ArticleNature communications2022
Sampling of structure and sequence space of small protein folds.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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
14 citing papers in PubMed.
- Mapping targetable sites on the human surfaceome for the design of novel binders.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- ProteinDJ: A high-performance and modular protein design pipeline.Protein science : a publication of the Protein Society · 2026Article
- Proposed EU NGT legislation in light of plant genetic variation.Plant biotechnology journal · 2025Review
- The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe.Biology · 2025Review
- An improved model for prediction of de novo designed proteins with diverse geometries.bioRxiv : the preprint server for biology · 2025Article
- Structural Homology Fails to Predict Secretion Efficiency inInternational journal of molecular sciences · 2025Article
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- Extracellular matrix-derived materials for tissue engineering and regenerative medicine: A journey from isolation to characterization and application.Bioactive materials · 2024Review
- Stepwise introduction of stabilizing mutations reveals nonlinear additive effects in de novo TIM barrels.Protein science : a publication of the Protein Society · 2024Article
- Review
- Tpgen: a language model for stable protein design with a specific topology structure.BMC bioinformatics · 2024Article
- Accelerating therapeutic protein design with computational approaches toward the clinical stage.Computational and structural biotechnology journal · 2023Review
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Nature only samples a small fraction of the sequence space that can fold into stable proteins. Furthermore, small structural variations in a single fold, sometimes only a few amino acids, can define a protein's molecular function. Hence, to design proteins with novel functionalities, such as molecular recognition, methods to control and sample shape diversity are necessary. To explore this space, we developed and experimentally validated a computational platform that can design a wide variety of small protein folds while sampling shape diversity. We designed and evaluated stability of about 30,000 de novo protein designs of eight different folds. Among these designs, about 6,200 stable proteins were identified, including some predicted to have a first-of-its-kind minimalized thioredoxin fold. Obtained data revealed protein folding rules for structural features such as helix-connecting loops. Beyond serving as a resource for protein engineering, this massive and diverse dataset also provides training data for machine learning. We developed an accurate classifier to predict the stability of our designed proteins. The methods and the wide range of protein shapes provide a basis for designing new protein functions without compromising stability.
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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.