CLAffinity: A Software Tool for Identification of Optimum Ligand Affinity for Competition-Based Primary Screens

A simplistic assumption
in setting up a competition assay is that
a low affinity labeled ligand can be more easily displaced from a
target protein than a high affinity ligand, which in turn produces
a more sensitive assay. An often-cited paper correctly rallies against
this assumption and recommends the use of the highest affinity ligand
available for experiments aiming to determine competitive inhibitor
affinities. However, we have noted this advice being applied incorrectly
to competition-based primary screens where the goal is optimum assay
sensitivity, enabling a clear yes/no binding determination for even
low affinity interactions. The published advice only applies to secondary,
confirmatory assays intended for accurate affinity determination of
primary screening hits. We demonstrate that using very high affinity
ligands in competition-based primary screening can lead to reduced
assay sensitivity and, ultimately, the discarding of potentially valuable
active compounds. We build on techniques developed in our PyBindingCurve
software for a mechanistic understanding of complex biological interaction
systems, developing the “CLAffinity tool” for simulating
competition experiments using protein, ligand, and inhibitor concentrations
common to drug screening campaigns. CLAffinity reveals optimum labeled
ligand affinity ranges based on assay parameters, rather than general
rules to optimize assay sensitivity. We provide the open source CLAffinity
software toolset to carry out assay simulations and a video summarizing
key findings to aid in understanding, along with a simple lookup table
allowing identification of optimal dynamic ranges for competition-based
primary screens. The application of our freely available software
and lookup tables will lead to the consistent creation of more performant
competition-based primary screens identifying valuable hit compounds,
particularly for difficult targets.