Evidence map›Paper›PMID 27279731›Full record

ArticleCancer informatics2016

Rational Design of Peptide Vaccines Against Multiple Types of Human Papillomavirus.

Sumanta Dey, Antara De, Ashesh Nandy

Open access · goldAbstract read
In one paragraph

Article in Cancer informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.7field-weighted citation impact, top 15% 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

7 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Article
  3. New Computational Approach for Peptide Vaccine Design Against SARS-COV-2.International journal of peptide research and therapeutics · 2021
    Article
  4. Review
  5. Article
  6. Article
  7. 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

3 authors at 1 institution in 1 country.

Sumanta DeyCentre for Interdisciplinary Research and Education, Kolkata, India.
Antara DeCentre for Interdisciplinary Research and Education, Kolkata, India.
Ashesh NandyCentre for Interdisciplinary Research and Education, Kolkata, India.
Centre for Interdisciplinary Research and Education · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human papillomavirus (HPV) occurs in many types, some of which cause cervical, genital, and other cancers. While vaccination is available against the major cancer-causing HPV types, many others are not covered by these preventive measures. Herein, we present a bioinformatics study for the designing of multivalent peptide vaccines against multiple HPV types as an alternative strategy to the virus-like particle vaccines being used now. Our technique of rational design of peptide vaccines is expected to ensure stability of the vaccine against many cycles of mutational changes, elicit immune response, and negate autoimmune possibilities. Using the L1 capsid protein sequences, we identified several peptides for potential vaccine design for HPV 16, 18, 33, 35, 45, and 11 types. Although there are concerns about the epitope-binding affinities for the peptides identified in this process, the technique indicates possibilities of multivalent, adjuvanted, peptide vaccines against a wider range of HPV types, and tailor-made different combinations of the peptides to address frequency variations of types over different population groups as required for prophylaxis and at lower cost than are in use at the present time.

Indexed as

continuous-discontinuous epitopesgraphical representationhuman papillomavirusmultivalent peptide vaccinesprotein graph radiusrational design of peptide vaccines

Identifiers

PMID27279731
PMCPMC4890726
OpenAlexW2417169344

What OpenQuestion holds

Textmetadata
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.