Back

A single dimer of the SARS-CoV-2 N protein can associate with multiple fragments of single-stranded and stem-loop RNAs

Kaneda, N.; Suzuki, L.; Endo, S.; Husna, S. M. A.; Ishikawa, S.; Takahashi, H.; Oikawa, H.; Itoh, Y.; Takahashi, S.

2025-01-24 biophysics
10.1101/2025.01.23.634602 bioRxiv
Show abstract

The nucleocapsid (N) protein of SARS-CoV-2 associates with the viral genomic RNA (gRNA) and forms the ribonucleoprotein (RNP) granules. However, the detailed molecular structures of RNP and their formation mechanism are largely unknown. We used circular dichroism (CD) spectroscopy, fluorescence correlation spectroscopy (FCS) and single-molecule Forster resonance energy transfer (sm-FRET) spectroscopy to understand the interaction between the N protein and different structural units of RNA. We chose polyadenylate chains with 40, 30 and 20 bases possessing a single-stranded structure and three stem loops with 50, 41 and 29 bases selected from gRNA, and labeled their 5 and 3 ends by Alexa488 and Alexa647, respectively, for the FCS and sm-FRET measurements. We found that the N protein started to bind to the single-stranded RNAs at the concentrations between 10 and 100 nM. The binding of the N protein to one of the stem loops occurred at the concentration less than 10 nM without melting the stem loop. For all the samples, the binding of multiple molecules of the RNA fragments to a single dimer of the N protein was observed. These results demonstrate that the N protein acts as a non-specific binder to both single-stranded and stem-loop structures of RNA, and that the N protein might contract a long RNA chain by bridging its multiple segments. We propose that the RNP granules might be folded by the association of the numerous stem loops of gRNA triggered by the assembly of the N protein.

Matching journals

The top 8 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.