A three-site mechanism for agonist/antagonist selective binding to vasopressin receptors

International audienc

Conformation and dynamics of human urotensin II and urotensin related peptide in aqueous solution

Conformation and dynamics of the vasoconstrictive peptides human urotensin II (UII) and urotensin related peptide (URP) have been investigated by both unrestrained and enhanced-sampling molecular-dynamics (MD) simulations and NMR spectroscopy. These peptides are natural ligands of the G-protein coupled urotensin II receptor (UTR) and have been linked to mammalian pathophysiology. UII and URP cannot be characterized by a single structure but exist as an equilibrium of two main classes of ring conformations, <i>open</i> and <i>folded</i>, with rapidly interchanging subtypes. The <i>open</i> states are characterized by turns of various types centered at K⁸Y⁹ or F⁶W⁷ predominantly with no or only sparsely populated transannular hydrogen bonds. The <i>folded</i> conformations show multiple turns stabilized by highly populated transannular hydrogen bonds comprising centers F⁶W⁷K⁸ or W⁷K⁸Y⁹. Some of these conformations have not been characterized previously. The equilibrium populations that are experimentally difficult to access were estimated by replica-exchange MD simulations and validated by comparison of experimental NMR data with chemical shifts calculated with density-functional theory. UII exhibits approximately 72% <i>open</i>:28% <i>folded</i> conformations in aqueous solution. URP shows very similar ring conformations as UII but differs in an <i>open:folded</i> equilibrium shifted further toward <i>open</i> conformations (86:14) possibly arising from the absence of folded N-terminal tail-ring interaction. The results suggest that the different biological effects of UII and URP are not caused by differences in ring conformations but rather by different interactions with UTR

A community effort in SARS-CoV-2 drug discovery.

peer reviewedThe COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.R-AGR-3826 - COVID19-14715687-CovScreen (01/06/2020 - 31/01/2021) - GLAAB Enric

Inhibition des membres de la superfamille des TGF-bs (implications dans la croissance musculaire)

CAEN-BU Médecine pharmacie (141182102) / SudocLYON1-BU Santé (693882101) / SudocSudocFranceF

Estimation des propriétés écotoxicologiques de substances chimiques par méthodes in silico (définition de modèles globaux ou spécifiques)

CAEN-BU Médecine pharmacie (141182102) / SudocSudocFranceF

Contribution à la conception du système d'information pharmaco-chimique du Centre d'Etudes et de Recherche sur le Médicament de Normandie

CAEN-BU Médecine pharmacie (141182102) / SudocLYON1-BU Santé (693882101) / SudocSudocFranceF

Approches 3D-(Q)SAR et modélisation des interactions ligand-récepteur (applications à l'urotensine-II humaine)

CAEN-BU Médecine pharmacie (141182102) / SudocSudocFranceF

Quantile de régression : application à l'analyse de l'écotoxicité de molécules chimiques

International audienceThe estimation of ecotoxicological properties of chemicals is a major environmental concern. The QSAR models (Quantitative Structure-Activity Relationship) are linear regression and classification models often used to predict the ecotoxicity of che-mical molecules. We consider in this paper quantile regression estimators which are more robust to outliers providing a more detailed focus on the entire conditional distribution of the dependent variable and not only on its mean as in linear regression. We propose here, in this concern of prediction, quantile models in regression and Support Vector Machines (SVM) in the field of chemoinformatics

Automated detection of structural alerts (chemical fragments) in (eco)toxicology

This mini-review describes the evolution of different algorithms dedicated to the automated discovery of chemical fragments associated to (eco)toxicological endpoints. These structural alerts correspond to one of the most interesting approach of in silico toxicology due to their direct link with specific toxicological mechanisms. A number of expert systems are already available but, since the first work in this field which considered a binomial distribution of chemical fragments between two datasets, new data miners were developed and applied with success in chemoinformatics. The frequency of a chemical fragment in a dataset is often at the core of the process for the definition of its toxicological relevance. However, recent progresses in data mining provide new insights into the automated discovery of new rules. Particularly, this review highlights the notion of Emerging Patterns that can capture contrasts between classes of data