An integrative structure-based framework for predicting biological effects mediated by antipeptide antibodies.

Area of Science:

• Immunology and Bioinformatics
• Computational Biology
• Structural Biology

Background:

• Predicting biological effects of antibodies is crucial for developing therapeutics.
• Antipeptide antibodies play a significant role in various biological processes.
• Understanding epitope-paratope interactions is key to predicting antibody efficacy.

Purpose of the Study:

• To present a general framework for predicting quantitative biological effects mediated by antipeptide antibodies.
• To estimate epitope-paratope binding affinities based on antigen structure and intrinsic disorder.
• To integrate binding affinity into dose-response relationships for antibody concentration.

Main Methods:

• Utilizing protein structural energetics to relate buried solvent-accessible surface area to binding affinity.
• Estimating binding affinity from B-cell epitope structure with implicit paratope structure treatment.
• Employing the SAPPHIRE/SUITE (Structural-energetic Analysis Program for Predicting Humoral Immune Response Epitopes/SAPPHIRE User Interface Tool Ensemble) software.

Main Results:

• Relating buried surface area upon binding to binding affinity for antipeptide antibodies.
• Comparing computational predictions with experimental data on binding affinities and biological effects.
• Accounting for paratope sidechain conformational entropy loss in binding affinity predictions.

Conclusions:

• The developed framework provides a quantitative approach to predict antibody-mediated biological effects.
• The findings have implications for refining B-cell epitope prediction methods.
• This work supports the development of prophylactic/therapeutic antibodies, peptide vaccines, and immunodiagnostics.