Evidence map›Paper›PMID 26770024›Full record

ArticleBioinformation2015

Insights from the predicted epitope similarity between Mycobacterium tuberculosis virulent factors and its human homologs.

Venkata Ravi Gutlapalli, Aparna Sykam, Anuraj Nayarisseri, Sujai Suneetha, Lavanya M Suneetha

Open access · hybridAbstract read
In one paragraph

Article in Bioinformation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.5field-weighted citation impact, top 33% 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

12 citing papers in PubMed, 33 citations in OpenAlex.

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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

5 authors at 2 institutions in 2 countries.

Venkata Ravi GutlapalliCODEWEL Nireekshana-ACET, Hyderabad, Telangana-500029, India; Centre for Biotechnology, AcharyaNagarjuna University, Nagarjuna Nagar, Guntur, Andhra Pradesh 522510, India.
Aparna SykamCODEWEL Nireekshana-ACET, Hyderabad, Telangana-500029, India; Centre for Biotechnology, AcharyaNagarjuna University, Nagarjuna Nagar, Guntur, Andhra Pradesh 522510, India.
Anuraj NayarisseriBioinformatics Research Laboratory, Eminent Biosciences, Vijaynagar, Indore - 452010, Madhya Pradesh, India.
Sujai SuneethaCODEWEL Nireekshana-ACET, Hyderabad, Telangana-500029, India.
Lavanya M SuneethaCODEWEL Nireekshana-ACET, Hyderabad, Telangana-500029, India.
Acharya Nagarjuna University · INNational Institute of Plant Health Management · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mycobacterium tuberculosis is known to be associated with several autoimmune diseases such as systemic lupus erythematous, rheumatoid arthritis and multiple sclerosis. This is attributed to sequence similarity between virulent factors and human proteins. Therefore, it is of interest to identify such regions in the virulent factors to assess potential autoimmune related information. M. tb specific virulent factors were downloaded from the VFDB database and its human homologs were identified using the sequence comparison search tool BLASTP. Both virulent proteins and their corresponding human homologs were further scanned for epitopes (B cell and HLA class I and II allele specific) using prediction programs (BCPRED and NETMHC). Data shows the presence of matching 22 B-cell, 79 HLA class II and 16 HLA class I specific predicted epitopes in these virulent factors having human homologs. A known peptide (HAFYLQYKNVKVDFA) associated with autoimmune atopic dermatitis is shown in the superoxide dismutase homolog structures of the bacterium (PDB ID: 1IDS) and human (PDB ID: 2QKC). This data provides insight into the understanding of infection-associated auto-immunity.

Identifiers

PMID26770024
PMCPMC4702028
OpenAlexW2403800803

What OpenQuestion holds

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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.