In-silico tool for predicting and scanning rheumatoid arthritis-inducing peptides in an antigen

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.

Methods

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–based methods were evaluated for predictive performance.

Results

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.

Discussion

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 https://webs.iiitd.edu.in/raghava/raipred/ .