Computational prediction of structure, function and interaction of aphid salivary effector proteins

Abstract

Similar to plant pathogens, phloem-feeding insects such as aphids deliver effector proteins inside their hosts that act to promote host susceptibility and enable feeding and infestation. Despite exciting progress towards identifying and characterizing effector proteins from these insects, their functions remain largely unknown. The recent ground-breaking development in protein structure prediction algorithms combined with the availability of proteomics and transcriptomic datasets for agriculturally important pests, such as the aphid Myzus persicae (green peach aphid), provides new opportunities to explore the structural and functional diversity of effector repertoires. In this study, we sought to gain insight into the the M. persicae effector repertoire by predicting and analysing the structures of a set of 71 effector candidate proteins. We used two protein structure prediction methods, AlphaFold and OmegaFold, which produced mutually consistent results. We observed a wide continuous spectrum of sizes and structures among the effector candidates, from disordered proteins to globular enzymes. We made use of the structural information and state-of-the-art computational methods to predict M. persicae effector protein properties, including function and interaction with host plant proteins. Overall, our investigation provides novel insights into the structure, function, and interaction prediction of aphid effector repertoires and will guide the necessary experimental characterization to address new hypotheses.