Accelerated NLRP3 inflammasome-inhibitory peptide design using a recurrent neural network model and molecular dynamics simulations

Anomalous NLRP3 inflammasome responses have been linked to multiple health issues, including but not limited to atherosclerosis, diabetes, metabolic syndrome, cardiovascular disease, and neurodegenerative disease. Thus, targeting NLRP3 and modulating its associated immune response might be a promising strategy for developing new anti-inflammatory drugs. Herein, we report a computational method for de novo peptide design for targeting NLRP3 inflammasomes. The described method leverages a long-short-term memory (LSTM) network based on a recurrent neural network (RNN) to model a valuable latent space of molecules. The resulting classifiers are utilized to guide the selection of molecules generated by the model based on circular dichroism spectra and physicochemical features derived from high-throughput molecular dynamics simulations. Of the experimentally tested sequences, 60% of the peptides showed NLRP3-mediated inhibition of IL-1 beta and IL-18. One peptide displayed high potency against NLRP3-mediated IL-1 beta inhibition. However, NLRC4 and AIM2 inflammasomemediated IL-1 beta secretion was uninterrupted by this peptide, demonstrating its selectivity toward the NLRP3 inflammasome. Overall, these results indicate that deep learning and molecular dynamics can accelerate the discovery of NLRP3 inhibitors with potent and selective activity.

Automated summary

The study presents a computational method for designing peptides targeting the NLRP3 inflammasome, which showed inhibitory effects on IL-1β and IL-18 mediated by NLRP3. Experimental results indicate the potential anti-inflammatory efficacy of these peptides, with one peptide demonstrating high potency in IL-1β inhibition and selectivity towards NLRP3.