Inorganic-organic hybrid materials such as organically templated metal oxides, metal-organic frameworks (MOFs) and organohalide perovskites have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility, photovoltaic properties, gas adsorption capacity or lithium-ion intercalation) to identify promising target candidates for synthetic efforts; determination of the structure-property relationship from large bodies of experimental data, enabled by integration with high-throughput synthesis and measurement tools; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification or gas adsorption properties). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on 'dark' reactions--failed or unsuccessful hydrothermal syntheses--collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully predicted conditions for new organically templated inorganic product formation with a success rate of 89 per cent. Inverting the machine-learning model reveals new hypotheses regarding the conditions for successful product formation.

The peptidoglycan (PG) cell wall is an extra-cytoplasmic glycopeptide polymeric structure that protects bacteria from osmotic lysis and determines cellular shape. Since the cell wall surrounds the cytoplasmic membrane, bacteria must add new material to the PG matrix during cell elongation and division. The lipid-linked precursor for PG biogenesis, Lipid II, is synthesized in the inner leaflet of the cytoplasmic membrane and is subsequently translocated across the bilayer so that the PG building block can be polymerized and cross-linked by complex multiprotein machines. This review focuses on major discoveries that have significantly changed our understanding of PG biogenesis in the past decade. In particular, we highlight progress made toward understanding the translocation of Lipid II across the cytoplasmic membrane by the MurJ flippase, as well as the recent discovery of a novel class of PG polymerases, the SEDS (shape, elongation, division, and sporulation) glycosyltransferases RodA and FtsW. Since PG biogenesis is an effective target of antibiotics, these recent developments may lead to the discovery of much-needed new classes of antibiotics to fight bacterial resistance.

The nonribosomal peptide synthetases (NRPSs) are one of the most promising resources for the production of new bioactive molecules. The mechanism of NRPS catalysis is based around sequential catalytic domains: these are organized into modules, where each module selects, modifies, and incorporates an amino acid into the growing peptide. The intermediates formed during NRPS catalysis are delivered between enzyme centers by peptidyl carrier protein (PCP) domains, which makes PCP interactions and movements crucial to NRPS mechanism. PCP movement has been linked to the domain alternation cycle of adenylation (A) domains, and recent complete NRPS module structures provide support for this hypothesis. However, it appears as though the A domain alternation alone is insufficient to account for the complete NRPS catalytic cycle and that the loaded state of the PCP must also play a role in choreographing catalysis in these complex and fascinating molecular machines.

The successful engineering of biosynthetic pathways hinges on understanding the factors that influence acyl carrier protein (ACP) stability and function. The stability and structure of ACPs can be influenced by the presence of divalent cations, but how this relates to primary sequence remains poorly understood. As part of a course-based undergraduate research experience, we investigated the thermostability of type II polyketide synthase (PKS) ACPs. We observed an approximate 40 °C range in the thermostability amongst the 14 ACPs studied, as well as an increase in stability (5 – 26 °C) of the ACPs in the presence of divalent cations. Distribution of charges in the helix II-loop-helix III region was found to impact the enthalpy of denaturation. Taken together, our results reveal clues as to how the sequence of type II PKS ACPs relates to their structural stability, information that can be used to study how ACP sequence relates to function.

Bacterial cell wall synthesis is an essential process in bacteria and one of the best targets for antibiotics. A critical step on this pathway is the export of the lipid-linked cell wall monomer, Lipid II, by its transporter MurJ. The mechanism by which MurJ mediates the transbilayer movement of Lipid II is not understood because intermediate states of this process have not been observed. Here we demonstrate a method to capture and detect interactions between MurJ and its substrate Lipid II by photo-cross-linking and subsequent biotin-tagging. We show that this method can be used to covalently capture intermediate transport states of Lipid II on MurJ in living cells. Using this strategy we probed several lethal arginine mutants and found that they retain appreciable substrate-binding ability despite being defective in Lipid II transport. We propose that Lipid II binding to these residues during transport induces a conformational change in MurJ required to proceed through the Lipid II transport cycle. The methods described to detect intermediate transport states of MurJ will be useful for characterizing mechanisms of inhibitors.