Alzheimer disease (AD) can be considered the most common age-related neurodegenerative disorder, and also, an important cause of death in elderly patients. A number of studies found the correlation of this disease pathology with BACE1 inhibitor and it is also evident that BACE1 inhibitor can function as a very potent treatment strategy in treating AD. In the present study, we aimed to prospect for novel plant-derived BACE1 inhibitors from Leea indica and to realize the structural basis of their interactions and mechanisms using combined molecular docking and molecular dynamics based approaches. An extensive library of Leea indica plant-derived molecule was compiled and computationally screened for inhibitory action against BACE1 by using virtual screening approaches. Furthermore, induced fit docking and classical molecular dynamics along with steered molecular dynamics simulations were employed to get insight the binding mechanisms. Two triterpenoids, ursolic acid and lupeol were identified through virtual screening; wherein, lupeol showed better binding free energy in MM/GBSA, MM/PBSA and MM/GBVI approaches. Furthermore classical and steered dynamics revealed the favourable hydrophobic interactions between the lupeol and the residues of flap or catalytic dyad of BACE1; however, ursolic acid showed disfavorable interactions with the BACE1. This study, therefore, unveiled the lupeol as a potent BACE1 inhibitor from a manually curated dataset of Leea indica molecules, which may provide a new dimension of designing novel BACE1 inhibitors for AD therapy.
Aims: One of the most important resources for the development of new drugs is a biologically active lead compound from natural sources. Biomedical researchers and pharmaceutical companies have a high interest in plant-derived molecules that can be used for drug development. Background: The collective knowledge of plants and their phytoconstituents would be of great benefit for the researchers involved in drug design. Therefore, we developed a unique and dynamic database GreenMolBD, to provide collective information of medicinal plants such as their individual profile, chemical constituents and pharmacological evidence, along with their plant parts and extract types based on different studies. Objective: We have also provided a complete profile of each compound, their physical, quantum, drug-likeliness, and toxicity properties (48 type’s descriptor) using in silico tools. Method: 1846 associated targets, related to individual compounds that are already explored in different studies, are also incorporated and synchronized. Result: GreenMolBD is freely accessible and searchable by keywords, plant name, synonym, common name, family name, family synonym, compound name, synonym, IUPAC name, InChI Key, target name and disease name. Conclusion: This is the first evidence-based database of bioactive molecules from medicinal plants specially grown in Bangladesh, which may help to explore and foster nature-inspired rational drug discovery in the future. Our database is continuously updating with the new information.
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