NMR-guided directed evolution.

Directed evolution is a powerful tool for improving existing properties and imparting completely new functionalities to proteins1-4. Nonetheless, its potential in even small proteins is inherently limited by the astronomical number of possible amino acid sequences. Sampling the complete sequence space of a 100-residue protein would require testing of 20100 combinations, which is beyond any existing experimental approach. In practice, selective modification of relatively few residues is sufficient for efficient improvement, functional enhancement and repurposing of existing proteins5. Moreover, computational methods have been developed to predict the locations and, in certain cases, identities of potentially productive mutations6-9. Importantly, all current approaches for prediction of hot spots and productive mutations rely heavily on structural information and/or bioinformatics, which is not always available for proteins of interest. Moreover, they offer a limited ability to identify beneficial mutations far from the active site, even though such changes may markedly improve the catalytic properties of an enzyme10. Machine learning methods have recently showed promise in predicting productive mutations11, but they frequently require large, high-quality training datasets, which are difficult to obtain in directed evolution experiments. Here we show that mutagenic hot spots in enzymes can be identified using NMR spectroscopy. In a proof-of-concept study, we converted myoglobin, a non-enzymatic oxygen storage protein, into a highly efficient Kemp eliminase using only three mutations. The observed levels of catalytic efficiency exceed those of proteins designed using current approaches and are similar with those of natural enzymes for the reactions that they are evolved to catalyse. Given the simplicity of this experimental approach, which requires no a priori structural or bioinformatic knowledge, we expect it to be widely applicable and to enable the full potential of directed enzyme evolution.

New insights into Gasdermin D pore formation.

Gasdermin D (GSDMD) is a pore-forming protein that perforates the plasma membrane (PM) during pyroptosis, a pro-inflammatory form of cell death, to induce the unconventional secretion of inflammatory cytokines and, ultimately, cell lysis. GSDMD is activated by protease-mediated cleavage of its active N-terminal domain from the autoinhibitory C-terminal domain. Inflammatory caspase-1, -4/5 are the main activators of GSDMD via either the canonical or non-canonical pathways of inflammasome activation, but under certain stimuli, caspase-8 and other proteases can also activate GSDMD. Activated GSDMD can oligomerize and assemble into various nanostructures of different sizes and shapes that perforate cellular membranes, suggesting plasticity in pore formation. Although the exact mechanism of pore formation has not yet been deciphered, cysteine residues are emerging as crucial modulators of the oligomerization process. GSDMD pores and thus the outcome of pyroptosis can be modulated by various regulatory mechanisms. These include availability of activated GSDMD at the PM, control of the number of GSDMD pores by PM repair mechanisms, modulation of the lipid environment and post-translational modifications. Here, we review the latest findings on the mechanisms that induce GSDMD to form membrane pores and how they can be tightly regulated for cell content release and cell fate modulation.

Systematic Assessment of the Accuracy of Subunit Counting in Biomolecular Complexes Using Automated Single-Molecule Brightness Analysis.

Analysis of single-molecule brightness allows subunit counting of high-order oligomeric biomolecular complexes. Although the theory behind the method has been extensively assessed, systematic analysis of the experimental conditions required to accurately quantify the stoichiometry of biological complexes remains challenging. In this work, we develop a high-throughput, automated computational pipeline for single-molecule brightness analysis that requires minimal human input. We use this strategy to systematically quantify the accuracy of counting under a wide range of experimental conditions in simulated ground-truth data and then validate its use on experimentally obtained data. Our approach defines a set of conditions under which subunit counting by brightness analysis is designed to work optimally and helps in establishing the experimental limits in quantifying the number of subunits in a complex of interest. Finally, we combine these features into a powerful, yet simple, software that can be easily used for the analysis of the stoichiometry of such complexes.