{"doi":"10.1007/s12551-024-01201-w","title":"Structure-based prediction of protein-nucleic acid binding using graph neural networks","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>Protein-nucleic acid (PNA) binding plays critical roles in the transcription, translation, regulation, and three-dimensional organization of the genome. Structural models of proteins bound to nucleic acids (NA) provide insights into the chemical, electrostatic, and geometric properties of the protein structure that give rise to NA binding but are scarce relative to models of unbound proteins. We developed a deep learning approach for predicting PNA binding given the unbound structure of a protein that we call PNAbind. Our method utilizes graph neural networks to encode the spatial distribution of physicochemical and geometric properties of protein structures that are predictive of NA binding. Using global physicochemical encodings, our models predict the overall binding function of a protein, and using local encodings, they predict the location of individual NA binding residues. Our models can discriminate between specificity for DNA or RNA binding, and we show that predictions made on computationally derived protein structures can be used to gain mechanistic understanding of chemical and structural features that determine NA recognition. Binding site predictions were validated against benchmark datasets, achieving AUROC scores in the range of 0.92–0.95. We applied our models to the HIV-1 restriction factor APOBEC3G and showed that our model predictions are consistent with and help explain experimental RNA binding data.</jats:p>","journal":"Biophysical Reviews","year":2024,"id":661154,"datarank":0.47032413238937254,"base_score":3.1354942159291497,"endowment":3.1354942159291497,"self_citation_contribution":0.47032413238937254,"citation_network_contribution":0.0,"self_endowment_contribution":0.47032413238937254,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":22,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1113798,"name":"Raktim Mitra","orcid":"0000-0003-1182-3742","position":1,"is_corresponding":false},{"id":1322451,"name":"Jiawei Huang","orcid":"0000-0003-4839-1971","position":2,"is_corresponding":false},{"id":318117,"name":"Xiaojiang S. Chen","orcid":"0000-0001-9574-0551","position":3,"is_corresponding":false},{"id":74845,"name":"Remo Rohs","orcid":"0000-0003-1752-1884","position":4,"is_corresponding":false},{"id":528301,"name":"Jared M. Sagendorf","orcid":"0009-0009-1195-1753","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Structure-based prediction of protein-nucleic acid binding using graph neural networks","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>Protein-nucleic acid (PNA) binding plays critical roles in the transcription, translation, regulation, and three-dimensional organization of the genome. Structural models of proteins bound to nucleic acids (NA) provide insights into the chemical, electrostatic, and geometric properties of the protein structure that give rise to NA binding but are scarce relative to models of unbound proteins. We developed a deep learning approach for predicting PNA binding given the unbound structure of a protein that we call PNAbind. Our method utilizes graph neural networks to encode the spatial distribution of physicochemical and geometric properties of protein structures that are predictive of NA binding. Using global physicochemical encodings, our models predict the overall binding function of a protein, and using local encodings, they predict the location of individual NA binding residues. Our models can discriminate between specificity for DNA or RNA binding, and we show that predictions made on computationally derived protein structures can be used to gain mechanistic understanding of chemical and structural features that determine NA recognition. Binding site predictions were validated against benchmark datasets, achieving AUROC scores in the range of 0.92–0.95. We applied our models to the HIV-1 restriction factor APOBEC3G and showed that our model predictions are consistent with and help explain experimental RNA binding data.</jats:p>","is_dataset_classified":null,"base_score":3.1354942159291497,"endowment":3.1354942159291497,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39345796","pmcid":"PMC11427629","openalex_id":"https://openalex.org/W4400039553","authors":[],"funders":[{"funder_name":"National Institute of Allergy and Infectious Diseases","grant_id":"R01AI150524","title":null},{"funder_name":"National Institute of General Medical Sciences","grant_id":"R35GM130376","title":null},{"funder_name":"Human Frontier Science Program","grant_id":"RGP0021/2018","title":null},{"funder_name":"National Institutes of Health","grant_id":"5R01AI150524-12","title":"Structural Basis of APOBEC Functions and HIV Restriction"},{"funder_name":"National Institutes of Health","grant_id":"5R35GM130376-03","title":"Quantitative Modeling of Transcription Factor-DNA Binding"},{"funder_name":"University of Southern California","grant_id":"","title":null}],"total_grants":6,"fwci":3.5544,"citation_percentile":0.93971387,"influential_citations":0,"citation_trend":[{"year":2024,"count":5},{"year":2025,"count":11},{"year":2026,"count":6}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://link.springer.com/content/pdf/10.1007/s12551-024-01201-w.pdf","host_type":"journal"},{"url":"https://link.springer.com/content/pdf/10.1007/s12551-024-01201-w.pdf","host_type":"publisher"},{"url":"https://link.springer.com/article/10.1007/s12551-024-01201-w/fulltext.html","host_type":"publisher"},{"url":"https://doi.org/10.1007/s12551-024-01201-w","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/39345796","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11427629","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11427629/pdf/12551_2024_Article_1201.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11427629","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11427629?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.1007/s12551-024-01201-w","host_type":""}],"fields_of_study":["Machine Learning in Bioinformatics","Protein Structure and Dynamics","Computational Drug Discovery Methods","0301 basic medicine","03 medical and health sciences","0206 medical engineering","02 engineering and technology"],"mesh_terms":[],"keywords":["Nucleic acid","Computational biology","RNA-binding protein","ENCODE","Binding site","RNA","Artificial neural network","DNA","Biological system","Chemistry","Artificial intelligence","Computer science","Biophysics","Biology","Biochemistry","Gene","Structural Bioinformatics","Protein-dna Binding","Deep Learning","Protein-rna Binding","Structure-function Prediction","Review"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Reduced inequalities"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"uniprot"},{"name":"pdb"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-12T10:30:09.627918Z","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":[]}