Computational framework integrating subtractive proteomics and structural bioinformatics to nominate candidate fungicide targets in Puccinia graminis f. sp. tritici

Puccinia graminis f. sp. tritici, the causal agent of wheat stem rust, continues to threaten global wheat production through recurring outbreaks and the erosion of host resistance and chemical control efficacy. This study aimed to identify candidate protein targets for fungicide development in P. graminis f. sp. tritici. An integrated subtractive proteomics pipeline was then applied, combining essentiality screening, pathway analysis, structural modeling, and exploratory molecular docking. A stringent filtering strategy excluding proteins homologous to Triticum aestivum, Homo sapiens, and beneficial microbes (Bacillus subtilis, Pseudomonas fluorescens, and Trichoderma harzianum) was applied, thereby maximizing selectivity while minimizing potential off-target ecological effects. From 36,348 proteins, four putative targets were identified: two β-glucan synthesis-associated KRE6 homologs, a cell wall α-1,3-glucan synthase (AGS1), and a Major Facilitator Superfamily (MFS) transporter. Guided by intrinsic disorder analysis, AlphaFold2 was used to generate high-quality 3D structural models of the prioritized targets. Network-based functional analysis linked these proteins to core cellular processes, while underscoring that their essentiality in P. graminis f. sp. tritici remains experimentally unconfirmed. Molecular docking with the antifungal agent poacic acid and the nucleotide sugar UDP-glucose suggested potential binding interactions within the predicted cavities. These exploratory simulations present preliminary evidence for plausible small-molecule binding pockets in the selected candidate targets. Collectively, this work provides a multi-layered computational framework for nominating and structurally characterizing candidate targets in wheat stem rust and generates testable hypotheses for future experimental work.