Quantifying Membrane Curvature Sensing of Peripheral Proteins by Simulated Buckling and Umbrella Sampling

A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions

The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and machine learning (ML) schemes are employed to obtain CVs from molecular dynamics (MD)/Monte Carlo (MC) trajectories or structural databanks for biomolecules. However, minimum sampling conditions to generate reliable CVs that accurately describe the underlying energy landscape remain unclear. Here, we address this issue by developing a Mode evolution Metric (MeM) to extract CVs that can pinpoint new states and describe local transitions in the vicinity of a reference minimum from nonequilibrated MD/MC trajectories. We present a general mathematical formulation of MeM for both statistical dimensionality reduction and machine learning approaches. Application of MeM to MC trajectories of model potential energy landscapes and MD trajectories of solvated alanine dipeptide reveals that the principal components which locate new states in the vicinity of a reference minimum emerge well before the trajectories locally equilibrate between the associated states. Finally, we demonstrate a possible application of MeM in designing efficient biased sampling schemes to construct accurate energy landscape slices that link transitions between states. MeM can help speed up the search for new minima around a biomolecular conformational state and enable the accurate estimation of thermodynamics for states lying on the energy landscape and the description of associated transitions.

Exploring the Properties of Curved Lipid Membranes: Comparative Analysis of Atomistic and Coarse-Grained Force Fields

Curvature emerges as a fundamental membrane characteristic crucial for diverse biological processes, including vesicle formation, cell signaling, and membrane trafficking. Increasingly valuable insights into atomistic details governing curvature-dependent membrane properties are provided by computer simulations. Nevertheless, the underlying force field models are conventionally calibrated and tested in relation to experimentally derived parameters of planar bilayers, thereby leaving uncertainties concerning their consistency in reproducing curved lipid systems. In this study we compare the depiction of buckled phosphatidylcholine (POPC) and POPC-cholesterol membranes by four popular force field models. Aside from agreement with respect to general trends in curvature dependence of a number of parameters, we observe a few qualitative differences. Among the most prominent ones is the difference between atomistic and coarse grained force fields in their representation of relative compressibility of the polar headgroup region and hydrophobic lipid core. Through a number of downstream effects, this discrepancy can influence the way in which curvature modulates the behavior of membrane bound proteins depending on the adopted simulation model.

Research progress on the regulatory role of cell membrane surface tension in cell behavior

Cell membrane surface tension has emerged as a pivotal biophysical factor governing cell behavior and fate. This review systematically delineates recent advances in techniques for cell membrane surface tension quantification, mechanosensing mechanisms, and regulatory roles of cell membrane surface tension in modulating major cellular processes. Micropipette aspiration, tether pulling, and newly developed fluorescent probes enable the measurement of cell membrane surface tension with spatiotemporal precision. Cells perceive cell membrane surface tension via conduits including mechanosensitive ion channels, curvature-sensing proteins (e.g. BAR domain proteins), and cortex-membrane attachment proteins (e.g. ERM proteins). Through membrane receptors like integrins, cells convert mechanical cues into biochemical signals. This conversion triggers cytoskeletal remodeling and extracellular matrix interactions in response to environmental changes. Elevated cell membrane surface tension suppresses cell spreading, migration, and endocytosis while facilitating exocytosis. Moreover, reduced cell membrane surface tension promotes embryonic stem cell differentiation and cancer cell invasion, underscoring cell membrane surface tension as a regulator of cell plasticity. Outstanding questions remain regarding cell membrane surface tension regulatory mechanisms and roles in tissue development/disease in vivo. Emerging tools to manipulate cell membrane surface tension with high spatiotemporal control in combination with omics approaches will facilitate the elucidation of cell membrane surface tension-mediated effects on signaling networks across various cell types/states. This will accelerate the development of cell membrane surface tension-based biomarkers and therapeutics for regenerative medicine and cancer. Overall, this review provides critical insights into cell membrane surface tension as a potent orchestrator of cell function, with broader impacts across mechanobiology.

Amphipathic Helices Can Sense Both Positive and Negative Curvatures of Lipid Membranes

Curvature sensing is an essential ability of biomolecules to preferentially localize to membrane regions of a specific curvature. It has been shown that amphipathic helices (AHs), helical peptides with both hydrophilic and hydrophobic regions, could sense a positive membrane curvature. The origin of this AH sensing has been attributed to their ability to exploit lipid-packing defects that are enhanced in regions of positive curvature. In this study, we revisit an alternative framework where AHs act as sensors of local internal stress within the membrane, suggesting the possibility of an AH sensing a negative membrane curvature. Using molecular dynamics simulations, we gradually tuned the hydrophobicity of AHs, thereby adjusting their insertion depth so that the curvature preference of AHs is switched from positive to negative. This study suggests that highly hydrophobic AHs could preferentially localize proteins to regions of a negative membrane curvature.

