In silico target identification of nootropic bioactive compounds from ayurvedic herbs

Numerous plants are listed in the Ayurvedic pharmacopoeia, and several different plant parts are being used in Ayurvedic formulations. The bioactive ingredients of many of these medicinal have been identified. A key step in Ayurvedic drug development is the identification and validation of biological targets of these bioactive ingredients. Most of the experimental techniques involving genomic, proteomic and metabolomic approaches for target identification are laborious and expensive. Computational approaches allow an efficient alternative approach for in silico target prediction of bioactive compounds. Here, we have used computational methods to predict the target proteins of major bioactive compounds present in seven medicinal plants (Bacopa monnieri, Centella asiatica, Clitoria ternatea, Acorus calamus Glycyrrhiza glabra Celastrus paniculatus Nardostachys jatamansi) known for their nootropic properties. These plants/plant parts are being used in various traditional Ayurveda formulations intended for cognitive enhancement and memory boosting. Even though these plants are widely used in the treatment of cognitive deficits, their scientific evaluation is lacking. Till date, very few studies have attempted to elucidate the targets or to explain the mode of action of bioactive ingredients in nootropic medicinal plants. We have chosen three databases for target prediction- ChEMBL, Swiss Target Prediction, and Binding DB. Based on available literature, we also examined if any of the predicted target proteins have brain-related functions. Pertinent to the nootropic properties of the medicinal plants, our study revealed several potential target proteins such as CYP19A1, MAPT, PTGS1, ACHE, SLC6A2, SLC6A3, MAOA and MAOB implicated in neurodevelopment, neuroprotection, learning and memory.