The coarse-grained Martini model is employed extensively to study membrane protein oligomerization. While this approach is exceptionally promising given its computational efficiency, it is alarming ...that a significant fraction of these studies demonstrate unrealistic protein clusters, whose formation is essentially an irreversible process. This suggests that the protein-protein interactions are exaggerated in the Martini model. If this held true, then it would limit the applicability of Martini to study multi-protein complexes, as the rapidly clustering proteins would not be able to properly sample the correct dimerization conformations. In this work we first demonstrate the excessive protein aggregation by comparing the dimerization free energies of helical transmembrane peptides obtained with the Martini model to those determined from FRET experiments. Second, we show that the predictions provided by the Martini model for the structures of transmembrane domain dimers are in poor agreement with the corresponding structures resolved using NMR. Next, we demonstrate that the first issue can be overcome by slightly scaling down the Martini protein-protein interactions in a manner, which does not interfere with the other Martini interaction parameters. By preventing excessive, irreversible, and non-selective aggregation of membrane proteins, this approach renders the consideration of lateral dynamics and protein-lipid interactions in crowded membranes by the Martini model more realistic. However, this adjusted model does not lead to an improvement in the predicted dimer structures. This implicates that the poor agreement between the Martini model and NMR structures cannot be cured by simply uniformly reducing the interactions between all protein beads. Instead, a careful amino-acid specific adjustment of the protein-protein interactions is likely required.
Biological membranes are tricky to investigate. They are complex in terms of molecular composition and structure, functional over a wide range of time scales, and characterized by nonequilibrium ...conditions. Because of all of these features, simulations are a great technique to study biomembrane behavior. A significant part of the functional processes in biological membranes takes place at the molecular level; thus computer simulations are the method of choice to explore how their properties emerge from specific molecular features and how the interplay among the numerous molecules gives rise to function over spatial and time scales larger than the molecular ones. In this review, we focus on this broad theme. We discuss the current state-of-the-art of biomembrane simulations that, until now, have largely focused on a rather narrow picture of the complexity of the membranes. Given this, we also discuss the challenges that we should unravel in the foreseeable future. Numerous features such as the actin-cytoskeleton network, the glycocalyx network, and nonequilibrium transport under ATP-driven conditions have so far received very little attention; however, the potential of simulations to solve them would be exceptionally high. A major milestone for this research would be that one day we could say that computer simulations genuinely research biological membranes, not just lipid bilayers.
Biological membranes generate specific functions through compartmentalized regions such as cholesterol-enriched membrane nanodomains that host selected proteins. Despite the biological significance ...of nanodomains, details on their structure remain elusive. They cannot be observed via microscopic experimental techniques due to their small size, yet there is also a lack of atomistic simulation models able to describe spontaneous nanodomain formation in sufficiently simple but biologically relevant complex membranes. Here we use atomistic simulations to consider a binary mixture of saturated dipalmitoylphosphatidylcholine and cholesterol - the "minimal standard" for nanodomain formation. The simulations reveal how cholesterol drives the formation of fluid cholesterol-rich nanodomains hosting hexagonally packed cholesterol-poor lipid nanoclusters, both of which show registration between the membrane leaflets. The complex nanodomain substructure forms when cholesterol positions itself in the domain boundary region. Here cholesterol can also readily flip-flop across the membrane. Most importantly, replacing cholesterol with a sterol characterized by a less asymmetric ring region impairs the emergence of nanodomains. The model considered explains a plethora of controversial experimental results and provides an excellent basis for further computational studies on nanodomains. Furthermore, the results highlight the role of cholesterol as a key player in the modulation of nanodomains for membrane protein function.
Biomembranes are exceptionally crowded with proteins with typical protein-to-lipid ratios being around 1∶50−1∶100 . Protein crowding has a decisive role in lateral membrane dynamics as shown by ...recent experimental and computational studies that have reported anomalous lateral diffusion of phospholipids and membrane proteins in crowded lipid membranes. Based on extensive simulations and stochastic modeling of the simulated trajectories, we here investigate in detail how increasing crowding by membrane proteins reshapes the stochastic characteristics of the anomalous lateral diffusion in lipid membranes. We observe that correlated Gaussian processes of the fractional Langevin equation type, identified as the stochastic mechanism behind lipid motion in noncrowded bilayer, no longer adequately describe the lipid and protein motion in crowded but otherwise identical membranes. It turns out that protein crowding gives rise to a multifractal, non-Gaussian, and spatiotemporally heterogeneous anomalous lateral diffusion on time scales from nanoseconds to, at least, tens of microseconds. Our investigation strongly suggests that the macromolecular complexity and spatiotemporal membrane heterogeneity in cellular membranes play critical roles in determining the stochastic nature of the lateral diffusion and, consequently, the associated dynamic phenomena within membranes. Clarifying the exact stochastic mechanism for various kinds of biological membranes is an important step towards a quantitative understanding of numerous intramembrane dynamic phenomena.
