Significance: Although polyethylene terephthalate (PET) is the most recycled plastic, its recycling typically involves thermomechanical means, producing a plastic of lower quality that cannot be recycled again. In contrast, breaking PET down to its building blocks allows for repolymerization into high-quality PET. Chemical depolymerization of PET requires hazardous chemicals and conditions. Enzymes are environmentally benign, renewable catalysts that work under mild conditions. Several enzymes capable of depolymerizing low-crystallinity PET have been reported; however, efficient depolymerization of highly crystalline forms of PET typically found in consumer products has remained elusive. Here, we report that, in moist-solid reaction mixtures, the cutinase from Humicola insolens can efficiently hydrolyze highly crystalline PET to terephthalic acid with a 50% yield, without any amorphization or pretreatment of PET.

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Covalent inhibition has seen a resurgence in the last several years. Although long-plagued by concerns of off-target effects due to nonspecific reactions leading to covalent adducts, there has been success in developing covalent inhibitors, especially within the field of anticancer therapy. Covalent inhibitors can have an advantage over noncovalent inhibitors since the formation of a covalent adduct may serve as an additional mode of selectivity due to the intrinsic reactivity of the target protein that is absent in many other proteins. Unfortunately, many covalent inhibitors form irreversible adducts with off-target proteins, which can lead to considerable side-effects. By designing the inhibitor to form reversible covalent adducts, one can leverage competing on/off kinetics in complex formation by taking advantage of the law of mass action. Although covalent adducts do form with off-target proteins, the reversible nature of inhibition prevents accumulation of the off-target adduct, thus limiting side-effects. In this perspective, we outline important characteristics of reversible covalent inhibitors, including examples and a guide for inhibitor development.

Rising drug resistance among pathogenic fungi, paired with a limited antifungal arsenal, poses an increasing threat to human health. To identify antifungal compounds, we screened the RIKEN natural product depository against representative isolates of four major human fungal pathogens. This screen identified NPD6433, a triazenyl indole with broad-spectrum activity against all screening strains, as well as the filamentous mold Aspergillus fumigatus. Mechanistic studies indicated that NPD6433 targets the enoyl reductase domain of fatty acid synthase 1 (Fas1), covalently inhibiting its flavin mononucleotide-dependent NADPH-oxidation activity and arresting essential fatty acid biosynthesis. Robust Fas1 inhibition kills Candida albicans, while sublethal inhibition impairs diverse virulence traits. At well-tolerated exposures, NPD6433 extended the lifespan of nematodes infected with azole-resistant C. albicans. Overall, identification of NPD6433 provides a tool with which to explore lipid homeostasis as a therapeutic target in pathogenic fungi and reveals a mechanism by which Fas1 function can be inhibited.