High-throughput developability assays enable library-scale identification of producible protein scaffold variants

Alexander W. Golinski, Katelynn M. Mischler, Sidharth Laxminarayan, Nicole L. Neurock, Matthew Fossing, Hannah Pichman, Stefano Martiniani, Benjamin J. Hackel

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Proteins require high developability-quantified by expression, solubility, and stability-for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low throughput in nature, often slowing the developmental pipeline. We evaluated the ability of 10 variations of three high-throughput developability assays to predict the bacterial recombinant expression of paratope variants of the protein scaffold Gp2. Enabled by a phenotype/genotype linkage, assay performance for 105 variants was calculated via deep sequencing of populations sorted by proxied developability. We identified the most informative assay combination via cross-validation accuracy and correlation feature selection and demonstrated the ability of machine learning models to exploit nonlinear mutual information to increase the assays' predictive utility. We trained a random forest model that predicts expression from assay performance that is 35% closer to the experimental variance and trains 80% more efficiently than a model predicting from sequence information alone. Utilizing the predicted expression, we performed a site-wise analysis and predicted mutations consistent with enhanced developability. The validated assays offer the ability to identify developable proteins at unprecedented scales, reducing the bottleneck of protein commercialization.

Original languageEnglish (US)
Article numbere2026658118
JournalProceedings of the National Academy of Sciences of the United States of America
Volume118
Issue number23
DOIs
StatePublished - Jun 8 2021

Bibliographical note

Funding Information:
This work was funded by the NIH (R01 EB023339) and an NSF Graduate Research Fellowship (to A.W.G.). We thank Daniel Woldring for useful feedback on the manuscript. We appreciate support from the University of Minnesota Flow Cytometry Core, University of Minnesota Genomics Center, and the Minnesota Supercomputing Institute at the University of Minnesota.

Funding Information:
ACKNOWLEDGMENTS. This work was funded by the NIH (R01 EB023339) and an NSF Graduate Research Fellowship (to A.W.G.). We thank Daniel Woldring for useful feedback on the manuscript. We appreciate support

Publisher Copyright:
© 2021 National Academy of Sciences. All rights reserved.

Keywords

  • Developability
  • Predictive modeling
  • Protein engineering

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