Evidence map›Paper›PMID 18315850›Full record

ArticleBMC bioinformatics2008

Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes.

Guang Lan Zhang, Asif M Khan, Kellathur N Srinivasan, At Heiny, Kx Lee, Chee Keong Kwoh, J Thomas August, Vladimir Brusic

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

15 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

8 authors at 6 institutions in 3 countries.

Guang Lan ZhangInstitute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. guanglanzhang@gmail.com
Asif M Khan
Kellathur N Srinivasan
At Heiny
Kx Lee
Chee Keong Kwoh
J Thomas August
Vladimir Brusic
National University of Singapore · SGHealth Sciences Authority · SGInstitute for Infocomm Research · SGJohns Hopkins University · USNanyang Technological University · SGUniversity of Queensland · AU

Funding

Dengue Epitope Vaccine,Tetravalent &MHCII-TargetedU19AI056541 · NIAID · JOHNS HOPKINS UNIVERSITY · PI AUGUST, J. THOMAS · 2003 to 2007
$6.8M
NIAID NIH HHS 5 U19 AI56541NIAID NIH HHS U19 AI056541PHS HHS HHSN2662-00400085C
6 · The paper itself

Abstract

backgroundT-cell epitopes that promiscuously bind to multiple alleles of a human leukocyte antigen (HLA) supertype are prime targets for development of vaccines and immunotherapies because they are relevant to a large proportion of the human population. The presence of clusters of promiscuous T-cell epitopes, immunological hotspots, has been observed in several antigens. These clusters may be exploited to facilitate the development of epitope-based vaccines by selecting a small number of hotspots that can elicit all of the required T-cell activation functions. Given the large size of pathogen proteomes, including of variant strains, computational tools are necessary for automated screening and selection of immunological hotspots.

resultsHotspot Hunter is a web-based computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes through analysis of antigenic diversity. It allows screening and selection of hotspots specific to four common HLA supertypes, namely HLA class I A2, A3, B7 and class II DR. The system uses Artificial Neural Network and Support Vector Machine methods as predictive engines. Soft computing principles were employed to integrate the prediction results produced by both methods for robust prediction performance. Experimental validation of the predictions showed that Hotspot Hunter can successfully identify majority of the real hotspots. Users can predict hotspots from a single protein sequence, or from a set of aligned protein sequences representing pathogen proteome. The latter feature provides a global view of the localizations of the hotspots in the proteome set, enabling analysis of antigenic diversity and shift of hotspots across protein variants. The system also allows the integration of prediction results of the four supertypes for identification of hotspots common across multiple supertypes. The target selection feature of the system shortlists candidate peptide hotspots for the formulation of an epitope-based vaccine that could be effective against multiple variants of the pathogen and applicable to a large proportion of the human population.

conclusionHotspot Hunter is publicly accessible at http://antigen.i2r.a-star.edu.sg/hh/. It is a new generation computational tool aiding in epitope-based vaccine design.

Indexed as

Amino Acid SequenceBinding SitesEpitope MappingEpitopes, T-LymphocyteImmunodominant EpitopesMolecular Sequence DataProtein BindingProteomeSequence Analysis, ProteinEpitopes, T-LymphocyteImmunodominant EpitopesProteome

Identifiers

PMID18315850
PMCPMC2259420
OpenAlexW2042279032

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.