{"doi":"10.1101/2022.06.22.497251","title":"A unified peptide array platform for antibody epitope binning, mapping, specificity and predictive off-target binding","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Therapeutic antibody efficacy is largely determined by the target epitope. In addition, off-target binding can result in unanticipated side-effects. Therefore, characterization of the epitope and binding specificity are critical in antibody discovery. Epitope binning provides low-resolution of an antibody epitope and is typically performed as a cross-blocking assay to group antibodies into overlapping or non-overlapping bins. Epitope mapping identifies the epitope with high resolution but requires low throughput methods. In addition to binning and mapping, there is a need for a scalable and predictive approach to reveal off-target binding early in antibody discovery to reduce the risk of in vivo side effects. Peptide microarrays are an information-rich platform for antibody characterization. However, the potential of peptide microarrays in early-stage antibody discovery has not been realized because they are not produced at the scale, quality and format needed for reliable high-throughput antibody characterization. A unified, peptide library platform for high-resolution antibody epitope binning, mapping and predictive off-target binding characterization is described here. This platform uses highly scalable array synthesis and photolithography to synthesize more than 3 million addressable peptides. These arrays conform to a microplate format and each synthesis is qualified with mass spectrometry. Using this platform, a scalable approach to early-stage epitope and specificity characterization, with prediction of off-target interaction(s), is demonstrated using a panel of anti-HER2 monoclonal antibodies. This study highlights the prospect of this platform to improve antibody discovery productivity by generating epitope and specificity information much earlier with potentially hundreds of antibody clones.</jats:p>","journal":null,"year":null,"id":596587,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"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":1527962,"name":"Anna Lei","orcid":null,"position":1,"is_corresponding":false},{"id":1041804,"name":"Patrick Walsh","orcid":"0000-0002-0130-8766","position":2,"is_corresponding":false},{"id":517037,"name":"Olgica Trenchevska","orcid":null,"position":3,"is_corresponding":false},{"id":1527963,"name":"Gaurav Saini","orcid":null,"position":4,"is_corresponding":false},{"id":689874,"name":"Theodore M. Tarasow","orcid":"0000-0003-0784-1219","position":5,"is_corresponding":false},{"id":1527964,"name":"Mohan Srinivasan","orcid":null,"position":6,"is_corresponding":false},{"id":1358455,"name":"David Smith","orcid":"0000-0001-5495-7981","position":7,"is_corresponding":false},{"id":1527965,"name":"Matthew P. Greving","orcid":null,"position":8,"is_corresponding":false},{"id":1527961,"name":"Cody Moore","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"A unified peptide array platform for antibody epitope binning, mapping, specificity and predictive off-target binding","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Therapeutic antibody efficacy is largely determined by the target epitope. In addition, off-target binding can result in unanticipated side-effects. Therefore, characterization of the epitope and binding specificity are critical in antibody discovery. Epitope binning provides low-resolution of an antibody epitope and is typically performed as a cross-blocking assay to group antibodies into overlapping or non-overlapping bins. Epitope mapping identifies the epitope with high resolution but requires low throughput methods. In addition to binning and mapping, there is a need for a scalable and predictive approach to reveal off-target binding early in antibody discovery to reduce the risk of in vivo side effects. Peptide microarrays are an information-rich platform for antibody characterization. However, the potential of peptide microarrays in early-stage antibody discovery has not been realized because they are not produced at the scale, quality and format needed for reliable high-throughput antibody characterization. A unified, peptide library platform for high-resolution antibody epitope binning, mapping and predictive off-target binding characterization is described here. This platform uses highly scalable array synthesis and photolithography to synthesize more than 3 million addressable peptides. These arrays conform to a microplate format and each synthesis is qualified with mass spectrometry. Using this platform, a scalable approach to early-stage epitope and specificity characterization, with prediction of off-target interaction(s), is demonstrated using a panel of anti-HER2 monoclonal antibodies. This study highlights the prospect of this platform to improve antibody discovery productivity by generating epitope and specificity information much earlier with potentially hundreds of antibody clones.</jats:p>","is_dataset_classified":null,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"23304386","pmcid":null,"openalex_id":"https://openalex.org/W4283585174","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2023,"count":1},{"year":2024,"count":1}],"oa_status":"green","license":null,"oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2022/06/26/2022.06.22.497251.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2022/06/26/2022.06.22.497251.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2022.06.22.497251","host_type":"publisher"},{"url":"https://doi.org/10.1101/2022.06.22.497251","host_type":"repository"}],"fields_of_study":["Monoclonal and Polyclonal Antibodies Research","Protein purification and stability","Glycosylation and Glycoproteins Research"],"mesh_terms":[],"keywords":["Epitope","Epitope mapping","Linear epitope","Computational biology","Monoclonal antibody","Antibody","Mimotope","Computer science","Biology","Immunology"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Decent work and economic growth"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-28T10:43:37.981497Z","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":[]}