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Reporting a Transcript from Iranian Viola Tricolor, Which May Encode a Novel Cyclotide-Like Precursor: Molecular and in silico Studies.

Authors :
Khoshkam Z
Zarrabi M
Sepehrizadeh Z
Naghdi E
Aftabi Y
Source :
Computational biology and chemistry [Comput Biol Chem] 2020 Feb; Vol. 84, pp. 107168. Date of Electronic Publication: 2019 Nov 21.
Publication Year :
2020

Abstract

The cyclotides are the largest known family of cyclic proteins, which are found in several plant families including Violaceae. They are circular bioactive peptides consisting of 28-37 amino acids, which possess a cyclic cystine knot (CCK) motif and could be useful in biotechnology and drug design as scaffolds for peptide-based drugs. This study describes our finding of a potentially novel gene transcript from the petals of the Iranian Viola tricolor (V. tricolor) flowers. This study is based on the cDNA screening method employed for isolation of cyclotide precursor genes and in silico analysis. Our study resulted in the finding of a novel cyclotide-like precursor from V. tricolor, which is documented in the NCBI by GenBank accession number: KP065812. The in silico analysis revealed that there are lots of similar sequences in many other plant families and they all exhibit some different features from previously discovered cyclotide precursors. The differences occur particularly in the main cyclotide domain that exists without the usual CCK structure. All of these hypothetical precursors have a conserved ER-signal sequence, a Cysteine (C)-rich sequence forming two zinc finger motifs and a cyclotide-like region containing several conserved elements including two highly conserved C residues. In conclusion, using the cDNA screening method we found a potentially new cyclotide-like precursor gene and in silico studies revealed its significant characteristics that may open up a new research line on the distribution and evolution of cyclotides.<br /> (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Details

Language :
English
ISSN :
1476-928X
Volume :
84
Database :
MEDLINE
Journal :
Computational biology and chemistry
Publication Type :
Academic Journal
Accession number :
31791808
Full Text :
https://doi.org/10.1016/j.compbiolchem.2019.107168