Back

Terminal Modification Reprograms Supramolecular Organization of Integrin Ligand Assemblies to Shape Cell Migration

Su, Z.; Lin, Y.; Zhang, Y.; Hu, X.

2026-09-29 bioengineering
10.64898/2026.09.29.755244 bioRxiv
Show abstract

Precise organization of bioactive ligands at material-cell interfaces is critical for directing integrin-mediated cell behavior, yet tuning supramolecular organization commonly necessitates orchestrated redesign of molecular composition or ligand sequence. Here we establish terminal modification as a minimal, sequence-preserving strategy to reprogram the organization and function of self-assembling integrin ligands. Using FFFIKVAV as a model system, N- and C-terminal modifications generated distinct supramolecular states with markedly different integrin activation and cell migration responses. Notably, stronger intermolecular association did not produce stronger biological activity, as N-terminal acetylation promoted scaffold consolidation while reducing the configurational freedom of the exposed IKVAV ligand and diminishing integrin activation and cell migration. Alongside modulating migration magnitude, terminal chemistry also diversified the cellular programs supporting migration, with the parent peptide and its C-terminally amidated analogue favoring protrusion-dominant and stress-fiber-dominant adhesion-cytoskeletal programs, respectively. These functional states were reproduced when the assemblies were introduced directly to pre-adherent cells, extending their activity beyond preformed material coatings. Our findings identify terminal chemistry as a compact molecular design variable that links supramolecular organization to ligand function while diversifying the biological output of a shared bioactive sequence.

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.