  The protein NLRP3 and its complexes are associated with an array of
inflammatory pathologies, among which neurodegenerative, autoimmune, and
metabolic diseases. Targeting the NLRP3 inflammasome represents a promising
strategy for easing the symptoms of pathologic neuroinflammation. When the
inflammasome is activated, NLRP3 undergoes a conformational change triggering
the production of pro-inflammatory cytokines IL-1\beta and IL-18, as well as
cell death by pyroptosis. NLRP3 nucleotide-binding and oligomerization (NACHT)
domain plays a crucial role in this function by binding and hydrolysing ATP and
is primarily responsible, together with conformational transitions involving
the PYD domain, for the complex-assembly process. Allosteric ligands proved
able to induce NLRP3 inhibition. Herein, we examine the origins of allosteric
inhibition of NLRP3. Through the use of molecular dynamics (MD) simulations and
advanced analysis methods, we provide molecular-level insights into how
allosteric binding affects protein structure and dynamics, remodelling of the
conformational ensembles populated by the protein, with key reverberations on
how NLRP3 is preorganized for assembly and ultimately function. The data are
used to develop a Machine Learning model to define the protein as Active or
Inactive, only based on the analysis of its internal dynamics. We propose this
model as a novel tool to select allosteric ligands.
