Using machine learning to identify the functional consequences of post-translational modifications in the rice proteome

For much of the world's poor, rice (O. sativa) provides the majority of daily calories. Rice productivity has more than doubled in recent decades, resulting from continued breeding efforts. However, to meet the demands imposed by the projected increase in population, rice production has to continue growing rapidly, while meeting challenges imposed by a changing climate. With the recent sequencing of >3000 different varieties, there is a huge genetic resource available for identifying genetic polymorphisms associated with desirable traits e.g. tolerance to biotic or abiotic stress, yield, nutritional content etc., which in due course could be bred into a major crop variety. However, there is a great knowledge gap at present between genetic variation and functional effects, in terms of how plants respond at the cellular level to different stresses.

Under normal field conditions, plants can be exposed to various biotic and abiotic environmental stresses. Plant stress tolerance and acclimation depends on significant changes in post-translational modifications (PTMs) of specific proteins to switch function rapidly. It is expected that environmental stresses, such as drought and heat will become more prevalent in the coming decades. Future successful solutions will undoubtedly involve applying next-generation technologies to improve the breeding of agro-economically important crops. This study aims to identify and quantify PTMs in rice, and in the model plant Arabidopsis thaliana, to improve our understanding of how plants respond rapidly to different types of stress. We are particularly interested to study lysine modifications that affect protein-protein interactions and turnover, such as SUMOylation and ubiquitination. We will study the evolutionary conservation of these sites, the extent to which they can be competition between different PTMs for the same site, and/or crosstalk with proximal sites. The acquired knowledge will be used to mine plant genomes for new alleles associated with desirable traits, with a long term objective of improving crop breeding efforts.