Application of RF diffusion to predict interspecies protein-protein interactions between fungal pathogens and cereal crops

Authors: HALEY, OLIVIA - Orise Fellow; Harding, Stephen; Sen, Taner; Woodhouse, Margaret; Kim, Hye-Seon; Andorf, Carson

Submitted to: bioRxiv
Publication Type: Pre-print Publication
Publication Acceptance Date: 9/19/2024
Publication Date: 9/19/2024
Citation: Haley, O.C., Harding, S., Sen, T.Z., Woodhouse, M.R., Kim, H.-S., Andorf, C. 2024. Application of RFdiffusion to predict interspecies protein-protein interactions between fungal pathogens and cereal crops. bioRxiv. https://doi.org/10.1101/2024.09.17.613523.
DOI: https://doi.org/10.1101/2024.09.17.613523

Interpretive Summary: Fungal diseases can severely impact crops, reducing yields, contaminating the grain, and threatening food security. To understand why some plants are more resistant to fungal infections than others, scientists study the interactions between proteins—molecules that play crucial roles in nearly all biological processes. When proteins interact, they can trigger various effects, including plant resistance or susceptibility to disease. However, predicting which proteins will interact, especially between plants and fungi, is challenging due to the complexity of their structures and functions. A new computational method to predict interactions between plant and fungal proteins was developed to address this challenge. This approach utilized artificial intelligence to generate novel protein structures likely to bind with proteins in the fungal genome known to infect plants and then identify proteins in the plants with similar structures. The method was tested using proteins from a fungus that causes rice blast, a major disease affecting rice crops. The method successfully identified 11 out of 14 known interactions between rice and the fungus, demonstrating a high recall rate of over 78%. This new method offers a way to predict protein interactions between fungi and crops, which can help us understand how some plants resist infections while others do not. By identifying these interactions, we can develop better strategies for managing fungal diseases and develop more resistant crops.

Technical Abstract: Plant pathogenic fungi secrete small proteins known as effectors which help overcome the plant defense response and cause disease. The concept of effector-triggered immunity in plants evolved from the “gene for gene hypothesis” which describes plant resistance or susceptibility to plant pathogens based on interspecies protein-protein interactions (PPIs) between plant-derived resistance (R) genes and pathogen-derived avirulence (Avr) effector genes. Understanding the molecular interactions mediating these host-pathogen interactions in effector-triggered immunity is thus essential to managing fungal disease. In silico methods of predicting interspecies PPIs have been heavily studied to identify target genes for crop resistance. But conventional sequence-based homology methods (i.e., interlog, domain-based inference) for predicting interspecies PPIs are not as powerful as methods that also incorporate structural homology. The objective of this study was to develop a computational workflow to predict PPIs between pathogenic fungi and their cereal hosts by leveraging recent advances in artificial intelligence and structural biology. This workflow proposes the use of a generative model, RFdiffusion, to predict the structure of truncated segments of proteins likely to bind to query effector proteins. The binder structures were filtered based on the number of contacts at the effectors’ known binding residues. Acceptable structures were then input into FoldSeek to search the host proteome for host proteins containing similar sub-structures. Experimentally-validated PPIs between rice (Oryzae sativa cv. ‘Japonica’) and rice blast fungus (Magnaporthe oryzae) were used for workflow validation. The effects of binder length and the binding residues’ mode of action (i.e., residues at active/substrate recognition sites) on the binder quality and presumptive host protein matches were explored. Ultimately, 11 out of 14 experimentally validated PPIs were recovered computationally, indicating a high recall (>78%) for the workflow. The shorter binders recovered most of the PPIs, but may have produced the most false positives, as functional analyses revealed that these host proteins displayed a wide variety of functions. These findings emphasize that subject matter expertise is still required to decipher the prediction results. Yet, this framework for elucidating interactions between fungal pathogens and host proteins could provide valuable insight into mechanisms of susceptibility or resistance at a scale friendly to limited computational resources and facilitate the development of control strategies that reduce crop diseases.