Computational Lipidomics of the Neuronal Plasma Membrane

Ingólfsson, Helgi I.; Carpenter, Timothy S.; Bhatia, Harsh; Bremer, Peer Timo; Marrink, Siewert J.; Lightstone, Felice C.

doi:10.1016/j.bpj.2017.10.017

Cited by 239 publications

(337 citation statements)

References 78 publications

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“…In other words, OMV uptake efficiency in vivo would depend on resident host cells, given that membrane composition differs for cells throughout the human body. [65][66][67] Our simulations were by no means comprehensive, but they were sufficiently diverse to resolve an interesting question: why does OMV lipid-mediated uptake depend on the cell wall architecture of parent (bacterial) cells? 11 The coarse-grained simulations answer this question, and in doing so, makes some headway in clarifying longstanding uncertainties associated with OMV uptake e.g.…”

Section: Resultsmentioning

confidence: 99%

To infect or not to infect: molecular determinants of bacterial outer membrane vesicle internalization by host membranes

Jefferies

Khalid

2019

Preprint

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Outer membrane vesicles (OMVs) are spherical liposomes that are secreted by almost all forms of Gram-negative bacteria. The nanospheres contribute to bacterial pathogenesis by trafficking molecular cargo from bacterial membranes to target cells at the host-pathogen interface. We have simulated the interaction of OMVs with host cell membranes to understand why OMV uptake depends on the length of constituent lipopolysaccharide macromolecules. Using coarse-grained molecular dynamics simulations, we show that lipopolysaccharide lipid length affects OMV shape at the host-pathogen interface: OMVs with long (smooth-type) lipopolysaccharide lipids retain their spherical shape when they interact with host cell membranes, whereasOMVs with shorter (rough-type) lipopolysaccharide lipids distort and spread over the host membrane surface. In addition, we show that OMVs preferentially coordinate domain-favoring ganglioside lipids within host membranes to enhance curvature and affect the local lipid composition. We predict that these differences in shape preservation affect OMV internalization on long timescales: spherical nanoparticles tend to be completely enveloped by host membranes, whereas low sphericity nanoparticles tend to remain on the surface of cells.

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Section: Resultsmentioning

confidence: 99%

To infect or not to infect: molecular determinants of bacterial outer membrane vesicle internalization by host membranes

Jefferies

Khalid

2019

Preprint

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“…Pioneering computational studies with the Martini CG force field allowed the characterization of not only lipid segregation and lipid phases, but also the relative partitioning of membrane proteins between these phases 9,[42][43][44] . More recently, Martini has been used in simulations of membranes with lipid compositions of comparable complexity to those found in specific tissues of living cells [45][46] .…”

Section: Generation Of Lipid Mixture Data Sets Using Coarse-grained Mmentioning

confidence: 99%

Unsupervised Machine Learning for Analysis of Coexisting Lipid Phases and Domain Growth in Biological Membranes

