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2. Protein interaction explorer (PIE): a comprehensive platform for navigating protein–protein interactions and ligand binding pockets.

Author

Mareuil, Fabien, Moine-Franel, Alexandra, Kar, Anuradha, Nilges, Michael, Ciambur, Constantin Bogdan, and Sperandio, Olivier

Subjects
*DRUG discovery, *LIGAND binding (Biochemistry), *PROTEIN-protein interactions, *HETERODIMERS, *LIGANDS (Biochemistry)
Abstract

Summary Protein Interaction Explorer (PIE) is a new web-based tool integrated to our database iPPI-DB, specifically crafted to support structure-based drug discovery initiatives focused on protein–protein interactions (PPIs). Drawing upon extensive structural data encompassing thousands of heterodimer complexes, including those with successful ligands, PIE provides a comprehensive suite of tools dedicated to aid decision-making in PPI drug discovery. PIE enables researchers/bioinformaticians to identify and characterize crucial factors such as the presence of binding pockets or functional binding sites at the interface, predicting hot spots, and foreseeing similar protein-embedded pockets for potential repurposing efforts. Availability and implementation PIE is user-friendly and readily accessible at https://ippidb.pasteur.fr/targetcentric/. It relies on the NGL visualizer. [ABSTRACT FROM AUTHOR]

Published
2024
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4. The iPPI-DB initiative: a community-centered database of protein–protein interaction modulators

Author

Torchet, Rachel, Druart, Karen, Ruano, Luis Checa, Moine-Franel, Alexandra, Borges, Hélène, Doppelt-Azeroual, Olivia, Brancotte, Bryan, Mareuil, Fabien, Nilges, Michael, Ménager, Hervé, Sperandio, Olivier, Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Département de Biologie structurale et Chimie - Department of Structural Biology and Chemistry, Institut Pasteur [Paris] (IP), none declared, This work used the computational and storage services (TARS cluster, VMHosting) provided by the IT department at Institut Pasteur, Paris. The authorswish to acknowledge in particular the help and technical advice of Eric Deveaud,Emmanuel Guichard, Thomas Me ́nard and Youssef Ghorbal (IT Department,Institut Pasteur). They also want to acknowledge the technical help of Tru Huynh(Structural Bioinformatics Unit, Institut Pasteur). They thank Jon Ison, BenjaminBardiaux and Pascal Campagne for their proofreading of the paper.Marvin JS (20.5.0, 2020, http://www.chemaxon.com) is used for drawingand displaying chemical structures in both Query mode and Contributionmode of iPPI-DB. Pipeline Pilot (server 19.1) is used to prepare the DrugBankdatabase from a SDF file prior to chemical similarity search 202 (2020), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris], Génomique métabolique (UMR 8030), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Spectrométrie de Masse pour la Biologie – Mass Spectrometry for Biology (UTechS MSBio), Centre National de la Recherche Scientifique (CNRS)-Centre de Ressources et de Recherche Technologique - Center for Technological Resources and Research (C2RT), Institut Pasteur [Paris]-Institut Pasteur [Paris], Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Etude de la dynamique des protéomes (EDyP), BioSanté (UMR BioSanté), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA), Biologie Computationnelle (ex C3BI), Bioinformatique structurale - Structural Bioinformatics, and Druart, Karen

Subjects
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], AcademicSubjects/SCI01060, Databases and Ontologies, [SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Original Papers, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [CHIM.CHEM]Chemical Sciences/Cheminformatics
Abstract

International audience; Abstract Motivation One avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets. Results Here, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database. This interface was created to enable community contributions, whereby external experts can suggest new database entries. Moreover, the data model, the graphical interface, and the tools to query the database have been completely modernized and improved. We added new PPI modulators, new PPI targets and extended our focus to stabilizers of PPIs as well. Availability and implementation The iPPI-DB server is available at https://ippidb.pasteur.fr The source code for this server is available at https://gitlab.pasteur.fr/ippidb/ippidb-web/ and is distributed under GPL licence (http://www.gnu.org/licences/gpl). Queries can be shared through persistent links according to the FAIR data standards. Data can be downloaded from the website as csv files. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2021
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7. InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein-protein interactions.

Author

Mallet V, Checa Ruano L, Moine Franel A, Nilges M, Druart K, Bouvier G, and Sperandio O

Subjects
Binding Sites, Protein Binding, Drug Design, Neural Networks, Computer, Proteins chemistry
Abstract

Motivation: Protein-protein interactions (PPIs) are key elements in numerous biological pathways and the subject of a growing number of drug discovery projects including against infectious diseases. Designing drugs on PPI targets remains a difficult task and requires extensive efforts to qualify a given interaction as an eligible target. To this end, besides the evident need to determine the role of PPIs in disease-associated pathways and their experimental characterization as therapeutics targets, prediction of their capacity to be bound by other protein partners or modulated by future drugs is of primary importance., Results: We present InDeep, a tool for predicting functional binding sites within proteins that could either host protein epitopes or future drugs. Leveraging deep learning on a curated dataset of PPIs, this tool can proceed to enhanced functional binding site predictions either on experimental structures or along molecular dynamics trajectories. The benchmark of InDeep demonstrates that our tool outperforms state-of-the-art ligandable binding sites predictors when assessing PPI targets but also conventional targets. This offers new opportunities to assist drug design projects on PPIs by identifying pertinent binding pockets at or in the vicinity of PPI interfaces., Availability and Implementation: The tool is available on GitLab at https://gitlab.pasteur.fr/InDeep/InDeep., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)

Published
2022
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