Structural and physical basis for the elasticity of elastin.

Elastin function is to endow vertebrate tissues with elasticity so that they can adapt to local mechanical constraints. The hydrophobicity and insolubility of the mature elastin polymer have hampered studies of its molecular organisation and structure-elasticity relationships. Nevertheless, a growing number of studies from a broad range of disciplines have provided invaluable insights, and several structural models of elastin have been proposed. However, many questions remain regarding how the primary sequence of elastin (and the soluble precursor tropoelastin) governs the molecular structure, its organisation into a polymeric network, and the mechanical properties of the resulting material. The elasticity of elastin is known to be largely entropic in origin, a property that is understood to arise from both its disordered molecular structure and its hydrophobic character. Despite a high degree of hydrophobicity, elastin does not form compact, water-excluding domains and remains highly disordered. However, elastin contains both stable and labile secondary structure elements. Current models of elastin structure and function are drawn from data collected on tropoelastin and on elastin-like peptides (ELPs) but at the tissue level, elasticity is only achieved after polymerisation of the mature elastin. In tissues, the reticulation of tropoelastin chains in water defines the polymer elastin that bears elasticity. Similarly, ELPs require polymerisation to become elastic. There is considerable interest in elastin especially in the biomaterials and cosmetic fields where ELPs are widely used. This review aims to provide an up-to-date survey of/perspective on current knowledge about the interplay between elastin structure, solvation, and entropic elasticity.

Differential MMP-14 targeting by biglycan, decorin, fibromodulin, and lumican unraveled by in silico approach.

Small leucine-rich proteoglycans (SLRPs) are major regulators of extracellular matrix assembly and cell signaling. Lumican, a member of the SLRPs family, and its derived peptides were shown to possess antitumor activity by interacting directly with the catalytic domain of MMP-14 leading to the inhibition of its activity. The aim of the present report was to characterize by in silico three-dimensional (3D) modeling the structure and the dynamics of four SLRPs including their core protein and their specific polysaccharide chains to assess their capacity to bind to MMP-14 and to regulate its activity. Molecular docking experiments were performed to identify the specific amino acids of MMP-14 interacting with each of the four SLRPs. The inhibition of each SLRP (100 nM) on MMP-14 activity was measured and the constants of inhibition (Ki) were evaluated. The impact of the number of glycan chains, structures, and dynamics of lumican on the interaction with MMP-14 was assessed by molecular dynamics simulations. Molecular docking analysis showed that all SLRPs bind to MMP-14 through their concave face, but in different regions of the catalytic domain of MMP-14. Each SLRPs inhibited significantly the MMP-14 activity. Finally, molecular dynamics showed the role of glycan chains in interaction with MMP-14 and shielding effect of SLRPs. Altogether, the results demonstrated that each SLRP exhibited inhibition of MMP-14 activity. However, the differential targeting of MMP-14 by the SLRPs was shown to be related not only to the core protein conformation but also to the glycan chain structures and dynamics.

Early Alterations of Intra-Mural Elastic Lamellae Revealed by Synchrotron X-ray Micro-CT Exploration of Diabetic Aortas.

Diabetes is a major concern of our society as it affects one person out of 11 around the world. Elastic fiber alterations due to diabetes increase the stiffness of large arteries, but the structural effects of these alterations are poorly known. To address this issue, we used synchrotron X-ray microcomputed tomography with in-line phase contrast to image in three dimensions C57Bl6J (control) and db/db (diabetic) mice with a resolution of 650 nm/voxel and a field size of 1.3 mm3. Having previously shown in younger WT and db/db mouse cohorts that elastic lamellae contain an internal supporting lattice, here we show that in older db/db mice the elastic lamellae lose this scaffold. We coupled this label-free method with automated image analysis to demonstrate that the elastic lamellae from the arterial wall are structurally altered and become 11% smoother (286,665 measurements). This alteration suggests a link between the loss of the 3D lattice-like network and the waviness of the elastic lamellae. Therefore, waviness measurement appears to be a measurable elasticity indicator and the 3D lattice-like network appears to be at the origin of the existence of this waviness. Both could be suitable indicators of the overall elasticity of the aorta.

