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Integrative approach for detecting membrane proteins.

Author(s): Alballa M, Butler G

BACKGROUND: Membrane proteins are key gates that control various vital cellular functions. Membrane proteins are often detected using transmembrane topology prediction tools. While transmembrane topology prediction tools can detect integral membrane protein...

Article GUID: 33349234
TooT-T: discrimination of transport proteins from non-transport proteins.

Author(s): Alballa M, Butler G

BMC Bioinformatics. 2020 Apr 23;21(Suppl 3):25 Authors: Alballa M, Butler G

Article GUID: 32321420
TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information.

Author(s): Alballa M, Aplop F, Butler G

PLoS One. 2020;15(1):e0227683 Authors: Alballa M, Aplop F, Butler G

Article GUID: 31935244
Title:TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information.
Authors:Alballa M,  Aplop F,  Butler G
Link:https://www.ncbi.nlm.nih.gov/pubmed/31935244?dopt=Abstract
DOI:10.1371/journal.pone.0227683
Category:PLoS One
PMID:31935244
Dept Affiliation: GENOMICS
1 Department of Computer Science and Software Engineering, Concordia University, Montréal, Québec, Canada.
2 College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
3 School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Malaysia.
4 Centre for Structural and Functional Genomics, Concordia University, Montréal, Québec, Canada.

Description:

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

PLoS One. 2020;15(1):e0227683

Authors: Alballa M, Aplop F, Butler G

Abstract

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.

PMID: 31935244 [PubMed - in process]