IsoCleft Finder – a web-based tool for the detection and analysis of protein binding-site geometric and chemical similarities

IsoCleft Finder is a web-based tool for the detection of local geometric and chemical similarities between potential small-molecule binding cavities and a non-redundant dataset of ligand-bound known small-molecule binding-sites. The non-redundant dataset developed as part of this study is composed of 7339 entries representing unique Pfam/PDB-ligand (hetero group code) combinations with known levels of cognate ligand similarity. The query cavity can be uploaded by the user or detected automatically by the system using existing PDB entries as well as user-provided structures in PDB format. In all cases, the user can refine the definition of the cavity interactively via a browser-based Jmol 3D molecular visualization interface. Furthermore, users can restrict the search to a subset of the dataset using a cognate-similarity threshold. Local structural similarities are detected using the IsoCleft software and ranked according to two criteria (number of atoms in common and Tanimoto score of local structural similarity) and the associated Z-score and p-value measures of statistical significance. The results, including predicted ligands, target proteins, similarity scores, number of atoms in common, etc., are shown in a powerful interactive graphical interface. This interface permits the visualization of target ligands superimposed on the query cavity and additionally provides a table of pairwise ligand topological similarities. Similarities between top scoring ligands serve as an additional tool to judge the quality of the results obtained. We present several examples where IsoCleft Finder provides useful functional information. IsoCleft Finder results are complementary to existing approaches for the prediction of protein function from structure, rational drug design and x-ray crystallography. IsoCleft Finder can be found at: http://bcb.med.usherbrooke.ca/isocleftfinder

GTP binding regulates cellular localization of Parkinso\u144s disease-associated LRRK2

Mutations in LRRK2 comprise the most common cause of familial Parkinso\u144s disease (PD), and sequence variants modify risk for sporadic PD. Previous studies indicate that LRRK2 interacts with microtubules and alters microtubule-mediated vesicular transport processes. However, the molecular determinants within LRRK2 required for such interactions have remained unknown. Here we report that most pathogenic LRRK2 mutants cause relocalization of LRRK2 to filamentous structures which colocalize with a subset of microtubules, and an identical relocalization is seen upon pharmacological LRRK2 kinase inhibition. The pronounced colocalization with microtubules does not correlate with alterations in LRRK2 kinase activity, but rather with increased GTP binding. Synthetic mutations which impair GTP binding, as well as LRRK2 GTP-binding inhibitors profoundly interfere with the abnormal localization of both pathogenic mutant as well as kinase-inhibited LRRK2. Conversely, addition of a non-hydrolyzable GTP analog to permeabilized cells enhances the association of pathogenic or kinase-inhibited LRRK2 with microtubules. Our data elucidate the mechanism underlying the increased microtubule association of select pathogenic LRRK2 mutants or of pharmacologically kinase-inhibited LRRK2, with implications for downstream MT-mediated transport events

High permittivity processed SrTiO3 for metamaterials applications at terahertz frequencies

High permittivity SrTiO3 for the realization of all-dielectric metamaterials operating at terahertz frequencies was fabricated. A comparison of different processing methods demonstrates that Spark Plasma Sintering is the most effective sintering process to yield high density ceramic with high permittivity. We compare this sintering process with two other processes. The fabricated samples are characterized in the low frequency and in the terahertz frequency ranges. Their relative permittivities are compared with that of a reference SrTiO3 single crystal. The permittivity of the sample fabricated by Spark Plasma Sintering is as high as that of the single crystal. The role of the signal-to-noise ratio in the measurements at terahertz frequency is detailed

PAK6-mediated phosphorylation of PPP2R2C regulates LRRK2-PP2A complex formation

Mutations in leucine-rich repeat kinase 2 (LRRK2) are a common cause of inherited and sporadic Parkinson’s disease (PD) and previous work suggests that dephosphorylation of LRRK2 at a cluster of heterologous phosphosites is associated to disease. We have previously reported subunits of the PP1 and PP2A classes of phosphatases as well as the PAK6 kinase as regulators of LRRK2 dephosphorylation. We therefore hypothesized that PAK6 may have a functional link with LRRK2’s phosphatases. To investigate this, we used PhosTag gel electrophoresis with purified proteins and found that PAK6 phosphorylates the PP2A regulatory subunit PPP2R2C at position S381. While S381 phosphorylation did not affect PP2A holoenzyme formation, a S381A phosphodead PPP2R2C showed impaired binding to LRRK2. Also, PAK6 kinase activity changed PPP2R2C subcellular localization in a S381 phosphorylation-dependent manner. Finally, PAK6-mediated dephosphorylation of LRRK2 was unaffected by phosphorylation of PPP2R2C at S381, suggesting that the previously reported mechanism whereby PAK6-mediated phosphorylation of 14-3-3 proteins promotes 14-3-3-LRRK2 complex dissociation and consequent exposure of LRRK2 phosphosites for dephosphorylation is dominant. Taken together, we conclude that PAK6-mediated phosphorylation of PPP2R2C influences the recruitment of PPP2R2C to the LRRK2 complex and PPP2R2C subcellular localization, pointing to an additional mechanism in the fine-tuning of LRRK2 phosphorylation

A French multicentric prospective prognostic cohort with epidemiological, clinical, biological and treatment information to improve knowledge on lymphoma patients: study protocol of the "REal world dAta in LYmphoma and survival in adults" (REALYSA) cohort.

