Evidence map›Paper›PMID 35539888›Full record

ArticleBioinformation2021

Molecular docking analysis of p53 with Toll-like receptors.

Mohammad Jahoor Alam, Fevzi Bardakci, Sadia Anjum, Shumayla Rasheed Mir, Irfan Ahmad, Mohd Saeed

Open access · hybridAbstract read
In one paragraph

Article in Bioinformation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 36% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
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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

6 authors at 1 institution in 1 country.

Mohammad Jahoor AlamDepartment of Biology, College of Science, University of Hail, Kingdom of Saudi Arabia.
Fevzi BardakciDepartment of Biology, College of Science, University of Hail, Kingdom of Saudi Arabia.
Sadia AnjumDepartment of Biology, College of Science, University of Hail, Kingdom of Saudi Arabia.
Shumayla Rasheed MirCollege of Computer Science and Engineering, University of Hail, Kingdom of Saudi Arabia.
Irfan AhmadDepartment of Clinical Laboratory Science, College of Applied Medical Science, King Khalid University, Kingdom of Saudi Arabia.
Mohd SaeedDepartment of Biology, College of Science, University of Hail, Kingdom of Saudi Arabia.
University of Ha'il · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

P53 is one of the most important proteins for its role in cellular signal transduction pathways. It regulates a wide variety of cellular processes, which includes apoptosis, senescence, cell cycle arrest, differentiation, and DNA repair and replication and cancer dynamics. It is a transcription factor for various cellular proteins. Recent report suggests that P53 is linked with transduction proteins involved in cellular immunity. Toll like receptors are needed for communication in cellular immunity. The interaction between p53 and toll like receptors is reported in various studies. Therefore, it is of interest to document the molecular docking analysis of p53 with Toll-like receptors for further consideration in therapeutic development. In the present paper we studied molecular interaction between p53 and toll like receptors using molecular docking approach. We used open-source tools for molecular docking and analyzing the data. Our molecular docking results suggest there is a promising interaction between p53 and toll like receptors. Our study will be a very useful for molecular therapeutics and drug design strategies. Further, molecular dynamics studies can be useful to determine of the stability of complex form by p53 and toll like receptors.

Indexed as

CommunicationDockingInteractionp53Toll-like receptor

Identifiers

PMID35539888
PMCPMC9049089
OpenAlexW4285746454

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.