{"doi":"10.1093/bioinformatics/btu348","title":"MPBind: a Meta-motif-based statistical framework and pipeline to Predict Binding potential of SELEX-derived aptamers","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>Summary: Aptamers are ‘synthetic antibodies’ that can bind to target molecules with high affinity and specificity. Aptamers are chemically synthesized and their discovery can be performed completely in vitro , rather than relying on in vivo biological processes, making them well-suited for high-throughput discovery. However, a large fraction of the most enriched aptamers in Systematic Evolution of Ligands by EXponential enrichment (SELEX) rounds display poor binding activity. Here, we present MPBind, a M eta-motif-based statistical framework and pipeline to P redict the Bind ing potential of SELEX-derived aptamers. Using human embryonic stem cell SELEX-Seq data, MPBind achieved high prediction accuracy for binding potential. Further analysis showed that MPBind is robust to both polymerase chain reaction amplification bias and incomplete sequencing of aptamer pools. These two biases usually confound aptamer analysis.</jats:p>\n               <jats:p>Availability and implementation : MPBind software and documents are available at http://www.morgridge.net/MPBind.html . The human embryonic stem cells whole-cell SELEX-Seq data are available at http://www.morgridge.net/Aptamer/ .</jats:p>\n               <jats:p>Contact : RStewart@morgridge.org</jats:p>\n               <jats:p>Supplementary information : Supplementary data are available at Bioinformatics online.</jats:p>","journal":"Bioinformatics","year":2014,"id":13889,"datarank":3.263649400659918,"base_score":4.143134726391533,"endowment":4.143134726391533,"self_citation_contribution":0.62147020895873,"citation_network_contribution":2.6421791917011883,"self_endowment_contribution":0.62147020895873,"citer_contribution":2.6421791917011883,"corpus_percentile":null,"corpus_rank":null,"citation_count":62,"citer_count":59,"citers_with_citation_signal":56,"citers_with_endowment":56,"datacite_reuse_total":2,"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":112137,"name":"Susanne Meyer","orcid":null,"position":1,"is_corresponding":false},{"id":112138,"name":"Zhonggang Hou","orcid":null,"position":2,"is_corresponding":false},{"id":110457,"name":"Nicholas E. Propson","orcid":"0000-0001-8205-3218","position":3,"is_corresponding":false},{"id":111889,"name":"H. Tom Soh","orcid":null,"position":4,"is_corresponding":false},{"id":111887,"name":"James A. Thomson","orcid":null,"position":5,"is_corresponding":false},{"id":3794,"name":"Ron Stewart","orcid":"0000-0002-9041-1828","position":6,"is_corresponding":false},{"id":43036,"name":"Peng Jiang","orcid":"0000-0002-7828-5486","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":4.143134726391533,"endowment":4.143134726391533,"datacite_reuse_total":2,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"24872422","pmcid":"PMC4155251","openalex_id":"https://openalex.org/W2099207551","authors":[],"funders":[{"funder_name":"NIDDK NIH HHS","grant_id":"1U54DK093467","title":null},{"funder_name":"NIDDK NIH HHS","grant_id":"U54 DK093467","title":null}],"total_grants":2,"fwci":4.409,"citation_percentile":0.94196445,"influential_citations":5,"citation_trend":[{"year":2015,"count":6},{"year":2016,"count":6},{"year":2017,"count":4},{"year":2018,"count":3},{"year":2019,"count":4},{"year":2020,"count":7},{"year":2021,"count":6},{"year":2022,"count":8},{"year":2023,"count":6},{"year":2024,"count":5},{"year":2025,"count":5},{"year":2026,"count":2}],"oa_status":"bronze","license":null,"oa_locations":[{"url":"https://academic.oup.com/bioinformatics/article-pdf/30/18/2665/48929430/bioinformatics_30_18_2665.pdf","host_type":"journal"},{"url":"https://academic.oup.com/bioinformatics/article-pdf/30/18/2665/48929430/bioinformatics_30_18_2665.pdf","host_type":"BRONZE"},{"url":"https://academic.oup.com/bioinformatics/article-pdf/30/18/2665/48929430/bioinformatics_30_18_2665.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1093/bioinformatics/btu348","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/24872422","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4155251","host_type":"repository"}],"fields_of_study":["Monoclonal and Polyclonal Antibodies Research","Advanced biosensing and bioanalysis techniques","vaccines and immunoinformatics approaches","Biology","Computer Science","Medicine","Chemistry","Aptamers, Nucleotide","Computational Biology","Embryonic Stem Cells","Humans","Ligands","Oligonucleotides","SELEX Aptamer Technique","Sequence Analysis","Software","Substrate Specificity"],"mesh_terms":["Humans","Ligands","Oligonucleotides","Software","Substrate Specificity","Sequence Analysis","Computational Biology","SELEX Aptamer Technique","Aptamers, Nucleotide","Embryonic Stem Cells"],"keywords":["Systematic evolution of ligands by exponential enrichment","Aptamer","Computational biology","SELEX Aptamer Technique","Biology","Molecular biology","Gene","Genetics","RNA"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"No poverty"}],"linked_datasets":[{"doi":"10.6084/m9.figshare.12560969.v1","title":"Additional file 1 of FSBC: fast string-based clustering for HT-SELEX data","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.12560969","title":"Additional file 1 of FSBC: fast string-based clustering for HT-SELEX data","publisher":"figshare","resource_type":"JournalArticle"}],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-05-31T20:01:00.445749Z","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":[]}