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 nanobody. Although sfGFP fluorescence exhibited no clear correlation with canonical linker properties like flexibility or rigidity, we identified a correlation between amino acid composition and functional output. Furthermore, AlphaFold-predicted substructures encompassing the linker and adjacent sfGFP regions revealed considerable structural diversity while maintaining the overall sfGFP fold. Notably, in silico structural features derived from the Cα–Cα distance matrix of these predicted substructures correlated with fluorescence, providing a structural rationale for the functional variation. By training on both sequence representations and in silico substructural features, we developed a multimodal deep learning framework to quantitatively customize linker sequences for high sfGFP fluorescence in special fusion constructs. This work presents a generalizable framework for engineering peptide linkers to assemble highly functional fusion proteins.