ArticleProceedings of the National Academy of Sciences of the United States of America2022
A generic framework for hierarchical de novo protein design.
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 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Rationally seeded computational protein design of ɑ-helical barrels.Nature chemical biology · 2024Article
- The structural landscape of the immunoglobulin fold by large-scale de novo design.Protein science : a publication of the Protein Society · 2024Article
- Review
- Single-chain dimers from de novo immunoglobulins as robust scaffolds for multiple binding loops.Nature communications · 2023Article
- De novo design of stable proteins that efficaciously inhibit oncogenic G proteins.Protein science : a publication of the Protein Society · 2023Article
- Article
- Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes.International journal of molecular sciences · 2023Review
- De novo protein fold design through sequence-independent fragment assembly simulations.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A generic framework for hierarchical de novo protein design.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test for our understanding of the underlying physical principles of protein folding and may lead to the development of proteins with unmatched functional characteristics. The first fundamental challenge of de novo design is to devise "designable" structural templates leading to sequences that will adopt the predicted fold. Here, we built on the TopoBuilder (TB) de novo design method, to automatically assemble structural templates with native-like features starting from string descriptors that capture the overall topology of proteins. Our framework eliminates the dependency of hand-crafted and fold-specific rules through an iterative, data-driven approach that extracts geometrical parameters from structural tertiary motifs. We evaluated the TopoBuilder framework by designing sequences for a set of five protein folds and experimental characterization revealed that several sequences were folded and stable in solution. The TopoBuilder de novo design framework will be broadly useful to guide the generation of artificial proteins with customized geometries, enabling the exploration of the protein universe.
Indexed as
Identifiers
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.