{"doi":"10.1101/2020.04.14.041962","title":"De novo 3D models of SARS-CoV-2 RNA elements and small-molecule-binding RNAs to aid drug discovery","abstract":"Abstract The rapid spread of COVID-19 is motivating development of antivirals targeting conserved SARS-CoV-2 molecular machinery. The SARS-CoV-2 genome includes conserved RNA elements that offer potential small-molecule drug targets, but most of their 3D structures have not been experimentally characterized. Here, we provide a compilation of chemical mapping data from our and other labs, secondary structure models, and 3D model ensembles based on Rosetta’s FARFAR2 algorithm for SARS-CoV-2 RNA regions including the individual stems SL1-8 in the extended 5’ UTR; the reverse complement of the 5’ UTR SL1-4; the frameshift stimulating element (FSE); and the extended pseudoknot, hypervariable region, and s2m of the 3’ UTR. For eleven of these elements (the stems in SL1-8, reverse complement of SL1-4, FSE, s2m, and 3’ UTR pseudoknot), modeling convergence supports the accuracy of predicted low energy states; subsequent cryo-EM characterization of the FSE confirms modeling accuracy. To aid efforts to discover small molecule RNA binders guided by computational models, we provide a second set of similarly prepared models for RNA riboswitches that bind small molecules. Both datasets (‘FARFAR2-SARS-CoV-2’, https://github.com/DasLab/FARFAR2-SARS-CoV-2 ; and ‘FARFAR2-Apo-Riboswitch’, at https://github.com/DasLab/FARFAR2-Apo-Riboswitch ’) include up to 400 models for each RNA element, which may facilitate drug discovery approaches targeting dynamic ensembles of RNA molecules.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":119359,"datarank":1.059978914339144,"base_score":3.091042453358316,"endowment":3.091042453358316,"self_citation_contribution":0.4636563680037475,"citation_network_contribution":0.5963225463353966,"self_endowment_contribution":0.4636563680037475,"citer_contribution":0.5963225463353966,"corpus_percentile":80.90817668445888,"corpus_rank":2469,"citation_count":21,"citer_count":17,"citers_with_citation_signal":16,"citers_with_endowment":16,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.762,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":58890,"name":"Andrew M. Watkins","orcid":"0000-0003-1617-1720","position":1,"is_corresponding":false},{"id":554162,"name":"Jose Chacon","orcid":"0000-0001-7965-3976","position":2,"is_corresponding":false},{"id":252521,"name":"Wipapat Kladwang","orcid":null,"position":3,"is_corresponding":false},{"id":235779,"name":"Ivan N. Zheludev","orcid":"0000-0002-9572-0574","position":4,"is_corresponding":false},{"id":554163,"name":"Jill Townley","orcid":"0000-0001-8528-2227","position":5,"is_corresponding":false},{"id":492053,"name":"Mats Rynge","orcid":"0000-0002-1779-7189","position":6,"is_corresponding":false},{"id":554164,"name":"Greg Thain","orcid":"0000-0002-2296-3735","position":7,"is_corresponding":false},{"id":235783,"name":"Rhiju Das","orcid":"0000-0001-7497-0972","position":8,"is_corresponding":false},{"id":235778,"name":"Ramya Rangan","orcid":"0000-0002-0960-0825","position":0,"is_corresponding":true}],"reference_count":68,"raw_metadata":null,"created_at":"2026-07-18T23:14:08.313144Z","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":[]}