Effects of changes in glycan composition on glycoprotein dynamics: example of N-glycans on insulin receptor.

Glycosylation is among the most common post-translational modifications in proteins, although it is observed in only about 10% of all the protein structures in protein data bank (PDB). Modifications of sugar composition in glycoproteins profoundly impact the overall physiology of the organism. One such example is the development of insulin resistance, which has been attributed to the removal of sialic acid residues from N-glycans of insulin receptor (IR) from various experimental studies. How such modifications affect the glycan-glycoprotein dynamics, and ultimately their function is not clearly understood to date. In this study, we performed molecular dynamics simulations of glycans in different environments. We studied the effects of removal of sialic acid on the glycan, as well as on the dynamics of leucine-rich repeat L1 domain of the IR ectodomain. We observed perturbations in L1 domain dynamics as a result of the removal of sialic acid. The perturbations include an increase in the flexibility of insulin-binding residues, which may affect insulin binding with IR. These changes are accompanied by perturbations in glycan-protein interactions and perturbation of long-range allosteric dynamics. Our observations will further aid in understanding the role of sugars in maintaining homeostasis and how changes in glycan composition may lead to perturbations in homeostasis, ultimately leading to conditions such as insulin resistance.

Umbrella Visualization: A method of analysis dedicated to glycan flexibility with UnityMol.

N-glycosylation is a post-translational modification heavily impacting protein functions. Some alterations of glycosylation, such as sialic acid hydrolysis, are related to protein dysfunction. Because of their high flexibility and the many reactive groups of the glycan chains, studying glycans with in vitro methods is a challenging task. Molecular dynamics is a useful tool and probably the only one in biology able to overcome this problem and gives access to conformational information through exhaustive sampling. To better decipher the impact of N-glycans, the analysis and visualization of their influence over time on protein structure is a prerequisite. We developed the Umbrella Visualization, a graphical method that assigns the glycan intrinsic flexibility during a molecular dynamics trajectory. The density plot generated by this method brought relevant informations regarding glycans dynamics and flexibility, but needs further development in order to integrate an accurate description of the protein topology and its interactions. We propose here to transform this analysis method into a visualization mode in UnityMol. UnityMol is a molecular editor, viewer and prototyping platform, coded in C#. The new representation of glycan chains presented in this study takes into account both the main positions adopted by each antenna of a glycan and their statistical relevance. By displaying the collected data on the protein surface, one is then able to investigate the protein/glycan interactions.

AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability.

Molecular docking is widely used in computed drug discovery and biological target identification, but getting fast results can be tedious and often requires supercomputing solutions. AMIDE stands for AutoMated Inverse Docking Engine. It was initially developed in 2014 to perform inverse docking on High Performance Computing. AMIDE version 2 brings substantial speed-up improvement by using AutoDock-GPU and by pulling a total revision of programming workflow, leading to better performances, easier use, bug corrections, parallelization improvements and PC/HPC compatibility. In addition to inverse docking, AMIDE is now an optimized tool capable of high throughput inverse screening. For instance, AMIDE version 2 allows acceleration of the docking up to 12.4 times for 100 runs of AutoDock compared to version 1, without significant changes in docking poses. The reverse docking of a ligand on 87 proteins takes only 23 min on 1 GPU (Graphics Processing Unit), while version 1 required 300 cores to reach the same execution time. Moreover, we have shown an exponential acceleration of the computation time as a function of the number of GPUs used, allowing a significant reduction of the duration of the inverse docking process on large datasets.

Structural Analysis of Nonapeptides Derived from Elastin.

