Abstract：
Researchers are investigating the medicinal properties of herbal plants throughout the world, which often leads to the discovery of novel plants and their chemicals for prophylactic needs of humans. Natural phytochemicals continue to be sought as alternative treatments for various diseases because of their non-toxic and therapeutic properties. In recent years, computational phytochemistry has enabled large-scale screening of phytochemicals, enabling researchers to pursue a wide range of therapeutic research alternatives to traditional ethnopharmacology. We propose to identify an anti-diabetic plant by computational screening on Indian herbal plants in conjunction with experimental characterization and biological validation. The methodology involves the creation of an in-house Indian herbal plant database. Molecular docking is used to screen against alpha amylase for anti-diabetic prophylaxis. Cassia angustifolia was chosen because its phytochemicals are able to bind to alpha amylase. Plants were experimentally extracted, botanically studied and their biological activity was evaluated. Further, the use of molecular dynamics was then applied to pinpoint the phytochemicals responsible for the affinity of alpha amylase. Results in the phytochemical analysis of the extracts revealed strong presence of alkaloids, flavonoids and cardiac glycosides. Moreover, alpha amylase biological activity with C. angustifolia extracts of chloroform, hexane and ethyl acetate demonstrated activity of 3.26, 8.01 and 30.33 µg/ml validating computational predictions. In conclusion, this study developed, validated computational predictions of identifying potential anti-diabetic plants \'Cassia angustifolia\' from house herbal databases. Hope this study shall inspire explore plant therapeutic repurposing using computational methods of drug discovery.Communicated by Ramaswamy H. Sarma.
In-house database phytochemicals preparation using Indian medicinal plants for repurposing plant therapeutics screening.Virtual screening of in-house database against alpha amylase for anti-diabetic therapeutics.The highest affinity plants Cassia angustifolia were identified, collected, processed four solvent extracts, along with qualitative and quantitative estimations.All plant extracts are subjected to botanical and biological experimental perspective.Advanced molecular dynamics simulations are used to understand the non-bonding interactions of phytochemicals with alpha amylase.
In-house database phytochemicals preparation using Indian medicinal plants for repurposing plant therapeutics screening.Virtual screening of in-house database against alpha amylase for anti-diabetic therapeutics.The highest affinity plants Cassia angustifolia were identified, collected, processed four solvent extracts, along with qualitative and quantitative estimations.All plant extracts are subjected to botanical and biological experimental perspective.Advanced molecular dynamics simulations are used to understand the non-bonding interactions of phytochemicals with alpha amylase.
摘要：
研究人员正在研究世界各地草药的药用特性，这通常导致发现新的植物及其化学物质，用于人类的预防性需求。天然植物化学物质由于其无毒和治疗性质而继续寻求作为各种疾病的替代疗法。近年来,计算植物化学使大规模筛选植物化学物质，使研究人员能够追求广泛的治疗研究替代传统的民族药理学。我们建议通过对印度草药植物进行计算筛选并结合实验表征和生物学验证来鉴定抗糖尿病植物。该方法涉及创建内部印度草药植物数据库。分子对接用于筛选抗α淀粉酶以预防糖尿病。选择决明子是因为其植物化学物质能够与α淀粉酶结合。通过实验提取植物，植物学研究并评估其生物活性。Further,然后使用分子动力学来确定负责α淀粉酶亲和力的植物化学物质。提取物的植物化学分析结果表明生物碱的强烈存在，类黄酮和强心苷。此外，α淀粉酶与三氯甲烷提取物的生物活性，己烷和乙酸乙酯的活性为3.26、8.01和30.33µg/ml，验证了计算预测。总之,这项研究发展，验证了从室内草药数据库中识别潜在抗糖尿病植物“决明子”的计算预测。希望这项研究能够启发使用药物发现的计算方法探索植物治疗的再利用。由RamaswamyH.Sarma沟通。
使用印度药用植物进行植物疗法筛选的内部数据库植物化学物质制备。用于抗糖尿病治疗的α淀粉酶内部数据库的虚拟筛选。鉴定出亲和力最高的植物决明子，收集,处理了四种溶剂萃取物，以及定性和定量估计。所有植物提取物均接受植物学和生物学实验观点。先进的分子动力学模拟用于了解植物化学物质与α淀粉酶的非键合相互作用。
使用印度药用植物进行植物疗法筛选的内部数据库植物化学物质制备。用于抗糖尿病治疗的α淀粉酶内部数据库的虚拟筛选。鉴定出亲和力最高的植物决明子，收集,处理了四种溶剂萃取物，以及定性和定量估计。所有植物提取物均接受植物学和生物学实验观点。先进的分子动力学模拟用于了解植物化学物质与α淀粉酶的非键合相互作用。
