Predicting a protein’s three-dimensional structure from its amino acid sequence is one of the most fundamental and challenging problems in molecular biology. Although AI-based approaches like AlphaFold have achieved groundbreaking results, they still struggle when no structural homologs are available.

Physics-based free modeling techniques, while more accurate in principle, require immense computational power—making classical simulations extremely slow or even infeasible.

Quantum computing introduces a new paradigm. By leveraging quantum algorithms together with coarse-grained lattice models, quantum processors can efficiently explore the enormous conformational space that proteins can adopt.

The choice of lattice model is critical:

• Tetrahedral lattices: Efficient but too rigid to represent key secondary structures.
• Face-centered cubic (FCC) lattices: Higher geometric fidelity, allowing realistic α-helices and β-sheets, though requiring greater quantum resources.

The goal of this hackathon is to explore how quantum algorithms and lattice-based representations can help overcome classical limitations, opening new pathways in protein structure prediction and molecular modeling.

For more information, check this document.