Nikkomycin Z

Phytophthora root and stem rot in soybeans results in substantial economic losses worldwide. In this study, a machine learning model based on a heterogeneous interaction graph attention network model was constructed. The PDBbind data set, comprising 13,285 complexes with experimental pK_a or pK_i values, was utilized to train and evaluate the model, which was subsequently employed to screen candidate compounds against chitin synthase of Phytophthora sojae (PsChs1) in the Traditional Chinese Medicine Systems Pharmacology database, comprising 14,249 compounds. High-scoring candidate compounds were docked with PsChs1 protein using Discovery Studio, and their interaction energies were evaluated. Molecular dynamic simulations spanning 50 ns were performed using GROMACS to explore the stability of the complexes, trajectory analysis was conducted with root-mean-square deviations, and the hydrogen bonds, radius of gyration, MMPBSA binding free energy, and binding modes were analyzed. MOL011832 and MOL011833 were identified as potential pesticides, both of which were present in the herb Schizonepeta through database retrieval. The inhibitory effects of an ethanol extract of Schizonepeta against P. sojae were subsequently explored and confirmed in biological experiments. Overall, this study proves the feasibility and high efficiency of pesticide discovery using graph neural network-based models.

Potato common scab is an important bacterial disease afflicting potatoes around the world. Better knowledge of the local Streptomyces spp. populations causing this disease is key to developing durable control strategies. In this study, we isolated 230 Streptomyces strains from scab-infected potato tubers harvested from commercial potato fields located across the province of Quebec, Canada. The genetic diversity of this collection was first studied using repetitive element-based PCR fingerprinting, and the genomes of 36 representative strains were sequenced using PacBio’s sequencing technology. This enabled us to identify the strains to the species level, to study the distribution of previously characterized virulence-associated genes and clusters, and to explore the repertoires of putative plant cell wall-degrading enzymes. In parallel, the virulence of the 36 strains was evaluated using a potato tuber slice assay. The diversity was higher than previously reported, as 11 phytopathogenic species were found across the province. Among them, S. scabiei and S. acidiscabies were the most abundant as well as the most virulent. Strains belonging to these two species harbored numerous virulence determinants, including the thaxtomin biosynthetic gene cluster. By contrast, most weakly virulent strains lacked this cluster but harbored at least one known virulence determinant. The results obtained suggest that a higher number of virulence-associated genes and clusters in the genome of phytopathogenic Streptomyces spp. are associated with greater virulence. This study contributes to increasing the publicly available genomic resources of scab-causing Streptomyces spp. and expands our knowledge on the diversity and virulence of this important bacterial pathogen.