ReviewNature2026
The past, present and future of de novo protein design.
Review in Nature, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Programming protein shape as an explicit design layer via CAD blueprint-guided diffusion.bioRxiv : the preprint server for biology · 2026Article
- Amplicon/Protein Bead Display enables quantitativebioRxiv : the preprint server for biology · 2026Article
- Membrane-Associated Biomolecules for Synthetic Cell Signalling.Chembiochem : a European journal of chemical biology · 2026Review
- Stepping ahead toward custom-designed autonomous motor proteins.Nature nanotechnology · 2026Article
- Modular Input-Output Biosensor Design UsingACS sensors · 2026Article
- A membrane-permeable small molecule biosensor accesses intractable cells and animals without genetic manipulation.bioRxiv : the preprint server for biology · 2026Article
- Modeling Reveals How Direct-Acting Antivirals Redirect HBV Capsid Assembly Pathways to Noninfectious Products.bioRxiv : the preprint server for biology · 2026Article
- Bayesian-Steered Structure Prediction of Mechanical Biomolecules Using Twisted Diffusion.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With deep-learning-powered advances in protein design methods, there is an ongoing paradigm shift in protein engineering from random selection to intentional computational design methods. Here we describe the current state of de novo protein design. While there is still room for improvement in success rates and activities, the long-standing challenges of designing new protein structures, assemblies and protein binders are close to being solved. The key current questions in these areas are not how to design, but what to design, and open-source design methodology such as RFdiffusion and ProteinMPNN together with protein structure prediction tools enable biochemists and molecular biologists to broadly explore possible applications. There has also been considerable progress in the de novo design of small-molecule target binders, enzymes and multistate protein systems. Current challenges for methods development include design of catalysts for reactions with high energy barriers and, more generally, design of switches and nanomachines that integrate binding, conformational change and catalysis. Over the next five to ten years, we anticipate the design of sophisticated protein nanomachines and materials with functionality ranging far beyond that generated during natural evolution for a wide range of applications in medicine, technology and sustainability.
Indexed as
Identifiers
42056544What Socratic holds
Registered trials
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 Socratic graph.