Back

A Novel {psi}-χ Fusion Protein for Unravelling the Contributions of χ to DNA Replication and Repair

Padgett-Pagliai, K. A.; Grant, J. D.; Petrides, M. J.; Newcomb, E.; Lovett, S. T.; Bloom, L.

2025-10-15 biochemistry
10.1101/2025.10.15.682636 bioRxiv
Show abstract

Faithful DNA replication in Escherichia coli requires the DNA polymerase III holoenzyme (DNA pol III HE) and its clamp loader complex, which couples processive DNA synthesis with {beta}2 clamp loading. The clamp loader accessory subunits {chi} and {psi} link single-stranded DNA-binding protein (SSB) to the replisome, stabilizing replication on SSB-coated templates and coordinating access for other SSB-interacting proteins (SIPs). Chi has also been implicated in tolerance to the chain-terminating nucleotide analog azidothymidine (AZT), though whether this function depends on {chi} within DNA pol III HE or on its independent interaction with the YoaA helicase remains unclear. To address this, we engineered {psi}-{chi} fusion proteins with flexible glycine-serine linkers to tether the two subunits while preserving folding and activity. Both fusions were biochemically competent, supporting ATP hydrolysis and clamp loading on SSB-coated DNA. In vivo, however, neither the {psi}-GS12-{chi} fusion nor expression of a {psi}{chi} operon restored AZT tolerance in {Delta}holC cells, whereas expression of {chi} alone was sufficient. Fusion expression also impaired growth in both WT and {Delta}holC backgrounds, a phenotype partially alleviated by disrupting {chi}-SSB binding. These findings support a model in which {chi} must dynamically engage SSB and YoaA outside of the clamp loader to promote AZT tolerance, highlighting the importance of regulated {chi}-SSB interactions in genome maintenance.

Published in Nucleic Acids Research (predicted rank #2) · training set

Matching journals

The top 2 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.