ArticleScience (New York, N.Y.)2020
Expanding the space of protein geometries by computational design of de novo fold families.
Article in Science (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- A Hybrid Physics-Deep Learning Framework for Combinatorial De Novo Design of Small-Molecule Binding Proteins.bioRxiv : the preprint server for biology · 2026Article
- Review
- Accurate single-domain scaffolding of three nonoverlapping protein epitopes using deep learning.Nature chemical biology · 2026Article
- Buttressing ligand-binding pockets in de novo designed NTF2-like domains.Protein science : a publication of the Protein Society · 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
- An improved model for prediction of de novo designed proteins with diverse geometries.bioRxiv : the preprint server for biology · 2025Article
- Deep learning-guided design of dynamic proteins.Science (New York, N.Y.) · 2025Article
- Opportunities and challenges in design and optimization of protein function.Nature reviews. Molecular cell biology · 2024Review
- Rationally seeded computational protein design of ɑ-helical barrels.Nature chemical biology · 2024Article
- Review
- Advances in ligand-specific biosensing for structurally similar molecules.Cell systems · 2023Review
- Article
- Manganese(ii) complexes stimulate antitumor immunityChemical science · 2023Article
- Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes.International journal of molecular sciences · 2023Review
- Sampling of structure and sequence space of small protein folds.Nature communications · 2022Article
- 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
- The road to fully programmable protein catalysis.Nature · 2022Review
- Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2022Article
- Accurate positioning of functional residues with robotics-inspired computational protein design.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Naturally occurring proteins vary the precise geometries of structural elements to create distinct shapes optimal for function. We present a computational design method, loop-helix-loop unit combinatorial sampling (LUCS), that mimics nature's ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space. Biophysical characterization showed that 17 (38%) of 45 tested LUCS designs encompassing two different structural topologies were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely matched the designs. LUCS greatly expands the designable structure space and offers a new paradigm for designing proteins with tunable geometries that may be customizable for novel functions.
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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.