1
|
Petersen BM, Kirby MB, Chrispens KM, Irvin OM, Strawn IK, Haas CM, Walker AM, Baumer ZT, Ulmer SA, Ayala E, Rhodes ER, Guthmiller JJ, Steiner PJ, Whitehead TA. An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries. Nat Commun 2024; 15:3974. [PMID: 38730230 PMCID: PMC11087541 DOI: 10.1038/s41467-024-48072-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 04/19/2024] [Indexed: 05/12/2024] Open
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.
Collapse
Affiliation(s)
- Brian M Petersen
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Monica B Kirby
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Karson M Chrispens
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Olivia M Irvin
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Isabell K Strawn
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Cyrus M Haas
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Alexis M Walker
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Zachary T Baumer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Sophia A Ulmer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Edgardo Ayala
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Emily R Rhodes
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Jenna J Guthmiller
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Paul J Steiner
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA.
| |
Collapse
|
2
|
Petersen BM, Kirby MB, Chrispens KM, Irvin OM, Strawn IK, Haas CM, Walker AM, Baumer ZT, Ulmer SA, Ayala E, Rhodes ER, Guthmiller JJ, Steiner PJ, Whitehead TA. An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.16.575852. [PMID: 38293170 PMCID: PMC10827193 DOI: 10.1101/2024.01.16.575852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
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 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.
Collapse
Affiliation(s)
- Brian M. Petersen
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Monica B. Kirby
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Karson M. Chrispens
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Olivia M. Irvin
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Isabell K. Strawn
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Cyrus M. Haas
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Alexis M. Walker
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Zachary T. Baumer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Sophia A. Ulmer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Edgardo Ayala
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045
| | - Emily R. Rhodes
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Jenna J. Guthmiller
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045
| | - Paul J. Steiner
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Timothy A. Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| |
Collapse
|