Back

cDNA display coupled with next-generation sequencing for rapid activity-based screening: Comprehensive analysis of transglutaminase substrate preference

Damnjanovic, J.; Odake, N.; Fan, J.; Jia, B.; KOJIMA, T.; Nemoto, N.; Hitomi, K.; Nakano, H.

2021-09-08 bioengineering
10.1101/2021.09.08.459404 bioRxiv
Show abstract

cDNA display is an in vitro display technology based on a covalent linkage between a protein and its corresponding mRNA/cDNA, where a stable complex is formed suitable for a wide range of selection conditions. A great advantage of cDNA display is the ability to handle enormous library size (1012) in a microtube scale, in a matter of days. To harness its benefits, we aimed at developing a platform which combines the advantages of cDNA display with high-throughput and accuracy of next-generation sequencing (NGS) for the selection of preferred substrate peptides of transglutaminase 2 (TG2), a protein cross-linking enzyme. After the optimization of the platform by the repeated screening of binary model libraries consisting of the substrate and non-substrate peptides at different ratios, screening and selection of combinatorial peptide library randomized at positions -1, +1, +2, and +3 from the glutamine residue was carried out. Enriched cDNA complexes were analyzed by NGS and bioinformatics, revealing the comprehensive amino acid preference of the TG2 at targeted positions of the peptide backbone. This is the first report on the cDNA display/NGS screening system to yield comprehensive data on TG substrate preference. Although some issues remain to be solved, this platform can be applied to the selection of other TGs and easily adjusted for the selection of other peptide substrates and even larger biomolecules.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.