{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Makowski EK"],"funding":["University of Michigan","Pfizer","National Institutes of Health","NIGMS NIH HHS","National Science Foundation"],"pagination":["2146629"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9704398"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(1)"],"pubmed_abstract":["Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of"],"journal":["mAbs"],"pubmed_title":["Reduction of therapeutic antibody self-association using yeast-display selections and machine learning."],"pmcid":["PMC9704398"],"funding_grant_id":["1T32GM140223-01","Rackham Predoctoral Fellowship","T32 GM140223","Graduate Research Fellowship"],"pubmed_authors":["Lambert M","Zupancic JM","Makowski EK","Tessier PM","Bennett EM","Lou W","Chen H","Desai AA","Gallo CJ","Zhang Y","Tully T","Smith MD","Lin L","Eschmann NS","Fernando A"],"additional_accession":[]},"is_claimable":false,"name":"Reduction of therapeutic antibody self-association using yeast-display selections and machine learning.","description":"Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan-Dec","modification":"2025-05-18T13:23:33.98Z","creation":"2025-05-18T13:23:33.98Z"},"accession":"S-EPMC9704398","cross_references":{"pubmed":["36433737"],"doi":["10.1080/19420862.2022.2146629"]}}