Mahoney, E. M. | bioRxiv Science

bioRxiv2025

DefensePredictor: A Machine Learning Model to Discover Novel Prokaryotic Immune Systems

Anti-phage defense systems protect bacteria from viruses. Studying defense systems has begun to reveal the evolutionary roots of eukaryotic innate immunity and produced important biotechnologies such as CRISPR-Cas9. Dozens of new systems have been discovered by looking for systems that co-localize in genomes, but this approach cannot identify systems outside defense islands. Here, we present DefensePredictor, a machine-learning model that leverages embeddings from a protein language model to classify proteins as defensive. We applied DefensePredictor to 69 diverse E. coli strains and validated 45 previously unknown systems, with >750 additional unique proteins receiving high confidence predictions. Our model, provided as open-source software, will help comprehensively map the anti-phage defense landscape of bacteria, further reveal connections between prokaryotic and eukaryotic immunity, and accelerate biotechnology development.

microbiology↗

bioRxiv2023

Marginal specificity in protein interactions constrains evolution

The evolution of novel functions in biology relies heavily on gene duplication and divergence, creating large paralogous protein families. Selective pressure to avoid detrimental cross-talk often results in paralogs that exhibit exquisite specificity for their interaction partners. But how robust or sensitive is this specificity to mutation? Here, using deep mutational scanning, we demonstrate that a paralogous family of bacterial signaling proteins exhibits marginal specificity, such that many individual substitutions give rise to substantial cross-talk between normally insulated pathways. Our results indicate that sequence space is locally crowded despite overall sparseness, and we provide evidence that this crowding has constrained the evolution of bacterial signaling proteins. These findings underscore how evolution selects for good enough rather than optimized phenotypes, leading to restrictions on the subsequent evolvability of paralogs. Significance StatementLarge paralogous protein families are found throughout biology, the product of extensive gene duplication. To execute different functions inside cells, paralogs typically acquire different specificities, interacting with only desired, cognate partners and avoiding cross-talk with non-cognate proteins. But how robust is this interaction specificity to mutation? Can individual mutations lead to cross-talk or do paralogs diverge enough such that multiple mutations would be required, providing a mutational buffer against cross-talk? To address these questions, we built mutant libraries that produce all possible single substitutions of a bacterial kinase and then screened for cross-talk to non-cognate proteins. Strikingly, we find that many single substitutions can produce cross-talk, meaning that these pathways typically exhibit only marginal specificity, and demonstrate that this restricts their evolvability.

evolutionary biology↗