ReviewNature2016
The coming of age of de novo protein design.
Review in Nature, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 609 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
609 citing papers in PubMed.
- Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry.bioRxiv : the preprint server for biology · 2026Article
- Generative AI designs functional thiolation domains for reprogramming non-ribosomal peptide synthetases.Nature communications · 2026Article
- Latent generative search unlocks de novo design of untapped biomolecular interactions at scale.bioRxiv : the preprint server for biology · 2026Article
- Scalable Production of aPharmaceutics · 2026Article
- Review
- Functional customization of peptide linkers in fusion proteins through multimodal deep learning approach.Synthetic and systems biotechnology · 2026Article
- Intracellular protein binders for imaging, control and future therapeutics.Nature biomedical engineering · 2026Review
- AbioRxiv : the preprint server for biology · 2026Article
- Molecular Access Engineering for Microbial Biocatalysis: from Enzyme Tunnels to Microbial Cell Factories.Journal of microbiology and biotechnology · 2026Review
- PythiaStudio: a one-stop protein engineering platform powered by Pythia model suite.Nucleic acids research · 2026Article
- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Artificial intelligence aided design of peptides with custom secondary structure motifs and reduced amino acid alphabets.bioRxiv : the preprint server for biology · 2026Article
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- Emergence and evolution of protein-coding de novo genes.Nature reviews. Genetics · 2026Review
- Review
- Emergence of specific binding and catalysis from a designed generalist binding protein.Nature chemistry · 2026Article
- MAHLER: Integrating Metadynamics and Inverse Folding to Predict Antibody-Antigen Kinetics.bioRxiv : the preprint server for biology · 2026Article
- Entropy quantum computing for fixed-backbone protein design.Scientific reports · 2026Article
- Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.Journal of chemical theory and computation · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
549 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are 20(200) possible amino-acid sequences for a 200-residue protein, of which the natural evolutionary process has sampled only an infinitesimal subset. De novo protein design explores the full sequence space, guided by the physical principles that underlie protein folding. Computational methodology has advanced to the point that a wide range of structures can be designed from scratch with atomic-level accuracy. Almost all protein engineering so far has involved the modification of naturally occurring proteins; it should now be possible to design new functional proteins from the ground up to tackle current challenges in biomedicine and nanotechnology.
Indexed as
Identifiers
27629638What OpenQuestion 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 OpenQuestion graph.