W526 Nucleic Acids Research, 2012, Vol. 40, Web Server issue Table 1. An overview of immune-epitope related bioinformatics tools provided by the IEDB-AR Tools category Prediction Epitope analysis T-cell B-cell MHC-I MHC-II Antigen processing New or updated tools Descriptions Consensus, SMMPMBEC, NetMHCpan Consensus, NN-align, NetMHC-IIpan NetCTLpan ElliPro Homology Mapping Predict peptides that bind to MHC class I molecules Predict peptides that bind to MHC class II molecules Predict epitopes by integrating proteasomal cleavage, TAP efﬁciency, peptide:MHC-I binding scores Predict protein regions that are most likely to be bound by antibodies Map linear and discontinuous epitopes onto 3D structures publication on the analysis resource, major updates and additions have been made to tools in all categories. The following sections describe these updates in detail. All T-cell epitope predictive tools take protein se- quences represented by single letter amino acids as input. Once submitted, protein sequences are broken into appropriate peptide lengths as speciﬁed by the user. Predictions are made against the set of MHC molecules chosen by the user. Outputs consist of a list of peptides and their predicted scores, indicating their likelihood of binding or being epitopes. MHC class I peptide binding predictions For peptide:MHC-I binding prediction, a number of im- provements and additions have been made since the last web-server issue on the IEDB-AR in 2008. Speciﬁcally, larger datasets have been used to re-train all existing prediction methods, adding multiple new MHC molecules in the process. Currently binding afﬁnity data and corres- ponding predictors are available for 56 human, 19 non-human primate and six mouse molecules. This repre- sents an increase of 42% in the number of molecules covered. Furthermore, three powerful new prediction methods have been added. The ﬁrst, SMMPMBEC (2) is an improve- ment of the SMM scoring matrix-based predictor (3). SMMPMBEC implements a Bayesian approach that prefers scoring matrices that are consistent with known amino acid similarities. This is particularly helpful when estimating the contribution of speciﬁc residues to peptide binding for molecules characterized by limited binding data. The second newly added method, NetMHCpan (4), is a neural-network based predictor, similar to NetMHC (5), but with the crucial difference that for NetMHCpan, a single network-ensemble is trained on all MHC molecules simultaneously, incorporating both the peptide and contact residues from the MHC se- quences. This allows the method to extrapolate and estimate binding predictions for any MHC molecule, including those not included in the training set (6), by leveraging known sequence:binding data afﬁnity relation- ships and extending this to those MHC molecules with no binding data. Consensus is the third newly added method, which has not been previously described. The method was motivated by observations made by investigators in the machine- learning community that combining predictions from different predictors may yield higher predictive perform- ance than any of the individual predictors (7). In the current implementation, predictions from various pre- dictors are ﬁrst transformed into percentile scores, thereby allowing comparisons across predictors on a uniform scale. For a given peptide and a predictor, a per- centile score is deﬁned as a percentage of random peptides sampled from naturally occurring proteins that score better than the peptide. Using the consensus approach, the ﬁnal predicted binding afﬁnity score for the peptide is a median of percentile scores from the different pre- dictors. Notable use of an early version of a consensus method for a large-scale prediction has been described in Moutaftsi et al. (8). Benchmarks for the different tools have been performed in the respective publications with average predictive per- formances of 0.881 AUC for the class I (NetMHCpan-2.0 against HLA A and B molecules) (4). Of special note, an MHC class I prediction competition was held recently for the ﬁrst time (9). Tested on blind peptide:MHC binding datasets generated by an independent group, the consen- sus method hosted at the IEDB-AR has consistently ranked high (i.e. within the top 5 entries out of 20 total) among competitors. The blind datasets were generated for three molecules, each involving 9- and 10-mers. The combined dataset consisted of 1200 measurements with 20% composed of binders, and the average predictive per- formance on this dataset for the IEDB consensus predic- tion was 0.96 AUC, notably higher than our own prediction performance estimates, only surpassed by the NetMHCcons (10), NetMHC and NetMHCpan methods. To date, top-performing methods have been entirely sequence based, despite potential advantages of struc- ture-based methods. It is, however, expected that as struc- tural modeling techniques improve, predictive methods based on 3D structures with comparable accuracy will emerge (11, 12). MHC class II peptide-binding predictions Similar to the class I tools, all class II tools have been re-trained using newly available binding data. Importantly, a large set of data have become available that covers a set of prevalent HLA-DP and DQ molecules, for which previously there were very little data available. This was not due to lack of importance of these molecules but rather due to the signiﬁcantly greater experimental effort involved in characterizing them when compared to