{"doi":"10.1101/2022.01.12.476085","title":"An <i>in silico</i> method to assess antibody fragment polyreactivity","abstract":"ABSTRACT Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. We designed a set of experiments using a diverse naïve synthetic camelid antibody fragment (‘nanobody’) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC &gt; 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally tested our model’s performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the model allowed us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its pharmacological properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":300755,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9584,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":553635,"name":"Jung-Eun Shin","orcid":"0000-0002-0039-7373","position":1,"is_corresponding":false},{"id":239845,"name":"Meredith A. Skiba","orcid":"0000-0003-4615-6775","position":2,"is_corresponding":false},{"id":498193,"name":"Genevieve R. Nemeth","orcid":"0000-0001-6930-5767","position":3,"is_corresponding":false},{"id":859213,"name":"Joseph D. Hurley","orcid":"0000-0002-3903-318X","position":4,"is_corresponding":false},{"id":553633,"name":"Alon Wellner","orcid":"0000-0001-5247-189X","position":5,"is_corresponding":false},{"id":859214,"name":"Ada Y. Shaw","orcid":"0000-0002-5283-9559","position":6,"is_corresponding":false},{"id":859215,"name":"Victor G. Miranda","orcid":"0000-0001-8631-4622","position":7,"is_corresponding":false},{"id":805113,"name":"Joseph Min","orcid":"0000-0002-2781-7390","position":8,"is_corresponding":false},{"id":272468,"name":"Chang C. Liu","orcid":"0000-0002-3290-2880","position":9,"is_corresponding":false},{"id":56406,"name":"Debora S. Marks","orcid":"0000-0001-9388-2281","position":10,"is_corresponding":false},{"id":239851,"name":"Andrew C. Kruse","orcid":"0000-0002-1467-1222","position":11,"is_corresponding":false},{"id":350348,"name":"Edward P. Harvey","orcid":"0000-0002-3710-3594","position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":null,"created_at":"2026-07-19T00:31:57.812980Z","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":[]}