{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T03:19:28Z","timestamp":1785899968242,"version":"3.56.0"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"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. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Rheumatoid arthritis (RA) is an autoimmune disorder in which the immune system mounts an abnormal response to self-antigens, resulting in chronic inflammation and joint damage. Identifying antigenic regions in proteins that trigger RA is essential for the development of protein-based therapeutics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed predictive models for HLA class II binding RA-inducing peptides using a dataset of 291 experimentally validated RA-inducing peptides and 165 RA non-inducing peptides. Positional and compositional analyses were performed to identify residue preferences. Alignment-based approaches (BLAST and MERCI), machine learning classifiers, deep learning, and protein language model\u2013based methods were evaluated for predictive performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compositional analysis revealed significant enrichment of glycine, proline, and tyrosine in RA-inducing peptides. Alignment-based approaches provided high precision but limited coverage. Among machine learning methods, XGBoost achieved the best performance (AUC = 0.75) on the validation dataset, while ProtBERT was the top-performing protein language model (AUC = 0.72). The ensemble model integrating XGBoost with MERCI-derived motifs yielded the highest overall performance (AUC = 0.80; MCC = 0.45) on an independent validation dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>\n                      This study presents computational strategies for identifying RA-inducing peptides and demonstrates the advantage of combining motif-based and machine learning approaches for improved performance. The findings are valuable for evaluating the safety of proteins in probiotics, genetically modified foods, and protein-based therapeutics. To facilitate broader use, the best-performing approach has been implemented in RAIpred, a web server and standalone software tool for predicting and scanning RA-inducing peptides, available at\n                      <jats:uri>https:\/\/webs.iiitd.edu.in\/raghava\/raipred\/<\/jats:uri>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1630863","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T05:30:43Z","timestamp":1756704643000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["In-silico tool for predicting and scanning rheumatoid arthritis-inducing peptides in an antigen"],"prefix":"10.3389","volume":"16","author":[{"given":"Ritu","family":"Tomer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shipra","family":"Jain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pushpendra Singh","family":"Gahlot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nisha","family":"Bajiya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gajendra P. 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