Abstract

Plant-pathogenic fungi secrete effector proteins to facilitate infection. We describe extensive improvements to EffectorP, the first machine learning classifier for fungal effector prediction. EffectorP 2.0 is now trained on a larger set of effectors and utilizes a different approach based on an ensemble of classifiers trained on different subsets of negative data, offering different views on classification. EffectorP 2.0 achieves an accuracy of 89%, compared with 82% for EffectorP 1.0 and 59.8% for a small size classifier. Important features for effector prediction appear to be protein size, protein net charge as well as the amino acids serine and cysteine. EffectorP 2.0 decreases the number of predicted effectors in secretomes of fungal plant symbionts and saprophytes by 40% when compared with EffectorP 1.0. However, EffectorP 1.0 retains value, and combining EffectorP 1.0 and 2.0 results in a stringent classifier with a low false positive rate of 9%. EffectorP 2.0 predicts significant enrichments of effectors in 12 of 13 sets of infection-induced proteins from diverse fungal pathogens, whereas a small cysteine-rich classifier detects enrichment in only seven of 13. EffectorP 2.0 will fast track the prioritization of high-confidence effector candidates for functional validation and aid in improving our understanding of effector biology. EffectorP 2.0 is available at http://effectorp.csiro.au.