1 Department of Computer Science and Software Engineering, Concordia University, Montréal, Québec, Canada. m_alball@encs.concordia.ca.
2 Department of Computer Science and Software Engineering, Concordia University, Montréal, Québec, Canada.
3 Centre for Structural and Functional Genomics, Concordia University, Montréal, Québec, 24105, Canada.

TooT-T: discrimination of transport proteins from non-transport proteins.

BMC Bioinformatics. 2020 Apr 23;21(Suppl 3):25

Authors: Alballa M, Butler G

Abstract

BACKGROUND: Membrane transport proteins (transporters) play an essential role in every living cell by transporting hydrophilic molecules across the hydrophobic membranes. While the sequences of many membrane proteins are known, their structure and function is still not well characterized and understood, owing to the immense effort needed to characterize them. Therefore, there is a need for advanced computational techniques takes sequence information alone to distinguish membrane transporter proteins; this can then be used to direct new experiments and give a hint about the function of a protein.

RESULTS: This work proposes an ensemble classifier TooT-T that is trained to optimally combine the predictions from homology annotation transfer and machine-learning methods to determine the final prediction. Experimental results obtained by cross-validation and independent testing show that combining the two approaches is more beneficial than employing only one.

CONCLUSION: The proposed model outperforms all of the state-of-the-art methods that rely on the protein sequence alone, with respect to accuracy and MCC. TooT-T achieved an overall accuracy of 90.07% and 92.22% and an MCC 0.80 and 0.82 with the training and independent datasets, respectively.

PMID: 32321420 [PubMed - in process]