Protein-protein interactions are an important component of health and disease. However, identifying the many interacting target ligands for multiple proteins in parallel is challenging. Most methods for testing possible ligands of affinity reagents are of low throughput and have limited dynamic range, typically requiring large quantities of the protein mix and antibody/antibodies. Moreover, many methods are time consuming and the success rate is often low.
Professor Josh LaBaer at the Biodesign Institute of Arizona State University has developed a high throughput and comprehensive method for identifying ligands to affinity reagents. Targeted, non-random libraries of ligands are utilized to identify binding partners for antibodies and monitor antibody responses. This method produces signatures of information reflecting the binding specificity of the antibody or antibody mixture that can be useful in a variety of applications.
Using carefully produced libraries and next generation sequencing, this technology requires only a small amount of material to find very high affinity ligands for diagnostic, vaccine and therapeutic applications.
- Rapidly characterization of affinity reagent responses to:
- Identify antigens/biomarkers for diagnostic assays
- Develop diagnostic tests to stratify patients into useful disease subtypes
- Improve companion diagnostics
- Diagnose past infections/pathogen exposures
- Understand immune response and/or pathophysiology to pathogens
- Improve and/or develop vaccines
- Develop strategies to prevent damage caused by autoantibodies or other immune system components
Benefits and Advantages
- Requires minimal material - only a few microliters of antibody (or serum) is required
- Very high diversity of protein species can be detected - based on carefully produced libraries instead of random sequences
- Antibodies measure their response against whole proteins, not fragments - measures the response as it actually occurs
- High throughput - next generation sequencing and barcoding enables dramatic data density