[3S03e] 08. Functional Molecules and Materials for Biological Systems - Part 3

6:00 PM - 6:15 PM JST(9:00 AM - 9:15 AM UTC)

[3FMBS-13] Integration of cDNA display with next-generation sequencing and bioinformatics for substrate profiling of transglutaminases

○Jasmina Damnjanovic¹, Kalhari Munaweera¹, Maurizio Camagna¹, Kiyotaka Hitomi², Hideo Nakano¹ (1. Nagoya University, Graduate School of Bioagricultural Sciences (Japan), 2. Nagoya University, Graduate School of Pharmaceutical Sciences (Japan))

Keywords:

transglutaminase,molecular engineering,cDNA display,next-generation sequencing,bioinformatics

Transglutaminases (TGs) are enzymes best known for catalyzing the stable crosslinking of proteins at their glutamine (Gln) and lysine (Lys) sequences, which is crucial for many biological processes in bacteria, animal and plant kingdoms. TGs are very diverse in size and shape, as well as in their substrate specificity, specifically amino acid sequence and length surrounding the reactive Gln. While human isozymes (TG1 - TG7 and Factor XIII) demonstrate strict substrate specificity, bacterial enzymes often have higher stability and catalytic turnover but display broad substrate specificity. Differences in substrate specificity are fundamental for control of the TG activity and their use in biotechnological processes. While mammalian TGs are studied in the context of drug targets where isozyme-specific inhibitors are desired drug options, bacterial TGs are studied as tools for protein crosslinking in the food and pharmaceutical industry, where site-specific crosslinking is often desired. Both directions of TG research heavily depend on comprehensive knowledge of their substrate profiles, which remains inaccessible to date.

To tackle the above problem, our group has established an in vitro TG substrate profiling platform based on cDNA display, next-generation sequencing (NGS), and bioinformatics. Our platform utilizes cDNA-displayed peptide libraries as Gln substrate pools, which are reacted with biotinylated primary amine in the presence of the TG of interest under increasingly strict selection conditions. The pool of reactive Gln sequences is recovered by the streptavidin pull-down and identified by the sequencing of their respective cDNA. The obtained NGS data are analyzed by the in-house Python scripts to deduce the preferred Gln sequences. We have applied this platform to study substrate profiles of TG1, TG2, TG3, and bacterial TG, and established the use of NGS data for the identification of natural protein substrates of these enzymes.

References:
(1) Damnjanovic et al., Sci. Rep. 12(1):13578, 2022.
(2) Munaweera et al., Biosci. Biotechnol. Biochem. 88(6):620-629, 2024.