BACKGROUND: Age-adjusted lymphoma incidence rates continue to rise in France since the early 80's, although rates have slowed since 2010 and vary across subtypes. Recent improvements in patient survival in major lymphoma subtypes at population level raise new questions about patient outcomes (i.e. quality of life, long-term sequelae). Epidemiological studies have investigated factors related to lymphoma risk, but few have addressed the extent to which socioeconomic status, social institutional context (i.e. healthcare system), social relationships, environmental context (exposures), individual behaviours (lifestyle) or genetic determinants influence lymphoma outcomes, especially in the general population. Moreover, the knowledge of the disease behaviour mainly obtained from clinical trials data is partly biased because of patient selection. METHODS: The REALYSA ("REal world dAta in LYmphoma and Survival in Adults") study is a real-life multicentric cohort set up in French areas covered by population-based cancer registries to study the prognostic value of epidemiological, clinical and biological factors with a prospective 9-year follow-up. We aim to include 6000 patients over 4 to 5 years. Adult patients without lymphoma history and newly diagnosed with one of the following 7 lymphoma subtypes (diffuse large B-cell, follicular, marginal zone, mantle cell, Burkitt, Hodgkin, mature T-cell) are invited to participate during a medical consultation with their hematologist. Exclusion criteria are: having already received anti-lymphoma treatment (except pre-phase) and having a documented HIV infection. Patients are treated according to the standard practice in their center. Clinical data, including treatment received, are extracted from patients' medical records. Patients' risk factors exposures and other epidemiological data are obtained at baseline by filling out a questionnaire during an interview led by a clinical research assistant. Biological samples are collected at baseline and during treatment. A virtual tumor biobank is constituted for baseline tumor samples. Follow-up data, both clinical and epidemiological, are collected every 6 months in the first 3 years and every year thereafter. DISCUSSION: This cohort constitutes an innovative platform for clinical, biological, epidemiological and socio-economic research projects and provides an opportunity to improve knowledge on factors associated to outcome of lymphoma patients in real life. TRIAL REGISTRATION: 2018-A01332-53, ClinicalTrials.gov identifier: NCT03869619

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Development and applications of a bioinformatic tool to detect molecular interaction field similarities

Résumé : Les méthodes de détection de similarités de sites de liaison servent entre autres à la prédiction de fonction et à la prédiction de cibles croisées. Ces méthodes peuvent aider à prévenir les effets secondaires, suggérer le repositionnement de médicament existants, identifier des cibles polypharmacologiques et des remplacements bio-isostériques. La plupart des méthodes utilisent des représentations basées sur les atomes, même si les champs d’interaction moléculaire (MIFs) représentent plus directement ce qui cherche à être identifié. Nous avons développé une méthode bio-informatique, IsoMif, qui détecte les similarités de MIF entre différents sites de liaisons et qui ne nécessite aucun alignement de séquence ou de structure. Sa performance a été comparée à d’autres méthodes avec des bancs d’essais, ce qui n’a jamais été fait pour une méthode basée sur les MIFs. IsoMif performe mieux en moyenne et est plus robuste. Nous avons noté des limites intrinsèques à la méthodologie et d’autres qui proviennent de la nature. L’impact de choix de conception sur la performance est discuté. Nous avons développé une interface en ligne qui permet la détection de similarités entre une protéine et différents ensembles de MIFs précalculés ou à des MIFs choisis par l’utilisateur. Des sessions PyMOL peuvent être téléchargées afin de visualiser les similarités identifiées pour différentes interactions intermoléculaires. Nous avons appliqué IsoMif pour identifier des cibles croisées potentielles de drogues lors d’une analyse à large échelle (5,6 millions de comparaisons). Des simulations d’arrimage moléculaire ont également été effectuées pour les prédictions significatives. L’objectif est de générer des hypothèses de repositionnement et de mécanismes d’effets secondaires observés. Plusieurs exemples sont présentés à cet égard.Abstract : Methods that detect binding site similarities between proteins serve for the prediction of function and the identification of potential off-targets. These methods can help prevent side-effects, suggest drug repurposing and polypharmacological strategies and suggest bioisosteric replacements. Most methods use atom-based representations despite the fact that molecular interaction fields (MIFs) represents more closely the nature of what is meant to be identified. We developped a computational algorithm, IsoMif, that detects MIF similarities between binding sites. We benchmark IsoMif to other methods which has not been previously done for a MIF-based method. IsoMif performed best in average and more consistently accross datasets. We highlight limitations intrinsic to the methodology or to nature. The impact of design choices on performance is discussed. We built a freely available web interface that allows the detection of similarities between a protein and pre-calculated MIFs or user defined MIFs. PyMOL sessions can be downloaded to visualize similarities for the different intermolecular interactions. IsoMif was applied for a large-scale analysis (5,6 millions of comparisons) to predict offtargets of drugs. Docking simulations of the drugs in the binding site of their top hits were performed. The primary objective is to generate hypotheses that can be further investigated and validated regarding drug repurposing opportunities and side-effect mechanisms

