Evidence map›Paper›PMID 31209296›Full record

ArticleNature chemistry2019

Stimulus-responsive self-assembly of protein-based fractals by computational design.

Nancy E Hernández, William A Hansen, Denzel Zhu, Maria E Shea, Marium Khalid, Viacheslav Manichev, Matthew Putnins, Muyuan Chen, Anthony G Dodge, Lu Yang and 9 more

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In one paragraph

Article in Nature chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Identifying nature's smallest fractals.Nature structural & molecular biology · 2024
    Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Lighting up solid states using a rubber.Nature communications · 2021
    Article
  8. Manifold of self-assembly of aRSC advances · 2020
    Article
  9. Fabrication of Pascal-triangle Lattice of Proteins by Inducing Ligand Strategy.Angewandte Chemie (International ed. in English) · 2020
    Article
  10. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Nancy E HernándezDepartment of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, USA.
William A HansenInstitute for Quantitative Biomedicine, Rutgers University, Piscataway, NJ, USA.
Denzel ZhuDepartment of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.ORCID http://orcid.org/0000-0003-0798-3823
Maria E SheaDepartment of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ, USA.ORCID http://orcid.org/0000-0002-7433-1967
Marium KhalidDepartment of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA.ORCID http://orcid.org/0000-0002-0815-4093
Viacheslav ManichevDepartment of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, USA.
Matthew PutninsInstitute for Quantitative Biomedicine, Rutgers University, Piscataway, NJ, USA.
Muyuan ChenProgram in Structural and Computational Biology and Molecular Biophysics, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA.
Anthony G DodgeBioTechnology Institute, University of Minnesota, St Paul, MN, USA.
Lu YangDepartment of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, USA.
Ileana Marrero-BerríosDepartment of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA.
Melissa BanalDepartment of Cell Biology and Neuroscience, Rutgers, University, Piscataway, NJ, USA.
Phillip RechaniDepartment of Physics and Astronomy, Rutgers University, Piscataway, NJ, USA.
Torgny GustafssonInstitute of Advanced Materials, Devices and Nanotechnology, Rutgers University, Piscataway, NJ, USA.
Leonard C FeldmanInstitute of Advanced Materials, Devices and Nanotechnology, Rutgers University, Piscataway, NJ, USA.
Sang-Hyuk LeeInstitute for Quantitative Biomedicine, Rutgers University, Piscataway, NJ, USA.ORCID http://orcid.org/0000-0001-9036-7292
Lawrence P WackettBioTechnology Institute, University of Minnesota, St Paul, MN, USA.
Wei DaiInstitute for Quantitative Biomedicine, Rutgers University, Piscataway, NJ, USA.
Sagar D KhareDepartment of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, USA. sagar.khare@rutgers.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fractal topologies, which are statistically self-similar over multiple length scales, are pervasive in nature. The recurrence of patterns in fractal-shaped branched objects, such as trees, lungs and sponges, results in a high surface area to volume ratio, which provides key functional advantages including molecular trapping and exchange. Mimicking these topologies in designed protein-based assemblies could provide access to functional biomaterials. Here we describe a computational design approach for the reversible self-assembly of proteins into tunable supramolecular fractal-like topologies in response to phosphorylation. Guided by atomic-resolution models, we develop fusions of Src homology 2 (SH2) domain or a phosphorylatable SH2-binding peptide, respectively, to two symmetric, homo-oligomeric proteins. Mixing the two designed components resulted in a variety of dendritic, hyperbranched and sponge-like topologies that are phosphorylation-dependent and self-similar over three decades (~10 nm-10 μm) of length scale, in agreement with models from multiscale computational simulations. Designed assemblies perform efficient phosphorylation-dependent capture and release of cargo proteins.

Indexed as

FractalsProtein AggregatesAlgorithmsBacterial ProteinsEscherichia coliHumansModels, ChemicalModels, MolecularPhosphorylationProtein EngineeringProtein MultimerizationRecombinant Fusion Proteinssrc-Family Kinasessrc Homology DomainsBacterial ProteinsProtein AggregatesRecombinant Fusion Proteinssrc-Family Kinases

Identifiers

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Registered trials

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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.