We have investigated the deamidation patterns of gluten peptides by tTG. The results demonstrate clear and selective deamidation patterns that to a large extent can be explained by strong effects of the spacing between the target glutamine residue and COOH-terminal proline residues. It has been described previously that a glutamine between proline residues is not modified by tTG (17
). An important general role for proline in guiding tTG specificity, however, has not been appreciated. Proline, after glutamine, is the second most abundant amino acid in gluten (18
) and thus the dominant factor in the selective deamidation of gluten peptides observed. In addition, the presence of a large hydrophobic amino acid three positions COOH terminally from the target glutamine has a strong positive influence on deamidation. This is highly significant since the sequence QXPF(Y) is one of the most frequently found sequences in gluten molecules and is predicted to be invariably deamidated by tTG. Moreover, cleavage by the enzyme pepsin is known to occur after phenylalanine and to a lesser extent after tyrosine, leucine, and isoleucine, and will thus generate many gluten peptides with the tTG substrate sequence QXPF(Y) at the COOH terminus. The sequential activity of pepsin and tTG, therefore, favors the generation of gluten peptides with an appropriate p7 anchor for binding to HLA-DQ2. We also demonstrate that by combining the HLA-DQ2 peptide-binding motif with two deamidation patterns an algorithm is obtained that predicts novel T cell stimulatory peptides in gluten. Strikingly, in this predictive algorithm (X X X Q4
) we had incorporated a deamidation pattern that results in peptides that do not optimally fit the HLA-DQ2 peptide-binding motif, e.g., they lack an amino acid with a large hydrophobic side chain at position 9 in the peptide. The lack of this anchor is probably compensated for by the introduction of two negative charged anchor residues in the peptide at p4 and p7 as the result of deamidation. Thus, the T cell stimulatory activity of these peptides appears to depend more on optimal deamidation than on strict adherence to the exact HLA-DQ2 peptide-binding motif.
A less stringent search algorithm, combining only one deamidation rule with the HLA-DQ2 peptide-binding motif, proved equally successful since it predicted a previously identified T cell stimulatory α-gliadin peptide (11
). Importantly, the first predictive algorithm identified 13 peptides, eight out of which stimulated T cells. The second, less stringent algorithm predicted 261 peptides. 18 of those (1 in 15) matched a known T cell stimulatory gliadin peptide. These algorithms, therefore, have a high predictive value. Therefore, it is striking that these algorithms identified very similar and sometimes identical peptides in the barley- and rye-derived hordeins and secalins but not in the oats-derived avenins. Oats is considered safe for CD patients (15
). While gliadins, hordeins, and secalins contain ~36% glutamine and 20% proline, avenins contain a similar percentage of glutamine (34%) but half the amount of proline (10%) (18
). Deamidation of avenins is thus likely to occur in a much more random fashion. Indeed, the lack of proline residues in Q-rich regions leads to nonselective deamidation of the glutamine residues in such regions (data not shown). Collectively, our results indicate that due to nonselective deamidation no T cell stimulatory neoepitopes can be generated from the avenins and thus offer an explanation for the clinical observation that oats is not toxic for CD patients. Moreover, our results indicate that the replacement of particular proline residues in gluten by other amino acids would yield gluten molecules with considerably less toxicity for CD patients but that would retain many of the chemical and physical properties desired in gluten.
tTG is a ubiquitous enzyme and could thus be involved in the modification of proteins at other sites in the body. Therefore, it will be of interest to investigate if the defined deamidation patterns and search algorithms can be used to identify novel T cell stimulatory peptides that are relevant for autoimmune diseases.