Literature DB >> 31787028

Computational investigation reveals Picrasidine C as selective PPARα lead: binding pattern, selectivity mechanism and ADME/tox profile.

Fangfei Li1, Hanxun Wang1, Ying Wang1,2, Shasha Feng1, Baichun Hu1,3, Xiangyu Zhang1, Jian Wang1, Wei Li4, Maosheng Cheng1.   

Abstract

Natural products and their derivatives have been recognized as an important source of therapeutic agents for many years. Previously we isolated a dimeric β-carboline-type alkaloid Picrasidine C from the root of Picrasma quassioides as subtype-selective peroxisome proliferator-activated receptor α (PPARα) agonist. In order to modify this natural product for better affinity and druggability, we investigated a series of properties exhibited by Picrasidine C, such as its binding mode with PPARα, the selectivity mechanism over PPARγ, as well as ADME/Tox profile through computational methods including sequence alignment, molecular docking, pharmacophore modeling and molecular dynamics simulations. The detailed information of binding pattern and affinity for Picrasidine C elucidated here will be valuable for chemical modification. Besides, the steric hindrance of residue Phe363 in PPARγ pocket was speculated as the main isoform selectivity mechanism for Picrasidine C, which would be helpful for the design of selective derivatives. ADME/Tox prediction was conducted to avoid potential undesirable pharmacokinetic properties for reducing the risk of failure. Finally, novel skeletons were derived from lead compound by core hopping method, validated through molecular dynamic simulations and MM-GBSA calculation. In short, the information obtained from computational strategy would be valuable for us to find more potent, safe and selective PPARα agonists during structural optimization.

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Keywords:  ADME/Tox prediction; PPARα agonist; Picrasidine C; molecular docking; molecular dynamics simulation

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Year:  2019        PMID: 31787028     DOI: 10.1080/07391102.2019.1699861

Source DB:  PubMed          Journal:  J Biomol Struct Dyn        ISSN: 0739-1102


  1 in total

1.  Anatomy of noncovalent interactions between the nucleobases or ribose and π-containing amino acids in RNA-protein complexes.

Authors:  Katie A Wilson; Ryan W Kung; Simmone D'souza; Stacey D Wetmore
Journal:  Nucleic Acids Res       Date:  2021-02-26       Impact factor: 16.971

  1 in total

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