Using B cell receptor lineage structures to predict affinity.

Area of Science:

• Immunology
• Computational Biology
• Bioinformatics

Background:

• Selecting high-affinity antibodies from large collections is crucial for therapeutic development, especially for novel pathogens.
• Experimental methods like cell sorting and baiting are effective but time-consuming and costly.
• Next-generation sequencing of B cell receptor (BCR) repertoires offers a rich source of antibody sequences.

Purpose of the Study:

• To develop a computational method for predicting antibody affinity using evolutionary information from related BCR sequences.
• To identify key mutations within an antibody's ancestral lineage that enhance affinity.
• To provide a reliable and efficient approach for discovering effective therapeutic antibodies.

Main Methods:

• Utilizing evolutionary information from naive ancestor-related B cell receptor sequences to predict antibody affinity.
• Integrating antigen identity with predicted affinity for enhanced antibody selection.
• Developing a method to pinpoint affinity-increasing mutations by analyzing ancestral lineage branches.

Main Results:

• The developed methods demonstrate reliable performance in predicting antibody affinity.
• The approach successfully identifies potential key affinity-enhancing mutations within antibody lineages.
• Evaluation on simulated and real data validates the efficacy of the prediction methods.

Conclusions:

• The novel computational method offers an efficient alternative to experimental techniques for identifying high-affinity antibodies.
• This approach can accelerate the discovery and development of therapeutic antibodies.
• The methods are integrated into the partis BCR inference package for broader accessibility.