ArticleProceedings of the National Academy of Sciences of the United States of America2023
De novo design of small beta barrel proteins.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 35 citations in OpenAlex.
- Accelerating protein design by scaling experimental characterization.Nature communications · 2026Article
- β-barrels from short macrocyclic peptides.Chemical communications (Cambridge, England) · 2026Article
- Computational design of metalloproteases.bioRxiv : the preprint server for biology · 2025Article
- Protein Design Meets Single-Molecule Detection: Towards Programmable Nanopore Sensors.International journal of molecular sciences · 2025Review
- Proposed EU NGT legislation in light of plant genetic variation.Plant biotechnology journal · 2025Review
- Parametrically guided design of beta barrels and transmembrane nanopores using deep learning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Unexplored regions of the protein sequence-structure map revealed at scale by a library of foldtuned language models.bioRxiv : the preprint server for biology · 2025Article
- Enzyme miniaturization: Revolutionizing future biocatalysts.Biotechnology advances · 2025Review
- Multistate and functional protein design using RoseTTAFold sequence space diffusion.Nature biotechnology · 2025Article
- Replicating PET Hydrolytic Activity by Positioning Active Sites with Smaller Synthetic Protein Scaffolds.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Parametrically guided design of beta barrels and transmembrane nanopores using deep learning.bioRxiv : the preprint server for biology · 2025Article
- Article
- Incremental Inverse Design of Desired Soybean Phenotypes.ACS omega · 2024Article
- Opportunities and challenges in design and optimization of protein function.Nature reviews. Molecular cell biology · 2024Review
- Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models.Cold Spring Harbor perspectives in biology · 2024Review
- Diversifying de novo TIM barrels by hallucination.Protein science : a publication of the Protein Society · 2024Article
- Optimization strategies and advances in the research and development of AAV-based gene therapy to deliver large transgenes.Clinical and translational medicine · 2024Review
- Recent Advances in Modeling Membrane β-Barrel Proteins Using Molecular Dynamics Simulations: From Their Lipid Environments to Their Assemblies.Methods in molecular biology (Clifton, N.J.) · 2024Article
- AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design.JACS Au · 2023Review
- Design of Diverse Asymmetric Pockets inBiochemistry · 2023Article
Corrections and comments
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Authors and funding
16 authors at 3 institutions in 1 country.
Funding
Abstract
Small beta barrel proteins are attractive targets for computational design because of their considerable functional diversity despite their very small size (<70 amino acids). However, there are considerable challenges to designing such structures, and there has been little success thus far. Because of the small size, the hydrophobic core stabilizing the fold is necessarily very small, and the conformational strain of barrel closure can oppose folding; also intermolecular aggregation through free beta strand edges can compete with proper monomer folding. Here, we explore the de novo design of small beta barrel topologies using both Rosetta energy-based methods and deep learning approaches to design four small beta barrel folds: Src homology 3 (SH3) and oligonucleotide/oligosaccharide-binding (OB) topologies found in nature and five and six up-and-down-stranded barrels rarely if ever seen in nature. Both approaches yielded successful designs with high thermal stability and experimentally determined structures with less than 2.4 Å rmsd from the designed models. Using deep learning for backbone generation and Rosetta for sequence design yielded higher design success rates and increased structural diversity than Rosetta alone. The ability to design a large and structurally diverse set of small beta barrel proteins greatly increases the protein shape space available for designing binders to protein targets of interest.
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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.