pyBinder: Quantitation to Advance Affinity Selection-Mass Spectrometry.

Area of Science:

• Biochemistry
• Analytical Chemistry
• Computational Biology

Background:

• Affinity selection-mass spectrometry (AS-MS) is a powerful ligand discovery platform.
• High sample complexity in AS-MS can overwhelm mass spectrometry capacity, leading to incomplete data and limited ligand identification.
• Existing methods struggle with large peptide libraries (10^8 members).

Purpose of the Study:

• Introduce pyBinder, a novel computational tool for analyzing AS-MS data.
• Develop quantitative scores to rank peptide ligands based on target selectivity and concentration-dependent enrichment.
• Enhance the efficiency and scope of ligand discovery using AS-MS.

Main Methods:

• Developed pyBinder to process primary MS^1 data from AS-MS experiments.
• Integrated peak area quantitation to create target selectivity and concentration-dependent enrichment scores.
• Benchmarked pyBinder using AS-MS data against antihemagglutinin antibody 12ca5 and WD Repeat Domain 5 (WDR5).

Main Results:

• pyBinder effectively characterizes peptides with known high-affinity binding motifs.
• Confirmed pyBinder scores reliably identify target-specific, motif-containing peptides for WDR5.
• Achieved a 4-fold increase in motif-containing sequence identification for WDR5 (from 3 to 14 ligands).

Conclusions:

• pyBinder significantly improves ligand discovery outcomes in AS-MS by enabling more identifications.
• The developed scores provide a robust method for comparing AS-MS data across diverse experimental conditions.
• Establishes an improved approach for AS-MS, facilitating broader application and discovery of selective ligands.