Location: Plant, Soil and Nutrition ResearchTitle: Predicting protein domain temperature adaptation across the prokaryote-eukaryote divide
|JENSEN, SARAH - Syngenta|
|JOHNSON, LYNN - Cornell University - New York|
|CASSTEVENS, TERRY - Cornell University - New York|
|Buckler, Edward - Ed|
Submitted to: bioRxiv
Publication Type: Pre-print Publication
Publication Acceptance Date: 7/13/2021
Publication Date: 7/13/2021
Citation: Jensen, S.E., Johnson, L.C., Casstevens, T., Buckler IV, E.S. 2021. Predicting protein domain temperature adaptation across the prokaryote-eukaryote divide. bioRxiv. 2021.07.13.452245. https://doi.org/10.1101/2021.07.13.452245.
Interpretive Summary: Proteins carry out all of the major biological functions that make life possible, and their function is linked to the temperature of their environment. The relationship between temperature and protein activity differs for each type of protein and is difficult to measure experimentally for a large number of proteins. This study uses genome features to build a model that predicts protein temperature sensitivity across a wide range of species. This project tests three models to predict temperature sensitivity in both prokaryote and eukaryote species and successfully developed a species-agnostic model that can be used to evaluate protein temperature sensitivity. By developing a model that is species-agnostic, this project lays the groundwork for future studies to compare protein adaptation profiles across any protein in any species. This model makes it possible to evaluate protein temperature sensitivity across thousands of proteins in multiple species; a question that would be extremely difficult and expensive to address experimentally. One possible application of this model is to compare adaptation profiles across plant proteins to identify ‘weak link’ proteins that are sensitive to high temperatures and may negatively impact plant development.
Technical Abstract: Protein thermostability is important for fitness but difficult to measure across the proteome. Fortunately, protein thermostability is correlated with prokaryote optimal growth temperatures (OGTs), which can be predicted from genome features. Models that can predict temperature sensitivity across the prokaryote-eukaryote divide would help inform how eukaryotes adapt to elevated temperatures, such as those predicted by climate change models. In this study we test whether prediction models can cross the prokaryote-eukaryote divide to predict protein stability in both prokaryotes and eukaryotes. We compare models built using a) the whole proteome, b) Pfam domains, and c) individual amino acid residues. Proteome-wide models accurately predict prokaryote optimal growth temperatures (r2 up to 0.93), while site-specific models demonstrate that nearly half of the proteome is associated with optimal growth temperature in both Archaea and Bacteria. Comparisons with the small number of eukaryotes with temperature sensitivity data suggest that site-specific models are the most transferable across the prokaryote-eukaryote divide. Using the site-specific models, we evaluated temperature sensitivity for 323,850 amino acid residues in 2,088 Pfam domain clusters in Archaea and Bacteria species separately. 59.0% of tested residues are significantly associated with OGT in Archaea and 75.2% of tested residues are significantly associated with OGT in Bacteria species at a 5% false discovery rate. These models make it possible to identify which Pfam domains and amino acid residues are involved in temperature adaptation and facilitate future research questions about how species will fare in the face of increasing environmental temperatures.