Predicting Fungal Contaminants for Space Missions Using Proteome-Wide Screening for Protein Orthologs

Fungal contamination poses a growing threat to spacecraft integrity, crew health, and planetary protection efforts. We describe a scalable and interpretable pipeline for identifying fungi with adaptation potential to spaceflight-associated stress conditions such as extreme temperatures, radiation levels, etc., and pathogenicity risks. Starting with proteins known to confer stress resistance, we identify orthologs across over fifteen hundred fungal species and evaluate their contamination potential via comparative proteome analysis. Our pipeline integrates proteins with known functional inference, cross-database proteome matching, and identity-based scoring to generate a ranked list of fungal species of concern. We apply this approach to detections from spacecraft assembly facilities, highlighting species with combined stress-tolerance and pathogenic potential. This study establishes a foundation for future AI-based risk assessments that can scale to orders of magnitude more fungal species, thus laying the foundation for systematic identification and assessment of fungal contaminants with potential adaptation and pathogenicity risks in spaceflight environments, thereby supporting contamination control strategies for future space missions. We also present an interactive visual online tool for researchers to trivially check the contamination potential of species in their own samples.