Advancing Collision-Induced Affinity Selection Mass Spectrometry for Quantitative Ligand Analysis in Complex Mixtures.

Native mass spectrometry (MS) is a powerful technique for studying protein-ligand interactions in their native states by employing soft electrospray ionization (ESI). It enables the direct observation of all species in equilibrium within a solution. Building on these advantages, Collision-Induced Affinity Selection MS (CIAS-MS) was introduced as an alternative approach, incorporating quadrupole mass selection, controlled complex dissociation, and ligand detection to enhance the study of protein-ligand interactions. This method retains the native ionization properties of proteins while enabling high-sensitivity detection of released ligands. This study presents an optimized CIAS-MS platform for improved ligand binding detection and quantitative affinity ranking. The integration of both positive and negative ion modes significantly broadens the detection of structurally diverse ligands, overcoming biases from single-mode analysis. More importantly, the collision-induced dissociation (CID) slope, derived from dissociation curves, is introduced as a robust parameter for affinity ranking. Unlike absolute intensities or dissociation thresholds, the CID slope reflects solution-phase affinity order across ligands and remains accurate in highly complex backgrounds, including 100-compound mixtures and bacterial lysates. These advances highlight the robustness of CIAS-MS for detecting bound ligands and ranking their affinities even under challenging conditions where traditional native MS fails. These findings establish CIAS-MS as a scalable and efficient platform for high-throughput ligand discovery and proteome-wide target engagement studies, offering a powerful addition to the biophysical toolkit for modern drug discovery.