Data-driven evaluation of suitable immunogens for improved antibody selection.

Area of Science:

• Immunology and Biotechnology
• Bioinformatics and Computational Biology

Background:

• Antibodies are crucial reagents in research and clinical settings due to their specific antigen-binding capabilities.
• The selection of optimal protein fragments (immunogens) for antibody generation is a significant challenge, impacting research reproducibility.

Purpose of the Study:

• To systematically evaluate immunogen properties influencing antibody production success and specificity.
• To identify key structural and functional characteristics of immunogens that correlate with antibody performance.
• To develop a tool to aid researchers in selecting suitable immunogens for antibody development.

Main Methods:

• Utilized the Human Protein Atlas to analyze immunogen properties.
• Classified antibodies as successful or unsuccessful based on standardized validation experiments.
• Performed structural and functional analysis of immunogen sequences, including length, secondary structure, and post-translational modification sites.

Main Results:

• Shorter immunogens (≤50 residues) with disordered N/C-termini and long coil regions were associated with higher success rates.
• Immunogens rich in beta sheets, transmembrane regions, or disulfide bridges correlated with poorer antibody performance.
• Post-translational modification sites within immunogens indicated potentially beneficial regions for antibody generation.

Conclusions:

• Immunogen characteristics significantly influence antibody success and specificity.
• Developed the R package 'immunogenViewer' to assist researchers in selecting optimal immunogens.
• Improved immunogen selection can enhance antibody reliability and reproducibility in scientific research.