AMAPEC: accurate antimicrobial activity prediction for fungal effector proteins

Abstract

Fungi typically occur in environments where numerous and diverse other microbes occur as well, often resulting in fierce competition for nutrients and habitat. To support fungal fitness in these environments, they evolved various mechanisms that mediate direct antagonism towards niche competitors. Among these, the secretion of proteins with antimicrobial activities has been reported in fungi with diverse lifestyles. Recently, several plant-associated fungi were shown to rely on the secretion of antimicrobial effector proteins to antagonize certain members of plant hosts' microbiota and to successfully colonize plant tissues. Some of these effectors do not share homology with known antimicrobials and represent novel antibiotics. Accordingly, the occurrence and conservation of proteinaceous antimicrobials throughout the fungal tree of life remains enigmatic. Here we present a computational approach to annotate candidate antimicrobial effectors in fungal secretomes based on protein physicochemical properties. After curating a set of proteins that were experimentally verified to display antimicrobial activity and a set of proteins that lack such activity, we trained a machine learning classifier on properties of protein sequences and predicted structures. This predictor performs particularly well on fungal proteins (R2=0.89) according to our validations and is delivered as a software package named AMAPEC, dedicated to antimicrobial activity prediction for effector candidates. We subsequently used this novel software to predict antimicrobial effector catalogs in three phylogenetically distant fungi with distinct lifestyles, revealing relatively large catalogs of candidate antimicrobials for each of the three fungi, and suggesting a broad occurrence of such proteins throughout the fungal kingdom. Thus, AMAPEC is a unique method to uncover antimicrobials in fungal secretomes that are often sparsely functionally annotated, and may assist biological interpretations during omic analyses. It is freely available at https://github.com/fantin-mesny/amapec.