{"doi":"10.1002/advs.202513641","title":"A Quantum Framework for Protein Binding‐Site Structure Prediction on Utility‐Level Quantum Processors","abstract":"Accurate prediction of protein active-site structures remains a central challenge in structural biology, especially for short and flexible peptide fragments where conventional and simulation-based methods often fail. Here, we present a quantum computing framework designed for utility-level quantum processors to address this problem. Starting from an amino acid sequence, we cast structure prediction as a ground-state energy minimization task using the Variational Quantum Eigensolver (VQE). Amino acid connectivity is represented on a tetrahedral lattice, and steric, geometric, and chirality constraints are encoded into a problem-specific Hamiltonian expressed as sparse Pauli operators. A two-stage architecture separates energy estimation from measurement decoding, enabling noise mitigation under realistic device conditions. We evaluate the method on 23 real protein fragments from the PDBbind dataset and 7 fragments from therapeutically relevant proteins, executing all experiments on the IBM-Cleveland Clinic quantum processor. Structural predictions are benchmarked against AlphaFold3 (AF3) and classical simulation-based approaches using identical postprocessing and docking procedures. Our quantum framework outperforms both AF3 and classical baselines in Root-Mean-Square Deviation (RMSD) and docking efficacy, demonstrating a practical end-to-end pipeline for biologically relevant structure prediction on real quantum hardware and highlighting its engineering feasibility for near-term quantum devices.","journal":"Advanced Science","year":2025,"id":529448,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9516,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1211361,"name":"Yuxin Yang","orcid":"0000-0002-8316-7636","position":1,"is_corresponding":false},{"id":1408726,"name":"W T Martin","orcid":null,"position":2,"is_corresponding":false},{"id":1408727,"name":"Ko-Chow Lin","orcid":null,"position":3,"is_corresponding":false},{"id":1408263,"name":"Zixu Wang","orcid":"0009-0001-0770-0515","position":4,"is_corresponding":false},{"id":1408264,"name":"Cheng‐Chang Lu","orcid":"0000-0002-7194-9147","position":5,"is_corresponding":false},{"id":807633,"name":"Weiwen Jiang","orcid":"0000-0002-9004-487X","position":6,"is_corresponding":false},{"id":70301,"name":"Ruth Nussinov","orcid":"0000-0002-8115-6415","position":7,"is_corresponding":false},{"id":87927,"name":"Joseph Loscalzo","orcid":"0000-0002-1153-8047","position":8,"is_corresponding":false},{"id":919369,"name":"Qiang Guan","orcid":"0000-0002-3804-8945","position":9,"is_corresponding":false},{"id":69831,"name":"Feixiong Cheng","orcid":"0000-0002-1736-2847","position":10,"is_corresponding":false},{"id":1399173,"name":"Yuqi Zhang","orcid":"0000-0001-5210-538X","position":0,"is_corresponding":true}],"reference_count":75,"raw_metadata":null,"created_at":"2026-07-19T02:50:56.971987Z","pmid":"41315890","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}