Genomewide Conserved Epitope Profiles of HIV-1 Predicted from MHC: Peptide Binding Classifiers

• Activity Number: 14
• Type: Topic Contributed
• Date/Time: Sunday, August 7, 2005 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Mark R. Segal*+ and Yuanyuan Xiao
• Companies: University of California, San Francisco and University of California, San Francisco
• Keywords: Random forests ; Classification ; HIV-1 ; Peptide binding ; Epitopes

Abstract

Following infection, HIV-1 proteins are digested into short peptides that bind to major histocompatibility complex (MHC) molecules. Subsequently, these bound complexes are displayed by antigen-presenting cells. T cells with receptors that recognize the complexes are activated, triggering an immune response. Peptides with this ability to induce T-cell response are called T-cell epitopes---prediction thereof is important for vaccine development. Sung and Simon (JCB 2004) start with compilations of peptide sequences that bind/don't bind to specific MHC molecules and, using biophysical properties of the constituent amino acids, develop a classifier. Biophysical properties are used because of the inability of select classifiers to effectively handle amino acid sequence. Tree-structured methods are not so limited (Segal et al. 2001). Here, we apply these methods, along with their ensemble extensions (i.e., bagging, boosting, random forests), and show they provide improved accuracy. Both additional properties (QSAR-derived) and classifiers (SVMs, ANNs) also are investigated. Finally, comparisons with respect to predicted/conserved epitopes are presented.