Structural studies of virulence proteins from rust fungi: combining large-scale machine learning and experimental validation to guide the engineering of durable disease resistance in crops
Supervisors: Peter Dodds (CSIRO), Melania Figueroa (CSIRO), Jana Sperschneider (CSIRO), Megan Outram (CSIRO), Mike Kuiper (CSIRO) and Simon Williams (ANU).
Rust fungi represent the largest group of plant pathogens, and are responsible for some of the most devastating diseases in agriculture. Collectively, they cause disease in a broad range of plant species, including economically important food crops, such as wheat, soybean and coffee. During infection of the plant, rust fungi secrete proteins known as effectors, which are delivered into host plant cells to promote virulence and facilitate colonisation. Despite their role in pathogenicity, little is known about the pathogenic function of most rust effectors. Understanding their functions is challenging, as most effectors lack sequence similarity with each other or proteins of known function; and therefore studies are often limited to individual effector proteins and are costly (both in time and consumables).
Recent advances in the accuracy of protein structural prediction provided by artificial intelligence (AlphaFold) have enabled our team to perform large-scale structural prediction of effector proteins from rust fungi that infect cereal crops. Structural comparisons between predicted and experimentally derived rust effector structures have revealed that many of the virulence proteins used by rust fungi can be grouped into structural families. We now seek to understand if effectors within structural families have shared virulence functions. We also seek to apply this knowledge to engineer plant immunity receptors to detect structurally related effectors to enhance plant disease resistance. We anticipate that new knowledge in this area will help generate durable disease management strategies against rust pathogens, including the identification of fungicide targets in rust fungi and/or crop engineering to enhance disease resistance.
The PhD candidate will combine structural biology, computational modelling, and biochemistry to link the effector structure predictions to virulence functions. The project will be supported by a multi-disciplinary team of researchers with expertise in structural biology, computational biology, crop genetics/breeding and machine learning.
The successful candidate will be provided with:
- Training in the use of structural modelling, structural comparison and clustering to identify targets for experimental validation.
- Skills in protein biochemistry, experimental protein structural solution, and in-planta experiments.
- An in-depth understanding of plant-microbe interactions and the engineering of novel strategies to prevent fungal virulence (fungicides) and enhance plant disease resistance (engineering of plant immunity receptors).
- Skills in working within a multidisciplinary team.
- Skills in data analysis, presentation and management, manuscript preparation and peer review.