{"doi":"10.1101/2022.07.25.501437","title":"TM-Vec: template modeling vectors for fast homology detection and alignment","abstract":"Abstract Exploiting sequence-structure-function relationships in molecular biology and computational modeling relies on detecting proteins with high sequence similarities. However, the most commonly used sequence alignment-based methods, such as BLAST, frequently fail on proteins with low sequence similarity to previously annotated proteins. We developed a deep learning method, TM-Vec, that uses sequence alignments to learn structural features that can then be used to search for structure-structure similarities in large sequence databases. We train TM-Vec to accurately predict TM-scores as a metric of structural similarity for pairs of structures directly from sequence pairs without the need for intermediate computation or solution of structures. For remote homologs (sequence similarity ≤ 10%) that are highly structurally similar (TM-score ? 0.6), we predict TM-scores within 0.026 of their value computed by TM-align. TM-Vec outperforms traditional sequence alignment methods and performs similar to structure-based alignment methods. TM-Vec was trained on the CATH and SwissModel structural databases and it has been tested on carefully curated structure-structure alignment databases that were designed specifically to test very remote homology detection methods. It scales sub-linearly for search against large protein databases and is well suited for discovering remotely homologous proteins.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":296871,"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":16,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.953,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":255017,"name":"James T. Morton","orcid":"0000-0003-3189-2681","position":1,"is_corresponding":false},{"id":58845,"name":"Daniel Berenberg","orcid":"0000-0003-4631-0947","position":2,"is_corresponding":false},{"id":90199,"name":"Nicholas Carriero","orcid":null,"position":3,"is_corresponding":false},{"id":58896,"name":"Vladimir Gligorijević","orcid":"0000-0002-5165-0973","position":4,"is_corresponding":false},{"id":317242,"name":"Robert Blackwell","orcid":"0000-0002-9450-9240","position":5,"is_corresponding":false},{"id":459298,"name":"Charlie E. M. Strauss","orcid":"0000-0003-3639-4673","position":6,"is_corresponding":false},{"id":459290,"name":"Julia Koehler Leman","orcid":"0000-0002-5693-3593","position":7,"is_corresponding":false},{"id":58895,"name":"Kyunghyun Cho","orcid":"0000-0003-1669-3211","position":8,"is_corresponding":false},{"id":476,"name":"Richard Bonneau","orcid":"0000-0003-4354-7906","position":9,"is_corresponding":false},{"id":562945,"name":"Tymor Hamamsy","orcid":"0000-0003-0820-2208","position":0,"is_corresponding":true}],"reference_count":62,"raw_metadata":null,"created_at":"2026-07-19T00:31:21.257700Z","pmid":null,"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":[]}