Membrane lipids drive formation of KRAS4b-RAF1 RBDCRD nanoclusters on the membrane.

The oncogene RAS, extensively studied for decades, presents persistent gaps in understanding, hindering the development of effective therapeutic strategies due to a lack of precise details on how RAS initiates MAPK signaling with RAF effector proteins at the plasma membrane. Recent advances in X-ray crystallography, cryo-EM, and super-resolution fluorescence microscopy offer structural and spatial insights, yet the molecular mechanisms involving protein-protein and protein-lipid interactions in RAS-mediated signaling require further characterization. This study utilizes single-molecule experimental techniques, nuclear magnetic resonance spectroscopy, and the computational Machine-Learned Modeling Infrastructure (MuMMI) to examine KRAS4b and RAF1 on a biologically relevant lipid bilayer. MuMMI captures long-timescale events while preserving detailed atomic descriptions, providing testable models for experimental validation. Both in vitro and computational studies reveal that RBDCRD binding alters KRAS lateral diffusion on the lipid bilayer, increasing cluster size and decreasing diffusion. RAS and membrane binding cause hydrophobic residues in the CRD region to penetrate the bilayer, stabilizing complexes through ß-strand elongation. These cooperative interactions among lipids, KRAS4b, and RAF1 are proposed as essential for forming nanoclusters, potentially a critical step in MAP kinase signal activation.

Computationally Designed Molecules Modulate ALS-Related Amyloidogenic TDP-43307-319 Aggregation.

Abnormal cytosolic aggregation of TAR DNA-binding protein of 43 kDa (TDP-43) is observed in multiple diseases, including amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration, and Alzheimer's disease. Previous studies have shown that TDP-43307-319 located at the C-terminal of TDP-43 can form higher-order oligomers and fibrils. Of particular interest are the hexamers that adopt a cylindrin structure that has been strongly correlated to neurotoxicity. In this study, we use the joint pharmacophore space (JPS) model to identify and generate potential TDP-43 inhibitors. Five JPS-designed molecules are evaluated using both experimental and computational methods: ion mobility mass spectrometry, thioflavin T fluorescence assay, circular dichroism spectroscopy, atomic force microscopy, and molecular dynamics simulations. We found that all five molecules can prevent the amyloid fibril formation of TDP-43307-319, but their efficacy varies significantly. Furthermore, among the five molecules, [AC0101] is the most efficient in preventing the formation of higher-order oligomers and dissociating preformed higher-order oligomers. Molecular dynamics simulations show that [AC0101] both is the most flexible and forms the most hydrogen bonds with the TDP-43307-319 monomer. The JPS-designed molecules can insert themselves between the ß-strands in the hexameric cylindrin structure of TDP-43307-319 and can open its structure. Possible mechanisms for JPS-designed molecules to inhibit and dissociate TDP-43307-319 oligomers on an atomistic scale are proposed.

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.

Martini 3 Coarse-Grained Force Field for Cholesterol.

Cholesterol plays a crucial role in biomembranes by regulating various properties, such as fluidity, rigidity, permeability, and organization of lipid bilayers. The latest version of the Martini model, Martini 3, offers significant improvements in interaction balance, molecular packing, and inclusion of new bead types and sizes. However, the release of the new model resulted in the need to reparameterize many core molecules, including cholesterol. Here, we describe the development and validation of a Martini 3 cholesterol model, addressing issues related to its bonded setup, shape, volume, and hydrophobicity. The proposed model mitigates some limitations of its Martini 2 predecessor while maintaining or improving the overall behavior.

An anchor-tether 'hindered' HCN1 inhibitor is antihyperalgesic in a rat spared nerve injury neuropathic pain model.

