TEPITOPEpan statistics

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Number of citations per year for the bioinformatics software tool TEPITOPEpan
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Protocols

TEPITOPEpan specifications

Information


Unique identifier OMICS_06757
Name TEPITOPEpan
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes

Versioning


No version available

Information


Unique identifier OMICS_06757
Name TEPITOPEpan
Interface Web user interface
Restrictions to use None
Computer skills Basic
Stability Stable
Maintained Yes

Publication for TEPITOPEpan

TEPITOPEpan citations

 (8)
library_books

Population specific design of de immunized protein biotherapeutics

2018
PLoS Comput Biol
PMCID: 5851651
PMID: 29499035
DOI: 10.1371/journal.pcbi.1005983

[…] models.Structure-based fitness prediction for validation purposes of the de-immunized Factor VIII C2 domain constructs were performed with FoldX [] using default settings for the obtained mutations. TEPITOPEpan 1.0 was used for epitope prediction. […]

library_books

Integrated computational prediction and experimental validation identifies promiscuous T cell epitopes in the proteome of Mycobacterium bovis

2016
Microb Genom
PMCID: 5320590
PMID: 28348866
DOI: 10.1099/mgen.0.000071

[…] o generate a control set of peptides to compare with our filtering methods we chose a random selection of 20 mers from the total list of 74 525 high promiscuity binders. Half of these were taken from TEPITOPEpan and half from netMHCIIpan predictions. They did not need to be present in both prediction methods. The only other filter placed on this selection was that they be contained in proteins wit […]

library_books

Immunoinformatics and epitope prediction in the age of genomic medicine

2015
Genome Med
PMCID: 4654883
PMID: 26589500
DOI: 10.1186/s13073-015-0245-0

[…] LA peptide binding groove into account []. In 2005, Zhang and colleagues published MULTIPRED [], one of the first pan-specific predictors. Other pan-specific methods are netMHCpan [], Pick-Pocket [], TEPITOPEpan [], ADT [], UniTope [] and KISS []. MULTI-PRED trains one predictor per super-class (alleles with similar binding properties), whereas PickPocket and TEPITOPEpan calculate the binding spec […]

library_books

Development and validation of an epitope prediction tool for swine (PigMatrix) based on the pocket profile method

2015
BMC Bioinformatics
PMCID: 4570239
PMID: 26370412
DOI: 10.1186/s12859-015-0724-8

[…] ly applied to develop TEPITOPE, an algorithm for prediction of peptide ligands to 51 HLA class II alleles with known pocket residues [] and then extended to any HLA-DR molecules with similar pockets (TEPITOPEpan) []. A similar method has also been used in the PickPocket algorithm for MHC class I prediction []. Whereas TEPITOPEpan uses pocket profiles from TEPITOPE, PickPocket generates binding pre […]

library_books

MHC2MIL: a novel multiple instance learning based method for MHC II peptide binding prediction by considering peptide flanking region and residue positions

2014
BMC Genomics
PMCID: 4290625
PMID: 25521198
DOI: 10.1186/1471-2164-15-S9-S9

[…] pecific score matrix (PSSM) based methods, artificial neural network (ANN) based methods, kernel based methods, and multiple instance learning based methods. Although TEPITOPE [], ARB [], CombLib [], TEPITOPEpan [] and SMM-align [] are all PSSM based methods, they differ significantly in the way of generating the score matrices. TEPITOPE is the first PSSM method in which the score matrix is obtain […]

call_split

An effective and effecient peptide binding prediction approach for a broad set of HLA DR molecules based on ordered weighted averaging of binding pocket profiles

2013
Proteome Sci
PMCID: 3908610
PMID: 24565049
DOI: 10.1186/1477-5956-11-S1-S15
call_split See protocol

[…] imgt/hla/) provided by the IMGT/HLA database.Five independent benchmark datasets are employed to evaluate the performance of OWA-PSSM through comparing with the TEPITOPE, MultiRTA, NetMHCIIpan2.0 and TEPITOPEpan methods. […]


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TEPITOPEpan institution(s)
School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China

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