Discovering Selected Antibodies From Deep-Sequenced Phage-Display Antibody Library Using ATTILA

Area of Science:

• Biotechnology
• Immunology
• Bioinformatics

Background:

• Phage display is crucial for selecting high-affinity antibodies for biopharmaceutical applications.
• Next-generation sequencing (NGS) enables analysis of billions of antibody variable domain sequences.
• Identifying specific enriched antibody clones from NGS data presents a significant challenge.

Purpose of the Study:

• To introduce the AutomaTed Tool For Immunoglobulin Analysis (ATTILA), a novel computational workflow.
• To facilitate the analysis of NGS data for identifying enriched antibody variable domains from biopanning experiments.
• To improve the efficiency and accuracy of monoclonal antibody discovery.

Main Methods:

• ATTILA integrates publicly available tools with custom scripts for data analysis.
• The workflow analyzes fold-change frequency of amplified variable heavy (VH) and variable light (VL) domains from NGS data.
• Performance and accuracy were evaluated across multiple biopanning experiments using human Fab libraries.

Main Results:

• ATTILA successfully assessed library variability and identified highly enriched variable domains.
• The tool generates comprehensive reports including amino acid sequences, CDRs, germline classification, and fold change.
• Demonstrated suitability for analyzing deep sequencing amplicon data.

Conclusions:

• ATTILA provides a robust method for analyzing NGS data in antibody discovery.
• The workflow aids in the identification and characterization of promising monoclonal antibody candidates.
• This approach effectively combines amplicon generation and bioinformatics for discovering new therapeutic antibodies.