{"doi":"10.1101/2025.10.03.679414","title":"RNA sequence design and protein–DNA specificity prediction with NA-MPNN","abstract":"Abstract RNA sequence design and protein–DNA binding specificity prediction can both be framed as nucleic acid inverse-folding problems: finding the most likely nucleic acid sequences given a fixed three-dimensional structure of a nucleic acid or nucleic acid–protein complex. While task-specific tools have been developed, no unified deep learning model for nucleic acid inverse folding has been described; a single model would have larger and more diverse datasets available for training and a considerably greater range of applicability. Here we introduce Nucleic Acid MPNN (NA-MPNN), a message-passing neural network that treats proteins, DNA, and RNA within a unified biopolymer graph representation. NA-MPNN outperforms previous methods on RNA sequence design and fixed-dock protein–DNA specificity prediction, and should be broadly useful for de novo RNA structure design and prediction of DNA-binding specificity.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":554688,"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":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.949,"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":888224,"name":"Andrew Favor","orcid":"0000-0002-9977-2785","position":1,"is_corresponding":false},{"id":1160037,"name":"Ryan McHugh","orcid":"0000-0003-0291-2196","position":2,"is_corresponding":false},{"id":1113798,"name":"Raktim Mitra","orcid":"0000-0003-1182-3742","position":3,"is_corresponding":false},{"id":1160036,"name":"Robert Pecoraro","orcid":"0000-0002-1656-0470","position":4,"is_corresponding":false},{"id":552413,"name":"Justas Dauparas","orcid":"0000-0002-0030-144X","position":5,"is_corresponding":false},{"id":682310,"name":"Cameron J. Glasscock","orcid":"0000-0001-5223-6339","position":6,"is_corresponding":false},{"id":105706,"name":"David Baker","orcid":"0000-0001-7896-6217","position":7,"is_corresponding":false},{"id":1452543,"name":"Andrew Kubaney","orcid":"0009-0009-4982-6050","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:54:54.542303Z","pmid":"41256668","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":[]}