Back

Focused framework sampling recovers binding-positive humanized anti-amyloid-β antibodies in a single sorting round

Kim, Y.; Kwon, H.; Song, J.; Lee, Y.; Park, M.; Lee, C.-H.

2026-08-18 bioengineering
10.64898/2026.08.14.744767 bioRxiv
Show abstract

Therapeutic antibody development requires workflows that integrate antigen-reactive clone discovery with efficient humanization and early developability assessment. Here, we combined immune yeast fragment antigen-binding (Fab) display with single-round focused humanization and applied the workflow to antibodies against amyloid-{beta} (A{beta})-derived preparations. Immunization with A{beta}1-42 aggregate preparations generated a Fab-display library with a diversity of approximately 3.5 x 108. Magnetic enrichment followed by fluorescence-activated cell sorting (FACS) identified three sequence-distinct immunoglobulin G (IgG)-format candidates, of which CLAB17 and CLAB45 were advanced to humanization. Structure-guided libraries sampled framework positions predicted to support complementarity-determining regions (CDRs) or heavy-and light-chain variable-domain packing, and a single FACS round recovered binding-positive variants CLAB17-h2 and CLAB45-h8. Both retained the parental CDRs and showed increased predicted humanness, favorable computational developability triage profiles, and high purity by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). By enzyme-linked immunosorbent assay (ELISA), CLAB17-h2 showed a lower apparent half-maximal effective concentration (EC50) for A{beta}1-42AggreSure, whereas CLAB45-h8 showed a lower apparent EC50 for pyroglutamate-modified A{beta}3-42 (A{beta}pE3-42). Because the preparations were not resolved into defined assembly states, these antibodies are considered A{beta}-preparation-binding rather than aggregate-state-selective candidates. This workflow provides a practical route from immune-repertoire discovery to binding-positive humanized antibodies.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.