Elastin-derived peptides are released from the extracellular matrix remodeling by numerous proteases and seem to regulate many biological processes, notably cancer progression. The canonical elastin peptide is VGVAPG, which harbors the XGXXPG consensus pattern, allowing interaction with the elastin receptor complex located at the surface of cells. Besides these elastokines, another class of peptides has been identified. This group of bioactive elastin peptides presents the XGXPGXGXG consensus sequence, but the reason for their bioactivity remains unexplained. To better understand their nature and structure-function relationships, herein we searched the current databases for this nonapeptide motif and observed that the XGXPGXGXG elastin peptides define a specific group of tandemly repeated patterns. Further, we focused on four tandemly repeated human elastin nonapeptides, i.e., AGIPGLGVG, VGVPGLGVG, AGVPGLGVG, and AGVPGFGAG. These peptides were analyzed by means of optical spectroscopies and molecular dynamics. Ultraviolet-circular dichroism and Raman spectra are consistent with a mixture of ß-turn, ß-strand, and random-chain secondary elements in aqueous media. Quantitative analysis of their conformations suggested that turns corresponded to half of the total population of structural elements, whereas the remaining half were equally distributed between ß-strand and unordered chains. These distributions were confirmed by molecular dynamics simulations. Altogether, our data suggest that these highly dynamic peptides harbor a type II ß-turn located in their central part. We hypothesize that this structural element could explain their specific bioactivity.

Proteoglycan Chemical Diversity Drives Multifunctional Cell Regulation and Therapeutics.

The extracellular matrix (ECM) constitutes a highly dynamic three-dimensional structural network comprised of macromolecules, such as proteoglycans/glycosaminoglycans (PGs/GAGs), collagens, laminins, fibronectin, elastin, other glycoproteins and proteinases. In recent years, the field of PGs has expanded rapidly. Due to their high structural complexity and heterogeneity, PGs mediate several homeostatic and pathological processes. PGs consist of a protein core and one or more covalently attached GAG chains, which provide the protein cores with the ability to interact with several proteins. The GAG building blocks of PGs significantly influence the chemical and functional properties of PGs. The primary goal of this comprehensive review is to summarize major achievements and paradigm-shifting discoveries made on the PG/GAG chemistry-biology axis, focusing on structural variability, structure-function relationships, metabolic, molecular, and epigenetic mechanisms underlying their synthesis. Recent insights related to exosome biogenesis, degradation, and cell signaling, their status as diagnostic tools and potential pharmacological targets in diseases as well as current applications in nanotechnology and biotechnology are addressed. Moreover, issues related to docking studies, molecular modeling, GAG/PG interaction networks, and their integration are discussed.

Tuning self-assembly in elastin-derived peptides.

Elastin-derived peptides are gaining increasing interest as potential biomaterials. Previous studies have demonstrated that short elastin-derived peptides are able to self-assemble into fibrils as the entire elastin protein. The motif responsible for that is the XGGZG motif at least three-fold repeated. In this work we have synthesized and studied, at molecular and supramolecular levels, four pentadecapeptides obtained by switching the X and Z residue with leucine and/or valine. We found that the four peptides formed different supramolecular structures corresponding to specific molecular conformations. Our results show that not only the residue type but also the exact position occupied by the residue in the motif is crucial in driving the self-aggregation. The aim of this work is to provide the basis for designing elastin-derived peptides with tunable supramolecular architecture.

Interaction between the elastin peptide VGVAPG and human elastin binding protein.

The elastin binding protein (EBP), a spliced variant of lysosomal ß-galactosidase, is the primary receptor of elastin peptides that have been linked to emphysema, aneurysm and cancer progression. The sequences recognized by EBP share the XGXXPG consensus pattern found in numerous matrix proteins, notably in elastin where the VGVAPG motif is repeated. To delineate the elastin binding site of human EBP, we built a homology model of this protein and docked VGVAPG on its surface. Analysis of this model suggested that Gln-97 and Asp-98 were required for interaction with VGVAPG because they contribute to the definition of a pocket thought to represent the elastin binding site of EBP. Additionally, we proposed that Leu-103, Arg-107, and Glu-137 were essential residues because they could interact with VGVAPG itself. Site-directed mutagenesis experiments at these key positions validated our model. This work therefore provides the first structural data concerning the interaction of the VGVAPG with its cognate receptor. The present structural data should now allow the development of EBP-specific antagonists.

