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Peer-reviewedCC BYNature Communications

Accelerating protein design by scaling experimental characterization

  • Jason Qian1,2
  • Lukas F. Milles3,4
  • Basile I. M. Wicky5,6
  • Robert J. Ragotte1,2
  • Amir Motmaen7,1,2
  • Andrew J. Borst1,2
  • Rebecca Skotheim1,2
  • Sebastian Ols8,1,9,2
  • Brian Coventry1,10,2
  • Xinting Li1,2
  • Ryan D. Kibler1,2
  • Inna Goreshnik1,2
4 more authors
  • Marc Expòsit1,2,11
  • Karin Loré8,9
  • Lance J. Stewart1,2
  • David A. Baker12,13,14
14 affiliations
  1. 1Department of Biochemistry, University of Washington, Seattle, WA, USA
  2. 2Institute for Protein Design, University of Washington, Seattle, WA, USA
  3. 3Department of Biochemistry, University of Washington, Seattle, WA, USA. [email protected]
  4. 4Institute for Protein Design, University of Washington, Seattle, WA, USA. [email protected]
  5. 5Department of Biochemistry, University of Washington, Seattle, WA, USA. [email protected]
  6. 6Institute for Protein Design, University of Washington, Seattle, WA, USA. [email protected]
  7. 7Bioengineering Graduate Program, University of Washington, Seattle, WA, USA
  8. 8Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
  9. 9Department of Medicine Solna, Division of Immunology and Allergy, Karolinska Institutet, Stockholm, Sweden
  10. 10Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
  11. 11Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA, USA
  12. 12Department of Biochemistry, University of Washington, Seattle, WA, USA. [email protected]
  13. 13Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA. [email protected]
  14. 14Institute for Protein Design, University of Washington, Seattle, WA, USA. [email protected]
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Abstract

Recent advances in de novo protein design have greatly outpaced standard protein biochemistry workflows, and experimental testing has been a bottleneck in the validation of new designs and methodologies. Here, we describe experimental and computational workflows to address the issues of scale, speed and reproducibility of common in vitro protein testing methods, enabling at least an order of magnitude increase in throughput while reducing wetlab time. Semi-Automated Protein Production (SAPP) is a rapid, modular, scalable and cost-effective protocol, enabling up to milligram-scale protein production, and standardized characterization including yield, dispersity, and oligomeric state of hundreds of designs per day, at the cost-equivalent of a few DNA oligos per construct. End-to-end protocol execution takes 48 hours, with about 6 hours spent benchside using mostly standard laboratory equipment. This protocol has become the standard at our institute, providing critical experimental validation for dozens of projects spanning tens of thousands of designs. We showcase the power of the platform by using it to rapidly characterize de novo designed inhibitors of respiratory syncytial virus. Since at least 80% of SAPP’s total cost comes from synthetic DNA, we also developed a scalable demultiplexing protocol (DMX) to leverage oligo pools as input DNA, providing a further 5-fold reduction in costs, enabling >1000 designs to be purified and characterized in arrayed, clonal format at a cost of $5 per construct. By reframing standard molecular biology practices and orchestrating wetlab workflows with partial automation instead of complex end-to-end robotics, these protocols should be widely adoptable, accelerating protein design.

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Cite

BibTeXRIS
BibTeX
@article{qian2026accelerating,
  title = {{Accelerating protein design by scaling experimental characterization}},
  author = {Jason Qian and Lukas F. Milles and Basile I. M. Wicky and Robert J. Ragotte and Amir Motmaen and Andrew J. Borst and Rebecca Skotheim and Sebastian Ols and Brian Coventry and Xinting Li and Ryan D. Kibler and Inna Goreshnik and Marc Expòsit and Karin Loré and Lance J. Stewart and David A. Baker},
  journal = {Nature Communications},
  year = {2026},
  month = aug,
  doi = {10.1038/s41467-026-76740-9},
  url = {https://doi.org/10.1038/s41467-026-76740-9},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Accelerating protein design by scaling experimental characterization
AU  - Jason Qian
AU  - Lukas F. Milles
AU  - Basile I. M. Wicky
AU  - Robert J. Ragotte
AU  - Amir Motmaen
AU  - Andrew J. Borst
AU  - Rebecca Skotheim
AU  - Sebastian Ols
AU  - Brian Coventry
AU  - Xinting Li
AU  - Ryan D. Kibler
AU  - Inna Goreshnik
AU  - Marc Expòsit
AU  - Karin Loré
AU  - Lance J. Stewart
AU  - David A. Baker
PY  - 2026
DA  - 2026/08/20
JO  - Nature Communications
DO  - 10.1038/s41467-026-76740-9
UR  - https://doi.org/10.1038/s41467-026-76740-9
AB  - Recent advances in de novo protein design have greatly outpaced standard protein biochemistry workflows, and experimental testing has been a bottleneck in the validation of new designs and methodologies. Here, we describe experimental and computational workflows to address the issues of scale, speed and reproducibility of common in vitro protein testing methods, enabling at least an order of magnitude increase in throughput while reducing wetlab time. Semi-Automated Protein Production (SAPP) is a rapid, modular, scalable and cost-effective protocol, enabling up to milligram-scale protein production, and standardized characterization including yield, dispersity, and oligomeric state of hundreds of designs per day, at the cost-equivalent of a few DNA oligos per construct. End-to-end protocol execution takes 48 hours, with about 6 hours spent benchside using mostly standard laboratory equipment. This protocol has become the standard at our institute, providing critical experimental validation for dozens of projects spanning tens of thousands of designs. We showcase the power of the platform by using it to rapidly characterize de novo designed inhibitors of respiratory syncytial virus. Since at least 80% of SAPP’s total cost comes from synthetic DNA, we also developed a scalable demultiplexing protocol (DMX) to leverage oligo pools as input DNA, providing a further 5-fold reduction in costs, enabling >1000 designs to be purified and characterized in arrayed, clonal format at a cost of $5 per construct. By reframing standard molecular biology practices and orchestrating wetlab workflows with partial automation instead of complex end-to-end robotics, these protocols should be widely adoptable, accelerating protein design.
SN  - 2041-1723
ER  -