Fatty acid synthases are dynamic ensembles of enzymes that can efficiently biosynthesize long hydrocarbon chains. Here we visualize the interaction between the Escherichia coli acyl carrier protein (AcpP) and β-ketoacyl-ACP-synthase I (FabB) using X-ray crystallography, NMR, and MD simulations. We leveraged this structural information to alter lipid profiles in vivo and provide a molecular basis for how protein-protein interactions can regulate the fatty acid profile in E. coli. The E. coli fatty acid synthase (FAS) produces fatty acids through an iterative cycle via the

The terminal reductase (R) domain from the non-ribosomal peptide synthetase (NRPS) module MxaA in Stigmatella aurantiaca Sga15 catalyzes a non-processive four-electron reduction to produce the myxalamide family of secondary metabolites. Despite widespread use in nature, a lack of structural and mechanistic information concerning reductive release from polyketide synthase (PKS) and NRPS assembly lines principally limits our ability to redesign R domains with altered or improved activity. Here we report crystal structures for MxaA R, both in the absence and, for the first time, in the presence of the NADPH cofactor. Molecular dynamics simulations were employed to provide a deeper understanding of this domain and further identify residues critical for structural integrity, substrate binding, and catalysis. Aggregate computational and structural findings provided a basis for mechanistic investigations and, in the process, delivered a rationally altered variant with improved activity toward highly reduced substrates.

The mechanistic details of many polyketide synthases (PKSs) remain elusive due to the instability of transient intermediates that are not accessible via conventional methods. Here we report an atom replacement strategy that enables the rapid preparation of polyketone surrogates by selective atom replacement, thereby providing key substrate mimetics for detailed mechanistic evaluations. Polyketone mimetics are positioned on the actinorhodin acyl carrier protein (actACP) to probe the underpinnings of substrate association upon nascent chain elongation and processivity. Protein NMR is used to visualize substrate interaction with the actACP, where a tetraketide substrate is shown not to bind within the protein, while heptaketide and octaketide substrates show strong association between helix II and IV. To examine the later cyclization stages, we extended this strategy to prepare stabilized cyclic intermediates and evaluate their binding by the actACP. Elongated monocyclic mimics show much longer residence time within actACP than shortened analogs. Taken together, these observations suggest ACP-substrate association occurs both before and after ketoreductase action upon the fully elongated polyketone, indicating a key role played by the ACP within PKS timing and processivity. These atom replacement mimetics offer new tools to study protein and substrate interactions and are applicable to a wide variety of PKSs.

The incorporation of nonacetate starter units during type II polyketide biosynthesis helps diversify natural products. Currently, there are few enzymatic strategies for the incorporation of nonacetate starter units in type II polyketide synthase (PKS) pathways. Here we report the crystal structure of AuaEII, the anthranilate:CoA ligase responsible for the generation of anthraniloyl-CoA, which is used as a starter unit by a type II PKS in aurachin biosynthesis. We present structural and protein sequence comparisons to other aryl:CoA ligases. We also compare the AuaEII crystal structure to a model of a CoA ligase homologue, AuaE, which is present in the same gene cluster. AuaE is predicted to have the same fold as AuaEII, but instead of CoA ligation, AuaE catalyzes acyl transfer of anthranilate from anthraniloyl-CoA to the acyl carrier protein (ACP). Together, this work provides insight into the molecular basis for starter unit selection of anthranilate in type II PKS biosynthesis.

Masks constructed of a variety of materials are in widespread use due to the COVID-19 pandemic, and people are exposed to chemicals inherent in the masks through inhalation. This work aims to survey commonly available mask materials to provide an overview of potential exposure. A total of 19 mask materials were analyzed using a nontargeted analysis twodimensional gas chromatography (GCxGC)−mass spectrometric (MS) workflow. Traditionally, there has been a lack of GCxGC− MS automated high-throughput screening methods, resulting in trade-offs with throughput and thoroughness. This work addresses the gap by introducing new machine learning software tools for high-throughput screening (Floodlight) and subsequent pattern analysis (Searchlight). A recursive workflow for chemical prioritization suitable for both manual curation and machine learning is introduced as a means of controlling the level of effort and equalizing sample loading while retaining key chemical signatures. Manual curation and machine learning were comparable with the mask materials clustering into three groups. The majority of the chemical signatures could be characterized by chemical class in seven categories: organophosphorus, long chain amides, polyethylene terephthalate oligomers, n-alkanes, olefins, branched alkanes and long-chain organic acids, alcohols, and aldehydes. The olefin, branched alkane, and organophosphorus components were primary contributors to clustering, with the other chemical classes having a significant degree of heterogeneity within the three clusters. Machine learning provided a means of rapidly extracting the key signatures of interest in agreement with the more traditional time-consuming and tedious manual curation process. Some identified signatures associated with plastics and flame retardants are potential toxins, warranting future study to understand the mask exposure route and potential health effects.