SGPocket: A New Graph Convolutional Neural Network for Ligand-protein Binding Site Prediction.

BACKGROUND: Drug research is a long process, taking more than 10 years and requiring considerable financial resources. Therefore, researchers and industrials aim to reduce time and cost. Thus, they use computational simulations like molecular docking to explore huge databases of compounds and extract the most promising ones for further tests. Structure-based molecular docking is a complex process mixing surface exploration and energy computation to find the minimal free energy of binding corresponding to the best interaction location. OBJECTIVE: Our work is developed in the ligand-protein context, where ligands are small compounds like drugs. In most cases, no information is known about where on the protein surface the ligand will bind. Thus, the whole protein surface must be explored, which takes a huge amount of time. METHODS: We have developed SGPocket (meaning Spherical Graph Pocket), a binding site prediction method. Our method allows us to reduce the explored protein surface using deep learning without any information about a ligand. SGPocket uses the spherical graph convolutional operator working on a spherical relative positioning of amino acids in the protein. Then, a final step of clustering extracts the binding sites. RESULTS: Tested and compared (with well-known binding site prediction methods) on a hand-made dataset, our method performed well and can reduce the docking computing time. CONCLUSION: Thus, SGPocket allows the reduction of the exploration surface in the molecular docking process by restricting the simulation only to the site(s) predicted to be interesting.

Structural impact of a new spike Y170W mutation detected in early emerging SARS-CoV-2 Omicron variants in France.

To assess the genetic characteristics of the early emerging SARS-CoV-2 Omicron variant strains, we retrospectively analyzed a collection of 150 nasopharyngeal samples taken from a series of outpatient cases tested positive by a referenced qRT-PCR assay during the reported period of Omicron variant emergence in December 2021, in northeastern region of France. Next Generation Sequencing (NGS) analysis of SARS-CoV-2 spike sequences revealed that only 3 (2 %) of these detected strains were Omicron variants, while 147 (98 %) were identified as previously described delta variants. Our phylogenetic analyzes of SARS-CoV-2 RNA genomes showed that these French early emerging Omicron variants may have originated from South Africa or India. In addition, whole viral genome sequences NGS comparison analyzes allowed us to identify an original and uncharacterized Y170W spike mutation that was weakly and transiently detected during the period of SARS-CoV-2 Omicron variant emergence in human populations. Molecular modeling and docking experiments indicated that this original mutated residue Y170W was neither directly involved in binding to the SARS-CoV-2 receptor ACE2 nor in interacting with known neutralizing antibody sites. However, this new mutation may be responsible for preventing the transition from the closed to the open Spike conformation, thus promoting the early emergence of the Omicron variant. Overall, these results underscore the epidemiological utility of a routine whole-genome viral NGS strategy that enables genotypic characterization of emerging or mutant SARS-CoV-2 variants, which could have significant implications for public health policy.

Sialic acids cleavage induced by elastin-derived peptides impairs the interaction between insulin and its receptor in adipocytes 3T3-L1.

The insulin receptor (IR) plays an important role in insulin signal transduction, the defect of which is believed to be the root cause of type 2 diabetes. In 3T3-L1 adipocytes as in other cell types, the mature IR is a heterotetrameric cell surface glycoprotein composed of two α subunits and two ß subunits. Our objective in our study, is to understand how the desialylation of N-glycan chains, induced by elastin-derived peptides, plays a major role in the function of the IR. Using the 3T3-L1 adipocyte line, we show that removal of the sialic acid from N-glycan chains (N893 and N908), induced by the elastin receptor complex (ERC) and elastin derived-peptides (EDPs), leads to a decrease in the autophosphorylation activity of the insulin receptor. We demonstrate by molecular dynamics approaches that the absence of sialic acids on one of these two sites is sufficient to generate local and general modifications of the structure of the IR. Biochemical approaches highlight a decrease in the interaction between insulin and its receptor when ERC sialidase activity is induced by EDPs. Therefore, desialylation by EDPs is synonymous with a decrease of IR sensitivity in adipocytes and could thus be a potential source of insulin resistance associated with diabetic conditions.

