ArticleProceedings of the National Academy of Sciences of the United States of America2022
Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 24 citations in OpenAlex.
- Applications and limitations of AI tools in enzyme design.Protein science : a publication of the Protein Society · 2026Review
- RINAMI: Residue-attributed interpretable neural network for predicting absolute folding free energy by merging structure and sequence information.Protein science : a publication of the Protein Society · 2026Article
- Article
- Accurate protein stability prediction for small domains using mega-scale experiments.bioRxiv : the preprint server for biology · 2026Article
- Using experimental results of protein design to guide biomolecular energy-function development.PLoS computational biology · 2026Article
- Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN.Protein science : a publication of the Protein Society · 2026Article
- ALG3 as a PanCancer Oncogene: Bioinformatics Analysis and Identification of Small-Molecule Inhibitors.Anti-cancer agents in medicinal chemistry · 2026Article
- Stable de novo protein design via joint conformational landscape and sequence optimization.Nature communications · 2025Article
- Global Analysis of Aggregation Determinants in Small Protein Domains.bioRxiv : the preprint server for biology · 2025Article
- Expert-guided protein language models enable accurate and blazingly fast fitness prediction.Bioinformatics (Oxford, England) · 2024Article
- Rationally seeded computational protein design of ɑ-helical barrels.Nature chemical biology · 2024Article
- Hydrophobicity-Based Force Field In Enzymes.ACS omega · 2024Article
- Article
- Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Scaffolding protein functional sites using deep learning.Science (New York, N.Y.) · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 3 countries.
Funding
Abstract
Designing entirely new protein structures remains challenging because we do not fully understand the biophysical determinants of folding stability. Yet, some protein folds are easier to design than others. Previous work identified the 43-residue ɑββɑ fold as especially challenging: The best designs had only a 2% success rate, compared to 39 to 87% success for other simple folds [G. J. Rocklin et al., Science 357, 168-175 (2017)]. This suggested the ɑββɑ fold would be a useful model system for gaining a deeper understanding of folding stability determinants and for testing new protein design methods. Here, we designed over 10,000 new ɑββɑ proteins and found over 3,000 of them to fold into stable structures using a high-throughput protease-based assay. NMR, hydrogen-deuterium exchange, circular dichroism, deep mutational scanning, and scrambled sequence control experiments indicated that our stable designs fold into their designed ɑββɑ structures with exceptional stability for their small size. Our large dataset enabled us to quantify the influence of universal stability determinants including nonpolar burial, helix capping, and buried unsatisfied polar atoms, as well as stability determinants unique to the ɑββɑ topology. Our work demonstrates how large-scale design and test cycles can solve challenging design problems while illuminating the biophysical determinants of folding.
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Registered trials
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