Anti-Cancer and Anti-Inflammatory Activities of a Short Molecule, PS14 Derived from the Virulent Cellulose Binding Domain of Aphanomyces invadans, on Human Laryngeal Epithelial Cells and an In Vivo Zebrafish Embryo Model

Molecules, Journal Year: 2022, Volume and Issue: 27(21), P. 7333 - 7333

Published: Oct. 28, 2022

In this study, the anti-cancer and anti-inflammatory activities of PS14, a short peptide derived from cellulase binding domain pathogenic fungus, Aphanomyces invadans, have been evaluated, in vitro vivo. Bioinformatics analysis PS14 revealed physicochemical properties web-based predictions, which indicate that is non-toxic, it has potential to elicit activities. These silico results were experimentally validated through (L6 or Hep-2 cells) vivo (zebrafish embryo larvae) models. Experimental showed non-toxic L6 cells zebrafish embryo, elicits an antitumor effect embryos. Anticancer activity assays, terms MTT, trypan blue LDH dose-dependent inhibitory on cell proliferation. Moreover, epithelial cancer embryos, challenge (i) caused significant changes cytomorphology induced apoptosis; (ii) triggered ROS generation; (iii) up-regulation genes including BAX, Caspase 3, 9 down-regulation Bcl-2, vitro. The was observed cell-free assays for inhibition proteinase lipoxygenase, heat-induced hemolysis hypotonicity-induced hemolysis. Together, study identified activities, while being Future experiments can focus clinical pharmacodynamics aspects PS14.

Language: Английский

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Peptide-Membrane Docking and Molecular Dynamic Simulation of In Silico Detected Antimicrobial Peptides from Portulaca oleracea’s Transcriptome

Probiotics and Antimicrobial Proteins, Journal Year: 2024, Volume and Issue: 16(5), P. 1501 - 1515

Published: May 4, 2024

Language: Английский

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VISH-Pred: an ensemble of fine-tuned ESM models for protein toxicity prediction

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(4)

Published: May 23, 2024

Abstract Peptide- and protein-based therapeutics are becoming a promising treatment regimen for myriad diseases. Toxicity of proteins is the primary hurdle therapies. Thus, there an urgent need accurate in silico methods determining toxic to filter pool potential candidates. At same time, it imperative precisely identify non-toxic expand possibilities biologics. To address this challenge, we proposed ensemble framework, called VISH-Pred, comprising models built by fine-tuning ESM2 transformer on large, experimentally validated, curated dataset protein peptide toxicities. The steps VISH-Pred framework efficiently estimate toxicities taking just sequence as input, employing under sampling technique handle humongous class-imbalance data learning representations from fine-tuned language which then fed machine techniques such Lightgbm XGBoost. able correctly both peptides/proteins with toxicity proteins, achieving Matthews correlation coefficient 0.737, 0.716 0.322 F1-score 0.759, 0.696 0.713 three non-redundant blind tests, respectively, outperforming other over $10\%$ these quality metrics. Moreover, achieved best accuracy area receiver operating curve scores independent test sets, highlighting robustness generalization capability framework. By making available easy-to-use web server, expect serve valuable asset future endeavors aimed at discerning peptides enabling efficient therapeutics.

Language: Английский

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Discovery of potential antidiabetic peptides using deep learning

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 180, P. 109013 - 109013

Published: Aug. 12, 2024

Language: Английский

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Modern machine learning methods for protein property prediction

Current Opinion in Structural Biology, Journal Year: 2025, Volume and Issue: 90, P. 102990 - 102990

Published: Jan. 28, 2025

Language: Английский

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