In Silico Residue Scanning Guides Affinity Maturation

End-to-End Residue Scanning Workflow with Schrödinger's BioLuminate

1. 01

   Identify Interface Residues (A6–IFNGR1)

   Interface residues on the heavy chain were identified from the A6–IFNGR1 complex structure in BioLuminate.

2. 02

   In Silico Mutagenesis (Residue Scanning)

   Each selected residue (e.g., H50, Y99) was mutated one at a time to assess the impact on binding affinity.

3. 03

   ΔAffinity Calculation (Binding Free Energy)

   Binding free energy changes (ΔAffinity) were computed using BioLuminate's Prime MM-GBSA workflow.

4. 04

   Interpret & Prioritize Mutations

   Mutations predicted to improve affinity are prioritized, while detrimental ones are avoided in experimental testing.

Problem Statement

Antibody A6 binds IFNGR1, but its affinity is limited by suboptimal interactions at specific heavy chain interface residues. Improving affinity requires identifying which residues can be safely modified without disrupting critical hotspots that stabilize the antigen–antibody interface.

This case study addresses the challenge of distinguishing tunable positions like H50 from essential hotspots like Y99 to guide rational affinity maturation.

Why This Problem Matters

Improving affinity enhances therapeutic potency and reduces dose. Mutating essential hotspots such as Tyr99 can severely weaken binding. A rational, structure-based approach minimizes risk, accelerates engineering, and improves developability.

• Higher Affinity = Greater Potency
• Protect Critical Hotspots
• Enable Targeted, Low-Risk Optimization
• Accelerate Development Timeline

Key Results: ΔAffinity from Residue Scanning

↓ Negative ΔAffinity = stronger binding · ↑ Positive ΔAffinity = weaker binding

Structural Context of Key Residues

Correlation with Experimental Binding Data

Strong correlation (R² = 0.86) between predicted ΔAffinity and observed ΔBinding validates the computational approach across both H50 and Y99 mutants.

Our Approach with BioLuminate

1. 01

   Complex Preparation

   Prepare A6–IFNGR1 complex structure and optimize using Protein Preparation Wizard.

2. 02

   Interface Analysis

   Identify heavy chain interface residues using BioLuminate's structural tools.

3. 03

   In Silico Mutagenesis

   Perform targeted single-point mutations at selected residues.

4. 04

   ΔAffinity Calculation

   Compute binding free energy changes (Prime MM-GBSA) for each mutant.

5. 05

   Interpretation & Prioritization

   Rank mutations to guide experimental validation and affinity maturation.

Key Insights

• Distinguishes hotspots from tunable residues

  Computational scanning clearly separates essential hotspots (Y99) from modifiable positions (H50).

• Affinity improvement is context-dependent

  Adding charge or polarity is not sufficient — structural role, packing and H-bonding are critical.

• Second-shell positions offer opportunities

  Residues like H50 can be optimized to improve complementarity without destabilizing the interface.

• Reduces experimental burden

  In silico energetics guides rational decisions early, saving time, cost and resources.

• Accelerates path to higher affinity candidates

  Enables focused, low-risk engineering toward better therapeutics.

Impact

• Improved Affinity

  Guides selection of mutations with potential to significantly enhance binding.

• Preserved Developability

  Avoids mutations that disrupt key hotspots and compromise stability.

• Faster Decision-Making

  Prioritizes the best candidates for lab validation, accelerating timelines.

• Rational Engineering

  Data-driven, structure-guided approach improves success rate of optimization.

• Stronger Therapeutic Potential

  Supports development of more potent and effective antibody therapeutics.

Conclusion

Using Schrödinger BioLuminate, in silico residue scanning enabled accurate identification of tunable and essential interface residues. This structure-guided approach provides actionable insights that accelerate affinity maturation while preserving antibody integrity and developability.