Prokaryotic nanocompartments, also known as encapsulins, are a recently discovered proteinaceous organelle-like compartments in prokaryotes that compartmentalize cargo enzymes. While initial studies have begun to elucidate the structure and physiological roles of encapsulins, bioinformatic evidence suggests that a great diversity of encapsulin nanocompartments remains unexplored. Here, we describe a novel encapsulin in the freshwater cyanobacterium Synechococcus elongatus PCC 7942. This nanocompartment is upregulated upon sulfate starvation and encapsulates a cysteine desulfurase enzyme via an N-terminal targeting sequence. Using cryo-electron microscopy, we have determined the structure of the nanocompartment complex to 2.2 Å resolution. Lastly, biochemical characterization of the complex demonstrated that the activity of the cysteine desulfurase is enhanced upon encapsulation. Taken together, our discovery, structural analysis, and enzymatic characterization of this prokaryotic nanocompartment provide a foundation for future studies seeking to understand the physiological role of this encapsulin in various bacteria.

Assessment of reaction substrate scope is often a qualitative endeavor that provides general indications of substrate sensitivity to a measured reaction outcome. Unfortunately, this field standard typically falls short of enabling the quantitative prediction of new substrates' performance. The disconnection between a reaction's development and the quantitative prediction of new substrates' behavior limits the applicative usefulness of many methodologies. Herein, we present a method by which substrate libraries can be systematically developed to enable quantitative modeling of reaction systems and the prediction of new reaction outcomes. Presented in the context of rhodium-catalyzed asymmetric transfer hydrogenation, these models quantify the molecular features that influence enantioselection and, in so doing, lend mechanistic insight to the modes of asymmetric induction.asymmetric catalysis | free-energy relationships | computational chemistry H uman brains are highly experienced at recognizing patterns in observed data. Organizing information and drawing connections between data enables general conclusions to be made, whether fast or slow, good or bad, or high or low. Although these qualitative assessments are routinely crafted they are subject to biases, causing evaluations to differ from one individual to another (1). The examination of a reaction's substrate scope often takes on a similarly qualitative air (2-5). A substrate scope for a developed synthetic method typically provides an indication of functional group tolerance and general trends in reaction outcomes for sterically and/or electronically varied substrates. This qualitative approach, which lacks quantitation of how substrate features will influence a reaction's outcome, particularly product selectivity, often limits a reaction's application to contexts with high degrees of similarity to the initial scope library. Additionally, it can be difficult to predict, beyond generalities such as poorly versus well-behaved, how a new substrate will perform under the reaction conditions. Addressing this limitation through quantitative prediction of reaction outcomes would significantly affect how one both develops and applies a new synthetic method while simultaneously imparting fundamental mechanistic insight (6).To accomplish this goal, an entirely new approach to examining a reaction's substrate scope is required. Because the ultimate goal is to mathematically predict a broad range of reaction outcomes, an initial library of substrates would need to be carefully designed to represent many of the impactful features influencing the reaction. Specifically, one would need to include systematic variation of steric and electronic features of a given substrate class while also limiting the initial size of the substrate library to make this a practical venture. With this in mind, the tenets of design of experiments (DoE) and regression modeling will need to be exploited, where broadly descriptive models are built from data that systematically sample the experimen...

A new enzymatic method is reported for constructing protein-and DNA-AuNP conjugates. The strategy relies on the initial functionalization of AuNPs with phenols, followed by activation with the enzyme tyrosinase. Using an oxidative coupling reaction, the activated phenols are coupled to proteins bearing proline, thiol, or aniline functional groups. Activated phenol-AuNPs are also conjugated to a small molecule biotin and commercially available thiol-DNA. Advantages of this approach for AuNP bioconjugation include: (1) initial formation of highly stable AuNPs that can be selectively activated with an enzyme, (2) the ability to conjugate either proteins or DNA through a diverse set of functional handles, (3) site-specific immobilization, and (4) facile conjugation that is complete within 2 h at room temperature under aqueous conditions. The enzymatic oxidative coupling on AuNPs is applied to the construction of tobacco mosaic virus (TMV)-AuNP conjugates, and energy transfer between the AuNPs and fluorophores on TMV is demonstrated.

Instrumental resolution of Fourier transform-charge detection mass spectrometry instruments with electrostatic ion trap detection of individual ions depends on the precision with which ion energy is determined. Energy can be selected using ion optic filters or from harmonic amplitude ratios (HARs) that provide Fellgett's advantage and eliminate the necessity of ion transmission loss to improve resolution. Unlike the ion energyfiltering method, the resolution of the HAR method increases with charge (improved S/N) and thus with mass. An analysis of the HAR method with current instrumentation indicates that higher resolution can be obtained with the HAR method than the best resolution demonstrated for instruments with energy-selective optics for ions in the low MDa range and above. However, this gain is typically unrealized because the resolution obtainable with molecular systems in this mass range is limited by sample heterogeneity. This phenomenon is illustrated with both tobacco mosaic virus (0.6−2.7 MDa) and AAV9 (3.7−4.7 MDa) samples where mass spectral resolution is limited by the sample, including salt adducts, and not by instrument resolution. Nevertheless, the ratio of full to empty AAV9 capsids and the included genome mass can be accurately obtained in a few minutes from 1× PBS buffer solution and an elution buffer containing 300+ mM nonvolatile content despite extensive adduction and lower resolution. Empty and full capsids adduct similarly indicating that salts encrust the complexes during late stages of droplet evaporation and that mass shifts can be calibrated in order to obtain accurate analyte masses even from highly salty solutions.