CLAffinity

CLAffinity simulates competition binding assays and optimizes labeled ligand affinities to improve sensitivity and dynamic range in drug-discovery primary screens.

Key Features:

• Simulation of Competition Experiments: Simulates competition experiments using realistic concentrations of proteins, ligands, and inhibitors typical of drug discovery assays.
• Optimization of Labeled Ligand Affinity: Determines optimal labeled ligand affinity ranges for specified assay parameters to avoid sensitivity loss associated with very high-affinity labeled ligands.
• Assay Sensitivity Enhancement: Identifies labeled ligand affinities that maximize sensitivity for detecting low-affinity interactions in primary screens and support clear yes/no binding determinations.
• Lookup Table for Dynamic Ranges: Generates a lookup table of dynamic ranges to guide selection of labeled ligand concentrations and affinities for competition-based primary screens.
• Mechanistic Modeling via PyBindingCurve Extension: Implements a mechanistic modeling approach by extending techniques from PyBindingCurve to represent complex biological interaction systems.

Scientific Applications:

• Drug-Discovery Assay Design: Design and optimize competition assays to screen potential inhibitors against target proteins using affinity-informed assay parameters.
• Primary Screen Optimization and Hit Identification: Improve sensitivity and reliability of primary screens to detect low-affinity binders and increase consistent identification of hit compounds.

Methodology:

Mechanistic simulation of competition binding experiments using techniques extended from PyBindingCurve, including generation of lookup tables for dynamic ranges based on specified concentrations of proteins, ligands, and inhibitors.