A rapid, automation-friendly, cell-free and purification-free pipeline for binding and kinetic characterisation of de novo designed binders with high quality standardised per-design data
- 1Technical University of Denmark (DTU)
Abstract
De novo protein binders can now be generated far faster than they can be tested. Current validation routes - display selection, cell-free two-hybrid assays, or surface-based biophysics - require cell culture, colony picking, protein purification, target labeling or sensor immobilisation, take 2 to 8 weeks, and require additional methods for affinity and kinetics characterisation. Here we report an end-to-end, cell- and purification-free pipeline that goes from gene to binding affinity and kinetics in three days without genetically modified organisms, cell culture, purification, labelling or immobilisation. An automated, metric-driven loop of RFdiffusion, ProteinMPNN and AlphaFold2 initial-guess generates and filters designs, which are ranked by Boltz-2 ipSAE, assembled by Golden Gate cloning from linear DNA, expressed in a cell-free protein synthesis lysate as fluorescent protein fusions, and screened directly in the unpurified product by flow-induced dispersion analysis (FIDA). Against human growth hormone, 11 of 30 designs (37%) bound, and the four strongest had equilibrium dissociation constants of 0.93-9.98 nM with on- and off-rates resolved from the same cell-free material. Each sample run measures expression level, hydrodynamic radius, oligomeric and aggregation state alongside the binding readout, producing the standardised, per-design datasets that model training requires.
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BibTeX
@misc{espi2026rapid,
title = {{A rapid, automation-friendly, cell-free and purification-free pipeline for binding and kinetic characterisation of de novo designed binders with high quality standardised per-design data}},
author = {D. Ruiz Espi and G. Avelar Molina and O. Sonne Buch and J. Heyd and A. Unger and L. Winther Gormsen and K. Boldsen Lund and K. Sahni Ray and C. Williams and A. Hald and T. P. Jenkins and E. Rivera-de-Torre},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.19.759856},
url = {https://www.biorxiv.org/content/10.64898/2026.10.19.759856v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A rapid, automation-friendly, cell-free and purification-free pipeline for binding and kinetic characterisation of de novo designed binders with high quality standardised per-design data
AU - D. Ruiz Espi
AU - G. Avelar Molina
AU - O. Sonne Buch
AU - J. Heyd
AU - A. Unger
AU - L. Winther Gormsen
AU - K. Boldsen Lund
AU - K. Sahni Ray
AU - C. Williams
AU - A. Hald
AU - T. P. Jenkins
AU - E. Rivera-de-Torre
PY - 2026
DA - 2026/10/19
PB - bioRxiv
DO - 10.64898/2026.10.19.759856
UR - https://www.biorxiv.org/content/10.64898/2026.10.19.759856v1
AB - De novo protein binders can now be generated far faster than they can be tested. Current validation routes - display selection, cell-free two-hybrid assays, or surface-based biophysics - require cell culture, colony picking, protein purification, target labeling or sensor immobilisation, take 2 to 8 weeks, and require additional methods for affinity and kinetics characterisation. Here we report an end-to-end, cell- and purification-free pipeline that goes from gene to binding affinity and kinetics in three days without genetically modified organisms, cell culture, purification, labelling or immobilisation. An automated, metric-driven loop of RFdiffusion, ProteinMPNN and AlphaFold2 initial-guess generates and filters designs, which are ranked by Boltz-2 ipSAE, assembled by Golden Gate cloning from linear DNA, expressed in a cell-free protein synthesis lysate as fluorescent protein fusions, and screened directly in the unpurified product by flow-induced dispersion analysis (FIDA). Against human growth hormone, 11 of 30 designs (37%) bound, and the four strongest had equilibrium dissociation constants of 0.93-9.98 nM with on- and off-rates resolved from the same cell-free material. Each sample run measures expression level, hydrodynamic radius, oligomeric and aggregation state alongside the binding readout, producing the standardised, per-design datasets that model training requires.
ER -