BACKGROUND: Neuropathic pain impairs quality of life, is widely prevalent, and incurs significant costs. Current pharmacological therapies have poor/no efficacy and significant adverse effects; safe and effective alternatives are needed. Hyperpolarisation-activated cyclic nucleotide-regulated (HCN) channels are causally implicated in some forms of peripherally mediated neuropathic pain. Whilst 2,6-substituted phenols, such as 2,6-di-tert-butylphenol (26DTB-P), selectively inhibit HCN1 gating and are antihyperalgesic, the development of therapeutically tolerable, HCN-selective antihyperalgesics based on their inverse agonist activity requires that such drugs spare the cardiac isoforms and do not cross the blood-brain barrier. METHODS: In silico molecular dynamics simulation, in vitro electrophysiology, and in vivo rat spared nerve injury methods were used to test whether 'hindered' variants of 26DTB-P (wherein a hydrophilic 'anchor' is attached in the para-position of 26DTB-P via an acyl chain 'tether') had the desired properties. RESULTS: Molecular dynamics simulation showed that membrane penetration of hindered 26DTB-Ps is controlled by a tethered diol anchor without elimination of head group rotational freedom. In vitro and in vivo analysis showed that BP4L-18:1:1, a variant wherein a diol anchor is attached to 26DTB-P via an 18-carbon tether, is an HCN1 inverse agonist and an orally available antihyperalgesic. With a CNS multiparameter optimisation score of 2.25, a >100-fold lower drug load in the brain vs blood, and an absence of adverse cardiovascular or CNS effects, BP4L-18:1:1 was shown to be poorly CNS penetrant and cardiac sparing. CONCLUSIONS: These findings provide a proof-of-concept demonstration that anchor-tethered drugs are a new chemotype for treatment of disorders involving membrane targets.

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure.

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 µm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions.

The confluence of machine learning and multiscale simulations.

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. This confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.

Exploring CRD mobility during RAS/RAF engagement at the membrane.

During the activation of mitogen-activated protein kinase (MAPK) signaling, the RAS-binding domain (RBD) and cysteine-rich domain (CRD) of RAF bind to active RAS at the plasma membrane. The orientation of RAS at the membrane may be critical for formation of the RAS-RBDCRD complex and subsequent signaling. To explore how RAS membrane orientation relates to the protein dynamics within the RAS-RBDCRD complex, we perform multiscale coarse-grained and all-atom molecular dynamics (MD) simulations of KRAS4b bound to the RBD and CRD domains of RAF-1, both in solution and anchored to a model plasma membrane. Solution MD simulations describe dynamic KRAS4b-CRD conformations, suggesting that the CRD has sufficient flexibility in this environment to substantially change its binding interface with KRAS4b. In contrast, when the ternary complex is anchored to the membrane, the mobility of the CRD relative to KRAS4b is restricted, resulting in fewer distinct KRAS4b-CRD conformations. These simulations implicate membrane orientations of the ternary complex that are consistent with NMR measurements. While a crystal structure-like conformation is observed in both solution and membrane simulations, a particular intermolecular rearrangement of the ternary complex is observed only when it is anchored to the membrane. This configuration emerges when the CRD hydrophobic loops are inserted into the membrane and helices α3-5 of KRAS4b are solvent exposed. This membrane-specific configuration is stabilized by KRAS4b-CRD contacts that are not observed in the crystal structure. These results suggest modulatory interplay between the CRD and plasma membrane that correlate with RAS/RAF complex structure and dynamics, and potentially influence subsequent steps in the activation of MAPK signaling.

Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework.

The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.

Regulation of Gramicidin Channel Function Solely by Changes in Lipid Intrinsic Curvature.

