6 research outputs found
Phytochemicals of âMagahi Panâ (Piper betle L. var. magahi) as Potential H+/K+-ATPase Inhibitors: In-Silico Study and ADME Profile
Background and objectives: Â in India, peptic ulcer is most prevalent gastrointestinal disease. Historically Piper betle has been used to treat stomach problems. In order to identify the phytochemicals present in Piper betle. L. var magahi LC/MS spectroscopic analysis was performed, following which, potential phytomolecules with H+/K+-ATPase inhibitory activity were chosen using in-silico evaluation. Methods: Phytochemicals in âMagahi panâ were investigated and potential H+/K+-ATPase inhibitor phytochemicals that were screened through in-silico analysis and ADME profile of selected phytochemicals were evaluated. Phytochemical characterization was done with the help of LC/MS followed by molecular docking against enzyme H+/K+-ATPase (PDBID:5YLV) using Autodock4.2 and Swiss ADME. The binding affinity, free energy, physicochemical property, saturation of carbon atoms, number of hydrogen bond acceptors-donors, molar refractivity, lipophilicity, water solubility, and drug likeliness property were evaluated in-silico for their predicted bioactivity against H+/K+-ATPase. Results: A total of 67 phytoconstituents were identified through LC/MS positive and negative ionization mode spectral analysis and six were selected on the basis of binding energy. Molecular docking results revealed that the isolated compounds interacted with target protein H+/K+-ATPase with minimum binding energy ranging from (1) netilmicin (-9.29 kcal/mol); (2) benztropine (-9.07 kcal/mol); (3) 5,6,7,3â,4â pentahydroxyisoflavone (-8.45 kcal/mol); (4) 2-O-acetylpseudolycorine (-8.02 kcal/mol);Â (5) R-95913 (-7.73 kcal/mol) and (6) luteolin (-6.93 kcal/mol), respectively. Conclusion: The ADME profile analysis and docking studies revealed 5,6,7,3â,4â pentahydroxy-isoflavone and luteolin as potential molecules for inhibiting H+/K+-ATPase
Additional file 2: of Prediction of protein solvent accessibility using PSO-SVR with multiple sequence-derived features and weighted sliding window scheme
Performance of Various window sizes for the least square linear regression model