Construction of a rationally designed antibody platform for sequencing-assisted selection

Area of Science:

• Biotechnology
• Immunology
• Computational Biology

Background:

• Antibody discovery platforms are crucial for developing therapeutics and research tools.
• Deep sequencing enhances antibody selection by monitoring libraries during the process.

Purpose of the Study:

• To create a rationally designed, fully defined single-chain variable fragment (scFv) library for deep sequencing analysis.
• To optimize antibody discovery using a hidden Markov model (HMM) trained on antibody-antigen structures.

Main Methods:

• Synthesized sequence-defined oligonucleotide libraries encoding complementarity-determining regions (CDRs) on a microarray.
• Cloned CDRs into an scFv framework for molecular display and produced a ~10^12 member library via ribosome display.
• Analyzed the library over four rounds of antigen selection using multiplex paired-end Illumina sequencing.

Main Results:

• The HMM scFv library successfully generated multiple antibody binders against an emerging cancer antigen.
• Deep sequencing provided comprehensive analysis of library members throughout the selection process.
• The platform demonstrated significant power in identifying specific antibody candidates.

Conclusions:

• The developed HMM scFv library and deep sequencing platform represent a next-generation approach for antibody production.
• This method significantly enhances the efficiency and scope of antibody discovery.
• The platform is effective for identifying binders against challenging targets like cancer antigens.