The use of bioinformatics for identifying class II-restricted T-cell epitopes

Bian, Hongjin

doi:10.1016/s1046-2023(02)00352-3

Cited by 70 publications

(64 citation statements)

References 12 publications

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“…For the peptides listed in the first column, 19 different core sequences were predicted and are indicated in the second column of the Table. The peptides indicated in bold were tested with patients' PBMC ( Table 2). Results are expressed as prediction threshold from 1 to 10% (percentage of the best scoring peptides in natural peptide frames) as described [32]. for the medium background.…”

Section: Hnrnp-a2-peptide Specific T-cell Responses Detected By Ifn-cmentioning

confidence: 99%

“…A prediction threshold of 1% indicates very good and a threshold of 10% very bad putative binders (see Table 1 (footnote b)) and ref. [32]). (Table 2, and Supporting Information Table 2).…”

Section: B-cell Epitope Analysis In Ra Patients Compared To DC and Hementioning

confidence: 99%

“…TEPITOPE is a T-cell epitope prediction software that enables the identification of ligands binding in a promiscuous or allelespecific mode to HLA-DR molecules [32]. We set the TEPITOPE prediction threshold to 10% to select all potential peptide binders to HLA-DR Ã 0401, DR Ã 0404, DR Ã 0101 (which includes weak putative binders and may yield false positives, i.e.…”

Section: Epitope Predictionmentioning

confidence: 99%

“…We set the TEPITOPE prediction threshold to 10% to select all potential peptide binders to HLA-DR Ã 0401, DR Ã 0404, DR Ã 0101 (which includes weak putative binders and may yield false positives, i.e. peptides not able to bind to these alleles; see [32]). …”

Section: Epitope Predictionmentioning

confidence: 99%

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Immunodominant T‐cell epitopes of hnRNP‐A2 associated with disease activity in patients with rheumatoid arthritis

et al. 2010

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The heterogeneous nuclear ribonucleoprotein A2 (hnRNP-A2) has been described as an important autoantigen in rheumatoid arthritis (RA) since it is targeted by autoantibodies, autoreactive T cells, and is aberrantly expressed in synovial cells in patients. To identify hnRNP-A2-specific T-cell epitopes possibly associated with pathogenicity, we used an innovative approach. We first scanned 280 overlapping hnRNP-A2 peptides for binding to the RA-associated class II molecules HLA-DR4 and HLA-DR1, leading to a comprehensive selection of binders. The selected peptides were tested in IFN-c-specific ELISPOT assay: PBMC from 18% of RA patients showed a significant IFN-c response to hnRNP-A2 peptides, 15% to the overlapping sequences 117-133 and/or 120-133, whereas PBMC from healthy individuals tested negative. We measured proliferative responses to these two peptides in another cohort of patients with RA or osteoarthritis: positive responses were found in 28% of RA, but also in 11% of osteoarthritis patients and these responses could be blocked by anti-MHC class II Ab. Remarkably, the presence of 117/120-133-specific T cells was significantly associated with active disease in RA patients, and bone erosion appeared to be more common in T-cell positive patients. These data suggest involvement of hnRNP-A2 specific cellular autoimmune responses in RA pathogenesis.

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Section: Hnrnp-a2-peptide Specific T-cell Responses Detected By Ifn-cmentioning

confidence: 99%

“…A prediction threshold of 1% indicates very good and a threshold of 10% very bad putative binders (see Table 1 (footnote b)) and ref. [32]). (Table 2, and Supporting Information Table 2).…”

Section: B-cell Epitope Analysis In Ra Patients Compared To DC and Hementioning

confidence: 99%

Section: Epitope Predictionmentioning

confidence: 99%

Section: Epitope Predictionmentioning

confidence: 99%

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Immunodominant T‐cell epitopes of hnRNP‐A2 associated with disease activity in patients with rheumatoid arthritis

et al. 2010

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“…Recently, immunoinformatics, a developed branch of bioinformatics tools, is used for selecting epitopes from immunological target proteins (Bian et al, 2003;Li et al, 2005). In this work, PEPOP (Moreau et al, 2008) for prediction of peptides representative of discontinuous epitopes (in short "discontinuous peptides") and SYFPEITHI (Rammensee et al, 1999), MHCPred (Guan et al, 2003), Propred (Singh and Raghava, 2001), SVMHC (Dönnes and Elofsson, 2002), and HLA peptide motif search (Parker et al, 1994) for prediction of T-cell epitope peptides were used.…”

Section: Introductionmentioning

confidence: 99%

In silico Design of Discontinuous Peptides Representative of B and T-cell Epitopes from HER2-ECD as Potential Novel Cancer Peptide Vaccines

Mahdavi

Keyhanfar²,

Moreau³

et al. 2013

Asian Pacific Journal of Cancer Prevention

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At present, the most common cause of cancer-related death in women is breast cancer. In a large proportion of breast cancers, there is the overexpression of human epidermal growth factor receptor 2 (HER2). This receptor is a 185 KDa growth factor glycoprotein, also known as the first tumor-associated antigen for different types of breast cancers. Moreover, HER2 is an appropriate cell-surface specific antigen for passive immunotherapy, which relies on the repeated application of monoclonal antibodies that are transferred to the patient. However, vaccination is preferable because it would stimulate a patient's own immune system to actively respond to a disease. In the current study, several bioinformatics tools were used for designing synthetic peptide vaccines. PEPOP was used to predict peptides from HER2 ECD subdomain III in the form of discontinuous-continuous B-cell epitopes. Then, T-cell epitope prediction web servers MHCPred, SYFPEITHI, HLA peptide motif search, Propred, and SVMHC were used to identify class-I and II MHC peptides. In this way, PEPOP selected 12 discontinuous peptides from the 3D structure of the HER2 ECD subdomain III. Furthermore, T-cell epitope prediction analyses identified four peptides containing the segments 77 (384-391) and 99 (495-503) for both B and T-cell epitopes. This work is the only study to our knowledge focusing on design of in silico potential novel cancer peptide vaccines of the HER2 ECD subdomain III that contain epitopes for both B and T-cells. These findings based on bioinformatics analyses may be used in vaccine design and cancer therapy; saving time and minimizing the number of tests needed to select the best possible epitopes.

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Peptide‐Based Immunotherapy for Food Allergy

Yang

Mine

2010

Bioactive Proteins and Peptides as Functional Foods and Nutraceuticals

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The use of bioinformatics for identifying class II-restricted T-cell epitopes

Cited by 70 publications

References 12 publications

Immunodominant T‐cell epitopes of hnRNP‐A2 associated with disease activity in patients with rheumatoid arthritis

Immunodominant T‐cell epitopes of hnRNP‐A2 associated with disease activity in patients with rheumatoid arthritis

In silico Design of Discontinuous Peptides Representative of B and T-cell Epitopes from HER2-ECD as Potential Novel Cancer Peptide Vaccines

Peptide‐Based Immunotherapy for Food Allergy

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