Application of support vector machines for T-cell epitopes prediction.
BACKGROUND
The T-cell receptor, a major histocompatibility complex (MHC) molecule, and a bound antigenic peptide, play major roles in the process of antigen-specific T-cell activation. T-cell recognition was long considered exquisitely specific. Recent data also indicate that it is highly flexible, and one receptor may recognize thousands of different peptides. Deciphering the patterns of peptides that elicit a MHC restricted T-cell response is critical for vaccine development.


RESULTS
For the first time we develop a support vector machine (SVM) for T-cell epitope prediction with an MHC type I restricted T-cell clone. Using cross-validation, we demonstrate that SVMs can be trained on relatively small data sets to provide prediction more accurate than those based on previously published methods or on MHC binding.


BACKGROUND
Data for 203 synthesized peptides is available at http://linus.nci.nih.gov/Data/LAU203_Peptide.pdf
Zhao
Yingdong
Y
Biometric Research Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Pinilla
Clemencia
C
Valmori
Danila
D
Martin
Roland
R
Simon
Richard
R
D000465:Algorithms; D000936:Antigen-Antibody Complex / Q000737:chemistry* / Q000378:metabolism*; D001185:Artificial Intelligence; D016000:Cluster Analysis; D030562:Databases, Protein; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry* / Q000378:metabolism*; D015395:Histocompatibility Antigens Class I / Q000737:chemistry / Q000378:metabolism; D016571:Neural Networks, Computer; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
D000936:Antigen-Antibody Complex; D018984:Epitopes, T-Lymphocyte; D015395:Histocompatibility Antigens Class I