Quantitative structure-activity relationship and machine learning studies of 2-thiazolylhydrazone derivatives with anti-Cryptococcus neoformans activity

Understanding the Enzyme (S)-Norcoclaurine Synthase Promiscuity to Aldehydes and Ketones

The (S)-norcoclaurine synthase from Thalictrum flavum (TfNCS) stereoselectively catalyzes the Pictet-Spengler reaction between dopamine and 4-hydroxyphenylacetaldehyde to give (S)-norcoclaurine. TfNCS can catalyze the Pictet-Spengler reaction with various aldehydes and ketones, leading to diverse tetrahydroisoquinolines. This substrate promiscuity positions TfNCS as a highly promising enzyme for synthesizing fine chemicals. Understanding carbonyl-containing substrates' structural and electronic signatures that influence TfNCS activity can help expand its applications in the synthesis of different compounds and aid in protein optimization strategies. In this study, we investigated the influence of the molecular properties of aldehydes and ketones on their reactivity in the TfNCS-catalyzed Pictet-Spengler reaction. Initially, we compiled a library of reactive and unreactive compounds from previous publications. We also performed enzymatic assays using nuclear magnetic resonance to identify some reactive and unreactive carbonyl compounds, which were then included in the library. Subsequently, we employed QSAR and DFT calculations to establish correlations between substrate-candidate structures and reactivity. Our findings highlight correlations of structural and stereoelectronic features, including the electrophilicity of the carbonyl group, to the reactivity of aldehydes and ketones toward the TfNCS-catalyzed Pictet-Spengler reaction. Interestingly, experimental data of seven compounds out of fifty-three did not correlate with the electrophilicity of the carbonyl group. For these seven compounds, we identified unfavorable interactions between them and the TfNCS. Our results demonstrate the applications of in silico techniques in understanding enzyme promiscuity and specificity, with a particular emphasis on machine learning methodologies, DFT electronic structure calculations, and molecular dynamic (MD) simulations.

Discovery of Lead 2-Thiazolylhydrazones with Broad-Spectrum and Potent Antifungal Activity

Opportunistic fungal infections represent a global health problem, mainly for immunocompromised individuals. New therapeutical options are needed since several fungal strains show resistance to clinically available antifungal agents. 2-Thiazolylhydrazones are well-known as potent compounds against Candida and Cryptococcus species. A scaffold-focused drug design using machine-learning models was established to optimize the 2-thiazolylhydrazone skeleton and obtain novel compounds with higher potency, better solubility in water, and enhanced absorption. Twenty-nine novel compounds were obtained and most showed low micromolar MIC values against different species of Candida and Cryptococcus spp., including Candida auris, an emerging multidrug-resistant yeast. Among the synthesized compounds, 2-thiazolylhydrazone 28 (MIC value ranging from 0.8 to 52.17 μM) was selected for further studies: cytotoxicity evaluation, permeability study in Caco-2 cell model, and in vivo efficacy against Cryptococcus neoformans in an invertebrate infection model. All results obtained indicate the great potential of 28 as a novel antifungal agent.