Evolutionary relationships of microbial transglutaminases

Istituto di Scienze dell'Alimentazione, National Research Council, Avellino, Italy
Dottorato di Ricerca in “Innovazione e management di alimenti ad elevata valenza salutistica”, Università di Foggia, Foggia, Italy
DOI
10.7287/peerj.preprints.3320v1
Subject Areas
Biochemistry, Bioinformatics
Keywords
phylogeny, microbial transglutaminase, sequence analysis
Copyright
© 2017 Giordano et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Giordano D, Facchiano A. 2017. Evolutionary relationships of microbial transglutaminases. PeerJ Preprints 5:e3320v1

Abstract

Transglutaminases (TGases) are a class of enzyme widely spread in nature, and observed in plants, microorganisms, vertebrates and invertebrates. This enzyme catalyzes post-translational protein modification, by acyl transfer reactions, deamidation and crosslinking (polymerisation). There is a large interest for TGases functions, in particular for human TGases and their involvement in physiopathological processes. In bacteria, TGases appear largely present, although the function of this enzyme is still unknown. Microbial TGases (MTG, or MTGase) are of extreme interest, in particular MTGase from Streptomyces mobaraensis, used in biopolymers industry, in cosmetics production, in wool textiles, and in the food processing. We present the results of bioinformatics analysis on MTGases sequences, based on database searching, sequence comparisons and alignments, phylogenetic tree constructions, with the aim of improving the knowledge of MTGases, in the perspective of investigating by protein modelling and simulations techniques the functional features.

Author Comment

This is an abstract which has been accepted for the NETTAB 2017 Workshop