Multiscale Modeling and Simulation Approaches to Lipid-Protein Interactions

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.

Systems biology approaches to study lipidomes in health and disease

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.

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.

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.

Binding of the Anti-FIV Peptide C8 to Differently Charged Membrane Models: From First Docking to Membrane Tubulation

Gp36 is the virus envelope glycoproteins catalyzing the fusion of the feline immunodeficiency virus with the host cells. The peptide C8 is a tryptophan-rich peptide corresponding to the fragment ⁷⁷⁰W-I⁷⁷⁷ of gp36 exerting antiviral activity by binding the membrane cell and inhibiting the virus entry. Several factors, including the membrane surface charge, regulate the binding of C8 to the lipid membrane. Based on the evidence that imperceptible variation of membrane charge may induce a dramatic effect in several critical biological events, in the present work we investigate the effect induced by systematic variation of charge in phospholipid bilayers on the aptitude of C8 to interact with lipid membranes, the tendency of C8 to assume specific conformational states and the re-organization of the lipid bilayer upon the interaction with C8. Accordingly, employing a bottom-up multiscale protocol, including CD, NMR, ESR spectroscopy, atomistic molecular dynamics simulations, and confocal microscopy, we studied C8 in six membrane models composed of different ratios of zwitterionic/negatively charged phospholipids. Our data show that charge content modulates C8-membrane binding with significant effects on the peptide conformations. C8 in micelle solution or in SUV formed by DPC or DOPC zwitterionic phospholipids assumes regular β-turn structures that are progressively destabilized as the concentration of negatively charged SDS or DOPG phospholipids exceed 40%. Interaction of C8 with zwitterionic membrane surface is mediated by Trp1 and Trp4 that are deepened in the membrane, forming H-bonds and cation-π interactions with the DOPC polar heads. Additional stabilizing salt bridge interactions involve Glu2 and Asp3. MD and ESR data show that the C8-membrane affinity increases as the concentration of zwitterionic phospholipid increases. In the lipid membrane characterized by an excess of zwitterionic phospholipids, C8 is adsorbed at the membrane interface, inducing a stiffening of the outer region of the DOPC bilayer. However, the bound of C8 significantly perturbs the whole organization of lipid bilayer resulting in membrane remodeling. These events, measurable as a variation of the bilayer thickness, are the onset mechanism of the membrane fusion and vesicle tubulation observed in confocal microscopy by imaging zwitterionic MLVs in the presence of C8 peptide.