Home / TooT Suite: Prediction and classification of membrane transport proteins
TooT Suite: Prediction and classification of membrane transport proteins
Generating solutions
Status
Competition
Genome Centre(s)
GE3LS
Project Leader(s)
- Gregory Butler,
- Concordia University
Fiscal Year Project Launched
Project Description
Increasing population + improved standard of living = a threat to the adequacy of our food supply. Indeed, by 2050, when the world population is expected to reach nine billion people, agricultural production will need to increase 60-70% to feed all these people. But with most of the world’s arable land already in production, the solution has to involve improving the yields from both crops and animals. Plants and animals are part of a complex ecosystem, co-habiting symbiotically with microbial communities, called microbiomes, that live in, on or near them, affecting their health and growth and, therefore, their productivity as a food source. Scientists currently use genomics and metagenomics to study these microbiomes, to better understand how microbiome-host interactions affect that health and growth. These interactions happen by exchanging chemical compounds, facilitated by transport proteins, which carry the compounds across the membranes of a cell. Dr. Gregory Butler of Concordia University is developing TooT Suite, a way to annotate the membrane transport proteins both in an organism, be it plant or animal, and in a microbiome, thus providing information about potential interactions between them. TooT Suite will be an open-source set of easy-to-use bioinformatics tools that will help genomics researchers in agriculture to better understand these interactions. It will work with different existing classification systems by predicting the protein’s most appropriate term for each of these systems, thus overcoming a lack of consistency. TooT Suite will open an era of agricultural research based on system-level thinking and metagenomics that is needed to address future food supply and food security challenges.