Personne : Biron, Éric
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Biron
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Éric
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Université Laval. Faculté de pharmacie
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ncf11849118
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Publication Restreint Collagencin, an antibacterial peptide from fish collagen : activity, structure and interaction dynamics with membrane(Elsevier, 2016-03-30) Biron, Éric; Gomaa, Ahmed; Fliss, Ismaïl; Beaulieu, Lucie; Bédard, François; Subirade, Muriel; Ennaas, Nadia; Hammami, RiadhIn this study, we first report characterization of collagencin, an antimicrobial peptide identified from fish collagen hydrolysate. The peptide completely inhibited the growth of Staphylococcus aureus at 1.88 mM. Although non-toxic up to 470 μM, collagencin was hemolytic at higher concentrations. The secondary structure of collagencin was mainly composed by β-sheet and β-turn as determined by CD measurements and molecular dynamics. The peptide is likely to form β-sheet structure under hydrophobic environments and interacts with both anionic (phosphatidylglycerol) and zwitterionic (phosphoethanolamine and phosphatidylcholine) lipids as shown with CD spectroscopy and molecular dynamics. The peptide formed several hydrogen bonds with both POPG and POPE lipids and remained at membrane–water interface, suggesting that collagencin antibacterial action follows a carpet mechanism. Collagenous fish wastes could be processed by enzymatic hydrolysis and transformed into products of high value having functional or biological properties. Marine collagens are a promising source of antimicrobial peptides with new implications in food safety and human health.Publication Accès libre Machine learning assisted design of highly active peptides for drug discovery(Public Library of Science, 2015-04-07) Tremblay, Denise; Biron, Éric; Giguère, Sébastien; Moineau, Sylvain; Laviolette, François; Liang, Xinxia; Marchand, Mario; Corbeil, JacquesThe discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning approaches can greatly assist in the process and even partly replace expensive laboratory experiments by learning a predictor with existing data or with a smaller amount of data generation. Unfortunately, once the model is learned, selecting peptides having the greatest predicted bioactivity often requires a prohibitive amount of computational time. For this combinatorial problem, heuristics and stochastic optimization methods are not guaranteed to find adequate solutions. We focused on recent advances in kernel methods and machine learning to learn a predictive model with proven success. For this type of model, we propose an efficient algorithm based on graph theory, that is guaranteed to find the peptides for which the model predicts maximal bioactivity. We also present a second algorithm capable of sorting the peptides of maximal bioactivity. Extensive analyses demonstrate how these algorithms can be part of an iterative combinatorial chemistry procedure to speed up the discovery and the validation of peptide leads. Moreover, the proposed approach does not require the use of known ligands for the target protein since it can leverage recent multi-target machine learning predictors where ligands for similar targets can serve as initial training data. Finally, we validated the proposed approach in vitro with the discovery of new cationic antimicrobial peptides.Publication Accès libre Preparation of N-substituted N-arylsulfonylglycines and their use in peptoid synthesis(American Chemical Society, 2015-11-09) Biron, Éric; Jobin, Steve; Herby, Claire; Vézina-Dawod, Simon; Derson, AntoineTo increase the chemical diversity accessible with peptoids and peptide–peptoid hybrids, N-alkylated arylsulfonamides were used to prepare side chain protected N-substituted glycines compatible with solid-phase synthesis. The described procedures give access to peptoid monomers bearing a wide variety of functional groups from commercially available amines in four straightforward steps. The prepared N-substituted N-arylsulfonylglycines were used as monomers in solid-phase synthesis to introduce relevant functionalized side chains into peptoid oligomers and peptide–peptoid hybrids.Publication Accès libre Toward solid-phase peptide fragment ligation by a traceless-Ugi multicomponent reaction approach(Royal Society of Chemistry, 2016-11-14) Biron, Éric; Jobin, Steve; Galindo, Sindy-Marcela; Vézina-Dawod, Simon; Fontaine, Alexia; Liang, XinxiaA new methodology to couple peptide fragments on solid support using a traceless isocyanide-based multicomponent reaction is described. The approach uses a microwave-assisted on-resin Ugi four-component reaction to attach a carboxyl free peptide to a supported peptide bearing a free N-terminal amine via the formation of an N-protected amide bond at the ligation site. Afterward, the generated backbone amide protecting group can be efficiently removed by microwave-assisted acidolysis with trifluoroacetic acid to afford a fully deprotected peptide. This straightforward Ugi reaction/deprotection approach was applied to condense various fragment lengths and provided a variety of oligopeptides.