Abstract
ABSTRACT
Gadolinium‐based contrast agents (GBCAs) are indispensable tools in magnetic resonance imaging (MRI), yet their clinical use is limited by non‐specific tissue accumulation, low molecular specificity, and safety concerns. Protein and peptide scaffolds provide a promising alternative because they can bind metal ions with high selectivity and enable precise molecular targeting. However, identifying short peptide motifs with optimal gadolinium (Gd
3+
) coordination and high relaxivity remains a major challenge. Here, we used a machine‐learning‐driven peptide evolution platform, the Protein Optimization Engineering Tool (POET), to design and optimize short Gd‐binding motifs that enhance longitudinal relaxivity (
r
1
). Two algorithmic strategies were tested: motif‐based and regular‐expression‐based representations. Both algorithms were trained on an initial set of 74 twelve‐amino‐acid peptides derived from natural EF‐hand scaffolds. Through two rounds of directed evolution and experimental screening, POET predicted peptides with up to a 24% increase in
r
1
ratio compared with the best natural EF‐hand. Further analysis revealed that peptides exhibiting higher relaxivity generally possessed a more negative net charge and lower isoelectric point than the buffer pH, indicating stronger electrostatic stabilization of Gd
3+
. Sequence enrichment analysis showed that acidic and small polar residues, particularly aspartic acid, glycine, and threonine, were selectively favored during evolution, while bulky hydrophobic and basic residues were depleted. These compositional trends align with improved solubility and enhanced metal coordination. Together, these results demonstrate a generalizable framework that integrates computational evolution with biophysical screening to discover new biologically derived Gd‐binding motifs. This approach provides a scalable route to engineer responsive, tunable, and biocompatible MRI contrast tags for precision imaging and molecular diagnostics.