Biology subjects

Böckmann, R. A.

Publications and source records attributed to Böckmann, R. A..

7 recordsLinked to original sources

bioRxiv2026

Large phospholipid-number asymmetry is not required to reproduce plasma membrane physical properties

Plasma membranes are compositionally asymmetric, but whether this lipid-type asymmetry is accompanied by a substantial phospholipid-number imbalance between leaflets remains debated. Here, we use microsecond all-atom molecular dynamics simulations to compare human red blood cell plasma membrane models with either strong phospholipid-number asymmetry and exoplasmic cholesterol enrichment or near-symmetric phospholipid and cholesterol numbers but preserved lipid-type asymmetry. Both asymmetric models reproduce a densely packed, ordered exoplasmic leaflet and a more fluid cytoplasmic leaflet. Strong phospholipid-number asymmetry, however, drives extensive cholesterol enrichment in the exoplasmic leaflet and amplifies membrane asymmetry, leading to large cholesterol-rich clusters, enhanced shallow hydrophobic exposure, reduced exoplasmic lipid mobility, lower ethanol permeability, and increased area compressibility. Comparison with available diffusion and alcohol-permeability measurements indicates that the strongly asymmetric model overestimates the immobilization and barrier properties of the exoplasmic leaflet, whereas the near-symmetric-number model better captures these dynamic observables. Our results suggest that lipid-type asymmetry is sufficient to reproduce many physical hallmarks of plasma membranes without requiring a large phospholipid-number imbalance.

biophysics↗

bioRxiv2026

MemBack: An Equivariant Graph Neural Network for Backmapping Lipid Membranes

Backmapping coarse-grained simulations to atomistic resolution is central to multiscale molecular simulation but remains challenging for chemically complex lipid membranes. We introduce MemBack, an SE(3) equivariant graph neural network that reconstructs CHARMM36 lipid structures from Martini 3 configurations by single-pass heavy-atom prediction followed by automated post-processing. Across chemically diverse systems, MemBack achieved a mean superposition-free per-lipid heavy-atom RMSD of 0.65 [A], while retaining comparable accuracy for membrane systems and lipid species excluded from training. At the membrane scale, MemBack preserved structural organization across resolutions: in a 16-component red-blood-cell membrane model excluded from training, bilayer thickness, area per lipid, and acyl-chain order closely matched the atomistic reference after only restrained minimization; in a native phase-separated Martini 3 membrane, the lateral organization of ordered and disordered domains was likewise retained directly after backmapping, without atomistic equilibration. Native Martini 3 systems containing up to 1.4 million reconstructed atoms remained consistent with their parent coarse-grained configurations and could be propagated in atomistic simulations, providing an efficient interface between Martini 3 and CHARMM36 membrane simulations.

biophysics↗

bioRxiv2026

Membrane Environment Sets the Functional pKa of Ionizable Lipids

Ionizable aminolipids enable lipid nanoparticles (LNPs) to encapsulate nucleic acids at neutral pH and to release their cargo upon endosomal acidification. The discrepancy between this effective, acidic LNP pKa and the basic intrinsic pKa of aminolipids, however, remains poorly understood. Here, we performed microsecond constant-pH molecular dynamics simulations of five widely used aminolipids (DODAP, DLin-MC3-DMA, DLin-KC2-DMA, ALC-0315, and SM-102) embedded in different LNP-relevant ternary DOPC/D-SPC-cholesterol membranes to quantify how aminolipid structure and membrane composition jointly govern aminolipid protonation and the associated pH-dependent membrane remodeling. Across all systems, membrane embedding lowers the apparent aminolipid pKa, yielding physiologically relevant values of 6-7.5 corresponding to shifts by up to 3.5 pKa units or approx. 20 kJ mol-1 with respect to the intrinsic pKa. Strikingly, the magnitude of the pKa shift correlates with pH-driven membrane remodeling upon deprotonation: polyunsaturated aminolipids undergo surface-to-core translocation, branched aminolipids preferentially form laterally segregated surface domains, and DODAP remains interfacially anchored through sustained hydration and hydrogen bonding. Saturated helper lipids (DSPC) systematically enhance segregation and amplify pKa shifts relative to DOPC. Together, these results identify membrane phase behavior as a primary regulator of aminolipid protonation equilibria and establish quantitative design principles for tuning LNP composition toward desired pKa, membrane remodeling, and delivery performance.

biophysics↗

bioRxiv2024

Constant-pH MD Simulations of Lipids

Constant pH Molecular Dynamics (CpHMD) simulations represent a cutting-edge computational approach for studying biological systems with remarkable realism. Recent advancements have enhanced the accessibility and efficiency of CpHMD, significantly reducing the performance overhead compared to traditional constant-protonation MD simulations. This chapter guides the reader through the application of CpHMD to investigate the pH-dependent behavior of Cationic Ionizable Lipids (CILs) -- a critical component of Lipid Nanoparticles (LNPs), which are among the most promising platforms for drug delivery. LNPs, including those employed in mRNA-based vaccines, played a pivotal role in the global response to the SARS-CoV-2 pandemic, underscoring their potential in modern medicine. The chapter begins with a comprehensive introduction to the fundamental concepts of LNPs and provides a step-by-step protocol for setting up simulations of membranes containing CILs to calculate their apparent pKa. This parameter is crucial for governing the in vivo behavior of LNPs, where precise control is essential to optimize delivery efficiency while minimizing toxicity. By showcasing the ability of CpHMD simulations to unravel the intricate relationship between pH-dependent protonation, membrane structure, and lipid distribution, this chapter highlights their potential to inform the rational design of novel LNP formulations.

