Back

Single Domain Antibody Inhibitors Target the Coiled Coil Arms of the Bacillus subtilis SMC complex

Gosselin, O.; Taschner, M.; Huber-Hürlimann, L.; Seeger, M. A.; Gruber, S.

2025-10-17 molecular biology
10.1101/2025.10.16.682983 bioRxiv
Show abstract

Synthetic nanobodies--also called sybodies--have proven valuable for stabilizing conformations of purified proteins, advancing structural and functional studies for example of transmembrane protein complexes. However, their utility in modulating protein function in living cells has remained less well explored. Structural Maintenance of Chromosomes (SMC) complexes facilitate chromosome organization by DNA loop extrusion, a fundamental process in all domains of life. In this study, we target the bacterial SMC complex, Smc-ScpAB, in Bacillus subtilis with synthetic nanobodies, with the aim to identify key functional regions of the protein complex in a largely unbiased manner. We first isolate sybodies that specifically bind purified Smc-ScpAB and then express them in B. subtilis to select binders capable of disrupting Smc-ScpAB function, leading to chromosome segregation defects and cell death. Mapping and biochemical characterization show that the fourteen disruptive sybodies belong to one of three library designs, target the Smc subunit near the same coiled coil arm interface and modulate its ATPase activity in two principal ways, highlighting the mid-region of the Smc coiled coil as critical feature of the DNA folding process. These findings underscore the potential of sybodies--and, by extension, designed binders--as versatile tools for probing dynamic protein function in living cells, with potentially broad applications in cell and synthetic biology.

Published in eLife (predicted rank #1) · training set

Matching journals

The top 4 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.