EffectorO: motif-independent prediction of effectors in oomycete genomes using machine learning and lineage specificity

Oomycete plant pathogens cause a wide variety of diseases, including late blight of potato, sudden oak death, and downy mildew of many plants. These pathogens are major contributors to losses in many food crops. Oomycetes secrete "effector" proteins to manipulate their hosts to the advantage of the pathogen. Plants have evolved to recognize effectors, resulting in an evolutionary cycle of defense and counter-defense in plant-microbe interactions. This selective pressure results in highly diverse effector sequences that can be difficult to computationally identify using sequence similarity. We developed a pipeline, EffectorO, that uses two complementary approaches to predict effectors in oomycete pathogen genomes: (1) a machine learning-based pipeline that predicts effector probability based on the biochemical properties of the N-terminal amino acid sequence of a protein and is trained on experimentally verified oomycete effectors and (2) a pipeline based on lineage-specificity to find proteins that are unique to one species or genus, a sign of evolutionary divergence due to adaptation to the host. We tested EffectorO on Bremia lactucae, which causes lettuce downy mildew, and Phytophthora infestans, which causes late blight of potato and tomato, and predicted many novel effector candidates, while still recovering the majority of known effector candidates. EffectorO will be useful for discovering novel families of oomycete effectors without relying on sequence similarity to known effectors.