{"doi":"10.1101/2024.03.31.587283","title":"gRNAde: Geometric Deep Learning for 3D RNA inverse design","abstract":"Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D conformational diversity. We introduce gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. gRNAde uses a multi-state Graph Neural Network and autoregressive decoding to generates candidate RNA sequences conditioned on one or more 3D backbone structures where the identities of the bases are unknown. On a single-state fixed backbone re-design benchmark of 14 RNA structures from the PDB identified by Das et al. (2010), gRNAde obtains higher native sequence recovery rates (56% on average) compared to Rosetta (45% on average), taking under a second to produce designs compared to the reported hours for Rosetta. We further demonstrate the utility of gRNAde on a new benchmark of multi-state design for structurally flexible RNAs, as well as zero-shot ranking of mutational fitness landscapes in a retrospective analysis of a recent ribozyme. Experimental wet lab validation on 10 different structured RNA backbones finds that gRNAde has a success rate of 50% at designing pseudoknotted RNA structures, a significant advance over 35% for Rosetta. Open source code and tutorials are available at: https://github.com/chaitjo/geometric-rna-design.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":484015,"datarank":0.6955319490008378,"base_score":3.1354942159291497,"endowment":3.1354942159291497,"self_citation_contribution":0.47032413238937254,"citation_network_contribution":0.2252078166114653,"self_endowment_contribution":0.47032413238937254,"citer_contribution":0.2252078166114653,"corpus_percentile":null,"corpus_rank":null,"citation_count":22,"citer_count":21,"citers_with_citation_signal":13,"citers_with_endowment":13,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9571,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1163690,"name":"Arian R. Jamasb","orcid":"0000-0002-6727-7579","position":1,"is_corresponding":false},{"id":1163691,"name":"Ramón Viñas","orcid":"0000-0003-2568-4325","position":2,"is_corresponding":false},{"id":1163692,"name":"Charles B. Harris","orcid":"0009-0003-5897-0704","position":3,"is_corresponding":false},{"id":1325528,"name":"Simon V. Mathis","orcid":"0000-0002-5246-6481","position":4,"is_corresponding":false},{"id":806840,"name":"Alex Morehead","orcid":"0000-0002-0586-6191","position":5,"is_corresponding":false},{"id":1325529,"name":"Rishabh Anand","orcid":"0000-0002-1765-7952","position":6,"is_corresponding":false},{"id":53245,"name":"Píetro Lió","orcid":"0000-0002-0540-5053","position":7,"is_corresponding":false},{"id":996376,"name":"Chaitanya K. Joshi","orcid":"0000-0003-4722-1815","position":0,"is_corresponding":true}],"reference_count":59,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:07:38.055693Z","pmid":"38826198","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":[]}