Modelling and Simulations of Extracellular Glycoproteins.

While the knowledge of protein structure and function has seen vast advances in previous decades, the understanding of how their posttranslational modifications, such as glycosylations, influence their structure and function remains poor. However, advances in in silico methodologies to study glycosylations in recent past have enabled us to study this and understand the role of glycosylations in protein structure and function in ways that would not be possible by conventional experimental methods. In this chapter, we will demonstrate how to leverage these methodologies to study glycoproteins and their structural and dynamic properties using molecular modelling techniques.

Highlighting the hygroscopic capacities of apiogalacturonans.

To meet the needs of dehydrated skin, molecules with a high hygroscopic potential are necessary to hydrate it effectively and durably. In this context, we were interested in pectins, and more precisely in apiogalacturonans (AGA), a singular one that is currently only found in a few species of aquatic plants. As key structures in water regulation of these aquatic plants and thanks to their molecular composition and conformations, we hypothesized that they could have beneficial role for skin hydration. Spirodela polyrhiza is a duckweed known to be naturally rich in AGA. The aim of this study was to investigate the hygroscopic potential of AGA. Firstly, AGA models were built based on structural information obtained from previous experimental studies. Molecular dynamics (MD) simulations were performed, and the hygroscopic potential was predicted in silico by analyzing the frequency of interaction of water molecules with each AGA residue. Quantification of interactions identified the presence of 23 water molecules on average in contact with each residue of AGA. Secondly, the hygroscopic properties were investigated directly in vivo. Indeed, the water capture in the skin was measured in vivo by Raman microspectroscopy thanks to the deuterated water (D20) tracking. Investigations revealed that AGA significantly capture and retain more water in the epidermis and deeper than a placebo control. Not only do these original natural molecules interact with water molecules, but they capture and retain them efficiently in the skin.

How molecular modelling can better broaden the understanding of glycosylations.

Glycosylations are among the most ubiquitous post-translational modifications (PTMs) in proteins, and the effects of their perturbations are seen in various diseases such as cancers, diabetes and arthritis to name a few. Yet they remain one of the most enigmatic aspects of protein structure and function. On the other hand, molecular modelling techniques have been rapidly bridging this knowledge gap since the last decade. In this review, we discuss how these techniques have proven to be indispensable for a better understanding of the role of glycosylations in glycoprotein structure and function.

Machine-learning methods for ligand-protein molecular docking.

Artificial intelligence (AI) is often presented as a new Industrial Revolution. Many domains use AI, including molecular simulation for drug discovery. In this review, we provide an overview of ligand-protein molecular docking and how machine learning (ML), especially deep learning (DL), a subset of ML, is transforming the field by tackling the associated challenges.

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.

Anti-Toxoplasma gondii effect of lupane-type triterpenes from the bark of black alder (Alnus glutinosa) and identification of a potential target by reverse docking.

Toxoplasmosis is a worldwide parasitosis that is generally benign. The infestation may pose a risk to immunocompromized patients and to fetuses when pregnant women have recently seroconverted. Current treatments have numerous side effects and chemoresistance is emerging, hence the need to find new anti-Toxoplasma gondii substances. This study focuses on the antiparasitic potential of lupane-type pentacyclic triterpenes isolated from the bark of black alder (Alnus glutinosa), as well as the hypothesis of their macromolecular target by an original method of reverse docking. Among the isolated triterpenes, betulone was the most active compound with an IC50 of 2.7 ± 1.2 µM, a CC50 greater than 80 µM, and a selectivity index of over 29.6. An additional study of the anti-T. gondii potential of commercially available compounds (betulonic acid methyl ester and betulonic acid) showed the important role of the C3 ketone function and the C28 oxidation level on the lupane-type triterpene in the antiparasitic activity since their IC50 and CC50 were similar to that of betulone. Finally, the most active compounds were subjected to the AMIDE reverse docking workflow. A dataset of 87 T. gondii proteins from the Protein Data Bank was created. It identified calcium-dependent protein kinase CDPK3 as the most likely target of betulin derivatives.

