A Comparative Analysis of Monofunctional Biosynthetic Peptidoglycan Transglycosylase (MBPT) from Pathogenic and Non-pathogenic Bacteria

Monofunctional biosynthetic peptidoglycan transglycosylase (MBPT) catalyzes the formation of the glycan chain in bacterial cell walls from peptidoglycan subunits: N-acetylglucosamine (NAG) and acetylmuramic acid (NAM). Bifunctional glycosyltransferases such as the penicillin binding protein (PBP) have peptidoglycan glycosyltransferase (PGT) on their C terminal end which links together the peptidoglycan subunits while transpeptidase (TP) on the N terminal end cross-links the peptide moieties on the NAM monosaccharide of the peptide subunits to create the bacterial cell wall. The singular function of MBPT resembles the C terminal end of PBP as it too contains and utilizes a similar PGT domain. In this article we analyzed the infectious and non infectious protein sequences of MBPT from 31 different strains of bacteria using a variety of bioinformatic tools. Motif analysis, dot-plot comparison, and phylogenetic analysis identified a number of significant differences between infectious and non-infectious protein sequences. In this paper we have made an attempt to explain, analyze and discuss these differences from an evolutionary perspective. The results of our sequence analysis may open the door for utilizing MBPT as a new target to fight a variety of infectious bacteria.

Comparative Analysis of the Three Classes of Archaeal and Bacterial Ribonucleotide Reductase from Evolutionary Perspective

The Ribonucleotide reductases (RNR) are essential enzymes that catalyze the conversion of nucleotides to deoxynucleotides in DNA replication and repair in all living organisms. The RNRs operate by a free radical mechanism but differ in the composition of subunit, cofactor required and regulation by allostery. Based on these differences the RNRs are classified into three classes-class I, class II and class III which depend on oxygen, adenosylcobalamin and S-adenosylmethionine with an iron sulfur cluster respectively for radical generation. In this article thirty seven sequences belonging to each of the three classes of RNR were analyzed by using various tools of bioinformatics. Phylogenetic analysis, dot-plot comparisons and motif analysis was done to identify a number of differences in the three classes of RNRs. In this research article, we have attempted to decipher evolutionary relationship between the three classes of RNR by using bioinformatics approach.