Cymoxanil disrupts RNA synthesis through inhibiting the activity of dihydrofolate reductase.

The agricultural fungicide cymoxanil (CMX) is commonly used in the treatment of plant pathogens, such as Phytophthora infestans. Although the use of CMX is widespread throughout the agricultural industry and internationally, the exact mechanism of action behind this fungicide remains unclear. Therefore, we sought to elucidate the biocidal mechanism underlying CMX. This was accomplished by first performing a large-scale chemical-genomic screen comprising the 4000 haploid non-essential gene deletion array of the yeast Saccharomyces cerevisiae. We found that gene families related to de novo purine biosynthesis and ribonucleoside synthesis were enriched in the presence of CMX. These results were confirmed through additional spot-test and colony counting assays. We next examined whether CMX affects RNA biosynthesis. Using qRT-PCR and expression assays, we found that CMX appears to target RNA biosynthesis possibly through the yeast dihydrofolate reductase (DHFR) enzyme Dfr1. To determine whether DHFR is a target of CMX, we performed an in-silico molecular docking assay between CMX and yeast, human, and P. infestans DHFR. The results suggest that CMX directly interacts with the active site of all tested forms of DHFR using conserved residues. Using an in vitro DHFR activity assay we observed that CMX inhibits DHFR activity in a dose-dependent relationship.

Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing.

Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design through identifying novel therapeutics that exhibit enhanced pharmacokinetic properties and reduced toxicity. The process of in-silico peptide design involves the application of molecular docking, molecular dynamics simulations, and machine learning algorithms. Three primary approaches for peptide therapeutic design including structural-based, protein mimicry, and short motif design have been predominantly adopted. Despite the ongoing progress made in this field, there are still significant challenges pertaining to peptide design including: enhancing the accuracy of computational methods; improving the success rate of preclinical and clinical trials; and developing better strategies to predict pharmacokinetics and toxicity. In this review, we discuss past and present research pertaining to the design and development of in-silico peptide therapeutics in addition to highlighting the potential of computation and artificial intelligence in the future of disease therapeutics.