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.
Glucose homeostasis and growth essentially depend on the hormone insulin engaging its receptor. Despite biochemical and structural advances, a fundamental contradiction has persisted in the current ...understanding of insulin ligand-receptor interactions. While biochemistry predicts two distinct insulin binding sites, 1 and 2, recent structural analyses have resolved only site 1. Using a combined approach of cryo-EM and atomistic molecular dynamics simulation, we present the structure of the entire dimeric insulin receptor ectodomain saturated with four insulin molecules. Complementing the previously described insulin-site 1 interaction, we present the first view of insulin bound to the discrete insulin receptor site 2. Insulin binding stabilizes the receptor ectodomain in a T-shaped conformation wherein the membrane-proximal domains converge and contact each other. These findings expand the current models of insulin binding to its receptor and of its regulation. In summary, we provide the structural basis for a comprehensive description of ligand-receptor interactions that ultimately will inform new approaches to structure-based drug design.
The lateral diffusion of embedded proteins along lipid membranes in protein-poor conditions has been successfully described in terms of the Saffman–Delbrück (SD) model, which predicts that the ...protein diffusion coefficient D is weakly dependent on its radius R as D ∝ ln(1/R). However, instead of being protein-poor, native cell membranes are extremely crowded with proteins. On the basis of extensive molecular simulations, we here demonstrate that protein crowding of the membrane at physiological levels leads to deviations from the SD relation and to the emergence of a stronger Stokes-like dependence D ∝ 1/R. We propose that this 1/R law mainly arises due to geometrical factors: smaller proteins are able to avoid confinement effects much better than their larger counterparts. The results highlight that the lateral dynamics in the crowded setting found in native membranes is radically different from protein-poor conditions and plays a significant role in formation of functional multiprotein complexes.
Machine learning has rapidly become a key method for the analysis and organization of large-scale data in all scientific disciplines. In life sciences, the use of machine learning techniques is a ...particularly appealing idea since the enormous capacity of computational infrastructures generates terabytes of data through millisecond simulations of atomistic and molecular-scale biomolecular systems. Due to this explosion of data, the automation, reproducibility, and objectivity provided by machine learning methods are highly desirable features in the analysis of complex systems. In this review, we focus on the use of machine learning in biomolecular simulations. We discuss the main categories of machine learning tasks, such as dimensionality reduction, clustering, regression, and classification used in the analysis of simulation data. We then introduce the most popular classes of techniques involved in these tasks for the purpose of enhanced sampling, coordinate discovery, and structure prediction. Whenever possible, we explain the scope and limitations of machine learning approaches, and we discuss examples of applications of these techniques.
The ability of cells to generate forces through actin filament turnover was an early adaptation in evolution. While much is known about how actin filaments grow, mechanisms of their disassembly are ...incompletely understood. The best-characterized actin disassembly factors are the cofilin family proteins, which increase cytoskeletal dynamics by severing actin filaments. However, the mechanism by which severed actin filaments are recycled back to monomeric form has remained enigmatic. We report that cyclase-associated-protein (CAP) works in synergy with cofilin to accelerate actin filament depolymerization by nearly 100-fold. Structural work uncovers the molecular mechanism by which CAP interacts with actin filament pointed end to destabilize the interface between terminal actin subunits, and subsequently recycles the newly-depolymerized actin monomer for the next round of filament assembly. These findings establish CAP as a molecular machine promoting rapid actin filament depolymerization and monomer recycling, and explain why CAP is critical for actin-dependent processes in all eukaryotes.
We report parametrization of dipalmitoyl-phosphatidylcholine (DPPC) in the framework of the Optimized Parameters for Liquid Simulations all-atom (OPLS-AA) force field. We chose DPPC as it is one of ...the most studied phospholipid species and thus has plenty of experimental data necessary for model validation, and it is also one of the highly important and abundant lipid types, e.g., in lung surfactant. Overall, PCs have not been previously parametrized in the OPLS-AA force field; thus, there is a need to derive its bonding and nonbonding parameters for both the polar and nonpolar parts of the molecule. In the present study, we determined the parameters for torsion angles in the phosphatidylcholine and glycerol moieties and in the acyl chains, as well the partial atomic charges. In these calculations, we used three methods: (1) Hartree–Fock (HF), (2) second order Møller–Plesset perturbation theory (MP2), and (3) density functional theory (DFT). We also tested the effect of the polar environment by using the polarizable continuum model (PCM), and for acyl chains the van der Waals parameters were also adjusted. In effect, six parameter sets were generated and tested on a DPPC bilayer. Out of these six sets, only one was found to be able to satisfactorily reproduce experimental data for the lipid bilayer. The successful DPPC model was obtained from MP2 calculations in an implicit polar environment (PCM).
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