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Dioxygenase from Aspergillus fumigatus MC8: molecular modelling and in silico studies on enzyme-substrate interactions

Författare:
  • Roopesh Krishnankutty
  • Joseph Abhilash
  • M. Haridas
  • Abdulhameed Sabu
  • Perraud Gaime Isabelle
  • Sevastianos Roussos
  • Christopher Augur
Publiceringsår: 2012
Språk: Engelska
Sidor: 144-151
Publikation/Tidskrift/Serie: Molecular Simulation
Volym: 38
Nummer: 2
Dokumenttyp: Artikel
Förlag: Taylor & Francis Group

Sammanfattning

Flavoenzymes have been extensively studied for their structural and mechanistic properties because they find potential application as industrial biocatalysts. They are attractive for biocatalysis because of the selectivity, controllability and efficiency of their reactions. Some of these enzymes catalyse the oxidative modification of protein substrates. Among them oxygenases (monoxoygenases and dioxygenases) are of special interest because they are highly entantio as well as regio-selective and can be used for oxyfunctionalisation. Dioxygenase enzymes catalyse oxygenation reactions in which both dioxygen atoms are incorporated into the product. A dioxygenase enzyme purified from Aspergillus fumigatus MC8 was subjected to protein digestion followed by peptide sequencing. The sequence analysis of the peptide fragments resulted in identifying its match with that of an extracellular dioxygenase sequence from the same species of fungus existing in the protein database. The sequence was submitted to protein homology/analogy recognition engine online server for homology modelling and the 3D structure was predicted. Subsequently, the in silico studies of the enzyme-substrate (protein-ligand) interaction were carried out by using the method of molecular docking simulations wherein the modelled dioxygenase enzyme (protein) was docked with the substrates (ligands), catechin and epicatechin.

Disputation

Nyckelord

  • Biology and Life Sciences
  • dioxygenase
  • peptide sequencing
  • homology modelling
  • molecular docking

Övrigt

Published
Yes
  • ISSN: 0892-7022

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