Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

Smith, M. D.; Case, M. A.; Makowski, E. K.; Tessier, P. M.

doi:10.1101/2023.07.10.548448

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bioRxiv · 10.1101/2023.07.10.548448

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

Smith, M. D.·Case, M. A.·Makowski, E. K.·Tessier, P. M.

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Abstract

MotivationDeep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries. ResultsHere, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns. AvailabilityAll deep sequencing datasets and code to do the analyses presented within are available via GitHub. ContactPeter Tessier, ptessier@umich.edu Supplementary informationSupplementary data are available at Bioinformatics online.

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Smith, M. D., Case, M. A., Makowski, E. K., Tessier, P. M.. 2023-07-11. Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data. https://doi.org/10.1101/2023.07.10.548448

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