# Integrating Functional Response and Target Binding for Mechanism-Centered Drug Screening by High-Mass MALDI-MS

## TL;DR

This paper introduces a new drug screening method using MALDI-MS to combine functional and binding data, improving hit selection for challenging drug targets like protein-protein interactions.

## Contribution

A novel MALDI-MS platform that integrates functional response and target binding data in a single assay for mechanism-centered drug screening.

## Key findings

- The platform distinguishes drug candidates with similar functional outcomes but different binding mechanisms.
- High-affinity inhibitors showed improved cell viability in antiviral assays, aligning with MALDI-MS results.
- The method is compatible with high-throughput formats and provides multidimensional pharmacological profiles.

## Abstract

Early stage drug
discovery is limited by the disjunction of function
and binding assays, creating an information gap that leads to the
high failure rate in hit advancement. This limitation is particularly
pronounced for protein–protein interactions, whose large and
shallow interfaces make it difficult to distinguish hits mechanistically.
To address this, we developed a cross-linking matrix-assisted laser
desorption/ionization mass spectrometry (MALDI-MS) platform that integrates
biochemical functional response and target binding in a single assay,
thereby generating a multidimensional pharmacological profile. Using
the SARS-CoV-2 RBD–ACE2 interaction and a set of 17 drug candidates
for a proof-of-concept study, the platform revealed a clear difference
between two inhibitors that appeared indistinguishable in conventional
functional assays: one showed stronger affinity and preferential ACE2
binding, while the other showed weaker and less specific binding.
These mechanistic differences were consistent with the results of
a cellular antiviral assay, in which only the high-affinity inhibitor
improved cell viability. This work presents a mechanism-centered,
rapid screening strategy that provides early multiparameter insight,
enables rational selection of high-quality leads for challenging drug
targets, and is compatible with high-throughput formats.