico techniques

Lactoferrin is a protein, primarily found in milk that has attracted the interest of the food industries due to its health properties. Nevertheless, the instability of lactoferrin has limited its commercial application. Recent studies have focused on encapsulation to enhance the stability of lactoferrin. However, the molecular insights underlying the changes of structural properties of lactoferrin and the interaction with protectants remain poorly understood. Computational approaches have proven useful in understanding the structural properties of molecules and the key binding with other constituents. In this review, comprehensive information on the structure and function of lactoferrin and the binding with various molecules for food purposes are reviewed, with a special emphasis on the use of molecular dynamics simulations. The results demonstrate the application of modeling and simulations to determine key residues of lactoferrin responsible for its stability and interactions with other biomolecular components under various conditions, which are also associated with its functional benefits. These have also been extended into the potential creation of enhanced lactoferrin for commercial purposes. This review provides valuable strategies in designing novel nutraceuticals for food science practitioners and those who have interests in acquiring familiarity with the application of computational modeling for food and health purposes.

Lipid-Mediated Association of the Slg1 Transmembrane Domains in Yeast Plasma Membranes

Clustering of transmembrane proteins underlies a multitude of fundamental biological processes at the plasma membrane (PM) such as receptor activation, lateral domain formation, and mechanotransduction. The self-association of the respective transmembrane domains (TMDs) has also been suggested to be responsible for the micron-scaled patterns seen for integral membrane proteins in the budding yeast PM. However, the underlying interplay between the local lipid composition and the TMD identity is still not mechanistically understood. In this work, we combined coarse-grained molecular dynamics simulations of simplified bilayer systems with high-resolution live-cell microscopy to analyze the distribution of a representative helical yeast TMD from the PM sensor Slg1 within different lipid environments. In our simulations, we specifically evaluated the effects of acyl chain saturation and anionic lipid head groups on the association of two TMDs. We found that weak lipid-protein interactions significantly affect the configuration of TMD dimers and the free energy of association. Increased amounts of unsaturated phospholipids (PLs) strongly reduced the helix-helix interaction, while the presence of anionic phosphatidylserine (PS) hardly affected the dimer formation. We could experimentally confirm this surprising lack of effect of PS using the network factor, a mesoscopic measure of PM pattern formation in yeast cells. Simulations also showed that the formation of TMD dimers in turn increased the order parameter of the surrounding lipids and induced long-range perturbations in lipid organization. In summary, our results shed new light on the mechanisms of lipid-mediated dimerization of TMDs in complex lipid mixtures.

Multiscale molecular modelling: from electronic structure to dynamics of nanosystems and beyond

Important contemporary biological and materials problems often depend on interactions that span orders of magnitude differences in spatial and temporal dimensions. This Tutorial Review attempts to provide an introduction to such fascinating problems through a series of case studies, aimed at beginning researchers, graduate students, postdocs and more senior colleagues who are changing direction to focus on multiscale aspects of their research. The choice of specific examples is highly personal, with examples either chosen from our own work or outstanding multiscale efforts from the literature. I start with various embedding schemes, as exemplified by polarizable continuum models, 3-D RISM, molecular DFT and frozen-density embedding. Next, QM/MM (quantum mechanical/molecular mechanical) techniques are the workhorse of pm-to-nm/ps-to-ns simulations; examples are drawn from enzymes and from nanocatalysis for oil-sands upgrading. Using polarizable force-fields in the QM/MM framework represents a burgeoning subfield; with examples from ion channels and electron dynamics in molecules subject to strong external fields, probing the atto-second dynamics of the electrons with RT-TDDFT (real-time - time-dependent density functional theory) eventually coupled with nuclear motion through the Ehrenfest approximation. This is followed by a section on coarse graining, bridging dimensions from atoms to cells. The penultimate chapter gives a quick overview of multiscale approaches that extend into the meso- and macro-scales, building on atomistic and coarse-grained techniques to enter the world of materials engineering, on the one hand, and cell biology, on the other. A final chapter gives just a glimpse of the burgeoning impact of machine learning on the structure-dynamics front. I aim to capture the excitement of contemporary leading-edge breakthroughs in the description of physico-chemical systems and processes in complex environments, with only enough historical content to provide context and aid the next generation of methodological development. While I aim also for a clear description of the essence of methodological breakthroughs, equations are kept to a minimum and detailed formalism and implementation details are left to the references. My approach is very selective (case studies) rather than exhaustive. I think that these case studies should provide fodder to build as complete a reference tree on multiscale modelling as the reader may wish, through forward and backward citation analysis. I hope that my choices of cases will excite interest in newcomers and help to fuel the growth of multiscale modelling in general.

