A Quantum Framework for Protein Binding-Site Structure Prediction on Utility-Level Quantum Processors
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914309707137024 |
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| author | Zhang, Yuqi Yang, Yuxin Martin, William Lin, Kingsten Wang, Zixu Lu, Cheng-Chang Jiang, Weiwen Nussinov, Ruth Loscalzo, Joseph Guan, Qiang Cheng, Feixiong |
| author_facet | Zhang, Yuqi Yang, Yuxin Martin, William Lin, Kingsten Wang, Zixu Lu, Cheng-Chang Jiang, Weiwen Nussinov, Ruth Loscalzo, Joseph Guan, Qiang Cheng, Feixiong |
| contents | Accurate prediction of protein active-site structures remains a central challenge in structural biology, particularly for short and flexible peptide fragments where conventional and simulation-based methods often fail. Here, we present a quantum computing framework specifically developed for utility-level quantum processors to address this problem. Starting from an amino acid sequence, we formulate structure prediction as a ground-state energy minimization problem using the Variational Quantum Eigensolver (VQE). Amino acid connectivity is encoded on a tetrahedral lattice model, and structural constraints-including steric, geometric, and chirality terms-are mapped into a problem-specific Hamiltonian represented as sparse Pauli operators. Optimization is performed with a two-stage architecture that separates energy estimation from measurement decoding, enabling noise mitigation under realistic device conditions. We evaluate the framework on 23 randomly selected protein fragments from the PDBbind dataset and 7 fragments from therapeutically relevant proteins, and execute experiments on the IBM-Cleveland Clinic quantum processor. Predictions are benchmarked against AlphaFold 3 (AF3) and classical simulation-based approaches using identical postprocessing and docking procedures. Our method outperforms both AF3 and classical baselines in RMSD (root-mean-square deviation) and docking efficacy. These results demonstrate an end-to-end, hardware-executable pipeline for biologically relevant structure prediction on real quantum processors, highlighting its engineering feasibility and practical advantages over existing classical and deep learning approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22677 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Quantum Framework for Protein Binding-Site Structure Prediction on Utility-Level Quantum Processors Zhang, Yuqi Yang, Yuxin Martin, William Lin, Kingsten Wang, Zixu Lu, Cheng-Chang Jiang, Weiwen Nussinov, Ruth Loscalzo, Joseph Guan, Qiang Cheng, Feixiong Emerging Technologies Accurate prediction of protein active-site structures remains a central challenge in structural biology, particularly for short and flexible peptide fragments where conventional and simulation-based methods often fail. Here, we present a quantum computing framework specifically developed for utility-level quantum processors to address this problem. Starting from an amino acid sequence, we formulate structure prediction as a ground-state energy minimization problem using the Variational Quantum Eigensolver (VQE). Amino acid connectivity is encoded on a tetrahedral lattice model, and structural constraints-including steric, geometric, and chirality terms-are mapped into a problem-specific Hamiltonian represented as sparse Pauli operators. Optimization is performed with a two-stage architecture that separates energy estimation from measurement decoding, enabling noise mitigation under realistic device conditions. We evaluate the framework on 23 randomly selected protein fragments from the PDBbind dataset and 7 fragments from therapeutically relevant proteins, and execute experiments on the IBM-Cleveland Clinic quantum processor. Predictions are benchmarked against AlphaFold 3 (AF3) and classical simulation-based approaches using identical postprocessing and docking procedures. Our method outperforms both AF3 and classical baselines in RMSD (root-mean-square deviation) and docking efficacy. These results demonstrate an end-to-end, hardware-executable pipeline for biologically relevant structure prediction on real quantum processors, highlighting its engineering feasibility and practical advantages over existing classical and deep learning approaches. |
| title | A Quantum Framework for Protein Binding-Site Structure Prediction on Utility-Level Quantum Processors |
| topic | Emerging Technologies |
| url | https://arxiv.org/abs/2506.22677 |