Precision engineering of adalimumab fab variants enhances TNF-α neutralization and structural stability

Area of Science:

• Biotechnology
• Computational Biology
• Immunology

Background:

• Tumor necrosis factor-alpha (TNF-α) is a key pro-inflammatory cytokine in autoimmune diseases.
• Adalimumab (Humira) is a monoclonal antibody that suppresses TNF-α but can be improved.
• In-silico methods offer a rational approach to antibody engineering.

Purpose of the Study:

• To design and optimize adalimumab Fab mutants with enhanced TNF-α binding affinity using computational methods.
• To evaluate the stability, humanness, and expression of engineered antibody variants.
• To identify top-performing mutants for potential therapeutic development.

Main Methods:

• A Python script and FoldX were used to generate 512 adalimumab Fab mutants.
• Mutants were analyzed for stability (ΔG), humanness, and predicted E. coli expression.
• Molecular docking, FreeSASA, MM/GBSA, and molecular dynamics simulations were employed for evaluation.

Main Results:

• Eight stable mutants were selected, retaining human-like attributes and showing high predicted expression.
• Top candidates (Antibody-98, Antibody-354, Antibody-374) exhibited enhanced binding affinity and stability compared to wild-type.
• Specific point mutations (e.g., G28S, R90E, Y96W, W53Y/F, L102I/V) improved molecular interactions and complex stability.

Conclusions:

• Computational antibody engineering is effective for enhancing TNF-α binding affinity and stability.
• Optimized adalimumab Fab variants demonstrate potential for improved biologic therapies.
• This study provides insights for developing next-generation therapeutics targeting autoimmune diseases.