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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 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.Martini 3.0 is an updated and reparametrized force field for coarse-grained molecular dynamics simulations with new bead types and an expanded ability to model molecular packing and interactions.

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.

Psychedelics produce fast and persistent antidepressant effects and induce neuroplasticity resembling the effects of clinically approved antidepressants. We recently reported that pharmacologically diverse antidepressants, including fluoxetine and ketamine, act by binding to TrkB, the receptor for BDNF. Here we show that lysergic acid diethylamide (LSD) and psilocin directly bind to TrkB with affinities 1,000-fold higher than those for other antidepressants, and that psychedelics and antidepressants bind to distinct but partially overlapping sites within the transmembrane domain of TrkB dimers. The effects of psychedelics on neurotrophic signaling, plasticity and antidepressant-like behavior in mice depend on TrkB binding and promotion of endogenous BDNF signaling but are independent of serotonin 2A receptor (5-HT 2A ) activation, whereas LSD-induced head twitching is dependent on 5-HT 2A and independent of TrkB binding. Our data confirm TrkB as a common primary target for antidepressants and suggest that high-affinity TrkB positive allosteric modulators lacking 5-HT 2A activity may retain the antidepressant potential of psychedelics without hallucinogenic effects. Moliner et al. show that psychedelics directly bind to the BDNF receptor TrkB with high affinity and promote BDNF-mediated plasticity and antidepressant-like effects, whereas their hallucinogenic-like effects are independent of TrkB binding.

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. The cofilin family proteins are actin disassembly factors but the disassembly mechanism is poorly understood. Here authors show that cyclase-associated-protein (CAP) works in synergy with cofilin to accelerate actin filament depolymerization by nearly 100-fold and reveal how CAP destabilizes the interface between terminal actin subunits.

Seipin is a disk-like oligomeric endoplasmic reticulum (ER) protein important for lipid droplet (LD) biogenesis and triacylglycerol (TAG) delivery to growing LDs. Here we show through biomolecular simulations bridged to experiments that seipin can trap TAGs in the ER bilayer via the luminal hydrophobic helices of the protomers delineating the inner opening of the seipin disk. This promotes the nanoscale sequestration of TAGs at a concentration that by itself is insufficient to induce TAG clustering in a lipid membrane. We identify Ser166 in the α3 helix as a favored TAG occupancy site and show that mutating it compromises the ability of seipin complexes to sequester TAG in silico and to promote TAG transfer to LDs in cells. While the S166D-seipin mutant colocalizes poorly with promethin, the association of nascent wild-type seipin complexes with promethin is promoted by TAGs. Together, these results suggest that seipin traps TAGs via its luminal hydrophobic helices, serving as a catalyst for seeding the TAG cluster from dissolved monomers inside the seipin ring, thereby generating a favorable promethin binding interface.

A mutated KRAS protein is frequently observed in human cancers. Traditionally, the oncogenic properties of KRAS missense mutants at position 12 (G12X) have been considered as equal. Here, by assessing the probabilities of occurrence of all KRAS G12X mutations and KRAS dynamics we show that this assumption does not hold true. Instead, our findings revealed an outstanding mutational bias. We conducted a thorough mutational analysis of KRAS G12X mutations and assessed to what extent the observed mutation frequencies follow a random distribution. Unique tissue-specific frequencies are displayed with specific mutations, especially with G12R, which cannot be explained by random probabilities. To clarify the underlying causes for the nonrandom probabilities, we conducted extensive atomistic molecular dynamics simulations (170 μs) to study the differences of G12X mutations on a molecular level. The simulations revealed an allosteric hydrophobic signaling network in KRAS, and that protein dynamics is altered among the G12X mutants and as such differs from the wild-type and is mutation-specific. The shift in long-timescale conformational dynamics was confirmed with Markov state modeling. A G12X mutation was found to modify KRAS dynamics in an allosteric way, which is especially manifested in the switch regions that are responsible for the effector protein binding. The findings provide a basis to understand better the oncogenic properties of KRAS G12X mutants and the consequences of the observed nonrandom frequencies of specific G12X mutations.

Cellular cholesterol can be metabolized to its fatty acid esters, cholesteryl esters (CEs), to be stored in lipid droplets (LDs). With triacylglycerols (TGs), CEs represent the main neutral lipids in LDs. However, while TG melts at ~4 °C, CE melts at ~44 °C, raising the question of how CE-rich LDs form in cells. Here, we show that CE forms supercooled droplets when the CE concentration in LDs is above 20% to TG and, in particular, liquid-crystalline phases when the fraction of CEs is above 90% at 37 °C. In model bilayers, CEs condense and nucleate droplets when the CE/phospholipid ratio reaches over 10-15%. This concentration is reduced by TG pre-clusters in the membrane that thereby facilitate CE nucleation. Accordingly, blocking TG synthesis in cells is sufficient to strongly dampen CE LD nucleation. Finally, CE LDs emerged at seipins, which cluster and nucleate TG LDs in the ER. However, when TG synthesis is inhibited, similar numbers of LDs are generated in the presence and absence of seipin, suggesting that seipin controls CE LD formation via its TG clustering capacity. Our data point to a unique model whereby TG pre-clusters, favorable at seipins, catalyze the nucleation of CE LDs. Dumesnil et al. report that cholesterol esters (CE), which only melt above body temperature, form supercooled liquid crystalline lipid droplets (LD). Triacylglycerols (TG) solubilize CE to help CE LD nucleation. Through clustering TGs in the ER membrane, seipin controls CE LD nucleation sites.