Kynurenic acid is a neuroprotective metabolite of tryptophan formed by kynurenine aminotransferase (KAT) catalyzed transformation of kynurenine. However, its high brain levels are associated with cognitive deficit and with the pathophysiology of schizophrenia. Although several classes of KAT inhibitors have been published, the search for new inhibitor chemotypes is crucial for the process of finding suitable clinical candidates. Therefore, we used pharmacophore modeling and molecular docking, which predicted derivatives of heterocyclic amino ketones as new potential irreversible inhibitors of kynurenine aminotransferase II. Thiazole and triazole-based amino ketones were synthesized within a SAR study and their inhibitory activities were evaluated in vitro. The observed activities confirmed our computational model and, moreover, the best compounds showed sub-micromolar inhibitory activity with 2-alaninoyl-5-(4-fluorophenyl)thiazole having IC₅₀ = 0.097 µM.

Pose prediction of ligands to proteins remains a central challenge of structure-based drug design. Although data leakage and generalizability concerns remain, data-driven methods for pose prediction (i.e., based on deep learning and diffusion) now routinely outperform traditional techniques such as molecular docking. In this work, we propose a simple data-driven ligand-based baseline for pose prediction, which is based on maximal common substructure to reference molecules, followed by constrained 3D embedding. As this TEMplate-based Protein-Ligand (TEMPL) baseline is strictly data-driven, it is a particularly meaningful baseline for interpolative tasks, where physics-based methods sometimes underperform as they exploit data less directly. However, it can also highlight the added advantage of other interpolative data-driven methods that should outperform this simple approach. We applied our baseline method in the ASAP-Polaris-OpenADMET antiviral competition, achieving a result that outperformed some classic docking algorithms for the pose prediction of a series of ligands at the Main Protease of SARS-CoV-2 and MERS-CoV. Furthermore, we show that the performance of our baseline is relatively good on a protein-ligand pose prediction benchmark used for deep learning based pose prediction, PDBBind, highlighting the risk of data leakage and the necessity of challenging splits for other data-driven methods as well. We also show our baseline method has limited performance on more challenging benchmarks, such as PoseBusters. We provide our baseline method as open source software. For convenience and for nontechnical users, we also provide a web application to run the pipeline. These findings will aid in the evaluation of future pose prediction methods, especially more complex data-driven approaches that are increasing in popularity.

A critical assessment of computational hit finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comprised of computational chemists and data scientists used protein structure and data from fragment-screening paired with advanced computational and machine learning methods to each predict up to 100 inhibitory ligands. Across all teams, 1957 compounds were predicted and were subsequently procured from commercial catalogs for biophysical assays. Of these compounds, 0.7% were confirmed to bind to Nsp13 in a surface plasmon resonance assay. The six best performing computational workflows used fragment growing, active learning, or conventional virtual screening with and without complementary deep-learning scoring functions. Follow-up functional assays resulted in identification of two compound scaffolds that bound Nsp13 with a Kd below 10 µM and inhibited in vitro helicase activity. Overall, the CACHE #2 was successful in identifying hit compound scaffolds targeting Nsp13, a central component of the coronavirus replication-transcription complex. Computational design strategies recurrently successful across the first two CACHE challenges include linking or growing docked or crystallized fragments and docking small and diverse libraries to train ultra-fast machine-learning models. The CACHE#2 competition reveals how crowd-sourcing ligand prediction efforts using a distinct array of approaches followed with critical biophysical assays can result in novel lead compounds to advance drug discovery efforts.

Callunene, a natural component of heather (Calluna vulgaris) nectar, has previously been shown to protect bumblebees from infection by the trypanosomatid Crithidia bombi. Here, we demonstrate that callunene exhibits antiparasitic activity against several trypanosomatid species, including Crithidia bombi, Leishmania mexicana, and Trypanosoma brucei. Notably, callunene's in vitro efficacy against T. brucei was comparable to that of nifurtimox, although its cytotoxicity toward human cells may limit direct therapeutic application. Using a biotinylated callunene analog in the pull-down assay, we identified NIPSNAP, a mitochondrial protein involved in mitophagy regulation, as a primary molecular target of this compound in C. bombi. Moreover, callunene alters acidocalcisome abundance, further connecting its role to regulation of mitochondrial physiology. Given its effects on mitochondria and ability to interact with NIPSNAP, callunene represents a promising chemical probe for studying mitophagy, a poorly understood process in trypanosomatids, and may provide new insights into mitochondrial biology of these parasites.