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- W4318707439 abstract "Abstract Machine learning-guided protein engineering continues to rapidly progress, however, collecting large, well-labeled data sets remains time and resource intensive. Directed evolution and protein engineering studies often require extensive experimental processes to eliminate noise and fully label high-throughput protein sequence-function data. Meta learning methods established in other fields (e.g. computer vision and natural language processing) have proven effective in learning from noisy data, given the availability of a small data set with trusted labels and thus could be applied for protein engineering. Here, we generate yeast display antibody mutagenesis libraries and screen them for target antigen binding followed by deep sequencing. Meta learning approaches are able to learn under high synthetic and experimental noise as well as in under labeled data settings, typically outperforming baselines significantly and often requiring a fraction of the training data. Thus, we demonstrate meta learning may expedite and improve machine learning-guided protein engineering. Availability and implementation The code used in this study is publicly available at https://github.com/LSSI-ETH/meta-learning-for-protein-engineering . Graphical Abstract" @default.
- W4318707439 created "2023-02-01" @default.
- W4318707439 creator A5024917315 @default.
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- W4318707439 date "2023-01-30" @default.
- W4318707439 modified "2023-10-18" @default.
- W4318707439 title "Meta Learning Improves Robustness and Performance in Machine Learning-Guided Protein Engineering" @default.
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- W4318707439 doi "https://doi.org/10.1101/2023.01.30.526201" @default.
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