Hybrid Quantum–AI Framework for Protein Structure Prediction
Deep learning models and Variational Quantum Eigensolver (VQE) algorithms are used in the Hybrid Quantum–AI Framework for Protein Structure Prediction to overcome the disadvantages of each method alone. Our structure prediction method enhances accuracy and has applicability in near-term quantum computing by framing the task as “energy fusion.” Making structure prediction a “energy fusion” problem enhances accuracy and makes it suitable for near-term quantum computing (NISQ) devices.
The idea is to combine neural network data-derived biological priors with quantum algorithm physics-based modeling.
Below, we discuss the framework and its parts:
Considering restrictions requires a hybrid approach.
A challenging protein structure prediction starts with finding the lowest-energy conformation on a high-dimensional energy landscape.
AlphaFold3 and ColabFold limitations: These models are data-driven notwithstanding their success. They tend to use sequence alignments and huge training datasets instead of physical principles. This limits interpretability and generalizability, especially for brief peptide segments or conditions outside their training distribution.
VQE on NISQ devices' limitations: Quantum computing uses a novel physics-based paradigm to approximate a molecular Hamiltonian's ground-state energy. Noise, a paucity of qubits, and shallow circuit depth plague NISQ technology like the 127-qubit superconducting processor used in this study. This makes VQE predictions crude and often fail to reproduce fine-grained features like backbone dihedral angle distributions or secondary-structure motifs. The quantum energy landscape provides a reliable, low-resolution map.
Function of Fused Energy
For energy fusion, three normalized terms are weighted linearly combined:
Basic quantum-mechanical interactions and the conformational landscape's low-resolution global structure are reflected in normalized quantum energy.
Secondary Structure Distribution Divergence: This measure compares Ramachandran kernel-derived secondary structure probabilities from the quantum candidate's geometry to NSP3 predictions. It emphasises secondary structural pattern compliance.
The final metric, dihedral-angle consistency, measures how well the quantum candidate structure matches NSP3 predictions. This term encodes fine-grained backbone geometry.
Coefficients are user-specified trade-off weights that balance term contributions.
Importance and Results
Statistics showed statistically significant benefits for the hybrid framework over classical and quantum-only baselines.
The hybrid technique yielded a mean RMSD of 4.89 Ă… (median 4.70 Ă…) after analyzing 375 conformations from 75 protein fragments.
This accuracy lowers RMSD by 28.6%, ColabFold by 58.5%, and AlphaFold3 by 57.2% for predictions that are quantum-only.Significant improvements are shown by the Wilcoxon signed-rank and paired t-tests (p < 0.001).Practicality: Despite NISQ's low precision, the framework shows how data-driven models can benefit from it.
The fused energy score guides the re-ranking process towards structurally accurate, near-native conformations, as seen by its positive association with RMSD (R^2 = 0.322).
Energy fusion can be used to construct scalable hybrid quantum–classical modeling of RNA folding, ligand docking, and peptide–membrane interactions in addition to protein folding.