Improving Martini 3 for Disordered and Multidomain Proteins

Coarse-grained molecular dynamics simulations are a useful tool to determine conformational ensembles of proteins. Here, we show that the coarse-grained force field Martini 3 underestimates the global dimensions of intrinsically disordered proteins (IDPs) and multidomain proteins when compared with small-angle X-ray scattering (SAXS) data and that increasing the strength of protein-water interactions favors more expanded conformations. We find that increasing the strength of interactions between protein and water by ca. 10% results in improved agreement with the SAXS data for IDPs and multidomain proteins. We also show that this correction results in a more accurate description of self-association of IDPs and folded proteins and better agreement with paramagnetic relaxation enhancement data for most IDPs. While simulations with this revised force field still show deviations to experiments for some systems, our results suggest that it is overall a substantial improvement for coarse-grained simulations of soluble proteins.

Protein-Protein Interaction-Gaussian Accelerated Molecular Dynamics (PPI-GaMD): Characterization of Protein Binding Thermodynamics and Kinetics

Protein-protein interactions (PPIs) play key roles in many fundamental biological processes such as cellular signaling and immune responses. However, it has proven challenging to simulate repetitive protein association and dissociation in order to calculate binding free energies and kinetics of PPIs due to long biological timescales and complex protein dynamics. To address this challenge, we have developed a new computational approach to all-atom simulations of PPIs based on a robust Gaussian accelerated molecular dynamics (GaMD) technique. The method, termed "PPI-GaMD", selectively boosts interaction potential energy between protein partners to facilitate their slow dissociation. Meanwhile, another boost potential is applied to the remaining potential energy of the entire system to effectively model the protein's flexibility and rebinding. PPI-GaMD has been demonstrated on a model system of the ribonuclease barnase interactions with its inhibitor barstar. Six independent 2 μs PPI-GaMD simulations have captured repetitive barstar dissociation and rebinding events, which enable calculations of the protein binding thermodynamics and kinetics simultaneously. The calculated binding free energies and kinetic rate constants agree well with the experimental data. Furthermore, PPI-GaMD simulations have provided mechanistic insights into barstar binding to barnase, which involves long-range electrostatic interactions and multiple binding pathways, being consistent with previous experimental and computational findings of this model system. In summary, PPI-GaMD provides a highly efficient and easy-to-use approach for binding free energy and kinetics calculations of PPIs.

Challenges and frontiers of computational modelling of biomolecular recognition

Biomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterise biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future.

Multiscale simulations of protein and membrane systems

Classical multiscale simulations are perfectly suited to investigate biological soft matter systems. Owing to the bridging between microscopically realistic and lower-resolution models or the integration of a hierarchy of subsystems, one gets access to biologically relevant system sizes and timescales. In recent years, increasingly complex systems and processes have come into focus such as multidomain proteins, phase separation processes in biopolymer solutions, multicomponent biomembranes, or multiprotein complexes up to entire viruses. The review shows factors that have contributed to this progress - from improved models to machine-learning-based analysis and scale-bridging methods.

Theory and Practice of Coarse-Grained Molecular Dynamics of Biologically Important Systems

Molecular dynamics with coarse-grained models is nowadays extensively used to simulate biomolecular systems at large time and size scales, compared to those accessible to all-atom molecular dynamics. In this review article, we describe the physical basis of coarse-grained molecular dynamics, the coarse-grained force fields, the equations of motion and the respective numerical integration algorithms, and selected practical applications of coarse-grained molecular dynamics. We demonstrate that the motion of coarse-grained sites is governed by the potential of mean force and the friction and stochastic forces, resulting from integrating out the secondary degrees of freedom. Consequently, Langevin dynamics is a natural means of describing the motion of a system at the coarse-grained level and the potential of mean force is the physical basis of the coarse-grained force fields. Moreover, the choice of coarse-grained variables and the fact that coarse-grained sites often do not have spherical symmetry implies a non-diagonal inertia tensor. We describe selected coarse-grained models used in molecular dynamics simulations, including the most popular MARTINI model developed by Marrink's group and the UNICORN model of biological macromolecules developed in our laboratory. We conclude by discussing examples of the application of coarse-grained molecular dynamics to study biologically important processes.