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. 2020 Aug 5;11:1705. doi: 10.3389/fimmu.2020.01705

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Copyright © 2020 Wendorff, Garcia Alvarez, Østerbye, ElAbd, Rosati, Degenhardt, Buus, Franke and Nielsen.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Performance of the models on peptide microarray data and resulting motifs. (A) The Pearson correlation coefficient (PCC) and (B) the Spearman correlation coefficient (SCC) on the independent test dataset of the peptide microarray are shown. The pairwise p-values were calculated using a non-parametric bootstrap hypothesis test with 1,000,000 bootstrap iterations. *0.01 < p ≤ 0.05, **0.001 < p ≤ 0.01, ***0.0001 < p ≤ 0.001, and ****p ≤ 0.0001. Motif plots of (C) DRB1*01:03, (D) DRB1*03:01, (E) DRB1*15:01, and (F) DRB1*15:02 based on the top 1% (from a pool of 100,000 random natural peptide) binding peptides generated with the deep learning model (PIA), NNAlign model and NetMHCIIpan-3.2 (8). (G) Pearson correlation coefficient (PCC) of the position specific scoring matrices (PSSM) between the different models.