Evidence map›Paper›PMID 42754580›Full record

ArticleNature communications2026

Accelerating protein design by scaling experimental characterization.

Jason Qian, Lukas F Milles, Basile I M Wicky, Robert J Ragotte, Amir Motmaen, Andrew J Borst, Rebecca Skotheim, Sebastian Ols, Brian Coventry, Xinting Li and 6 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. AI-Driven De Novo Binder Design: From Structure Prediction to Closed-Loop Optimization.Computational and structural biotechnology journal · 2026
    Review
  5. Article
  6. bioRxiv : the preprint server for biology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Jason Qian *Department of Biochemistry, University of Washington, Seattle, WA, USA.
Lukas F Milles *Department of Biochemistry, University of Washington, Seattle, WA, USA. milles@lmu.de.
Basile I M Wicky *Department of Biochemistry, University of Washington, Seattle, WA, USA. basile.wicky@bsse.ethz.ch.ORCID http://orcid.org/0000-0002-2501-7875
Robert J Ragotte *Department of Biochemistry, University of Washington, Seattle, WA, USA.
Amir MotmaenDepartment of Biochemistry, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0003-4190-6215
Andrew J BorstDepartment of Biochemistry, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0003-4297-7824
Rebecca SkotheimDepartment of Biochemistry, University of Washington, Seattle, WA, USA.
Sebastian OlsDepartment of Biochemistry, University of Washington, Seattle, WA, USA.
Brian CoventryDepartment of Biochemistry, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-6910-6255
Xinting LiDepartment of Biochemistry, University of Washington, Seattle, WA, USA.
Ryan D KiblerDepartment of Biochemistry, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0001-6984-9887
Inna GoreshnikDepartment of Biochemistry, University of Washington, Seattle, WA, USA.
Marc ExpòsitDepartment of Biochemistry, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-2980-303X
Karin LoréDepartment of Medicine Solna, Division of Immunology and Allergy, Karolinska Institutet, Stockholm, Sweden.ORCID http://orcid.org/0000-0001-7679-9494
Lance StewartDepartment of Biochemistry, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0003-4264-5125
David BakerDepartment of Biochemistry, University of Washington, Seattle, WA, USA. dabaker@uw.edu.ORCID http://orcid.org/0000-0001-7896-6217

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in de novo protein design have greatly outpaced standard protein biochemistry workflows, making experimental validation a bottleneck. Here, we describe workflows to address the 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 ~6 hours spent benchside using standard laboratory equipment. We showcase the platform by rapidly screening redesigned fluorescent proteins, as well as identifying de novo binders that potently neutralize respiratory syncytial virus. We also developed a barcoding and demultiplexing protocol (DMX) to further reduce gene synthesis cost 5-fold by leveraging oligo pools as input DNA for the generation of thousands of sequence-verified arrayed clones. These protocols which combine optimized molecular biology, automated analysis, and optional open-source robotics should be widely adoptable, accelerating protein design.

Indexed as

Protein EngineeringProteinsReproducibility of ResultsRespiratory Syncytial VirusesProteins

Identifiers

PMID42754580
PMCPMC13586242

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.