Integrative Computational Prediction Strategy for Antibody-Antigen Binding: A Case Study on Interleukin-1 Beta

Area of Science:

• Computational biology
• Structural biology
• Immunology

Background:

• Accurate prediction of antibody-antigen interactions is crucial for drug development and understanding immune responses.
• Identifying critical epitope and paratope residues guides the design of therapeutic antibodies and vaccines.
• Current methods for predicting binding poses and key residues can be computationally intensive and require prior knowledge.

Purpose of the Study:

• To present a computational strategy for identifying critical epitope/paratope residues and predicting binding poses in antibody-antigen complexes.
• To develop and validate a novel workflow (EPIGUIDE) for automated epitope prediction without prior information.
• To assess the efficiency and accuracy of the ESSA-guided approach compared to traditional methods and AlphaFold3.

Main Methods:

• Utilized Essential Site Scanning Analysis (ESSA), an elastic network model-based method, to identify binding-related essential residues.
• Integrated ESSA with ClusPro for molecular docking, guiding the process with predicted essential residues.
• Employed molecular dynamics simulations to validate binding modes and uncover interaction patterns.
• Developed the EPIGUIDE workflow for automated epitope prediction, removing the need for prior epitope knowledge.

Main Results:

• ESSA-guided docking significantly improved antibody-antigen pose prediction accuracy compared to blind docking.
• Molecular dynamics simulations confirmed higher fidelity reproduction of known binding modes and revealed mechanistic insights.
• The EPIGUIDE workflow successfully predicted epitopes for diverse antibody-bound complexes, with ESSA identifying epitope residues in 10 out of 11 cases without prior information.
• The computational strategy demonstrated practical throughput, comparable success rates to AlphaFold3, and efficiency through coarse-grained modeling.

Conclusions:

• The ESSA-based computational strategy provides an efficient and accurate method for predicting antibody-antigen binding poses and identifying critical residues.
• The EPIGUIDE workflow offers an automated solution for epitope prediction, reducing reliance on prior experimental data.
• This approach accelerates the screening of key binding regions, making it a valuable tool for guiding experimental validation in antibody engineering and drug discovery.