SAHBNET, an accessible surface-based elastic network: an application to membrane protein

Insight into the Self-Assembling Properties of Peptergents: A Molecular Dynamics Simulation Study

By manipulating the various physicochemical properties of amino acids, the design of peptides with specific self-assembling properties has been emerging for more than a decade. In this context, short peptides possessing detergent properties (so-called "peptergents") have been developed to self-assemble into well-ordered nanostructures that can stabilize membrane proteins for crystallization. In this study, the peptide with "peptergency" properties, called ADA8 and extensively described by Tao et al., is studied by molecular dynamic simulations for its self-assembling properties in different conditions. In water, it spontaneously forms beta sheets with a β barrel-like structure. We next simulated the interaction of this peptide with a membrane protein, the bacteriorhodopsin, in the presence or absence of a micelle of dodecylphosphocholine. According to the literature, the peptergent ADA8 is thought to generate a belt of β structures around the hydrophobic helical domain that could help stabilize purified membrane proteins. Molecular dynamic simulations are here used to image this mechanism and provide further molecular details for the replacement of detergent molecules around the protein. In addition, we generalized this behavior by designing an amphipathic peptide with beta propensity, which was called ABZ12. Both peptides are able to surround the membrane protein and displace surfactant molecules. To our best knowledge, this is the first molecular mechanism proposed for "peptergency".

Elastic network model of learned maintained contacts to predict protein motion

We present a novel elastic network model, lmcENM, to determine protein motion even for localized functional motions that involve substantial changes in the protein's contact topology. Existing elastic network models assume that the contact topology remains unchanged throughout the motion and are thus most appropriate to simulate highly collective function-related movements. lmcENM uses machine learning to differentiate breaking from maintained contacts. We show that lmcENM accurately captures functional transitions unexplained by the classical ENM and three reference ENM variants, while preserving the simplicity of classical ENM. We demonstrate the effectiveness of our approach on a large set of proteins covering different motion types. Our results suggest that accurately predicting a "deformation-invariant" contact topology offers a promising route to increase the general applicability of ENMs. We also find that to correctly predict this contact topology a combination of several features seems to be relevant which may vary slightly depending on the protein. Additionally, we present case studies of two biologically interesting systems, Ferric Citrate membrane transporter FecA and Arachidonate 15-Lipoxygenase.

Interplay of G Protein-Coupled Receptors with the Membrane: Insights from Supra-Atomic Coarse Grain Molecular Dynamics Simulations

G protein-coupled receptors (GPCRs) are central to many fundamental cellular signaling pathways. They transduce signals from the outside to the inside of cells in physiological processes ranging from vision to immune response. It is extremely challenging to look at them individually using conventional experimental techniques. Recently, a pseudo atomistic molecular model has emerged as a valuable tool to access information on GPCRs, more specifically on their interactions with their environment in their native cell membrane and the consequences on their supramolecular organization. This approach uses the Martini coarse grain (CG) model to describe the receptors, lipids, and solvent in molecular dynamics (MD) simulations and in enough detail to allow conserving the chemical specificity of the different molecules. The elimination of unnecessary degrees of freedom has opened up large-scale simulations of the lipid-mediated supramolecular organization of GPCRs. Here, after introducing the Martini CGMD method, we review these studies carried out on various members of the GPCR family, including rhodopsin (visual receptor), opioid receptors, adrenergic receptors, adenosine receptors, dopamine receptor, and sphingosine 1-phosphate receptor. These studies have brought to light an interesting set of novel biophysical principles. The insights range from revealing localized and heterogeneous deformations of the membrane bilayer at the surface of the protein, specific interactions of lipid molecules with individual GPCRs, to the effect of the membrane matrix on global GPCR self-assembly. The review ends with an overview of the lessons learned from the use of the CGMD method, the biophysical-chemical findings on lipid-protein interplay.

Coarse-grained force fields for molecular simulations

Molecular dynamics (MD) simulations at the atomic scale are a powerful tool to study the structure and dynamics of model biological systems. However, because of their high computational cost, the time and length scales of atomistic simulations are limited. Biologically important processes, such as protein folding, ion channel gating, signal transduction, and membrane remodeling, are difficult to investigate using atomistic simulations. Coarse-graining reduces the computational cost of calculations by reducing the number of degrees of freedom in the model, allowing simulations of larger systems for longer times. In the first part of this chapter we review briefly some of the coarse-grained models available for proteins, focusing on the specific scope of each model. Then we describe in more detail the MARTINI coarse-grained force field, and we illustrate how to set up and run a simulation of a membrane protein using the Gromacs software package. We explain step-by-step the preparation of the protein and the membrane, the insertion of the protein in the membrane, the equilibration of the system, the simulation itself, and the analysis of the trajectory.

The influence of fatty acids on the GpA dimer interface by coarse-grained molecular dynamics simulation

The hydrophobic thickness of membranes, which is manly defined by fatty acids, influences the packing of transmembrane domains of proteins and thus can modulate the activity of these proteins. We analyzed the dynamics of the dimerization of Glycophorin A (GpA) by molecular dynamics simulations to describe the fatty acid dependence of the transmembrane region assembly. GpA represents a well-established model for dimerization of single transmembrane helices containing a GxxxG motif in vitro and in silico. We performed simulations of the dynamics of the NMR-derived dimer as well as self-assembly simulations of monomers in membranes composed of different fatty acid chains and monitored the formed interfaces and their transitions. The observed dimeric interfaces, which also include the one known from NMR, are highly dynamic and converted into each other. The frequency of interface formation and the preferred transitions between interfaces similar to the interface observed by NMR analysis strongly depend on the fatty acid used to build the membrane. Molecular dynamic simulations after adaptation of the helix topology parameters to better represent NMR derived structures of single transmembrane helices yielded an enhanced occurrence of the interface determined by NMR in molecular dynamics simulations. Taken together we give insights into the influence of fatty acids and helix conformation on the dynamics of the transmembrane domain of GpA.