An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

Brian M. Petersen, Monica B. Kirby, Karson M. Chrispens, Olivia M. Irvin, Isabell K. Strawn, Cyrus M. Haas, Alexis M. Walker, Zachary T. Baumer, Sophia A. Ulmer, Edgardo Ayala, Emily R. Rhodes, Jenna J. Guthmiller, Paul J. Steiner and Timothy A. Whitehead ()
Additional contact information
Brian M. Petersen: University of Colorado Boulder
Monica B. Kirby: University of Colorado Boulder
Karson M. Chrispens: University of Colorado Boulder
Olivia M. Irvin: University of Colorado Boulder
Isabell K. Strawn: University of Colorado Boulder
Cyrus M. Haas: University of Colorado Boulder
Alexis M. Walker: University of Colorado Boulder
Zachary T. Baumer: University of Colorado Boulder
Sophia A. Ulmer: University of Colorado Boulder
Edgardo Ayala: University of Colorado Anschutz Medical Campus
Emily R. Rhodes: University of Colorado Boulder
Jenna J. Guthmiller: University of Colorado Anschutz Medical Campus
Paul J. Steiner: University of Colorado Boulder
Timothy A. Whitehead: University of Colorado Boulder

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

Date: 2024
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DOI: 10.1038/s41467-024-48072-z

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