Development and applications of a bioinformatic tool to detect molecular interaction field similarities

Résumé : Les méthodes de détection de similarités de sites de liaison servent entre autres à la prédiction de fonction et à la prédiction de cibles croisées. Ces méthodes peuvent aider à prévenir les effets secondaires, suggérer le repositionnement de médicament existants, identifier des cibles polypharmacologiques et des remplacements bio-isostériques. La plupart des méthodes utilisent des représentations basées sur les atomes, même si les champs d’interaction moléculaire (MIFs) représentent plus directement ce qui cherche à être identifié. Nous avons développé une méthode bio-informatique, IsoMif, qui détecte les similarités de MIF entre différents sites de liaisons et qui ne nécessite aucun alignement de séquence ou de structure. Sa performance a été comparée à d’autres méthodes avec des bancs d’essais, ce qui n’a jamais été fait pour une méthode basée sur les MIFs. IsoMif performe mieux en moyenne et est plus robuste. Nous avons noté des limites intrinsèques à la méthodologie et d’autres qui proviennent de la nature. L’impact de choix de conception sur la performance est discuté. Nous avons développé une interface en ligne qui permet la détection de similarités entre une protéine et différents ensembles de MIFs précalculés ou à des MIFs choisis par l’utilisateur. Des sessions PyMOL peuvent être téléchargées afin de visualiser les similarités identifiées pour différentes interactions intermoléculaires. Nous avons appliqué IsoMif pour identifier des cibles croisées potentielles de drogues lors d’une analyse à large échelle (5,6 millions de comparaisons). Des simulations d’arrimage moléculaire ont également été effectuées pour les prédictions significatives. L’objectif est de générer des hypothèses de repositionnement et de mécanismes d’effets secondaires observés. Plusieurs exemples sont présentés à cet égard.Abstract : Methods that detect binding site similarities between proteins serve for the prediction of function and the identification of potential off-targets. These methods can help prevent side-effects, suggest drug repurposing and polypharmacological strategies and suggest bioisosteric replacements. Most methods use atom-based representations despite the fact that molecular interaction fields (MIFs) represents more closely the nature of what is meant to be identified. We developped a computational algorithm, IsoMif, that detects MIF similarities between binding sites. We benchmark IsoMif to other methods which has not been previously done for a MIF-based method. IsoMif performed best in average and more consistently accross datasets. We highlight limitations intrinsic to the methodology or to nature. The impact of design choices on performance is discussed. We built a freely available web interface that allows the detection of similarities between a protein and pre-calculated MIFs or user defined MIFs. PyMOL sessions can be downloaded to visualize similarities for the different intermolecular interactions. IsoMif was applied for a large-scale analysis (5,6 millions of comparisons) to predict offtargets of drugs. Docking simulations of the drugs in the binding site of their top hits were performed. The primary objective is to generate hypotheses that can be further investigated and validated regarding drug repurposing opportunities and side-effect mechanisms

Detection of Binding Site Molecular Interaction Field Similarities

Protein binding-site similarity detection methods can be used to predict protein function and understand molecular recognition, as a tool in drug design for drug repurposing and polypharmacology, and for the prediction of the molecular determinants of drug toxicity. Here, we present IsoMIF, a method able to identify binding site molecular interaction field similarities across protein families. IsoMIF utilizes six chemical probes and the detection of subgraph isomorphisms to identify geometrically and chemically equivalent sections of protein cavity pairs. The method is validated using six distinct data sets, four of those previously used in the validation of other methods. The mean area under the receiver operator curve (AUC) obtained across data sets for IsoMIF is higher than those of other methods. Furthermore, while IsoMIF obtains consistently high AUC values across data sets, other methods perform more erratically across data sets. IsoMIF can be used to predict function from structure, to detect potential cross-reactivity or polypharmacology targets, and to help suggest bioisosteric replacements to known binding molecules. Given that IsoMIF detects spatial patterns of molecular interaction field similarities, its predictions are directly related to pharmacophores and may be readily translated into modeling decisions in structure-based drug design. IsoMIF may in principle detect similar binding sites with distinct amino acid arrangements that lead to equivalent interactions within the cavity. The source code to calculate and visualize MIFs and MIF similarities are freely available