<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Makowski EK</submitter><funding>University of Michigan</funding><funding>Pfizer</funding><funding>National Institutes of Health</funding><funding>NIGMS NIH HHS</funding><funding>National Science Foundation</funding><pagination>2146629</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9704398</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>14(1)</volume><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</pubmed_abstract><journal>mAbs</journal><pubmed_title>Reduction of therapeutic antibody self-association using yeast-display selections and machine learning.</pubmed_title><pmcid>PMC9704398</pmcid><funding_grant_id>1T32GM140223-01</funding_grant_id><funding_grant_id>Rackham Predoctoral Fellowship</funding_grant_id><funding_grant_id>T32 GM140223</funding_grant_id><funding_grant_id>Graduate Research Fellowship</funding_grant_id><pubmed_authors>Lambert M</pubmed_authors><pubmed_authors>Zupancic JM</pubmed_authors><pubmed_authors>Makowski EK</pubmed_authors><pubmed_authors>Tessier PM</pubmed_authors><pubmed_authors>Bennett EM</pubmed_authors><pubmed_authors>Lou W</pubmed_authors><pubmed_authors>Chen H</pubmed_authors><pubmed_authors>Desai AA</pubmed_authors><pubmed_authors>Gallo CJ</pubmed_authors><pubmed_authors>Zhang Y</pubmed_authors><pubmed_authors>Tully T</pubmed_authors><pubmed_authors>Smith MD</pubmed_authors><pubmed_authors>Lin L</pubmed_authors><pubmed_authors>Eschmann NS</pubmed_authors><pubmed_authors>Fernando A</pubmed_authors></additional><is_claimable>false</is_claimable><name>Reduction of therapeutic antibody self-association using yeast-display selections and machine learning.</name><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</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Jan-Dec</publication><modification>2025-05-18T13:23:33.98Z</modification><creation>2025-05-18T13:23:33.98Z</creation></dates><accession>S-EPMC9704398</accession><cross_references><pubmed>36433737</pubmed><doi>10.1080/19420862.2022.2146629</doi></cross_references></HashMap>