Accurate modeling of peptide-MHC structures with AlphaFold

Abstract

Major histocompatibility complex (MHC) proteins present peptides on the cell surface for T-cell surveillance. Reliable in silico prediction of which peptides would be presented and which T-cell receptors would recognize them is an important problem in structural immunology. Here, we introduce an AlphaFold-based pipeline for predicting the three-dimensional structures of peptide-MHC complexes for class I and class II MHC molecules. Our method demonstrates high accuracy, outperforming existing tools in class I modeling precision and class II peptide register prediction. We explore applications of this method towards improving peptide-MHC binding prediction.

Figure 1.

Structure dataset and the modeling pipeline. (A) Counts of non-redundant pMHCs in the discovery and test datasets, for class I and class II. (B) A schematic of a peptide position relative to the MHC binding groove. (For class I, positions P2 and P9 are the primary anchors. For class II, positions P1 and P9 are the two ends of the peptide core.) Peptide registers can be parameterized by the lengths of the C-terminal and N-terminal regions (n_ℓ,n_r). The sets of class I and class II registers observed in the discovery dataset can be characterized by a few simple rules. (C) The four registers that are possible for a class I 9-mer peptide, according to our register selection rules. (D) For a pMHC sequence and a choice of peptide register, our pipeline assigns templates and a paired peptide-MHC multiple sequence alignment. From these data, AlphaFold (AF) produces a model and an error score. (We use 100-pLDDT averaged over the peptide core as the score.) (E) A neural net seqnn predicts the pMHC dissociation constant K_d for each peptide register. Only registers with K_d within a certain factor of the lowest K_d for a given pMHC are then considered in modeling.

Figure 2.

Results of pMHC modeling for class I structures. (A) Numbers of non-redundant pMHCs for different MHC loci and species in the class I dataset. (B) C_α-pRMSD (alpha-carbon peptide RMSD) for TFold models in the class I discovery and test datasets. RMSD is computed upon superimposing MHC chains of the model and the experimental structure. Results shown for models selected by predicted LDDT (“best pLDDT”) and by peptide RMSD (“best RMSD”). Box plots show median value and first quartiles. (C) Fractions of incorrect predictions of peptide registers for class I models in the discovery and test datasets, for different methods. The first method (“assign canonical”) assigns the canonical register to all pMHCs. Bars show 95% confidence intervals (Agresti-Coull estimate). (D) Score vs accuracy plots for TFold models in the discovery and test datasets. Four accuracy groups of models based on C_α-pRMSD are denoted by color: sub-angstrom (<1 Å), good (1–1.5 Å), poor (1.5–2.5 Å), and unacceptable (>2.5 Å). For every score cutoff (100-pLDDT plotted along the horizontal axis), the plot shows fractions of models in the four accuracy groups. A vertical dashed line marks the median score. For models below and above the median score, percentages of models with C_α-pRMSD>1.5Å are shown to illustrate the score’s discrimination ability. Spearman’s ρ for the score vs RMSD is also printed on the plots. (E) Detailed diagram of register errors made by different algorithms on the discovery and test datasets. Rows correspond to algorithms, and columns to structures, with PDB IDs indicated below. Columns are colored by MHC locus and species. Each filled square indicates that the corresponding algorithm predicted the register incorrectly for the corresponding pMHC structure. (F) Comparison of C_α-pRMSDs for class I models produced by PANDORA and TFold. Percentages in the left plot are fractions of pMHCs above and below the diagonal. Both algorithms were run on the subset of the test set that only includes human and mouse MHC proteins. (G) Score vs accuracy plots for models produced by PANDORA and TFold. (See caption for figure 2D for a description of such plots.) For PANDORA, the scores in the plot are values of the MODELLER [3] molpdf energy function. (H) TFold modeling results for the set of class I pMHC pairs that are similar in sequence but differ in geometry (“difficult pMHC pairs”). Each point in the scatterplot is a modeled pMHC, and the coordinates are C_α-pRMSD of the model relative to the native structure (“true RMSD”) or to the experimental structure for the other pMHC in the pair (“cross RMSD”). Percentages indicate fractions of points above and below the diagonal. Points are colored by error score (100-pLDDT) of the models. (I) Details on the modeling results for the class I difficult pMHC pairs. Each column corresponds to a pair of pMHCs similar in sequence. (Some of them differ only by mutations in the MHC sequence, which are not shown.) Markers indicate C_α-pRMSDs for models w.r.t. their experimental structures, between the two experimental structures, and between the models. Markers for model to native RMSDs are colored by the error scores, and average scores for each pair are used to sort the columns left to right. A perfect modeling algorithm would have low error for the models (colored markers near zero) and similar RMSDs between models and between true structures (crosses and empty circles overlapping).

Figure 3.

Results of pMHC modeling for class II structures. (A) Counts of non-redundant pMHCs for different MHC loci and species in the class II dataset. IA and IE denote the two class II mouse loci H2-IA and H2-IE. The dataset only includes human and mouse structures. (B) C_α-сRMSD (alpha-carbon peptide core RMSD) for TFold models in the class II discovery and test datasets. RMSD is computed upon superimposing MHC chains of the model and the experimental structure, and only includes residues of the extended core P0-P9. Results shown for models selected by predicted LDDT (“best pLDDT”) and by peptide RMSD (“best RMSD”). Box plots show median value and first quartiles. (C) Fractions of incorrect predictions of peptide registers for class II models in the discovery and test datasets, for different methods. Bars show 95% confidence intervals (Agresti-Coull estimate). p-value is computed by Fisher’s exact test. (D) Score vs accuracy plots for TFold models in the discovery and test datasets. (See caption for figure 2D for a description of such plots.) (E) Detailed diagram of register errors made by different algorithms on the discovery and test datasets. (See caption for figure 2E for a description of such diagrams.) (F) Spearman’s ρ for the predicted vs measured pMHC dissociation constant (K_d), for four different prediction algorithms. The test set here consists of K_d measurements for 472 pMHCs with data deposited to IEDB after the netMHCIIpan 4.0 training date. Bars show the 95% confidence intervals estimated by bootstrap (1000 draws).