AI-guided discovery of natural pocket proteins for portable small-molecule sensing

Recognition-element development remains an important practical challenge in small-molecule biosensing. Here, we present Dry-Wet Closed-Loop AI-driven Sensing Strategy (DWCLASS), an AI-guided workflow that combines screening of natural pocket proteins, molecular dynamics simulations, experimental binding assessment, and portable metasurface plasmonic sensing. Using thiamine (vitamin B1, thiamine) and 25-hydroxyvitamin D3 [25(OH)D ₃ ] as model analytes, the workflow identified binding responses in 7 of 16 tested analyte–protein pairs (43.8%). Selected proteins were incorporated into a competitive MetaSPR assay with gold nanoparticle amplification and acoustic mixing. The portable device provided a 240 s measurement step for prepared samples. Serum LODs were 2.98 ppt for thiamine and 3.82 ppt for 25(OH)D₃. Preliminary measurements of human serum specimens were compared with commercial ELISA to explore performance in biological samples. This study establishes an integrated route from AI-assisted screening of natural proteins to portable assay development for two chemically distinct vitamin analytes. Applicability to additional targets and clinical reference accuracy remain to be established.