Physics-based generative model of curvature sensing peptides; distinguishing sensors from binders

Proteins can specifically bind to curved membranes through curvature-induced hydrophobic lipid packing defects. The chemical diversity among such curvature "sensors" challenges our understanding of how they differ from general membrane "binders" that bind without curvature selectivity. Here, we combine an evolutionary algorithm with coarse-grained molecular dynamics simulations (Evo-MD) to resolve the peptide sequences that optimally recognize the curvature of lipid membranes. We subsequently demonstrate how a synergy between Evo-MD and a neural network (NN) can enhance the identification and discovery of curvature sensing peptides and proteins. To this aim, we benchmark a physics-trained NN model against experimental data and show that we can correctly identify known sensors and binders. We illustrate that sensing and binding are phenomena that lie on the same thermodynamic continuum, with only subtle but explainable differences in membrane binding free energy, consistent with the serendipitous discovery of sensors.

Molecular Dynamics Simulations of Curved Lipid Membranes

Eukaryotic cells contain membranes with various curvatures, from the near-plane plasma membrane to the highly curved membranes of organelles, vesicles, and membrane protrusions. These curvatures are generated and sustained by curvature-inducing proteins, peptides, and lipids, and describing these mechanisms is an important scientific challenge. In addition to that, some molecules can sense membrane curvature and thereby be trafficked to specific locations. The description of curvature sensing is another fundamental challenge. Curved lipid membranes and their interplay with membrane-associated proteins can be investigated with molecular dynamics (MD) simulations. Various methods for simulating curved membranes with MD are discussed here, including tools for setting up simulation of vesicles and methods for sustaining membrane curvature. The latter are divided into methods that exploit scaffolding virtual beads, methods that use curvature-inducing molecules, and methods applying virtual forces. The variety of simulation tools allow researcher to closely match the conditions of experimental studies of membrane curvatures.

Efficient Quantification of Lipid Packing Defect Sensing by Amphipathic Peptides: Comparing Martini 2 and 3 with CHARMM36

In biological systems, proteins can be attracted to curved or stretched regions of lipid bilayers by sensing hydrophobic defects in the lipid packing on the membrane surface. Here, we present an efficient end-state free energy calculation method to quantify such sensing in molecular dynamics simulations. We illustrate that lipid packing defect sensing can be defined as the difference in mechanical work required to stretch a membrane with and without a peptide bound to the surface. We also demonstrate that a peptide's ability to concurrently induce excess leaflet area (tension) and elastic softening─a property we call the "characteristic area of sensing" (CHAOS)─and lipid packing sensing behavior are in fact two sides of the same coin. In essence, defect sensing displays a peptide's propensity to generate tension. The here-proposed mechanical pathway is equally accurate yet, computationally, about 40 times less costly than the commonly used alchemical pathway (thermodynamic integration), allowing for more feasible free energy calculations in atomistic simulations. This enabled us to directly compare the Martini 2 and 3 coarse-grained and the CHARMM36 atomistic force fields in terms of relative binding free energies for six representative peptides including the curvature sensor ALPS and two antiviral amphipathic helices (AH). We observed that Martini 3 qualitatively reproduces experimental trends while producing substantially lower (relative) binding free energies and shallower membrane insertion depths compared to atomistic simulations. In contrast, Martini 2 tends to overestimate (relative) binding free energies. Finally, we offer a glimpse into how our end-state-based free energy method can enable the inverse design of optimal lipid packing defect sensing peptides when used in conjunction with our recently developed evolutionary molecular dynamics (Evo-MD) method. We argue that these optimized defect sensors─aside from their biomedical and biophysical relevance─can provide valuable targets for the development of lipid force fields.

Coarse-Grained Simulations Suggest Potential Competing Roles of Phosphoinositides and Amphipathic Helix Structures in Membrane Curvature Sensing of the AP180 N-Terminal Homology Domain

The generation and sensing of membrane curvature by proteins has become of increasing interest to researchers with multiple mechanisms, from hydrophobic insertion to protein crowding, being identified. However, the role of charged lipids in the membrane curvature-sensing process is still far from understood. Many proteins involved in endocytosis bind phosphatidylinositol 4,5-bisphosphate (PIP2) lipids, allowing these proteins to accumulate at regions of local curvature. Here, using coarse-grained molecular dynamics simulations, we study the curvature-sensing behavior of the ANTH domain, a protein crucial for endocytosis. We selected three ANTH crystal structures containing either an intact, split, or truncated terminal amphipathic helix. On neutral membranes, the ANTH domain has innate curvature-sensing ability. In the presence of PIP2, however, only the domain with an intact helix senses curvature. Our work sheds light on the role of PIP2 and its modulation of membrane curvature sensing by proteins.