AI-Guided Hydrophobic Core Design of Robust Six-Helix Bundle Proteins

α-Helical domains are widespread and versatile,
yet typically
fail under low mechanical load because backbone hydrogen bonds unzip
sequentially, limiting their use in force-bearing nanomaterials and
molecular devices. We present an AI-guided strategy to design six-helix
bundle proteins with densely packed hydrophobic cores that co-optimize
mechanical and thermal stability. Backbones were generated with RFdiffusion,
sequences designed with ProteinMPNN, and structures validated by AlphaFold2/ESMFold;
steered and annealing molecular dynamics simulation identified designs
with high predicted unfolding forces and heat resilience. Three selected
constructs (HP149, HP206, HP347) expressed solubly and folded as predominantly
α-helical by circular dichroism. AFM-based single-molecule force
spectroscopy revealed unfolding forces approaching 100 pN, much higher
than typical α-helical domains (∼20 pN). All three retained
substantial helical content to ≥100 °C. Mutating buried
hydrophobic residues (V17S, L104R in HP149) reduced unfolding forces,
confirming core packing as an important determinant. These results
establish hydrophobic-core design as a promising route to robust α-helical
scaffolds.