Phenotype-information-phenotype cycle for deconvolution of combinatorial antibody libraries selected against complex
Hongkai Zhang1, Ali Torkamani, Teresa M Jones
1Department of Chemistry, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.
Summary
This study integrates antibody selection with next-generation sequencing to recover full antibody library information. This phenotype-information-phenotype cycle enables efficient gene recovery for antibody discovery from complex biological systems.
Area of Science:
- Molecular Biology
- Immunology
- Bioinformatics
Background:
- Large combinatorial antibody libraries and next-generation sequencing are powerful molecular biology tools.
- Current methods often lose information about less-fit antibody variants during selection.
- Combining selection and sequencing could unlock the full potential of molecular libraries.
Purpose of the Study:
- To develop a method integrating phenotype selection with genetic information recovery.
- To realize the full potential of large molecular libraries by combining evolutionary selection and sequencing.
- To enable the isolation of specific antibody genes from complex, degenerate collections.
Main Methods:
- Implementation of a phenotype-information-phenotype cycle.
- Selection of phage-encoded antibodies binding to Escherichia coli surface targets.
- Information retrieval via pyrosequencing of the selected antibody pool.
- Bioinformatic analysis to identify specific antibody sequences.
- Affinity-based method for gene isolation from degenerate nucleic acid collections.
Main Results:
- Successful integration of phenotype selection with genetic information recovery.
- Identification and isolation of specific antibody genes from a selected pool.
- Demonstration of a method applicable to targets present as minor components in complex systems.
Conclusions:
- The developed phenotype-information-phenotype cycle effectively integrates information and gene recovery.
- This approach maximizes the utility of large antibody libraries by retaining comprehensive molecular information.
- The method is generalizable for selecting antibodies against diverse targets in complex biological contexts.
