Trp-Tyr is a dipeptide structure that potently stimulates GLP-1 secretion in a murine enteroendocrine cell model, identified by comprehensive analysis

Taguchi, Hayate; Murai, Kana; Hira, Tohru

doi:10.1016/j.bbrc.2023.04.042

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…27 More importantly, dipeptides and tripeptides are more stable during digestion and transepithelial transport and thus possess higher bioavailability. 28 However, the function of all dipeptides in stimulating GLP-1 secretion has been investigated by Taguchi et al 18 In this study, 307 tripeptides in PPHs were selected for GLP-1-secreting peptide screening, and the workflow of GLP-1-secreting peptide identification is shown in Figure 1.…”

Section: Identification Of Peptides In Pphs Bymentioning

confidence: 99%

“…PeptideRanker. We consider that PepT1 primarily binds with dipeptides or tripeptides, 6 and a comparative study of 339 different dipeptides in stimulating GLP-1 secretion was investigated by Taguchi et al 18 This study focused only on the virtual screening of tripeptides with the function of stimulating GLP-1 secretion (Figure 1). Accordingly, the bioactivity of tripeptides in PPHs was predicted via PeptideRanker, 19 and tripeptides with scores >0.5 were selected for further screening.…”

Section: Identification Of the Sensing Receptormentioning

confidence: 99%

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Virtual Screening of GLP-1-Secreting Peptides from Pea Protein Hydrolysates via Peptide Transporter 1 (PepT1) Activation-Based Molecular Docking

Zhang,

Zhu,

Zhang

et al. 2024

J. Agric. Food Chem.

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Dietary proteins regulate glucose homeostasis via intestinal protein sensing-induced glucagon-like peptide 1 (GLP-1) secretion. However, the reported GLP-1-secreting peptides derived from dietary proteins are few, and studies regarding GLP-1secreting peptide identification by traditional separation and purification methods are laborious. Herein, we have rapidly virtualscreened two GLP-1 secreting peptides from pea protein hydrolysates (PPHs) by peptidomic analysis and molecular docking with peptide transporter 1 (PepT1). PPH-stimulated GLP-1 secretion decreased after adding the PepT1 antagonist 4-aminobenzoic acid (4-AMBA), indicating that PepT1 activation was involved in PPH-induced GLP-1 secretion in NCI-H716 cells. Subsequently, 307 tripeptides in PPHs were obtained through peptidomic analysis. Among them, two GLP-1-secreting peptides, FLR and LRW, were identified via PepT1 activation-based molecular docking. FLR and LRW (1 mg/mL) increased GLP-1 levels to 170.20% ± 27.83% and 272.37% ± 45.96%, respectively (p < 0.05). More importantly, molecular docking implied that the interactions between peptides and the active center of PepT1 (especially Glu595, Asn329, and Asn171 in the N-pocket and Arg27 in the C-pocket) were crucial for peptide activity in stimulating GLP-1 secretion. Our study suggested that the combination of peptidomics and PepT1 activationbased molecular docking is a promising approach for identification of GLP-1-secreting peptides.

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Section: Identification Of Peptides In Pphs Bymentioning

confidence: 99%

Section: Identification Of the Sensing Receptormentioning

confidence: 99%

Virtual Screening of GLP-1-Secreting Peptides from Pea Protein Hydrolysates via Peptide Transporter 1 (PepT1) Activation-Based Molecular Docking

Zhang,

Zhu,

Zhang

et al. 2024

J. Agric. Food Chem.

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RNA-RBP interactions recognition using multi-label learning and feature attention allocation

Han,

Talpur,

Liu

et al. 2024

J Cloud Comp

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In this study, we present a sophisticated multi-label deep learning framework for the prediction of RNA-RBP (RNA-binding protein) interactions, a critical aspect in understanding RNA functionality modulation and its implications in disease pathogenesis. Our approach leverages machine learning to develop a rapid and cost-efficient predictive model for these interactions. The proposed model captures the complex characteristics of RNA and recognizes corresponding RBPs through its dual-module architecture. The first module employs convolutional neural networks (CNNs) for intricate feature extraction from RNA sequences, enabling the model to discern nuanced patterns and attributes. The second module is a multi-view multi-label classification system incorporating a feature attention mechanism. The second module is a multi-view multi-label classification system that utilizes a feature attention mechanism. This mechanism is designed to intricately analyze and distinguish between common and unique deep features derived from the diverse RNA characteristics. To evaluate the model's efficacy, extensive experiments were conducted on a comprehensive RNA-RBP interaction dataset. The results emphasize substantial improvements in the model's ability to predict RNA-RBP interactions compared to existing methodologies. This advancement emphasizes the model's potential in contributing to the understanding of RNA-mediated biological processes and disease etiology.

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GLP-1 Secretion Induced by Dietary Proteins and Peptides, and Its Hypoglycemic and Thermic Effects

Hira

2024

JJSNFS

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Trp-Tyr is a dipeptide structure that potently stimulates GLP-1 secretion in a murine enteroendocrine cell model, identified by comprehensive analysis

Cited by 3 publications

References 22 publications

Virtual Screening of GLP-1-Secreting Peptides from Pea Protein Hydrolysates via Peptide Transporter 1 (PepT1) Activation-Based Molecular Docking

Virtual Screening of GLP-1-Secreting Peptides from Pea Protein Hydrolysates via Peptide Transporter 1 (PepT1) Activation-Based Molecular Docking

RNA-RBP interactions recognition using multi-label learning and feature attention allocation

GLP-1 Secretion Induced by Dietary Proteins and Peptides, and Its Hypoglycemic and Thermic Effects

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