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AB-Panda: An AI-Generated Antibody Structure-Based Tool for Developability Prediction.
Wu Zou1, Jianjun Deng2, Yun Shen2
1School of Bioscience and Technology, Chengdu Medical College, Chengdu, Sichuan Province, China.
Biotechnology and Bioengineering
|November 1, 2025
Summary
A new tool, AB-Panda, uses antibody structures to predict developability issues like aggregation. It identifies problematic areas in antibody sequences, aiding in engineering better therapeutic antibodies.
Area of Science:
- Biochemistry
- Computational Biology
- Immunology
Background:
- Antibody developability is crucial for therapeutic success, impacting self-interaction, aggregation, and stability.
- Computational tools enhance antibody sequence evaluation, but high-concentration formulations require structure-based prediction improvements.
Purpose of the Study:
- To introduce AB-Panda, a novel tool for predicting antibody developability using structure-based metrics.
- To establish reference standards for antibody developability based on new metrics.
Main Methods:
- Utilized AlphaFold2-predicted antibody structures.
- Developed three structure-related metrics: unit-area hydrophobic value (UHV), unit-area positive charge (UPC), and unit-area negative charge (UNC).
- Analyzed 919 clinical stage therapeutic (CST) antibodies to define recommended metric ranges.
Main Results:
- AB-Panda identifies hydrophobic and charged patches in antibody complementarity determining regions (CDRs).
- Established recommended UHV, UPC, and UNC ranges for antibody developability.
- Provided visualizations for identifying problematic amino acids and guiding sequence engineering.
Conclusions:
- AB-Panda enhances early screening and optimization of antibody sequences for improved developability.
- The tool aids in designing antibodies suitable for high-concentration, subcutaneous formulations.
- Integrated into a web application for broader accessibility in antibody development.
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