biophysics↗

bioRxiv2024

Mechanistic Insight into pH-Driven Phase Transition of Lipid Nanoparticles

The functionality of lipid nanoparticles (LNPs) as delivery systems in mRNA-based therapeutics is intricately linked to the protonation behavior of their aminolipid components. This study employs large-scale constant-pH molecular dynamics (CpHMD) simulations to decode the environment-dependent pKa of aminolipids in the Comirnaty lipid formulation, providing a detailed view of their pH-dependent structural dynamics. Our results reveal a significant shift in the apparent pKa of the aminolipid ALC-0315, from an intrinsic value of 9.3 in water to 4.9 within the LNP environment. This shift arises from the interplay between lipid reorganization and local electrostatic interactions, resulting in distinct protonation states across the LNP core and surface. At low pH, protonated aminolipids dominate the LNP surface, promoting efficient mRNA encapsulation, whereas at neutral pH, deprotonated aminolipids migrate to the hydrophobic core, driving structural stabilization. Notably, the localized pKa of aminolipids varies significantly with their position, decreasing from near-surface regions (7 to 8) to the hydrophobic core ([≤]4). These findings elucidate the molecular mechanisms underpinning LNP phase transitions and highlight the key role of pKa shifts for the design of aminolipids and for optimizing LNP compositions for enhanced therapeutic delivery. This study bridges experimental observations with molecular-level insights, advancing the rational development of next-generation lipid-based nanocarriers.

biophysics↗

bioRxiv2023

Role of lipid nanodomains for inhibitory FcγRIIb function

The inhibitory Fc{gamma} receptor Fc{gamma}RIIb is involved in immune regulation and is known to localize to specific regions of the plasma membrane called lipid rafts. Previous studies suggested a link between the altered lateral receptor localization within the plasma membrane and the functional impairment of the Fc{gamma}RIIb-I232T variant that is associated with systemic lupus erythematosus. Here, we conducted microsecond all-atom molecular dynamics simulations and IgG binding assays to investigate the lipid nano-environment of Fc{gamma}RIIb monomers and of the Fc{gamma}RIIb-I232T mutant within a plasma membrane model, the orientation of the Fc{gamma}RIIb ectodomain, and its accessibility to IgG ligands. In contrast to previously proposed models, our simulations indicated that Fc{gamma}RIIb does not favor a cholesterol-or a sphingolipid-enriched lipid environment. Interestingly, cholesterol was depleted for all studied Fc{gamma}RIIb variants within a 2-3 nm environment of the receptor, counteracting the usage of raft terminology for models on receptor functionality. Instead, the receptor interacts with lipids that have poly-unsaturated fatty acyl chains and with (poly-) anionic lipids within the cytosolic membrane leaflet. We also found that Fc{gamma}RIIb monomers adopt a conformation that is not suitable for binding to its IgG ligand, consistent with a lack of detectable binding of monomeric IgG in experiments on primary immune cells. However, our results propose that multivalent IgG complexes might stabilize Fc{gamma}RIIb in a binding-competent conformation. We suggest differences in receptor complex formation within the membrane as a plausible cause of the altered membrane localization or clustering and the altered suppressive function of the Fc{gamma}RIIb-I232T variant. Significance StatementOur study sheds new light on the molecular mechanisms underlying the regulation of immune signaling mediated by the inhibitory Fc{gamma} receptor (Fc{gamma}RIIb). By utilizing atomistic simulations and experimental assays, we demonstrate that Fc{gamma}RIIb interacts with specific lipids in the plasma membrane. Notably, our findings challenge the current view of membrane heterogeneity in immune cells, as Fc{gamma}RIIb is not localized in specialized membrane domains known as rafts. Rather, we propose that receptor complex formation modulates receptor localization and conformation, thereby enabling ligand binding. Our findings have important implications for understanding how immune receptors function and communicate with each other, and may provide new opportunities for developing therapeutic strategies targeting Fc{gamma}RIIb in diseases such as autoimmunity and cancer.

immunology↗

bioRxiv2022

Lipid bicelles in the study of biomembrane characteristics

Simulations of lipid membranes typically make use of periodic boundary conditions to mimic macroscopically sized membranes and allow for comparison to experiments performed e.g. on planar lipid membranes or on unilamellar lipid vesicles. However, the lateral periodicity partly suppresses membrane fluctuations or membrane remodeling, processes that are of particular importance in the study of asymmetric membranes - i.e. membranes with integral or associated proteins and/or asymmetric lipid compositions. Here, we devised a simple albeit powerful lipid bicelle model system that (i) displays similar structural, dynamical and mechanical properties compared to infinite periodic lipid membrane systems, and allows (ii) for the study of asymmetric lipid bilayer systems, and (iii) the unperturbed formation of local spontaneous curvature induced by lipids or proteins in coarse-grained and all-atom molecular dynamics simulations. In addition, the system is characterized by largely unbiased thermal fluctuations as opposed to standard bilayer systems. Application of the bicelle system for an asymmetric lipid composition resembling the plasma membrane reveals that the cholesterol density for a tension-free plasma membrane with a vanishing spontaneous curvature is larger by 28% within the extracellular leaflet compared to the cytosolic leaflet. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/517649v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@56cb44org.highwire.dtl.DTLVardef@3b16faorg.highwire.dtl.DTLVardef@161a143org.highwire.dtl.DTLVardef@a248e1_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