Mesoscopic Rigid Body Modelling of the Extracellular Matrix Self-Assembly

Challenging level of rigid-body approach involving numerical elements (CHLORAINE) applied to repeated elastin peptides

Elastic proteins and derived biomaterials contain numerous tandemly repeated peptides along their sequences, ranging from a few copies to hundreds. These repetitions are responsible for their biochemical, biological and biomechanical properties. These sequences are considered to be intrinsically disordered, and the variations in their behavior are actually mainly due to their high flexibility and lack of stable secondary structures originating from their unique amino acid sequences. Consequently, the simulation of elastic proteins and large elastomeric biomaterials using classical molecular dynamics is an important challenge. Here, we propose a novel approach that allows the application of the DURABIN protocol to repeated elastin-like peptides (r-ELPs) in a simple way. Four large r-ELPs were studied to evaluate our method, which was developed for simulating extracellular matrix proteins at the mesoscopic scale. After structure clustering applied on molecular dynamic trajectories of constitutive peptides (5-mers and 6-mers), the main conformations were used as starting points to define the corresponding primitives, further used as rigid body fragments in our program. Contributions derived from electrostatic and molecular hydrophobicity potentials were tested to evaluate their influence on the interactions during simple mesoscopic simulations. The CHLORAINE approach, despite the thinner granularity due to the size of the patterns used, was included in the DURABIN protocol and emerges as a promising way to simulate elastic macromolecular systems.

UNILIPID, a Methodology for Energetically Accurate Prediction of Protein Insertion into Implicit Membranes of Arbitrary Shape

The insertion of proteins into membranes is crucial for understanding their function in many biological processes. In this work, we present UNILIPID, a universal implicit lipid-protein description as a methodology for dealing with implicit membranes. UNILIPID is independent of the scale of representation and can be applied at the level of all atoms, coarse-grained particles down to the level of a single bead per amino acid. We provide example implementations for these scales and demonstrate the versatility of our approach by accurately reflecting the free energy of transfer for each amino acid. In addition to single membranes, we describe the analytical implementation of double membranes and show that UNILIPID is well suited for modeling at multiple scales. We generalize to membranes of arbitrary shape. With UNILIPID, we provide a methodological framework for a simple and general parameterization tuned to reproduce a selected reference hydrophobicity scale. The software we provide along with the methodological description is optimized for specific user features such as real-time response, live visual analysis, and virtual reality experiences.

Multiscale modelling of the extracellular matrix

The extracellular matrix is a complex three-dimensional network of molecules that provides cells with a complex microenvironment. The major constituents of the extracellular matrix such as collagen, elastin and associated proteins form supramolecular assemblies contributing to its physicochemical properties and organization. The structure of proteins and their supramolecular assemblies such as fibrils have been studied at the atomic level (e.g., by X-ray crystallography, Nuclear Magnetic Resonance and cryo-Electron Microscopy) or at the microscopic scale. However, many protein complexes are too large to be studied at the atomic level and too small to be studied by microscopy. Most extracellular matrix components fall into this intermediate scale, so-called the mesoscopic scale, preventing their detailed characterization. Simulation and modelling are some of the few powerful and promising approaches that can deepen our understanding of mesoscale systems. We have developed a set of modelling tools to study the self-organization of the extracellular matrix and large motion of macromolecules at the mesoscale level by taking advantage of the dynamics of articulated rigid bodies as a mean to study a larger range of motions at the cost of atomic resolution.

Lactosylceramide induced by elastin-derived peptides decreases adipocyte differentiation

Elastin, the major protein of the extracellular matrix, is specially found in cardiovascular tissues and contributing to 30-50% of the dry weight of blood vessels. Elastin regulates cell signalling pathways involved in morphogenesis, injury response and inflammation. The function of elastin is frequently compromised in damaged or aged elastic tissues. Indeed, elastin degradation, observed during ageing, and the resulting production of elastin-derived peptides (EDPs), have crucial impacts on cardiovascular disease (atherosclerosis, thrombosis) or on metabolism disease progressions (type 2 diabetes or non-alcoholic steatohepatitis). In the present study, we analysed the EDP effects on 3T3 preadipocyte cell differentiation. In a first part, we treated 3T3-L1 cells with EDP and visualized the lipid droplet accumulation by the oil red O staining and measured the expression of various transcription factors and adipocyte-specific mRNAs by real-time RT-PCR. We demonstrated that the elastin receptor complex, ERC, is activated by EDPs and decreased adipocyte differentiation by a modulation of crucial adipogenesis transcriptional factor particularly PPARγ. In a second part, we identified the signalling pathway implicated in EDP-reduced cell differentiation. The flow cytometry and immunocytochemistry approaches showed that ERC activated by EDP produced a second messenger, lactosylceramide (Lac-Cer). Moreover, this Lac-Cer production favoured the phosphorylation of ERK1-2 (p-ERK1-2), to decrease adipocyte differentiation by a modulation of adipogenesis transcriptional factor PPARγ. To conclude, the EDP/Lac-Cer/p-ERK1-2 signalling pathway may be studied further as a critical target for treating complications associated with adipocyte dedifferentiation such as obesity and diabetes insulin resistance.

From Virtual Reality to Immersive Analytics in Bioinformatics

Bioinformatics-related research produces huge heterogeneous amounts of data. This wealth of information includes data describing metabolic mechanisms and pathways, proteomics, transcriptomics, and metabolomics. Often, the visualization and exploration of related structural - usually molecular - data plays an important role in the aforementioned contexts. For decades, virtual reality (VR)-related technologies were developed and applied to Bioinformatics problems. Often, these approaches provide "just" visual support of the analysis, e.g. in the case of exploring and interacting with a protein on a 3D monitor and compatible interaction hardware. Moreover, in the past these approaches were limited to cost-intensive professional visualization facilities. The advent of new affordable, and often mobile technologies, provides high potential for using similar approaches on a regular basis for daily research. Visual Analytics is successfully being used for several years to analyze complex and heterogeneous datasets. Immersive Analytics combines these approaches now with new immersive and interactive technologies. This publication provides a short overview of related technologies, their history and Bioinformatics-related approaches. Six new applications on the path from VR to Immersive Analytics are being introduced and discussed.