Engineering Biocatalysts for the C-H Activation of Fatty Acids by Ancestral Sequence Reconstruction.

Selective, one-step C-H activation of fatty acids from biomass is an attractive concept in sustainable chemistry. Biocatalysis has shown promise for generating high-value hydroxy acids, but to date enzyme discovery has relied on laborious screening and produced limited hits, which predominantly oxidise the subterminal positions of fatty acids. Herein we show that ancestral sequence reconstruction (ASR) is an effective tool to explore the sequence-activity landscape of a family of multidomain, self-sufficient P450 monooxygenases. We resurrected 11 catalytically active CYP116B ancestors, each with a unique regioselectivity fingerprint that varied from subterminal in the older ancestors to mid-chain in the lineage leading to the extant, P450-TT. In lineages leading to extant enzymes in thermophiles, thermostability increased from ancestral to extant forms, as expected if thermophily had arisen de novo. Our studies show that ASR can be applied to multidomain enzymes to develop active, self-sufficient monooxygenases as regioselective biocatalysts for fatty acid hydroxylation.

NLRP12 interacts with NLRP3 to block the activation of the human NLRP3 inflammasome.

Inflammasomes are multiprotein complexes that drive inflammation and contribute to protective immunity against pathogens and immune pathology in autoinflammatory diseases. Inflammasomes assemble when an inflammasome scaffold protein senses an activating signal and forms a signaling platform with the inflammasome adaptor protein ASC. The NLRP subfamily of NOD-like receptors (NLRs) includes inflammasome nucleators (such as NLRP3) and also NLRP12, which is genetically linked to familial autoinflammatory disorders that resemble diseases caused by gain-of-function NLRP3 mutants that generate a hyperactive NLRP3 inflammasome. We performed a screen to identify ASC inflammasome-nucleating proteins among NLRs that have the canonical pyrin-NACHT-LRR domain structure. Only NLRP3 and NLRP6 could initiate ASC polymerization to form "specks," and NLRP12 failed to nucleate ASC polymerization. However, wild-type NLRP12 inhibited ASC inflammasome assembly induced by wild-type and gain-of-function mutant NLRP3, an effect not seen with disease-associated NLRP12 mutants. The capacity of NLRP12 to suppress NLRP3 inflammasome assembly was limited to human NLRP3 and was not observed for wild-type murine NLRP3. Furthermore, peripheral blood mononuclear cells from patients with an NLRP12 mutant-associated inflammatory disorder produced increased amounts of the inflammatory cytokine IL-1ß in response to NLRP3 stimulation. Thus, our findings provide insights into NLRP12 biology and suggest that NLRP3 inhibitors in clinical trials for NLRP3-driven diseases may also be effective in treating NLRP12-associated autoinflammatory diseases.

Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP).

Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.

Using the Evolutionary History of Proteins to Engineer Insertion-Deletion Mutants from Robust, Ancestral Templates Using Graphical Representation of Ancestral Sequence Predictions (GRASP).

Analyzing the natural evolution of proteins by ancestral sequence reconstruction (ASR) can provide valuable information about the changes in sequence and structure that drive the development of novel protein functions. However, ASR has also been used as a protein engineering tool, as it often generates thermostable proteins which can serve as robust and evolvable templates for enzyme engineering. Importantly, ASR has the potential to provide an insight into the history of insertions and deletions that have occurred in the evolution of a protein family. Indels are strongly associated with functional change during enzyme evolution and represent a largely unexplored source of genetic diversity for designing proteins with novel or improved properties. Current ASR methods differ in the way they handle indels; inclusion or exclusion of indels is often managed subjectively, based on assumptions the user makes about the likelihood of each recombination event, yet most currently available ASR tools provide limited, if any, opportunities for evaluating indel placement in a reconstructed sequence. Graphical Representation of Ancestral Sequence Predictions (GRASP) is an ASR tool that maps indel evolution throughout a reconstruction and enables the evaluation of indel variants. This chapter provides a general protocol for performing a reconstruction using GRASP and using the results to create indel variants. The method addresses protein template selection, sequence curation, alignment refinement, tree building, ancestor reconstruction, evaluation of indel variants and approaches to library development.

Caspase-1 self-cleavage is an intrinsic mechanism to terminate inflammasome activity.

Host-protective caspase-1 activity must be tightly regulated to prevent pathology, but mechanisms controlling the duration of cellular caspase-1 activity are unknown. Caspase-1 is activated on inflammasomes, signaling platforms that facilitate caspase-1 dimerization and autoprocessing. Previous studies with recombinant protein identified a caspase-1 tetramer composed of two p20 and two p10 subunits (p20/p10) as an active species. In this study, we report that in the cell, the dominant species of active caspase-1 dimers elicited by inflammasomes are in fact full-length p46 and a transient species, p33/p10. Further p33/p10 autoprocessing occurs with kinetics specified by inflammasome size and cell type, and this releases p20/p10 from the inflammasome, whereupon the tetramer becomes unstable in cells and protease activity is terminated. The inflammasome-caspase-1 complex thus functions as a holoenzyme that directs the location of caspase-1 activity but also incorporates an intrinsic self-limiting mechanism that ensures timely caspase-1 deactivation. This intrinsic mechanism of inflammasome signal shutdown offers a molecular basis for the transient nature, and coordinated timing, of inflammasome-dependent inflammatory responses.