bioRxiv preprint doi: https://doi.org/10.1101/293159; this version posted April 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. TranCEP: Predicting transmembrane transport proteins using composition, evolutionary, and positional information Munira Alballa1, Faizah Aplop2, Gregory Butler1,3* 1 Department of Computer Science and Software Engineering, Concordia University, Montr´eal, Qu´ebec, Canada 2 School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Malaysia 3 Centre for Structural and Functional Genomics, Concordia University, Montr´eal,Qu´ebec, Canada *
[email protected] Abstract Transporters mediate the movement of compounds across the membranes that separate the cell from its environment, and across inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computation tools that predict the substrates which are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment, and restriction to important positions of the alignment that play a role in determining specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state of the art.