NetCTLpan: pan-specific MHC class I pathway epitope predictions

Abstract

Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at http://www.cbs.dtu.dk/services/NetCTLpan/.Electronic supplementary materialThe online version of this article (doi:10.1007/s00251-010-0441-4) contains supplementary material, which is available to authorized users.

Selective graft-versus-leukemia depends on magnitude and diversity of the alloreactive T cell response

“…For detailed data on the T cell clones, see Supplemental Table 4. For detailed data on TCR-β deep sequencing, see Supplemental (28). We tested T cell specificity for predicted patient-type epitopes but not donor-type controls after loading titrated synthetic peptides onto donor EBV-LCLs.…”

Bioinformatics Identification of Antigenic Peptide: Predicting the Specificity of Major MHC Class I and II Pathway Players

“…This opens for questions on how to de fi ne relevant thresholds relating prediction values to likelihoods of a peptide being a T cell epitope. It is becoming apparent that not all MHC molecules present peptides at the same binding threshold ( 29,30 ) . The two distinct prediction values (af fi nity and %Rank) are included to capture these intrinsic differences between MHC molecules in terms of binding threshold for presentation of peptides.…”

“…NetCTLpan is an extended and improved version of NetCTL , which is available at http://www.cbs.dtu.dk/services/NetCTLpan/ and described in detail in ( 30 ) . The C-terminal proteasomal cleavage and TAP transport ef fi ciency are predicted as for the NetCTL method, while MHC class I binding is based on the NetMHCpan method.…”

In silico identification of cytotoxic T lymphocyte epitopes encoded by RD5 region of Mycobacterium tuberculosis

“…In this study, HLA-A2, A3, and B7 supertypes were included in this analysis, as they were the most common HLA Class I supertypes across ethnicities, with an estimated frequency of > 80% [8]. Second, the predictive CTL epitopes by NetCTL 1.2 server were analyzed by the rest five prediction online tools: SYFPEITHI [13], BIMAS [14], NetCTLPan [15], Immune Epitope Database (IEDB) Analysis Resource [16], NetMHC [17], and NetMHCPan [18]. The default prediction value was selected.…”