These tools will provide scientists with insight into host-microbiome interactions. For agricultural scientists, the host-microbiome interactions are important for plant and animal health and growth, which are key factors in food productivity.
Due | Deliverable | Description | Link |
---|---|---|---|
Q4 | D.1.1 | TooT-SC: Given a fasta file of protein sequences, predict the substrate class that is transported. TooT-BERT-SC: Classifies transmembrane transporter proteins by substrate using a majority-vote approach with various standard predictors. |
Link Link |
Q4 | D.1.4 | Computational infrastructure for in silico experiments. | Link |
Q4 | D.4.1 | Experimental infrastructure. | Link |
Q6 | D.1.2 | TooT-Proteome: Given a fasta file of a proteome, predict the transporters and their properties. TooT-BERT-T: Predicts transmembrane transporter proteins from protein sequences using ProtBERT-BFD and logistic regression. TooT-Proteome: Predicts transmembrane transporter proteins and their substrate-specific properties using BERT-based predictors for transporter and substrate classification. |
Link Link Link |
Q6 | D.2.1 and D.3.1 (HPC) | TooT-TC: Given a fasta file of protein sequences, predict their TC family and subfamily. | |
Q8 | D.2.2 | TooT-All: Given a fasta file of protein sequences, classify against all transporter schemes. | |
Q8 | D.4.2 | Experimental infrastructure - version 2. | Link (boutiques) Link (CBRAIN) Link (biolab VM) |
Q12 | D.1.3 | TooT-BERT-ICAT: Utilizes ProtBERT-BFD to predict and classify inorganic ion-specific transmembrane transporter proteins with high precision and accuracy. TooT-SS: Given a fasta file of protein sequences, predict the specific substrate that is transported. |
Link |
Q12 | D.2.3 | TooT-All (version 2): Given a fasta file of protein sequences, classify against all transporter schemes. | |
Q12 | D.3.2 | TooT-Meta (= TooT-Proteome HPC): Given a fasta file of a meta-proteome, predict the transporters and their properties. |