Functional customization of peptide linkers in fusion proteins through multimodal deep learning approach

Peptide linkers are critical modulators of function in fusion proteins, a foundational technology in modern bioengineering. However, the rational customization of linkers for specific applications remains challenging, hindered by an insufficient understanding of the relationship between linker sequences and fused protein function. In this study, we systematically characterized 370 diverse linkers, generated from random 18-amino acid sequences with no homology to known proteins, fusing sfGFP to a