Massively parallel quantification and prediction of monomeric protein yield in Escherichia coli
- 1Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan; Department of Chemistry and Biotechnology, Graduate School of Engineering, The
Abstract
Obtaining sufficient quantities of purified proteins in their intended molecular states is essential for biological research and protein engineering. Advances in computational protein design have expanded the functional capabilities of de novo proteins and accelerated candidate generation. However, reliable methods for ranking expected monomeric yield of candidate de novo designs, including molecular binders, fluorescent proteins and enzymes, have yet to be established. Standardized measurements of monomeric yield across diverse sequences remain scarce, limiting our understanding of its determinants and the development of accurate predictors for candidate selection. Here we developed a scalable pooled assay combining peptide barcodes, size-exclusion chromatography and mass spectrometry to quantify monomeric yield for over 39,000 natural protein domain variants, de novo proteins and nanobodies under uniform and standardized Escherichia coli production conditions. We used this dataset to (1) quantify the relationship between folding stability and monomeric yield, (2) benchmark inverse-folding methods, (3) evaluate the utility of structure-prediction confidence metrics in selecting high-yield design candidates, and (4) identify sequence and structural patterns in nanobodies associated with monomeric yield and monomer-to-dimer yield ratios. We further developed ESMC-MonoYield, a protein language model fine-tuned to predict monomeric yield from amino acid sequence alone, and prospectively demonstrated enrichment of high-yield candidates among BindCraft-designed binders. Our approach enables standardized, large-scale measurement of monomeric yield across diverse proteins, and establishes monomeric yield as a sequence-encoded property that can be learned, predicted and ultimately designed.
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BibTeX
@misc{setsu2026massively,
title = {{Massively parallel quantification and prediction of monomeric protein yield in Escherichia coli}},
author = {S. Setsu and K. Kiyokawa and A. Y. W. Lam and E. Mishiro-Sato and Y. Tomari and K. Tsuboyama},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.02.763507},
url = {https://www.biorxiv.org/content/10.64898/2026.11.02.763507v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Massively parallel quantification and prediction of monomeric protein yield in Escherichia coli
AU - S. Setsu
AU - K. Kiyokawa
AU - A. Y. W. Lam
AU - E. Mishiro-Sato
AU - Y. Tomari
AU - K. Tsuboyama
PY - 2026
DA - 2026/11/02
PB - bioRxiv
DO - 10.64898/2026.11.02.763507
UR - https://www.biorxiv.org/content/10.64898/2026.11.02.763507v1
AB - Obtaining sufficient quantities of purified proteins in their intended molecular states is essential for biological research and protein engineering. Advances in computational protein design have expanded the functional capabilities of de novo proteins and accelerated candidate generation. However, reliable methods for ranking expected monomeric yield of candidate de novo designs, including molecular binders, fluorescent proteins and enzymes, have yet to be established. Standardized measurements of monomeric yield across diverse sequences remain scarce, limiting our understanding of its determinants and the development of accurate predictors for candidate selection. Here we developed a scalable pooled assay combining peptide barcodes, size-exclusion chromatography and mass spectrometry to quantify monomeric yield for over 39,000 natural protein domain variants, de novo proteins and nanobodies under uniform and standardized Escherichia coli production conditions. We used this dataset to (1) quantify the relationship between folding stability and monomeric yield, (2) benchmark inverse-folding methods, (3) evaluate the utility of structure-prediction confidence metrics in selecting high-yield design candidates, and (4) identify sequence and structural patterns in nanobodies associated with monomeric yield and monomer-to-dimer yield ratios. We further developed ESMC-MonoYield, a protein language model fine-tuned to predict monomeric yield from amino acid sequence alone, and prospectively demonstrated enrichment of high-yield candidates among BindCraft-designed binders. Our approach enables standardized, large-scale measurement of monomeric yield across diverse proteins, and establishes monomeric yield as a sequence-encoded property that can be learned, predicted and ultimately designed.
ER -