NLRP3 monomer Functional Dynamics: from the Effects of Allosteric Binding to Implications for Drug Design

TL;DR

This study investigates how allosteric binding influences NLRP3 protein dynamics and function, using molecular simulations and machine learning to aid in drug design targeting inflammatory diseases.

Contribution

It provides detailed molecular insights into NLRP3 allosteric inhibition and introduces a machine learning model to classify protein activity based on dynamics.

Findings

Allosteric binding remodels NLRP3 conformational ensembles.

Machine learning model accurately classifies NLRP3 activity states.

Insights support targeted drug design for inflammatory diseases.

Abstract

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.