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Arras P, Yoo HB, Pekar L, Clarke T, Friedrich L, Schröter C, Schanz J, Tonillo J, Siegmund V, Doerner A, Krah S, Guarnera E, Zielonka S, Evers A. AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study. Front Mol Biosci 2023; 10:1249247. [PMID: 37842638 PMCID: PMC10575757 DOI: 10.3389/fmolb.2023.1249247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/31/2023] [Indexed: 10/17/2023] Open
Abstract
Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles. Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production. Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.
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Affiliation(s)
- Paul Arras
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
- Institute for Organic Chemistry and Biochemistry, Technical University of Darmstadt, Darmstadt, Germany
| | - Han Byul Yoo
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Lukas Pekar
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Thomas Clarke
- Bioinformatics, EMD Serono, Billerica, MA, United States
| | - Lukas Friedrich
- Computational Chemistry and Biologics, Merck Healthcare KGaA, Darmstadt, Germany
| | | | - Jennifer Schanz
- ADCs & Targeted NBE Therapeutics, Merck KGaA, Darmstadt, Germany
| | - Jason Tonillo
- ADCs & Targeted NBE Therapeutics, Merck KGaA, Darmstadt, Germany
| | - Vanessa Siegmund
- Early Protein Supply and Characterization, Merck Healthcare KGaA, Darmstadt, Germany
| | - Achim Doerner
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Simon Krah
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Enrico Guarnera
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Stefan Zielonka
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
- Institute for Organic Chemistry and Biochemistry, Technical University of Darmstadt, Darmstadt, Germany
| | - Andreas Evers
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
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