TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information


Autoři: Munira Alballa aff001;  Faizah Aplop aff003;  Gregory Butler aff001
Působiště autorů: Department of Computer Science and Software Engineering, Concordia University, Montréal, Québec, Canada aff001;  College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia aff002;  School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Malaysia aff003;  Centre for Structural and Functional Genomics, Concordia University, Montréal, Québec, Canada aff004
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227683

Souhrn

Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the 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 computational tools that predict the substrates that 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 (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used.

Klíčová slova:

Anions – Cations – Membrane proteins – Multiple alignment calculation – Protein sequencing – Sequence alignment – Sequence databases – Transmembrane transport proteins


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