Membrane protein function is regulated by the lipid bilayer composition. In many cases the changes in function correlate with changes in the lipid intrinsic curvature (c 0), and c 0 is considered a determinant of protein function. Yet, water-soluble amphiphiles that cause either negative or positive changes in curvature have similar effects on membrane protein function, showing that changes in lipid bilayer properties other than c 0 are important-and may be dominant. To further investigate the mechanisms underlying the bilayer regulation of protein function, we examined how maneuvers that alter phospholipid head groups effective "size"-and thereby c 0-alter gramicidin (gA) channel function. Using dioleoylphospholipids and planar bilayers, we varied the head groups' physical volume and the electrostatic repulsion among head groups (and thus their effective size). When 1,2-dioleyol-sn-glycero-3-phosphocholine (DOPC), was replaced by 1,2-dioleyol-sn-glycero-3-phosphoethanolamine (DOPE) with a smaller head group (causing a more negative c 0), the channel lifetime (τ) is decreased. When the pH of the solution bathing a 1,2-dioleyol-sn-glycero-3-phosphoserine (DOPS) bilayer is decreased from 7 to 3 (causing decreased head group repulsion and a more negative c 0), τ is decreased. When some DOPS head groups are replaced by zwitterionic head groups, τ is similarly decreased. These effects do not depend on the sign of the change in surface charge. In DOPE:DOPC (3:1) bilayers, pH changes from 5→9 to 5→0 (both increasing head group electrostatic repulsion, thereby causing a less negative c 0) both increase τ. Nor do the effects depend on the use of planar, hydrocarbon-containing bilayers, as similar changes were observed in hydrocarbon-free lipid vesicles. Altering the interactions among phospholipid head groups may alter also other bilayer properties such as thickness or elastic moduli. Such changes could be excluded using capacitance measurements and single channel measurements on gA channels of different lengths. We conclude that changes gA channel function caused by changes in head group effective size can be predicted from the expected changes in c 0.

Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.

Atomistic Characterization of Gramicidin Channel Formation.

We investigated gramicidin A (gA) subunit dimerization in lipid bilayers using microsecond-long replica-exchange umbrella sampling simulations, millisecond-long unbiased molecular dynamics simulations, and machine learning. Our simulations led to a dimer structure that is indistinguishable from the experimentally determined gA channel structures, with the two gA subunits joined by six hydrogen bonds (6HB). The simulations also uncovered two additional dimer structures, with different gA-gA stacking orientations that were stabilized by four or two hydrogen bonds (4HB or 2HB). When examining the temporal evolution of the dimerization, we found that two bilayer-inserted gA subunits can form the 6HB dimer directly, with no discernible intermediate states, as well as through paths that involve the 2HB and 4HB dimers.

Assessing the Perturbing Effects of Drugs on Lipid Bilayers Using Gramicidin Channel-Based In Silico and In Vitro Assays.

Partitioning of bioactive molecules, including drugs, into cell membranes may produce indiscriminate changes in membrane protein function. As a guide to safe drug development, it therefore becomes important to be able to predict the bilayer-perturbing potency of hydrophobic/amphiphilic drugs candidates. Toward this end, we exploited gramicidin channels as molecular force probes and developed in silico and in vitro assays to measure drugs' bilayer-modifying potency. We examined eight drug-like molecules that were found to enhance or suppress gramicidin channel function in a thick 1,2-dierucoyl-sn-glycero-3-phosphocholine (DC22:1PC) but not in thin 1,2-dioleoyl-sn-glycero-3-phosphocholine (DC18:1PC) lipid bilayer. The mechanism underlying this difference was attributable to the changes in gramicidin dimerization free energy by drug-induced perturbations of lipid bilayer physical properties and bilayer-gramicidin interactions. The combined in silico and in vitro approaches, which allow for predicting the perturbing effects of drug candidates on membrane protein function, have implications for preclinical drug safety assessment.

Simulations of Asymmetric Membranes Illustrate Cooperative Leaflet Coupling and Lipid Adaptability.

