Roy Susanta, Teron Robindra, Nikku Linga Raju
Department of Life Science, Assam University - Diphu Campus, Diphu, Karbi Anglong, ASSAM - 782 462.
North Eastern Institute of Ayurveda and Folk Medicine Research (NEIAFMR) Pasighat, East Siang District, Arunachal Pradesh - 791102.
Bioinformation. 2023 Sep 30;19(9):908-917. doi: 10.6026/97320630019908. eCollection 2023.
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.
评估源自41种民族药用植物的生物活性肽的有效性，根据其抗糖尿病蛋白靶点（二肽基肽酶-IV、α-淀粉酶、α-葡萄糖苷酶、G蛋白偶联受体激酶2、糖原合成酶激酶3β、胰高血糖素样肽-1受体和脂联素受体1）对它们进行分类，并使用每个靶点的前七种肽创建一个名为PhytoSelectDBT的网络工具，这很有意义。如果PhytoSelectDBT提出的基于靶点的药用植物建议之一未成功，PhytoSelectDBT会提供其他基于靶点的治疗该病症及任何其他相关并发症的可能性。这些结果为2型糖尿病的管理提供了有用的资源，并强调了利用民族医学知识来鉴定有效的抗糖尿病植物及其肽的重要性。我们使用分子对接来研究抗糖尿病靶点（二肽基肽酶-IV、α-淀粉酶、α-葡萄糖苷酶、G蛋白偶联受体激酶2、糖原合成酶激酶3β、胰高血糖素样肽-1受体和脂联素受体1）与来自41种民族药用植物的预测生物活性肽之间的相互作用。所有生物活性肽都与几个数据库进行交叉核对，以确定其致敏性、毒性和交叉反应性。还检查了所有模拟消化的生物活性肽中B细胞和T细胞表位的存在情况以供参考。该数据存档于PhytoselectDBT数据库。
