Meta learning addresses noisy and under-labeled data in machine learning-guided antibody engineering
Open accessPublished version of a preprint
Abstract
Machine learning-guided protein engineering is rapidly progressing; however, collecting high-quality, large datasets remains a bottleneck. Directed evolution and protein engineering studies often require extensive experimental processes to eliminate noise and label protein sequence-function data. Meta learning has proven effective in other fields in learning from noisy data via bi-level optimization given the availability of a small dataset with trusted labels. Here, we leverage meta learning approaches to overcome noisy and under-labeled data and expedite workflows in antibody engineering. We generate yeast display antibody mutagenesis libraries and screen them for target antigen binding followed by deep sequencing. We then create representative learning tasks, including learning from noisy training data, positive and unlabeled learning, and learning out of distribution properties. We demonstrate that meta learning has the potential to reduce experimental screening time and improve the robustness of machine learning models by training with noisy and under-labeled training data.
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Flagged first, then by citations
- Authors
- Minot, M., & Reddy, S. T.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Reddy, Sai T.
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 15
- Issue
- 1
- Pages
- 4-18.e4
- ISSN
- 2405-4712 (ISSN-L)
- OpenAlex
- W4390674232
- Cited by
- 35
- References
- 11
- Citations by year
- 2026: 6 · 2025: 22 · 2024: 6 · 2023: 1
- FWCI
- 8.66
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 382 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell Systems (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Eidgenössische Technische Hochschule Zürich
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (310030_197941)
- 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 › Machine Learning in Bioinformatics
- Keywords
- meta-learning, antibody engineering, noisy training data, protein engineering, yeast surface display, positive-unlabeled learning, out-of-distribution robustness, bi-level optimization, deep sequencing
- MeSH terms
- Machine Learning, Amino Acid Sequence, Antibodies, Engineering, Mutagenesis
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