Biological membranes are composed of lipid bilayers that are often asymmetric with regards to the lipid composition and/or aqueous solvent they separate. Studying lipid asymmetry both experimentally and computationally is challenging. Molecular dynamics simulations of lipid bilayers with asymmetry are difficult due to finite system sizes and time scales accessible to simulations. Due to the very slow flip-flop rate for phospholipids, one must first choose how many lipids are on each side of the bilayer, but the resulting bilayer may be unstable (or metastable) due to differing tensile and compressive forces between leaflets. Here we use molecular dynamics simulations to investigate a number of different asymmetric membrane systems, both with atomistic and coarse-grained models. Asymmetries studied include differences in number of lipids, lipid composition (unsaturated and saturated tails and different headgroups), and chemical gradients between the aqueous phases. Extensive analysis of the bilayers' properties such as area per lipid, density, and lateral pressure profiles are used to characterize bilayer asymmetry. We also address how cholesterol (which flip-flops relatively quickly) influences membrane asymmetries. Our results show how each leaflet is influenced by the other and can mitigate the structural changes to the bilayer overall structure. Cholesterol can respond to changes in bilayer asymmetry to alleviate some of the effect on the bilayer structure, but that will alter its leaflet distribution, which in turn affects its chemical potential. Ionic imbalances are shown to have a modest change in bilayer structure, despite large changes in the electrostatic potential. Bilayer asymmetry can also induce a modest electrostatic potential across the membrane. Our results highlight the importance of membrane asymmetry on bilayer properties, the influence of lipid headgroups, tails and cholesterol on asymmetry, and the ability of lipids to adapt to different environments.

Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning.

Accurately predicting small molecule partitioning and hydrophobicity is critical in the drug discovery process. There are many heterogeneous chemical environments within a cell and entire human body. For example, drugs must be able to cross the hydrophobic cellular membrane to reach their intracellular targets, and hydrophobicity is an important driving force for drug-protein binding. Atomistic molecular dynamics (MD) simulations are routinely used to calculate free energies of small molecules binding to proteins, crossing lipid membranes, and solvation but are computationally expensive. Machine learning (ML) and empirical methods are also used throughout drug discovery but rely on experimental data, limiting the domain of applicability. We present atomistic MD simulations calculating 15,000 small molecule free energies of transfer from water to cyclohexane. This large data set is used to train ML models that predict the free energies of transfer. We show that a spatial graph neural network model achieves the highest accuracy, followed closely by a 3D-convolutional neural network, and shallow learning based on the chemical fingerprint is significantly less accurate. A mean absolute error of ∼4 kJ/mol compared to the MD calculations was achieved for our best ML model. We also show that including data from the MD simulation improves the predictions, tests the transferability of each model to a diverse set of molecules, and show multitask learning improves the predictions. This work provides insight into the hydrophobicity of small molecules and ML cheminformatics modeling, and our data set will be useful for designing and testing future ML cheminformatics methods.

ddcMD: A fully GPU-accelerated molecular dynamics program for the Martini force field.

We have implemented the Martini force field within Lawrence Livermore National Laboratory's molecular dynamics program, ddcMD. The program is extended to a heterogeneous programming model so that it can exploit graphics processing unit (GPU) accelerators. In addition to the Martini force field being ported to the GPU, the entire integration step, including thermostat, barostat, and constraint solver, is ported as well, which speeds up the simulations to 278-fold using one GPU vs one central processing unit (CPU) core. A benchmark study is performed with several test cases, comparing ddcMD and GROMACS Martini simulations. The average performance of ddcMD for a protein-lipid simulation system of 136k particles achieves 1.04 µs/day on one NVIDIA V100 GPU and aggregates 6.19 µs/day on one Summit node with six GPUs. The GPU implementation in ddcMD offloads all computations to the GPU and only requires one CPU core per simulation to manage the inputs and outputs, freeing up remaining CPU resources on the compute node for alternative tasks often required in complex simulation campaigns. The ddcMD code has been made open source and is available on GitHub at https://github.com/LLNL/ddcMD.

Capturing Biologically Complex Tissue-Specific Membranes at Different Levels of Compositional Complexity.

