Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors

Fragment-based
drug discovery (FBDD) has become a mainstream approach
in drug design because it allows the reduction of the chemical space
and screening libraries while identifying fragments with high protein–ligand
efficiency interactions that can later be grown into drug-like leads.
In this work, we leverage high-throughput molecular dynamics (MD)
simulations to screen a library of 129 fragments for a total of 5.85
ms against the CXCL12 monomer, a chemokine involved in inflammation
and diseases such as cancer. Our in silico binding
assay was able to recover binding poses, affinities, and kinetics
for the selected library and was able to predict 8 mM-affinity fragments
with ligand efficiencies higher than 0.3. All of the fragment hits present a similar chemical structure, with a hydrophobic
core and a positively charged group, and bind to either sY7 or H1S68
pockets, where they share pharmacophoric properties with experimentally
resolved natural binders. This work presents a large-scale screening
assay using an exclusive combination of thousands of short MD adaptive
simulations analyzed with a Markov state model (MSM) framework.