Using NetMHCpan to predict peptides that bind to SLA-1*0401 or to human-pig chimeric MHC class I molecules Our recently described neural network-driven bioinformatics predictor, NetMHCpan (version 2.0), has been trained on about 88,000 peptide-binding data points representing more than 80 different MHC-I molecules (primarily HLA-A and HLA-B molecules). We have previously shown that NetMHCpan is an efficient tool to identify peptides that bind to HLA molecules where no prior data exist (Nielsen et al. 2007 ) and demonstrated that NetMHCpan can be extended to MHC-I molecules of other species 1 (Hoof et al. 2009 ). We applied NetMHCpan to our peptide repository of about 10,000 peptides, which over the past decade have been selected to scan infectious agents (e.g., SARS and influenza, Sylvester-Hvid et al. 2004; Wang et al. 2010) , improve coverage of MHC-I specificities (e.g., Buus et al. 2003; Christensen et al. 2003) , etc. We extracted 29 peptides as predicted binders to either the SLA-1*0401, the HPP, or the PHP human/porcine chimeric class I molecules (some of the peptides were predicted to bind to two or even all three of these molecules). All these peptide-MHC-I combinations were tested for binding (Table 3) ; 13 of 14 peptides bound to the SLA-1*0401 molecule with an affinity (IC 50 value) better than 500 nM (6 with an affinity less than 50 nM); all 13 peptides tested on the PHP molecule were strong binders with IC 50 values below 50 nM; and 3 of 12 peptides tested on the HPP molecule bound with an affinity better than 500 nM. Of the 39 peptide-MHC-I combinations tested, 20 (51%) were found to be good binders, 9 (23%) were average binders, and 10 (26%) did not bind well (Table 3) . This is in stark contrast to the 0.5% frequency of binders among randomly selected peptides (Yewdell and Bennink 1999) .