ArticleScience (New York, N.Y.)2017
Global analysis of protein folding using massively parallel design, synthesis, and testing.
Article in Science (New York, N.Y.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 262 papers.
What it found
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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.
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Who cites it
262 citing papers in PubMed, 550 citations in OpenAlex.
- Quantifying protein unfolding kinetics with a high-throughput microfluidic platform.Cell systems · 2026Article
- Integrating Tumor Targeting and Half-Life Extension into a Dual-Surfaced Single-Domain ADAPT Protein.Molecular pharmaceutics · 2026Article
- Scalable Production of aPharmaceutics · 2026Article
- Aligning protein-generative models to experimental fitness with ProteinDPO.Nature methods · 2026Article
- Accelerating protein design by scaling experimental characterization.Nature communications · 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
- The Ascent of Miniproteins.Biochemistry · 2026Review
- LoMuS: low-rank adaptation with sequence multi-representation improves protein stability prediction.Bioinformatics (Oxford, England) · 2026Article
- Review
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Article
- Hypervariable loop profiling decodes sequence determinants of antibody stability.Nature structural & molecular biology · 2026Article
- Article
- Accurate protein stability prediction for small domains using mega-scale experiments.bioRxiv : the preprint server for biology · 2026Article
- Beyond Classical Force Fields: Physics-Driven Assessment of the Grappa Machine-Learned Force Field on the FoldBind Dataset.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- Accurate single-domain scaffolding of three nonoverlapping protein epitopes using deep learning.Nature chemical biology · 2026Article
- Using experimental results of protein design to guide biomolecular energy-function development.PLoS computational biology · 2026Article
- Supervised learning of protein variant effects across large-scale mutagenesis datasets.Protein science : a publication of the Protein Society · 2026Article
- Functional protein design and enhancement with ontology reinforcement iteration.Nature communications · 2026Article
- The allosteric landscape of the Src kinase.Science advances · 2026Article
202 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
Proteins fold into unique native structures stabilized by thousands of weak interactions that collectively overcome the entropic cost of folding. Although these forces are "encoded" in the thousands of known protein structures, "decoding" them is challenging because of the complexity of natural proteins that have evolved for function, not stability. We combined computational protein design, next-generation gene synthesis, and a high-throughput protease susceptibility assay to measure folding and stability for more than 15,000 de novo designed miniproteins, 1000 natural proteins, 10,000 point mutants, and 30,000 negative control sequences. This analysis identified more than 2500 stable designed proteins in four basic folds-a number sufficient to enable us to systematically examine how sequence determines folding and stability in uncharted protein space. Iteration between design and experiment increased the design success rate from 6% to 47%, produced stable proteins unlike those found in nature for topologies where design was initially unsuccessful, and revealed subtle contributions to stability as designs became increasingly optimized. Our approach achieves the long-standing goal of a tight feedback cycle between computation and experiment and has the potential to transform computational protein design into a data-driven science.
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What 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.