This work is the first to show that the ion specificity in tuning HIN on ionic surfaces follows the Hofmeister series.

Inflammasomes are large protein complexes that trigger host defense in cells by activating inflammatory caspases for cytokine maturation and pyroptosis. NLRP6 is a sensor protein in the nucleotide-binding domain (NBD) and leucine-rich repeat (LRR)-containing (NLR) inflammasome family that has been shown to play multiple roles in regulating inflammation and host defenses. Despite the significance of the NLRP6 inflammasome, little is known about the molecular mechanism behind its assembly and activation. Here we present cryo-EM and crystal structures of NLRP6 pyrin domain (PYD). We show that NLRP6 PYD alone is able to self-assemble into filamentous structures accompanied by large conformational changes and can recruit the ASC adaptor using PYD–PYD interactions. Using molecular dynamics simulations, we identify the surface that the NLRP6 PYD filament uses to recruit ASC PYD. We further find that full-length NLRP6 assembles in a concentration-dependent manner into wider filaments with a PYD core surrounded by the NBD and the LRR domain. These findings provide a structural understanding of inflammasome assembly by NLRP6 and other members of the NLR family.

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity and stability, rationalizing the laboratory evolution of de novo enzymes, and decoding protein sequence semantics and their application in enzyme engineering. Notably, the prediction of catalytic activity and stability of enzymes using natural protein sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.