Comparison of HLA ligand elution data and binding predictions reveals varying prediction performance for the multiple motifs recognized by HLA‐DQ2.5

Abstract

SummaryBinding prediction tools are commonly used to identify peptides presented on MHC class II molecules. Recently, a wealth of data in the form of naturally eluted ligands has become available and discrepancies between ligand elution data and binding predictions have been reported. Quantitative metrics for such comparisons are currently lacking. In this study, we assessed how efficiently MHC class II binding predictions can identify naturally eluted peptides, and investigated instances with discrepancies between the two methods in detail. We found that, in general, MHC class II eluted ligands are predicted to bind to their reported restriction element with high affinity. But, for several studies reporting an increased number of ligands that were not predicted to bind, we found that the reported MHC restriction was ambiguous. Additional analyses determined that most of the ligands predicted to not bind, are predicted to bind other co‐expressed MHC class II molecules. For selected alleles, we addressed discrepancies between elution data and binding predictions by experimental measurements and found that predicted and measured affinities correlate well. For DQA1*05:01/DQB1*02:01 (DQ2.5) however, binding predictions did miss several peptides that were determined experimentally to be binders. For these peptides and several known DQ2.5 binders, we determined key residues for conferring DQ2.5 binding capacity, which revealed that DQ2.5 utilizes two different binding motifs, of which only one is predicted effectively. These findings have important implications for the interpretation of ligand elution data and for the improvement of MHC class II binding predictions.

Human T cells recognize HLA-DP–bound peptides in two orientations

“…We also analyzed with MoDec a small dataset from published HLA-DR and HLA-DQ allotypes ( 9 , 10 ) ( SI Appendix , Fig. S2 ).…”

“…HLA-DP peptide elution datasets from EBV-LCL and K562 transduced were published earlier ( 5 , 23 ) and reanalyzed with MoDec. Datasets for HLA-DR and HLA-DQ were obtained from studies published by others ( 9 , 10 ) and were analyzed with MoDec.…”

Modeling Peptide–Protein Interactions by a Logo-Based Method: Application in Peptide–HLA Binding Predictions

“…To tackle this challenge, we devised an alternative approach by creating a set of non-binders. This involved considering the total combination of non-preferred amino acids across all nine positions within the binding core, according to the known binding motifs for DQ2.5 [ 11 ]. Given that the peptides encompass experimentally confirmed non-preferred amino acids at each position, the combination of these specific amino acids inevitably yields sequences classified as non-binders.…”

“…Given that the peptides encompass experimentally confirmed non-preferred amino acids at each position, the combination of these specific amino acids inevitably yields sequences classified as non-binders. The non-preferred amino acids for DQ2.5 are as follows: for position 1 (p1)—Ala, Arg, Asn, Asp, Glu, Gly, Lys, Ser, and Thr; for position 2 (p2)—Ala and Leu; for position 3 (p3)—Arg, Asp, Glu, Ile, Leu, and Lys; for position 4 (p4)—Ala, Arg, Gln, Gly, Ile, Leu, Lys, Met, Phe, Ser, Thr, Trp, and Val; for position 5 (p5)—Arg, Leu, Lys, and Thr; for position 6 (p6)—Arg, Asn, Gln, Gly, Leu, Lys, Pro, Ser, Thr, and Val; for position 7 (p7)—Ala, Asn, Gln, Gly, Leu, Lys, Pro, Ser, Thr, and Val; for position 8 (p8)—Ala, Gln, Ile, Leu, and Thr; for position 9 (p9)—Ala, Arg, Gln, Lys, Met, Pro, Ser, and Thr [ 11 ]. The total combination of them generated a pool of 24,710,400 non-binding nonamers.…”

preDQ – a software tool for peptide binding prediction to HLA‐DQ2 and HLA‐DQ8

“…The sets of non-binders were generated by a total combination of the non-preferred amino acids at all nine positions of the binding core, according to the known binding motifs for DQ2.5 [16] and DQ8.1 [17]. For DQ2.5, a pool of 27 799 200 non-binding nonamers was generated.…”