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Many Stramenopile species belonging to oomycetes from the genus  Saprolegnia  infect fish, amphibians, and crustaceans in aquaculture farms and natural ecosystems.  Saprolegnia parasitica  is one of the most severe fish pathogens, responsible for high losses in the aquaculture industry worldwide. Most of the molecules reported to date for the control of  Saprolegnia  infections either are inefficient or have negative impacts on the health of the fish hosts or the environment resulting in substantial economic losses. Until now, the whole proteome of  S. parasitica  has not been explored for a systematic screening of novel inhibitors against the pathogen. The present study was designed to develop a consensus computational framework for the identification of potential target proteins and their inhibitors and subsequent experimental validation of selected compounds. Comparative analysis between the proteomes of  Saprolegnia , humans and fish species identified proteins that are specific and essential for the survival of the pathogen. The DrugBank database was exploited to select food and drug administration (FDA)-approved inhibitors whose high binding affinity to their respective protein targets was confirmed by computational modeling. At least six of the identified compounds significantly inhibited the growth of  S. parasitica in vitro . Triclosan was found to be most effective with a minimum inhibitory concentration (MIC 100 ) of 4 μg/ml. Optical microscopy showed that the inhibitors affect the morphology of hyphal cells, with hyper-branching being commonly observed. The inhibitory effects of the compounds identified in this study on  Saprolegnia ’s mycelial growth indicate that they are potentially usable for disease control against this class of oomycete pathogens. Similar approaches can be easily adopted for the identification of potential inhibitors against other plant and animal pathogenic oomycete infections.

frontiers in microbiology

Identification of Growth Inhibitors of the Fish Pathogen Saprolegnia parasitica Using in silico Subtractive Proteomics, Computational Modeling, and Biochemical Validation