Plasma membranes (PMs) contain hundreds of different lipid species that contribute differently to overall bilayer properties. By modulation of these properties, membrane protein function can be affected. Furthermore, inhomogeneous lipid mixing and domains of lipid enrichment/depletion can sort proteins and provide optimal local environments. Recent coarse-grained (CG) Martini molecular dynamics efforts have provided glimpses into lipid organization of different PMs: an "Average" and a "Brain" PM. Their high complexity and large size require long simulations (∼80 µs) for proper sampling. Thus, these simulations are computationally taxing. This level of complexity is beyond the possibilities of all-atom simulations, raising the question-what complexity is needed for "realistic" bilayer properties? We constructed CG Martini PM models of varying complexity (63 down to 8 different lipids). Lipid tail saturations and headgroup combinations were kept as consistent as possible for the "tissues'" (Average/Brain) at three levels of compositional complexity. For each system, we analyzed membrane properties to evaluate which features can be retained at lower complexity and validate eight-component bilayers that can act as reliable mimetics for Average or Brain PMs. Systems of reduced complexity deliver a more robust and malleable tool for computational membrane studies and allow for equivalent all-atom simulations and experiments.

Decoupling copolymer, lipid and carbon nanotube interactions in hybrid, biomimetic vesicles.

Bilayer vesicles that mimic a real biological cell can be tailored to carry out a specific function by manipulating the molecular composition of the amphiphiles. These bio-inspired and bio-mimetic structures are increasingly being employed for a number of applications from drug delivery to water purification and beyond. Complex hybrid bilayers are the key building blocks for fully synthetic vesicles that can mimic biological cell membranes, which often contain a wide variety of molecular species. While the assembly and morpholgy of pure phospholid bilayer vesicles is well understood, the functionality and structure dramaticlly changes when copolymer and/or carbon nanotube porins (CNTP) are added. The aim of this study is to understand how the collective molecular interactions within hybrid vesicles affect their nanoscale structure and properties. In situ small and wide angle X-ray scattering (SAXS/WAXS) and molecular dynamics simulations (MD) are used to investigate the morphological effect of molecular interactions between polybutadiene polyethylene oxide, lipids and carbon nanotubes (CNT) within the hybrid vesicle bilayer. Within the lipid/copolymer system, the hybrid bilayer morphology transitions from phase separated lipid and compressed copolymer at low copolymer loadings to a mixed bilayer where opposing lipids are mostly separated from the inner region. This transition begins between 60 wt% and 70 wt%, with full homogenization observed by 80 wt% copolymer. The incorporation of CNT into the hybrid vesicles increases the bilayer thickness and enhances the bilayer symmetry. Analysis of the WAXS and MD indicate that the CNT-dioleoyl interactions are much stronger than the CNT-polybutadiene.

Molecular Mechanism for Gramicidin Dimerization and Dissociation in Bilayers of Different Thickness.

Membrane protein functions can be altered by subtle changes in the host lipid bilayer physical properties. Gramicidin channels have emerged as a powerful system for elucidating the underlying mechanisms of membrane protein function regulation through changes in bilayer properties, which are reflected in the thermodynamic equilibrium distribution between nonconducting gramicidin monomers and conducting bilayer-spanning dimers. To improve our understanding of how subtle changes in bilayer thickness alter the gramicidin monomer and dimer distributions, we performed extensive atomistic molecular dynamics simulations and fluorescence-quenching experiments on gramicidin A (gA). The free-energy calculations predicted a nonlinear coupling between the bilayer thickness and channel formation. The energetic barrier inhibiting gA channel formation was sharply increased in the thickest bilayer (1,2-dierucoyl-sn-glycero-3-phosphocholine). This prediction was corroborated by experimental results on gramicidin channel activity in bilayers of different thickness. To further explore the mechanism of channel formation, we performed extensive unbiased molecular dynamics simulations, which allowed us to observe spontaneous gA dimer formation in lipid bilayers. The simulations revealed structural rearrangements in the gA subunits and changes in lipid packing, as well as water reorganization, that occur during the dimerization process. Together, the simulations and experiments provide new, to our knowledge, insights into the process and mechanism of gramicidin channel formation, as a prototypical example of the bilayer regulation of membrane protein function.