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Updated: Dec 21, 2025

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries
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Discovering Selected Antibodies From Deep-Sequenced Phage-Display Antibody Library Using ATTILA.

Andréa Queiroz Maranhão1,2, Heidi Muniz Silva1, Waldeyr Mendes Cordeiro da Silva1,3

  • 1Department of Cellular Biology, Institute of Biological Science, University of Brasília, Brasília, Brazil.

Bioinformatics and Biology Insights
|May 20, 2020
PubMed
Summary
This summary is machine-generated.

We developed ATTILA, a new tool to analyze next-generation sequencing data from antibody discovery experiments. ATTILA efficiently identifies enriched antibody variable domains, aiding in the discovery of novel monoclonal antibodies.

Keywords:
Phage displayantibody variable domainsbiopanningcombinatorial librarynext-generation sequencing

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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.