Evidence map›Paper›PMID 31890727›Full record

ArticleMolecular therapy. Methods & clinical development2019

Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate.

Jared A Delmar, Jihong Wang, Seo Woo Choi, Jason A Martins, John P Mikhail

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Molecular therapy. Methods & clinical development, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
2.0field-weighted citation impact, top 13% of its field
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

23 citing papers in PubMed, 46 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. DOTAD: A Database of Therapeutic Antibody Developability.Interdisciplinary sciences, computational life sciences · 2024
    Article
  9. Review
  10. Heat-induced structural and chemical changes to a computationally designed miniprotein.Protein science : a publication of the Protein Society · 2024
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Deep Learning in Therapeutic Antibody Development.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  19. Article
  20. High-throughput developability assays enable library-scale identification of producible protein scaffold variants.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Jared A DelmarAnalytical Sciences, Biopharmaceutical Development, AstraZeneca, One MedImmune Way, Gaithersburg, MD 20878, USA.
Jihong WangAnalytical Sciences, Biopharmaceutical Development, AstraZeneca, One MedImmune Way, Gaithersburg, MD 20878, USA.
Seo Woo ChoiDavid H. Koch School of Chemical Engineering Practice, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Jason A MartinsDavid H. Koch School of Chemical Engineering Practice, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
John P MikhailDavid H. Koch School of Chemical Engineering Practice, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Massachusetts Institute of Technology · USAstraZeneca (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The spontaneous conversion of asparagine residues to aspartic acid or iso-aspartic acid, via deamidation, is a major pathway of protein degradation and is often seriously disruptive to biological systems. Deamidation has been shown to negatively affect both

Indexed as

antibodydeamidationdevelopabilitydrug developmentIgGmabmachine learningpredictionstabilitytherapeutic protein

Identifiers

PMID31890727
PMCPMC6923510
OpenAlexW2978043812

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.