RT Journal Article
SR Electronic
T1 Antibody affinity engineering using antibody repertoire data and machine learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2025.01.10.632313
DO 10.1101/2025.01.10.632313
A1 Erlach, Lena
A1 Friedensohn, Simon
A1 Neumeier, Daniel
A1 Mason, Derek M.
A1 Reddy, Sai T.
YR 2025
UL http://biorxiv.org/content/early/2025/01/14/2025.01.10.632313.abstract
AB Advanced antibody discovery and engineering workflows take advantage of the combination of high-throughput screening, deep sequencing and machine learning (ML). Most high-throughput methods, however, lack the resolution to provide absolute affinity values of antibody-antigen interactions, limiting their utility for precise engineering of binding kinetics. In this study, we utilize antibody repertoire data, affinity characterization and ML for antibody affinity engineering. Leveraging natural antibody sequence information from repertoires of immunized mice, we identified and experimentally measured affinities for 35 antigen-specific variants. Supervised ML models trained on these sequences achieved remarkable accuracy in predicting affinity, despite the limited dataset size. We utilized the trained ML model to in silico-design eight synthetic antibody variants, of which seven exhibited the desired affinities. Our study illustrates the potential of this streamlined and efficient approach for precise engineering of the affinity of antibodies while reducing extensive experimental screening.Competing Interest StatementS.T.R., S.F. and D.M. may hold shares of Alloy Therapeutics. S.T.R. may hold shares of Engimmune Therapeutics and S.T.R. is on the scientific advisory board of Alloy Therapeutics, Engimmune Therapeutics, Encelta and Fy Kappa Biologics. S.T.R. is a member of the board of directors of Engimmune Therapeutics and GlycoEra.