ArticlePLoS computational biology2024
Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Article
- The open-source Masala software suite: Facilitating rapid methods development for synthetic heteropolymer design.bioRxiv : the preprint server for biology · 2025Article
- Cryo-EM structures of HCV E2 glycoprotein bound to neutralizing and non-neutralizing antibodies determined using bivalent Fabs as fiducial markers.Communications biology · 2025Article
- Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.Science advances · 2025Article
- Glycan masking in immunogen design: computational and experimental methods.Frontiers in immunology · 2025Review
- Epitope mapping via in vitro deep mutational scanning methods and its applications.The Journal of biological chemistry · 2025Review
- HyperMPNN-A general strategy to design thermostable proteins learned from hyperthermophiles.bioRxiv : the preprint server for biology · 2024Article
- mRNA-LNP prime boost evolves precursors toward VRC01-like broadly neutralizing antibodies in preclinical humanized mouse models.Science immunology · 2024Article
- Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as Restraint.ACS synthetic biology · 2024Article
- Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins.PLoS computational biology · 2024Article
- Combinatorial immune refocusing within the influenza hemagglutinin RBD improves cross-neutralizing antibody responses.Cell reports · 2023Article
- De novo design of monomeric helical bundles for pH-controlled membrane lysis.Protein science : a publication of the Protein Society · 2023Article
- Structural and computational design of a SARS-CoV-2 spike antigen with improved expression and immunogenicity.Science advances · 2023Article
- Combinatorial immune refocusing within the influenza hemagglutinin head elicits cross-neutralizing antibody responses.bioRxiv : the preprint server for biology · 2023Article
- A versatile Halo- and SNAP-tagged BMP/TGFβ receptor library for quantification of cell surface ligand binding.Communications biology · 2023Article
- Antigen- and scaffold-specific antibody responses to protein nanoparticle immunogens.Cell reports. Medicine · 2022Article
- Article
- Mannose-binding lectin and complement mediate follicular localization and enhanced immunogenicity of diverse protein nanoparticle immunogens.Cell reports · 2022Article
- An analytical study on the identification of N-linked glycosylation sites using machine learning model.PeerJ. Computer science · 2022Article
- Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks.Nature communications · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, we developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement.
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.