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.

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.