Meta Learning Improves Robustness and Performance in Machine Learning-Guided Protein Engineering

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 implementationThe code used in this study is publicly available at https://github.com/LSSI-ETH/meta-learning-for-protein-engineering. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=184 SRC="FIGDIR/small/526201v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@11d643forg.highwire.dtl.DTLVardef@a07828org.highwire.dtl.DTLVardef@130c8fdorg.highwire.dtl.DTLVardef@1db796d_HPS_FORMAT_FIGEXP M_FIG C_FIG