{"doi":"10.1016/j.sbi.2024.102973","title":"Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction","abstract":"In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccurate, low-confidence, or simply not predicted at all. Here, we review three blind spots that alternative conformations reveal about AF-based protein structure prediction. First, proteins that assume conformations distinct from their training-set homologs can be mispredicted. Second, AF overrelies on its training set to predict alternative conformations. Third, degeneracies in pairwise representations can lead to high-confidence predictions inconsistent with experiment. These weaknesses suggest approaches to predict alternative folds more reliably. • AlphaFold-based methods often predict single protein structures with high accuracy. • However, these methods sometimes fail to predict alternative conformations. • Three explanations for these failures are discussed.","journal":"Current Opinion in Structural Biology","year":2025,"id":508891,"datarank":0.6394019815561974,"base_score":4.2626798770413155,"endowment":4.2626798770413155,"self_citation_contribution":0.6394019815561974,"citation_network_contribution":0.0,"self_endowment_contribution":0.6394019815561974,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":70,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9507,"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":681969,"name":"Myeongsang Lee","orcid":"0000-0003-2078-6023","position":1,"is_corresponding":false},{"id":285589,"name":"Lauren L. Porter","orcid":"0000-0003-2031-8326","position":2,"is_corresponding":false},{"id":440284,"name":"Devlina Chakravarty","orcid":"0000-0002-7499-9553","position":0,"is_corresponding":true}],"reference_count":54,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:47:00.188082Z","pmid":"39756261","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":[]}