the HLA DR molecules. With the new data available, for the ﬁrst time, the prediction methods cover a large fraction of human MHC class II molecules (13). The molecules covered (Table 2) were selected for experimental charac- terization based on their high frequency in the worldwide human population. Two new methods for class II binding prediction [NN-align (8) and NetMHCIIpan (9)] were added to the resource website. Both methods are artiﬁcial neural network based and are trained using a concurrent align- ment and weight optimization neural network training procedure described in (14–16). NN-align is molecule- speciﬁc (i.e. one neural network method is trained for each MHC class II molecule), and NetMHCIIpan is HLA-DR pan-speciﬁc (i.e. one neural network method is trained covering all HLA-DR molecules). Both methods include encoding of peptide ﬂanking regions (PFR), PFR length and the peptide length to boost the predictive per- formance. The pan speciﬁcity of NetMHCIIpan is achieved (as was the case for NetMHCpan) by including both the peptide and contact residues from the MHC se- quences in the network training. Both novel methods have demonstrated superior predictive performance compared to the earlier methods included in the resource website with average AUC performance values of 0.821 (NN-align) and 0.846 (NetMHCIIpan) when bench- marked against a large set of HLA-DR molecules (15). MHC class I antigen processing predictions In addition to binding of peptides to MHC molecules, there are additional steps in the MHC class I pathway that a peptide has to pass in order to be recognized by the immune system (17). This includes proteasomal cleavage (18, 19) and TAP transport (20), which have been utilized in combination with MHC binding predic- tions to identify T-cell epitopes (21). A new such integra- tive predictive approach has recently been developed called NetCTLpan (22), distinguished by the use of the NetMHCpan method for the peptide:MHC binding step. It has been demonstrated that for cases where a low false positive rate is desired, (high speciﬁcity predictions) pro- teasome cleavage and transport efﬁciency by TAP contrib- ute to improved predictive performances (22). User interface updates for MHC class I/II peptide binding tools The web interfaces to the MHC class I and II binding predictive tools have been updated based on user feedback. One example was that the selection of MHC molecules from a drop-down list used to make predictions had become cumbersome, especially with the addition of the NetMHCpan tools. To address this, there is now a checkbox that is selected by default that limits the MHC molecules included for selection to those that occur in at least 1% of the human population. The vast majority of users are focusing on such molecules, and the smaller list is much easier to navigate, whereas the entirety of alleles is still available by simply unselecting the checkbox. This feature is currently available only for MHC class I tools. Nucleic Acids Research, 2012, Vol. 40, Web Server issue W527 Table 2. HLA-DP, DQ, DR molecules chosen based on their high frequency in the human population. Allele frequency data are provided by dbMHC Allelic variant HLA-DPA1*0201-DPB1*0101 HLA-DPA1*0103-DPB1*0201 HLA-DPA1*01-DPB1*0401 HLA-DPA1*0301-DPB1*0402 HLA-DPA1*0201-DPB1*0501 HLA-DQA1*0501-DQB1*0201 HLA-DQA1*0501-DQB1*0301 HLA-DQA1*0301-DQB1*0302 HLA-DQA1*0401-DQB1*0402 HLA-DQA1*0101-DQB1*0501 HLA-DQA1*0102-DQB1*0602 HLA-DRB1*0101 HLA-DRB1*0301 HLA-DRB1*0401 HLA-DRB1*0404 HLA-DRB1*0405 HLA-DRB1*0701 HLA-DRB1*0802 HLA-DRB1*0901 HLA-DRB1*1101 HLA-DRB1*1302 HLA-DRB1*1501 HLA-DRB3*0101 HLA-DRB4*0101 HLA-DRB5*0101 Allele frequency 16 17.5 36.2 41.6 21.7 11.3 35.1 19 12.8 14.6 14.6 5.4 13.7 4.6 3.6 6.2 13.5 4.9 6.2 11.8 7.7 12.2 26.1 41.8 16 In addition, the user can select different combinations of MHC molecules and lengths, so that a different group of predictions can be run in one iteration rather than re- peatedly selecting prediction method and retrieving results. Also with the addition of NetMHCpan, one can upload any MHC molecule of interest to allow predictions for MHC molecules outside of those provided by the IEDB-AR. Once a table of predictions is generated, it can be ‘expanded’ to show greater detail of how individual components of the scores contribute to the ﬁnal scores (Figure 1). Finally, another user request was for the IEDB team to spell out, which prediction method is recommended for a given task. Therefore, a default choice is provided, named ‘IEDB Recommended’. Based on availability of predictors and previously observed predictive performance, this se- lection tries to use the best possible method for a given MHC molecule. Currently, for peptide:MHC-I binding prediction, for a given MHC molecule, ‘IEDB Recommended’ uses the consensus method consisting of NetMHC, SMM and CombLib if a trained predictor is available for the molecule. Otherwise, NetMHCpan is used. This choice was motivated by the expected predictive performance of the methods in decreasing order: Consensus > NetMHC > SMM > NetMHCpan > Comb- Lib. For peptide:MHC-II binding prediction, ‘IEDB Recommended’ again uses the ‘consensus’ approach, combining NN-align, SMM-align and CombLib. The expected predicted performance for MHC-II binding methods in decreasing order are Consensus > NetMHCIIpan > NN-align > SMM-align > CombLib. Of note, we fully expect the IEDB recommendation to