PhytoSelectDBT: A database for the molecular models of anti-diabetic targets docked with bioactive peptides from selected ethno-medicinal plants

PhytoSelectDBT:一个数据库,用于存储抗糖尿病靶点分子模型与精选民族药用植物中的生物活性肽对接的分子模型。

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Abstract

It is of interest to assess the effectiveness of bioactive peptides derived from 41 ethno-medicinal plants, classify them according to their anti-diabetic protein targets (DPP-IV, alpha-amylase, alpha-glucosidase, GRK2, GSK3B, GLP-1R, and AdipoR1), and create a web tool named PhytoSelectDBT by using the top seven peptides per target. If one of the target-based medicinal plant suggestions made by PhytoSelectDBT is unsuccessful, alternative target-based possibilities are presented by PhytoSelectDBT for treating the condition and any other related complications. The results provide a useful resource for the management of type 2 diabetes and emphasize the significance of utilising ethnomedical knowledge for the identification of potent anti-diabetic plants and their peptides. We used molecular docking to investigate interactions between anti-diabetic targets (DPP-IV, alpha-amylase, alpha-glucosidase, GRK2, GSK3B, GLP-1R, and AdipoR1) and projected bioactive peptides from 41 ethnomedicinal plants. All bioactive peptides were cross-checked against several databases to determine their allergenicity, toxicity, and cross-reactivity. The presence of B and T cell epitopes was also examined in all simulated digested bioactive peptides for reference. This data is archived at the PhytoselectDBT database.

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