AI-Guided Antibody Deimmunization Using Targeted Residue Optimization and Protein Language Models

Overview

Rheumatoid arthritis (RA) is an autoimmune disease affecting approximately 18 million people worldwide. Many treatments rely on monoclonal antibody drugs that block inflammatory signals such as TNF-α and IL-6. However, up to 40% of patients develop anti-drug antibodies (ADAs), which reduce treatment effectiveness by targeting these therapies as foreign proteins. Reducing immunogenicity while preserving drug function remains a major challenge in biologic design. This project presents a computational pipeline for identifying antibody sequence modifications that reduce predicted immunogenicity while maintaining structural stability. Antibody sequences were analyzed using NetMHCIIpan to identify immune-reactive regions, and alternative amino acids were generated using the protein language model ESM-2. A key innovation of this work is a targeted masking strategy that biases model training toward immunogenic “hotspot” residues, ensuring precise edits in high-risk regions. Through multiple iterations of optimization and hybridization with biological antibody design principles, the final approach achieved a 66% success rate in reducing predicted immunogenicity (meaning the model was able to successfully produce antibodies with reduced GIS scores for 66% of the antibodies in the test set) 106 test sequences, with an average sequence similarity of 88.3% and 13.5 targeted mutations per sequence. These results demonstrate that protein language models can be guided with biologically-informed strategies to perform targeted, clinically relevant antibody deimmunization, providing a robust framework for AI-assisted therapeutic design

Competition history

• CSEF 2026 Medicine & Physiology (Track 2) (Senior Division) · Entry S-21-32

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