{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T17:43:21Z","timestamp":1778003001893,"version":"3.51.4"},"reference-count":76,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Genet."],"abstract":"<jats:p>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\u2019s ability to produce biologically meaningful insights, we conducted a comprehensive assessment involving <jats:italic>i<\/jats:italic>) predicting associations between known HLA class II human autoepitopes and microbial-peptide mimicry, <jats:italic>ii<\/jats:italic>) 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 <jats:italic>iii<\/jats:italic>) analyzing illustrative cases in the context of SARS-CoV-2 infection and autoimmunity. MMPred code and user guide are made freely available at <jats:ext-link>https:\/\/github.com\/ComputBiol-IBB\/MMPRED<\/jats:ext-link>.<\/jats:p>","DOI":"10.3389\/fgene.2024.1500684","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T10:35:08Z","timestamp":1733826908000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["MMPred: a tool to predict peptide mimicry events in MHC class II recognition"],"prefix":"10.3389","volume":"15","author":[{"given":"Filippo","family":"Guerri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valentin","family":"Junet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Judith","family":"Farr\u00e9s","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xavier","family":"Daura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1603","DOI":"10.48550\/arXiv.1603.04467","article-title":"TensorFlow: large-scale machine learning on heterogeneous distributed systems","author":"Abadi","year":"2016","journal-title":"arXiv"},{"key":"B2","doi-asserted-by":"publisher","first-page":"102791","DOI":"10.1016\/j.autrev.2021.102791","article-title":"Molecular mimicry between SARS-CoV-2 and human proteins","volume":"20","author":"Adiguzel","year":"2021","journal-title":"Autoimmune. 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