Back

Hydration Energetics Shape Antibody Discrimination between Sulfotyrosine and Phosphotyrosine

Mori, T.; Yahagi, K.; Maruoka, S.; Toyoda, K.; Sonoshita, Y.; Kametani, Y.; Shiota, Y.; Yoshizawa, K.; Watanabe, K.; Okazaki, K.; Kobashigawa, Y.; Morioka, H.; Hirakawa, H.; Nishimoto, E.; Teramoto, T.; Kakuta, Y.

2026-08-11 biophysics
10.64898/2026.08.05.743142 bioRxiv
Show abstract

Chemically similar post-translational modifications can mediate distinct biological functions, but how proteins distinguish between them remains unclear. Sulfotyrosine (sTyr) and phosphotyrosine (pTyr) exemplify this problem because they have similar sizes, local geometries, and electrostatic properties but function in different biological contexts. Here, we used the monoclonal antibody PSG2, which recognizes sTyr independently of the surrounding peptide sequence, to examine how a protein distinguishes these modifications. The crystal structure of PSG2 bound to an sTyr-containing peptide revealed a deep electropositive pocket with no modeled water molecules in direct contact with the sulfate group. Gas-phase density functional theory calculations favored pTyr over sTyr, showing that direct protein-ligand interactions alone are insufficient to explain PSG2 selectivity. Explicit first-shell hydration calculations showed that pTyr has a larger desolvation penalty than sTyr, and accounting for this difference reversed the calculated energetic order. Isothermal titration calorimetry showed favorable enthalpic and entropic contributions to sTyr binding, whereas no detectable heat signal was observed for pTyr. These results show that PSG2 distinguishes sTyr from pTyr through the balance between direct protein-ligand interactions and ligand desolvation.

Matching journals

The top 7 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.