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https://scholarbank.nus.edu.sg/handle/10635/15704
DC Field | Value | |
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dc.title | Understanding peptide specificity through structural immunoinformatics | |
dc.contributor.author | TONG JOO CHUAN | |
dc.date.accessioned | 2010-04-08T10:56:28Z | |
dc.date.available | 2010-04-08T10:56:28Z | |
dc.date.issued | 2007-02-26 | |
dc.identifier.citation | TONG JOO CHUAN (2007-02-26). Understanding peptide specificity through structural immunoinformatics. ScholarBank@NUS Repository. | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/15704 | |
dc.description.abstract | Major histocompatibility complex (MHC) molecules bind peptides of diverse sequences in order to generate maximal immunological protection by covering the spectrum of peptides that may be seen by a host over the course of its lifetime. However, in many circumstances the immune system malfunctions and incorrectly recognizes a self-peptide that it should not. This results in disease characterized by recognition and attack of self. Pemphigus vulgaris (PV) is an example for such autoimmune disorder. In such situation, peptide-based modalities for immune therapy would be an advantage. However, peptide-based therapies require a thorough understanding of the sequence-structure-function relationship between MHC and its bound peptide. Advances in immunologic techniques and bioinformatics tools have enabled the generation of large amount of data that could be used for such studies. This thesis describes original findings from application of bioinformatic tools to the study of peptide/MHC interactions and its significance in PV. | |
dc.language.iso | en | |
dc.subject | MHC, epitope, bioinformatics, prediction | |
dc.type | Thesis | |
dc.contributor.department | BIOCHEMISTRY | |
dc.contributor.supervisor | RANGANATHAN, SHOBA | |
dc.contributor.supervisor | TAN TIN WEE | |
dc.description.degree | Ph.D | |
dc.description.degreeconferred | DOCTOR OF PHILOSOPHY | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Ph.D Theses (Open) |
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JooChuanTong_thesis.pdf | 3.11 MB | Adobe PDF | OPEN | None | View/Download |
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