Evidence map›Paper›PMID 34901481›Full record

ArticleSynthetic and systems biotechnology2021

Computational prediction and validation of specific EmbR binding site on PknH.

Insung Na, Huanqin Dai, Hantian Li, Anvita Gupta, David Kreda, Powell Zhang, Xiangyin Chen, Lixin Zhang, Gil Alterovitz

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Synthetic and systems biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 45% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 7 institutions in 2 countries.

Insung NaComputational Health Informatics Program, Boston Children's Hospital/Harvard Medical School, Boston, MA 02115, USA.
Huanqin DaiComputational Health Informatics Program, Boston Children's Hospital/Harvard Medical School, Boston, MA 02115, USA.
Hantian LiState Key Laboratory of Mycology, Institute of Microbiology, Beijing, 100101 China.
Anvita GuptaDepartment of Computer Sciences, Stanford University, Stanford CA 94305, USA.
David KredaCenter for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
Powell ZhangLexington High School, Boston, MA 02115, USA.
Xiangyin ChenState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China.
Lixin ZhangState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China.
Gil AlterovitzDepartment of Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA 02115, USA.
Boston Children's Hospital · USEast China University of Science and Technology · CNBrigham and Women's Hospital · USHarvard University · USJohns Hopkins University · USLexington City Schools · USStanford University · US

Funding

Towards a generalizable drug discovery framework based on intrinsically disordered regionsR01GM118467 · NIGMS · BRIGHAM AND WOMEN'S HOSPITAL · PI ALTEROVITZ, GIL · 2016 to 2021
$2.4M
NIGMS NIH HHS R01 GM118467
6 · The paper itself

Abstract

Tuberculosis drug resistance continues to threaten global health but the underline molecular mechanisms are not clear. Ethambutol (EMB), one of the well-known first - line drugs in tuberculosis treatment is, unfortunately, not free from drug resistance problems. Genomic studies have shown that some genetic mutations in

Indexed as

Binding site predictionDisorder-to-order transitionDrug resistanceMolecular simulationProtein intrinsic disorder

Identifiers

PMID34901481
PMCPMC8636726
OpenAlexW3215638063

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.