Gnanakaran

et al. 2019

Preprint

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2 Phase separation in mixed lipid systems has been extensively studied both experimentally and 3 theoretically because of its biological importance. A detailed description of such complex systems 4 undoubtedly requires novel mathematical frameworks that are capable to decompose and 5 categorize the evolution of thousands if not millions of lipids involved in the phenomenon. The 6 interpretation and analysis of Molecular Dynamics (MD) simulations representing temporal and 7 spatial changes in such systems is still a challenging task. Here, we present a new unsupervised 8 machine learning approach based on Nonnegative Matrix Factorization, called NMFk, that 9 successfully extracts physically meaningful features from neighborhood profiles derived from 10 coarse-grained MD simulations of ternary lipid mixture. Our results demonstrate that leveraging 11 NMFk can (a) determine the role of different lipid molecules in phase separation, (b) characterize 12 the formation of nano-domains of lipids, (c) determine the timescales of interest and (d) extract 13 physically meaningful features that uniquely describe the phase separation with broad 14 implications. 15 3 16 Author Summary17 Cell membranes contain mixtures of chemically diverse lipid molecules, dynamically structured, 18 that play a key role for cell survival. Under different conditions, lipids in the membrane can 19 segregate into liquid-ordered and liquid-disordered domains. It is well-known that such domains 20 play a critical role in cellular homeostasis. Unfortunately, the lack of molecular details hampers 21 the direct interpretation of the available experimental data for such lipid segregation dynamics.22 Molecular Dynamics (MD) simulations can potentially fill the gap between theory and 23 experiments, but this task requires rigorous methods for proper analysis and description of the MD 24 generated trajectories. Here, we present a new unsupervised machine learning analysis based on 25 Nonnegative Matrix Factorization that extracts physically meaningful features from pre-processed 26 datasets generated by MD simulations. We simulate and analyze phase separation in a well-studied 27 ternary lipid mixture. Our results demonstrate that via the proposed machine learning we can: (a) 28 determine the role of different lipid molecules in the phase separation, (b) characterize the 29 formation of nano-domains in lipid bilayers, (c) determine the timescales of the formations of these 30 nano-domains and (d) extract physically meaningful features that uniquely describe the dynamics 31 of the phase separation 33 34 Cell membranes contain mixtures of different lipid types, dynamically arranged, that play a key 35 role in various mechanisms responsible for cell survival [1]. In the past, membranes were thought 36 to be homogenous systems, however, new data suggests that under different stimulus, the lipids 37 can segregate [2,3] into detergent-resistant domains commonly called "rafts". These domains are 38 highly dynamic, varying in size and composition [4,5]. Important...

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“…Interestingly, when the alcohol chain consists of more than 8 carbons the reported shift for GUVs' T mix becomes non-monotonic, making it hard to predict what the effect of a new untested compound will be. Molecular dynamics (MD) simulations have a long history of complementing experiments when it comes to understanding the intricate details of phase separation in lipid membranes (8)(9)(10)(11)(12)(13)(14)(15)(16)(17). The nature of the problem inherently calls for long length-and time-scales.…”

Section: Introductionmentioning

confidence: 99%

Inserting small molecules across membrane mixtures: Insight from the potential of mean force

Centi

Dutta

Parekh

et al. 2019

Preprint

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Small solutes have been shown to alter the lateral organization of cell membranes and reconstituted phospholipid bilayers; however, the mechanisms by which these changes happen are still largely unknown. Traditionally, both experiment and simulation studies have been restricted to testing only a few compounds at a time, failing to identify general molecular descriptors or chemical properties that would allow extrapolating beyond the subset of considered solutes. In this work, we probe the competing energetics of inserting a solute in different membrane environments by means of the potential of mean force. We show that these calculations can be used as a computationally-efficient proxy to establish whether a solute will stabilize or destabilize domain phase separation. Combined with umbrella sampling simulations and coarse-grained molecular dynamics simulations, we are able to screen solutes across a wide range of chemistries and polarities. Our results indicate that, for the system under consideration, preferential partitioning and therefore effectiveness in altering membrane phase separation are strictly linked to the location of insertion in the bilayer (i.e., midplane or interface). Our approach represents a fast and simple tool for obtaining structural and thermodynamic insight into the partitioning of small molecules between lipid domains and its relation to phase separation, ultimately providing a platform for identifying the key determinants of this process. SIGNIFICANCE In this work we explore the relationship between solute chemistry and the thermodynamics of insertion in a mixed lipid membrane. By combining a coarse-grained resolution and umbrella-sampling simulations we efficiently sample conformational space to study the thermodynamics of phase separation. We demonstrate that measures of the potential of mean force-a computationally-efficient quantity-between different lipid environments can serve as a proxy to predict a compound's ability to alter the thermodynamics of the lipid membrane. This efficiency allows us to set up a computational screening across many compound chemistries, thereby gaining insight beyond the study of a single or a handful of compounds.

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Computational Lipidomics of the Neuronal Plasma Membrane

Cited by 239 publications

References 78 publications

To infect or not to infect: molecular determinants of bacterial outer membrane vesicle internalization by host membranes

To infect or not to infect: molecular determinants of bacterial outer membrane vesicle internalization by host membranes

Unsupervised Machine Learning for Analysis of Coexisting Lipid Phases and Domain Growth in Biological Membranes

Inserting small molecules across membrane mixtures: Insight from the potential of mean force

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