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

Autor:	Petersen BM; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA., Kirby MB; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA., Chrispens KM; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA., Irvin OM; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA., Strawn IK; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA., Haas CM; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA., Walker AM; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA., Baumer ZT; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA., Ulmer SA; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA., Ayala E; Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045., Rhodes ER; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA., Guthmiller JJ; Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045., Steiner PJ; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA., Whitehead TA; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Jan 16. Date of Electronic Publication: 2024 Jan 16.
DOI:	10.1101/2024.01.16.575852
Abstrakt:	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 ten 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. Competing Interests: Competing interests The authors declare no competing financial interest.
Databáze:	MEDLINE
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