Solving the Competitive Binding Equilibria between Many Ligands: Application to High-Throughput Screening and...

Area of Science:

• Biochemistry
• Computational Chemistry
• Pharmacology

Background:

• Drug discovery necessitates selective targeting of biological macromolecules by small molecules.
• Binding affinities are crucial for drug efficacy, and complex equilibria arise when multiple drug candidates bind to a single target.
• Analyzing these coupled equilibria complicates high-throughput drug discovery methods.

Purpose of the Study:

• To present an efficient and robust iterative computational algorithm for solving coupled binding equilibria.
• To enable improved analysis of mixture-based, high-throughput drug discovery techniques.
• To introduce a facile method for affinity-ranking drug candidates in library screening.

Main Methods:

• Developed an iterative computational algorithm to solve coupled equilibria between multiple ligands and a biomolecular target.
• Explored binding equilibria under conditions relevant to mixture-based library screening using affinity selection-mass spectrometry (AS-MS).
• Proposed a ranking method based on the relative change in bound ligand concentration by varying receptor-to-ligand ratios in sequential AS-MS analyses.

Main Results:

• The computational algorithm efficiently and robustly solves coupled equilibria without requiring initial value estimations.
• The proposed affinity-ranking method is insensitive to variations in individual compound concentrations.
• The method allows for the use of unpurified compounds from mixture-based combinatorial synthesis.

Conclusions:

• The developed algorithm simplifies the analysis of complex binding equilibria in drug discovery.
• The novel ranking method facilitates high-throughput screening and affinity assessment of drug candidates.
• This approach supports the development of advanced mixture-based drug discovery techniques.