GROQ-seq Datasets Across Transcription Factors (LacI, RamR, VanR), T7 RNA Polymerase and TEV Protease

Aviv Spinner, Shwetha Sreenivasan, James R. McLellan, Svetlana Ikonomova, Dana Cortade, Simon d’Oelsnitz, Kristen Sheldon, Olga Vasilyeva, Nina Y Alperovich, Anjali Chadha, Lily Nematollahi, Andi Dhroso, Zach Sisson, Corey M. Hudson, Erika DeBenedictis, Peter J. Kelly, Amanda Reider Apel, David Ross, Catherine Baranowski
2026 · doi:10.64898/2026.04.15.718744
preprint OA: closed CC-BY-ND-4.0
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Abstract

Predicting any protein’s function from its sequence alone would be a significant breakthrough in molecular biology. Although machine learning approaches have sought to tackle this, their limited generalizability reflects the absence of sufficiently large, open, diverse, and unified datasets. To address this data gap, we developed a high-throughput experimental platform called GROQ-seq ( Gro wth-based Q uantitative Seq uencing). In GROQ-seq, a protein’s function can be linked to a sequencing-based readout that enables scalable characterization of large variant libraries in Escherichia coli . Here, we present pilot datasets demonstrating its performance across three distinct protein function classes: transcription factors, polymerases, and proteases. The objective of this report is to present the datasets and to provide users with a clear and transparent characterization of their properties, including both the strengths and limitations.

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License: CC-BY-ND-4.0