Identification of Structurally Related Antibodies in Antibody Sequence Databases Using Rosetta-Derived...

Area of Science:

• Immunology
• Structural Biology
• Bioinformatics

Background:

• Antibody (Ab) variable gene sequence data is growing, but predicting antibody function solely from sequence remains difficult.
• Developing methods to link antibody sequence to its specific function is crucial for therapeutic antibody discovery.

Purpose of the Study:

• To develop and validate a novel sequence-to-function prediction method for identifying antibodies with specific structural and functional properties.
• To discover new human antibodies with structural and antiviral activity similar to the influenza virus-specific antibody CH65.

Main Methods:

• Coupling structural data of a single antibody/antigen complex with antibody repertoire data.
• Utilizing a position-specific structure-scoring matrix (P3SM) with Rosetta structure-prediction scores.
• Identifying antibody variable loops with structural similarity to a known antibody (CH65).
• Validating newly identified antibodies through recombinant expression, crystallography, and virus inhibition assays.

Main Results:

• The P3SM approach successfully identified novel antibody candidates belonging to the CH65 class.
• Newly identified antibodies exhibited HCDR3 loops with structural similarity to CH65.
• Recombinant expression and functional assays confirmed similar antiviral activity against influenza virus for the new antibodies compared to CH65.

Conclusions:

• The developed sequence-to-function prediction method enables the discovery of human antibodies with desired structures and functions.
• This approach leverages readily available cDNA repertoires from amplicon sequencing techniques.
• It offers a powerful strategy for identifying novel therapeutic antibodies based on sequence and predicted structure.