Next, the NetMHCpan method was used to generate PSSMs and sequence logos from the corresponding amino acid frequencies as described by Nielsen et al. (2004) . For each position, the frequencies of all 20 amino acids were displayed as a stack of letters showing the sequence conservation/information content (the height of the entire 1 A preliminary report of SLA-1*0401 binding was given in Hoof et al. (2009) The normalized relative binding (RB) value indicates whether an amino acid is favored (RB>2, bold numbers) or disfavored (RB<0.5, italic numbers) in a given peptide position. The anchor position (AP) value is given by the equation ∑(RB−1) 2 . The important anchor positions 2, 3, and 9 for SLA-1*0401 are underlined stack) and the relative frequency of amino acids (the height of the individual amino acids). Figure 7 shows a specificity tree clustering of the SLA-1*0401 molecule compared to prevalent representatives of the 12 common HLA supertypes that NetMHCpan originally intended to cover . By this token, SLA-1*0401 most closely resembles that of HLA-A*01:01. The limited PSCPL analysis of the chimeric MHC-I molecules revealed strong P9 signals with specificities that seemed to be determined by the origin of the α 1 domain: the HPP chimera showed an HLA-A*11:01-like P9 specificity, whereas the PHP chimera showed a SLA-1*0401/HLA-A*01:01-like specificity. Since the NetMHCpan predictor successfully captured these chimeric specificities (see above), we reasoned that the predictor might also be used to perform in silico dissection of these specificities and used the P9 specificity as an example of such an in silico analysis. The NetMHCpan predictor considers a pseudo-sequence consisting of 34 polymorphic positions, which contain residues that are within 4.0 Å of the atoms of bound nonamer peptides (Nielsen et al. 2007 ). Of the 34 positions of the pseudosequence, 10 delineates the P9 binding pocket; however, only 3 of these, positions 74, 77, and 97, differ between SLA-1*0401 and HLA-A*11:01. To explore the effect of these three residues, we performed in silico experiments where we examined single substitutions Y74D, G77D, and S97I (the letter before the position number indicates the SLA-A*0401 single letter residue, whereas the letter after indicates the HLA-A*11:01 residue) as well as the corresponding triple substitution (YGS-DDI). As described above, PSSMs were generated for each of the in silico molecules followed by a specificity tree clustering (including SLA-A*0401, HLA-A*01:01, and HLA-A*11:01). Figure 8 shows this tree along with the sequence logo plots showing the predicted binding specificity of each in silico MHC-I molecule. Albeit the Y74D and G77D single substitutions showing some of the positively charged P9 peptide residue preference of HLA-A*11:01, they still clustered with HLA-A*01:01. In contrast, the in silico (YGS-DDI) triple substitution clustered with the HLA-A*11:01. This suggests that the NetMHCpan method is capable of defining the residues of the F pocket that determine the specificity of position 9.