Prediction of MHC class I binding peptides using profile motifs

Abstract

ABSTRACT:Peptides that bind to a given major histocompatibility complex (MHC) molecule share sequence similarity. Therefore, a position specific scoring matrix (PSSM) or profile derived from a set of peptides known to bind to a specific MHC molecule would be a suitable predictor of whether other peptides might bind, thus anticipating possible T-cell epitopes within a protein. In this approach, the binding potential of any peptide sequence (query) to a given MHC molecule is linked to its similarity to a group of aligned peptides known to bind to that MHC, and can be obtained by comparing the query to the PSSM. This article describes the derivation of alignments and profiles from a collection of peptides known to bind a specific MHC, compatible with the structural and molecular basis of the peptide-MHC class I (MHCI) interaction. Moreover, in order to apply these profiles to the prediction of peptide-MHCI binding, we have developed a new search algorithm (RANKPEP) that ranks all possible peptides from an input protein using the PSSM coefficients. The predictive power of the method was evaluated by running RANKPEP on proteins known to bear MHCI K b -and D b -restricted T-cell epitopes. Analysis of the results indicates that Ͼ 80% of these epitopes are among the top 2% of scoring peptides. Prediction of peptide-MHC binding using a variety of MHCI-specific PSSMs is available on line at our RANK-PEP web server (www.mifoundation.org/Tools/rankpep.html). In addition, the RANKPEP server also allows the user to enter additional profiles, making the server a powerful and versatile computational biology benchmark for the prediction of peptide-MHC binding. Human Immunology 63, 701-709 (2002).

Identification of Antigenic Determinants, Solvent Accessibility and MHC Binders of Antigen NADH Dehydrogenase Subunit 5 from a Little Dragon from Medina (Dracunculusmedinensis)

“…RANKPEP web server is a variability masking feature to focus on the prediction of conserved epitopes, which could thus help to avoid immune evasion resulting from mutation. Support Vector Machine (SVM) based method for prediction of promiscuous MHC class II binding peptides from protein sequence; SVM has been trained on the binary input of single amino acid sequence [40][41][42][43].…”

Prediction of MHC Binding Peptides and Antigenic Peptides from Ascaris lumbricoides

“…RANKPEP web server is a variability masking feature to focus on the prediction of conserved epitopes, which could thus help to avoid immune evasion resulting from mutation. Support Vector Machine (SVM) based method for prediction of promiscuous MHC class II binding peptides from protein sequence; SVM has been trained on the binary input of single amino acid sequence [30][31][32][33].…”

Sampling of Major Histocompatibility Complex Class I-Associated Peptidome Suggests Relatively Looser Global Association of HLA-B*5101 With Peptides

“…The binding scores of each peptide to one of the six alleles expressed by U937 cells was obtained by using a profile-based prediction algorithm [14], accessible at http://mif.dfci.harvard.edu/Tools/rankpep.html. Each peptide received a numerical score for binding to any of the six alleles.…”

“…This analysis revealed presence of two alleles for each HLA-A (A*0301 and A*3101), HLA-B (B*1801 and B*5101) and HLA-C (Cw*0102 and Cw*0702) locus (data not shown). To assign particular peptides to given alleles we used a profile based prediction algorithm [14]. Binding of long peptides to MHC class I has been reported only sporadically [18][19][20][21][22][23], hence the rules for their binding to class I molecules are not well defined.…”