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β-Sitosterol, a phytocompound from Parthenium hysterophorus, reveals anti-diabetic properties through α-Amylase inhibition: an i n-silico and in-vitro analysis.

Lokesh RaviShabari GirishSharun Roy D'SouzaAnirudh Sreenivas BkShree Kumari Godidhar RaghuramArchana OAjith Kumar KrishnanReji Manjunathan
Published in: Journal of biomolecular structure & dynamics (2023)
The study aims to identify and validate a potential α-Amylase inhibitor from the leaf extract of the Parthenium hysterophorus. Molecular docking and dynamics analyses were performed to test the anti-diabetic efficacy of the compound by focusing on α-Amylase inhibition. The molecular docking study using AutoDock Vina (PyRx) and SeeSAR tools identified β-Sitosterol as an effective α-Amylase inhibitory compound. Among the analysed fifteen phytochemicals, β-Sitosterol demonstrated the most appreciable binding energy (-9.0 Kcal/mol) and is comparatively higher than the binding energy of the standard α-Amylase inhibitor, the Acarbose (-7.6 Kcal/mol). The significance of the interaction between β-Sitosterol and α-Amylase was further investigated using Molecular Dynamics Simulation (MDS) for 100 ns via GROMACS. The data reveals that the compound could exhibit the highest stability with α-Amylase regarding RMSD, RMSF, SASA and Potential Energy analysis. The key residue of α-Amylase (Asp -197) shows a significantly low fluctuation of 0.7 Å while interacting with β-Sitosterol. The data obtained from MDS results strongly suggested the potential inhibitory impact of β-Sitosterol on α-Amylase. In addition, the proposed phytochemical was purified from the leaf extracts of P.hysterophorus using the silica gel column chromatography and identified by GC-MS analysis. The purified β-Sitosterol demonstrated a significant 42.30% in-vitro α-Amylase enzyme inhibition property under 400 µg/ml concentration and thus supported the in-silico predictions. Further in-vivo investigations are necessary to analyse the efficiency of β-Sitosterol on α-Amylase inhibition to help the anti-diabetic potential of the phytocompound.Communicated by Ramaswamy H. Sarma.
Keyphrases
  • molecular docking
  • molecular dynamics simulations
  • type diabetes
  • high resolution
  • machine learning
  • zika virus
  • climate change
  • artificial intelligence
  • dengue virus
  • data analysis