TITLE: Potentiel anti-Toxoplasma gondii de triterpènes de type lupane de l'écorce de l'aulne glutineux, Alnus glutinosa, et identification d'une cible potentielle par docking inverse. ABSTRACT: La toxoplasmose est une parasitose mondiale, généralement bénigne. Les personnes à risque sont les patients immunodéprimés et les fœtus chez les femmes enceintes nouvellement séroconverties. Les traitements actuels ont de nombreux effets secondaires et des phénomènes de chimiorésistance apparaissent, d'où la nécessité de trouver de nouvelles substances actives contre T. gondii. Cette étude porte sur le potentiel antiparasitaire des triterpènes pentacycliques de type lupane isolés de l'écorce de l'aulne glutineux (Alnus glutinosa) et formule une hypothèse quant à leur cible protéique par l'utilisation d'une méthode originale de docking inverse. Parmi les triterpènes isolés, la bétulone s'est révélée être la plus active avec une CI50 de 2,7 µM ± 1,2 µM, une CC50 supérieure à 80 µM et un indice de sélectivité supérieur à 29,6. L'étude complémentaire du potentiel anti-T. gondii de composés disponibles commercialement et analogues à la bétulone (acide bétulonique et methyl ester de l'acide bétulonique) a montré le rôle important de la fonction cétone en C3 et du degré d'oxydation de la position 28 du squelette triterpénique de type lupane dans l'activité antiparasitaire puisque leurs CI50 et CC50 étaient similaires aux valeurs rencontrées pour la bétulone. Enfin, les composés les plus actifs ont été soumis au flux de travail de docking inverse d'AMIDE. Un ensemble de 87 protéines de T. gondii de la Protein Data Bank a été créé. La protéine kinase calcium dépendante CDPK3 a été identifiée comme la cible la plus probable des dérivés de la bétuline.

Molecular mechanism of ß-sheet self-organization at water-hydrophobic interfaces.

The capacity to form ß-sheet structure and to self-organize into amyloid aggregates is a property shared by many proteins. Severe neurodegenerative pathologies such as Alzheimer's disease are thought to involve the interaction of amyloidogenic protein oligomers with neuronal membranes. To understand the experimentally observed catalysis of amyloid formation by lipid membranes and other water-hydrophobic interfaces, we examine the physico-chemical basis of peptide adsorption and aggregation in a model membrane using atomistic molecular simulations. Blocked octapeptides with simple, repetitive sequences, (Gly-Ala)4, and (Gly-Val)4, are used as models of ß-sheet-forming polypeptide chains found in the core of amyloid fibrils. In the presence of an n-octane phase mimicking the core of lipid membranes, the peptides spontaneously partition at the octane-water interface. The adsorption of nonpolar sidechains displaces the peptides' conformational equilibrium from a heterogeneous ensemble characterized by a high degree of structural disorder toward a more ordered ensemble favoring ß-hairpins and elongated ß-strands. At the interface, peptides spontaneously aggregate and rapidly evolve ß-sheet structure on a 10 to 100 ns time scale, while aqueous aggregates remain amorphous. Catalysis of ß-sheet formation results from the combination of the hydrophobic effect and of reduced conformational entropy of the polypeptide chain. While the former drives interfacial partition and displaces the conformational equilibrium of monomeric peptides, the planar interface further facilitates ß-sheet organization by increasing peptide concentration and reducing the dimensionality of self-assembly from three to two. These findings suggest a general mechanism for the formation of ß-sheets on the surface of globular proteins and for amyloid self-organization at hydrophobic interfaces.