Bridging Manual and Computational Approaches: The Pseudophosphatase Scanner for Genome-Wide Fungal Pseudophosphatome...

Pseudophosphatases are proteins with mutations in their catalytic motifs, resulting in the predicted loss of enzymatic activity. Although pseudophosphatases are established regulators of various signaling pathways in humans and other metazoans, their biological roles in fungi remain largely unexplored. Identifying fungal-specific pseudophosphatases is particularly important because they control fungal growth, development, and virulence through noncatalytic mechanisms and could represent selective antifungal targets due to the absence of close human homologues. Here, we present a comprehensive overview of fungal pseudophosphatases identified across all major phosphatase families. To compile a robust data set, we integrated information from literature and databases as well as systematically scanned the phosphatomes of the human pathogenic yeast Cryptococcus neoformans, the plant pathogen Fusarium graminearum, and the neglected pathogenic fungus Talaromyces marneffei. We identified candidate pseudophosphatases and assessed their conservation through BLAST analysis across humans, true yeasts, filamentous ascomycetes, and basidiomycetes. We classified pseudophosphatase candidates into 10 distinct groups, including three groups (CC1-type Oca, CC1-type Yvh1, and HAD-type NIF) that appear to be fungal-specific with no human homologues and exhibit lineage-specific features. Available evidence indicates that fungal pseudophosphatases contribute to signaling pathways that regulate development, metabolism, stress responses, and virulence. In addition, we developed the Pseudophosphatase Scanner tool as a post-HMM (Hidden Markov Model) analysis pipeline to enable genome-wide pseudophosphatase detection. By combining HMM scan-based fold assignment with motif-level pattern matching, the Pseudophosphatase Scanner distinguishes canonical motifs, relaxed variants, and degenerated fold remnants, facilitating functional interpretation of phosphatase active sites at the sequence level. The Pseudophosphatase Scanner is accessible both as a Python script for local execution and as a Google Colab notebook, offering a point-and-click interface for cloud-based analysis. This study provides a catalog of fungal pseudophosphatases and a bioinformatics platform for efficient large-scale pseudophosphatase discovery.