Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Davide, Sabbadin"'
Autor:
Léa El Khoury, Zhifeng Jing, Alberto Cuzzolin, Alessandro Deplano, Daniele Loco, Boris Sattarov, Florent Hédin, Sebastian Wendeborn, Chris Ho, Dina El Ahdab, Theo Jaffrelot Inizan, Mattia Sturlese, Alice Sosic, Martina Volpiana, Angela Lugato, Marco Barone, Barbara Gatto, Maria Ludovica Macchia, Massimo Bellanda, Roberto Battistutta, Cristiano Salata, Ivan Kondratov, Rustam Iminov, Andrii Khairulin, Yaroslav Mykhalonok, Anton Pochepko, Volodymyr Chashka-Ratushnyi, Iaroslava Kos, Stefano Moro, Matthieu Montes, Pengyu Ren, Jay W. Ponder, Louis Lagardère, Jean-Philip Piquemal, Davide Sabbadin
Publikováno v:
Chemical Science
Chemical Science, 2022, ⟨10.1039/D1SC05892D⟩
Chemical Science, 2022, ⟨10.1039/D1SC05892D⟩
We report a fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (Mpro) inhibitors whose potency range from mM for initial non-covalent ligands to high nM for the final covalent compound (IC50=830 +/- 50 nM). The project extensi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7fb3c770ad5b156560e839423dea37d
https://hal.science/hal-03361062
https://hal.science/hal-03361062
Autor:
Léa, El Khoury, Zhifeng, Jing, Alberto, Cuzzolin, Alessandro, Deplano, Daniele, Loco, Boris, Sattarov, Florent, Hédin, Sebastian, Wendeborn, Chris, Ho, Dina, El Ahdab, Theo, Jaffrelot Inizan, Mattia, Sturlese, Alice, Sosic, Martina, Volpiana, Angela, Lugato, Marco, Barone, Barbara, Gatto, Maria Ludovica, Macchia, Massimo, Bellanda, Roberto, Battistutta, Cristiano, Salata, Ivan, Kondratov, Rustam, Iminov, Andrii, Khairulin, Yaroslav, Mykhalonok, Anton, Pochepko, Volodymyr, Chashka-Ratushnyi, Iaroslava, Kos, Stefano, Moro, Matthieu, Montes, Pengyu, Ren, Jay W, Ponder, Louis, Lagardère, Jean-Philip, Piquemal, Davide, Sabbadin
Publikováno v:
Chemical science. 13(13)
We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (M
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::09aa3a22bcf963a31ded78c4b6b5bf9a
https://pubmed.ncbi.nlm.nih.gov/35432906
https://pubmed.ncbi.nlm.nih.gov/35432906
Autor:
Penny Cutler, Yen-Hua Huang, Fergus Gerard Paul Earley, James A. Goodchild, Aurelien Bigot, Conan K. Wang, Davide Sabbadin, Chris Chambers, David J. Craik, Judith Blythe, Quentin Kaas
Publikováno v:
FEBS Letters. 593:1336-1350
The insecticidal effects of ω-hexatoxin-Hv1a, κ-hexatoxin-Hv1c and ω/κ-hexatoxin-Hv1h are currently attributed to action at calcium and potassium channels. By characterizing the binding of these toxins to neuronal membranes, we show that they hav
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ffe20b84183ea4a08aaba345686c483c
https://doi.org/10.1002/1873-3468.13435
https://doi.org/10.1002/1873-3468.13435
Publikováno v:
Molecular pharmaceutics. 16(10)
Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbe04ab8ea2e05b6e13c7a25a2293015
https://pubmed.ncbi.nlm.nih.gov/31437001
https://pubmed.ncbi.nlm.nih.gov/31437001
Autor:
Amita Kaundal, Sang-Youl Park, Erin L. Sternburg, Dezi Elzinga, Aditya S. Vaidya, Sean R. Cutler, Michael Bartsch, Mathilde Lachia, Assaf Mosquna, Sebastian Wendeborn, Fedor V. Karginov, Davide Sabbadin
Publikováno v:
ACS chemical biology. 14(3)
Pyrabactin resistance 1 (PYR1) and related abscisic acid (ABA) receptors are new targets for manipulating plant drought tolerance. Here, we identify and use PYR1 hypersensitive mutants to define ligand binding hotspots and show that these can guide i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75c79eedc06f7075e46572022d64f042
https://pubmed.ncbi.nlm.nih.gov/30668093
https://pubmed.ncbi.nlm.nih.gov/30668093
Autor:
Giuseppe Deganutti, Stefano Moro, Antonella Ciancetta, Veronica Salmaso, Mattia Sturlese, Davide Sabbadin, Alberto Cuzzolin
Publikováno v:
Journal of Chemical Information and Modeling. 56:687-705
Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active substances as well as to develop novel active candidates. Unfortunately, simulating the binding process is still a challenging task because it requires cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a68f55dfe16138f950c477461f6e1ba0
https://doi.org/10.1021/acs.jcim.5b00702
https://doi.org/10.1021/acs.jcim.5b00702
Autor:
Davide Sabbadin, Stefano Moro, Giuseppe Deganutti, Andrea Bortolato, Jonathan S. Mason, Francesca Deflorian
Publikováno v:
Biomolecular Simulations in Structure-Based Drug Discovery
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5bf4aed5a5157af533cd51000665ca39
https://doi.org/10.1002/9783527806836.ch9
https://doi.org/10.1002/9783527806836.ch9
Publikováno v:
Methods in molecular biology (Clifton, N.J.). 1824
Supervised MD (SuMD) is a computational method that enables the exploration of ligand-receptor recognition pathway in a reduced timescale. The performance speedup is due to the incorporation of a tabu-like supervision algorithm on the ligand-receptor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::83803ee5c55cdcae1693e3ce0a07bea3
https://pubmed.ncbi.nlm.nih.gov/30039414
https://pubmed.ncbi.nlm.nih.gov/30039414
Publikováno v:
Methods in Molecular Biology ISBN: 9781493986293
Supervised MD (SuMD) is a computational method that enables the exploration of ligand-receptor recognition pathway in a reduced timescale. The performance speedup is due to the incorporation of a tabu-like supervision algorithm on the ligand-receptor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3ccf7a0aef58068a4c09198ed1fef0b
https://doi.org/10.1007/978-1-4939-8630-9_17
https://doi.org/10.1007/978-1-4939-8630-9_17
Publikováno v:
Journal of Chemical Information and Modeling
In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image ana
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8fda8d5752577c66ca575ca6ae3b0a0a