SVRMHC prediction server for MHC-binding peptides
<p>Abstract</p> <p>Background</p> <p>The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate <it>in silico </it>prediction of epitope-MHC binding affinity can greatly expedite epitope scr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2006-10-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/7/463 |
| _version_ | 1811287905203650560 |
|---|---|
| author | Ren Yongliang Xu Qiqi Liu Wen Wan Ji Flower Darren R Li Tongbin |
| author_facet | Ren Yongliang Xu Qiqi Liu Wen Wan Ji Flower Darren R Li Tongbin |
| author_sort | Ren Yongliang |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate <it>in silico </it>prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort.</p> <p>Results</p> <p>Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods.</p> <p>Conclusion</p> <p>SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.</p> |
| first_indexed | 2024-04-13T03:26:42Z |
| format | Article |
| id | doaj.art-cedfa58caa804d3991659c318c3c97ec |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T03:26:42Z |
| publishDate | 2006-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-cedfa58caa804d3991659c318c3c97ec2022-12-22T03:04:38ZengBMCBMC Bioinformatics1471-21052006-10-017146310.1186/1471-2105-7-463SVRMHC prediction server for MHC-binding peptidesRen YongliangXu QiqiLiu WenWan JiFlower Darren RLi Tongbin<p>Abstract</p> <p>Background</p> <p>The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate <it>in silico </it>prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort.</p> <p>Results</p> <p>Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods.</p> <p>Conclusion</p> <p>SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.</p>http://www.biomedcentral.com/1471-2105/7/463 |
| spellingShingle | Ren Yongliang Xu Qiqi Liu Wen Wan Ji Flower Darren R Li Tongbin SVRMHC prediction server for MHC-binding peptides BMC Bioinformatics |
| title | SVRMHC prediction server for MHC-binding peptides |
| title_full | SVRMHC prediction server for MHC-binding peptides |
| title_fullStr | SVRMHC prediction server for MHC-binding peptides |
| title_full_unstemmed | SVRMHC prediction server for MHC-binding peptides |
| title_short | SVRMHC prediction server for MHC-binding peptides |
| title_sort | svrmhc prediction server for mhc binding peptides |
| url | http://www.biomedcentral.com/1471-2105/7/463 |
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