Hallucinating structure-conditioned antibody libraries for target-specific binders
Open accessPublished version of a preprint
Abstract
Antibodies are widely developed and used as therapeutics to treat cancer, infectious disease, and inflammation. During development, initial leads routinely undergo additional engineering to increase their target affinity. Experimental methods for affinity maturation are expensive, laborious, and time-consuming and rarely allow the efficient exploration of the relevant design space. Deep learning (DL) models are transforming the field of protein engineering and design. While several DL-based protein design methods have shown promise, the antibody design problem is distinct, and specialized models for antibody design are desirable. Inspired by hallucination frameworks that leverage accurate structure prediction DL models, we propose the F v Hallucinator for designing antibody sequences, especially the CDR loops, conditioned on an antibody structure. Such a strategy generates targeted CDR libraries that retain the conformation of the binder and thereby the mode of binding to the epitope on the antigen. On a benchmark set of 60 antibodies, F v Hallucinator generates sequences resembling natural CDRs and recapitulates perplexity of canonical CDR clusters. Furthermore, the F v Hallucinator designs amino acid substitutions at the V H -V L interface that are enriched in human antibody repertoires and therapeutic antibodies. We propose a pipeline that screens F v Hallucinator designs to obtain a library enriched in binders for an antigen of interest. We apply this pipeline to the CDR H3 of the Trastuzumab-HER2 complex to generate in silico designs predicted to improve upon the binding affinity and interfacial properties of the original antibody. Thus, the F v Hallucinator pipeline enables generation of inexpensive, diverse, and targeted antibody libraries enriched in binders for antibody affinity maturation.
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- Authors
- Mahajan, S. P., Ruffolo, J. A., Frick, R., & Gray, J. J.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Gray, Jeffrey J.
- Venue
- Frontiers in Immunology
- Publisher
- Frontiers Media SA
- Published
- 2022-10-21
- Volume
- 13
- Pages
- 999034-999034
- ISSN
- 1664-3224 (ISSN-L)
- PMC
- PMC9635398
- OpenAlex
- W4307166035
- Cited by
- 20
- References
- 12
- Citations by year
- 2025: 8 · 2024: 5 · 2023: 6 · 2022: 1
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Immunology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- AstraZeneca (R35-GM141881)
- GlaxoSmithKline
- Maryland Advanced Research Computing Center
- National Institutes of Health (GM-078221)
- National Institute of General Medical Sciences (R01GM078221, R35GM141881)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- antibody design, hallucinations, CDR loops, CDR-H3, affinity maturation, deep learning protein design
- MeSH terms
- Amino Acid Sequence, Antibodies, Antibody Affinity, Antigens, Hallucinations, Humans, Complementarity Determining Regions
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