MMPred: a tool to predict peptide mimicry events in MHC class II recognition

Abstract

We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. Starting with two protein or peptide sets (e.g., from human and SARS-CoV-2), MMPred facilitates the generation, investigation, and testing of mimicry hypotheses by providing epitope predictions specifically for MHC class II alleles, which are frequently implicated in autoimmunity. However, the tool is easily extendable to MHC class I predictions by incorporating pre-trained models from CNN-PepPred and NetMHCpan. To evaluate MMPred's ability to produce biologically meaningful insights, we conducted a comprehensive assessment involving i) predicting associations between known HLA class II human autoepitopes and microbial-peptide mimicry, ii) interpreting these predictions within a systems biology framework to identify potential functional links between the predicted autoantigens and pathophysiological pathways related to autoimmune diseases, and iii) analyzing illustrative cases in the context of SARS-CoV-2 infection and autoimmunity. MMPred code and user guide are made freely available at https://github.com/ComputBiol-IBB/MMPRED.

Keywords: MHC class II; SARS-CoV-2; autoimmune disease; epitope prediction; molecular mimicry; sequence alignment.

FIGURE 1

Scheme summarizing the overall workflow of MMPred when used with alignment. Input in purple, process in orange and output in grey.

FIGURE 2

Results of the functional analysis for prediction sets 3 (using BLASTp) and 4 (using PSI-BLAST). Dependence of the distribution of scores $S$ (box plots overlaid with scatterplots) on the following parameters: use of BLASTp or PSI-BLAST and threshold E-value for the alignment, %Rank threshold and allele-selection criterion (allHLA or one HLA, see Section 2.4.1) for the epitope prediction. The random distribution (Rnd) is represented in grey. The mean of the distribution is indicated with a cross.

FIGURE 3

Results of the ANNs analysis for the predicted autoantigens of the SARS-CoV-2 vs. Human protome prediction sets. For each of the autoimmune-disease motifs tested, a boxplot with overlapped scatterplot represent the background distribution of the score $S$ . The eight predicted autoantigens that satisfy Perc(S) $>$ 95 are shown. Those with Perc(S) $>$ 99 are marked with * and those with Perc(S) $>$ 99.9 are marked with **.

FIGURE 4

(A) Superposition of the crystallographic structure of NSP13 (PDB entry 6ZSL, chain B) (green) with the predicted AlphaFold structure of MOV10 (UniProt entry Q9HCE1) (pink), the highlighted $\alpha$ -helices in the center correspond to residues 557-569 of NSP13 and 901-913 of MOV10. Superposition and image were generated with PyMOL (https://www.pymol.org/). (B) Logo plot of the multiple sequence alignment for the NSP13 epitope. To facilitate visualization, a pseudocount of 0.1 is used and a min-max normalization of each position is applied. Image generated with the logomaker python package (Tareen and Kinney, 2020).