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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
Inter-protein residue covariation information unravels physically interacting protein dimers

Author(s): Salmanian S; Pezeshk H; Sadeghi M;

Background: Predicting physical interaction between proteins is one of the greatest challenges in computational biology. There are considerable various protein interactions and a huge number of protein sequences and synthetic peptides with unknown interacti...

Article GUID: 33334319
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
SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models.

Author(s): Reid I, O'Toole N, Zabaneh O, Nourzadeh R, Dahdouli M, Abdellateef M, Gordon PM, Soh J, Butler G, Sensen CW, Tsang A

BMC Bioinformatics. 2014 Jul 01;15:229 Authors: Reid I, O'Toole N, Zabaneh O, Nourzadeh R, Dahdouli M, Abdellateef M, Gordon PM, Soh J, Butler G, Sensen CW, Tsang A

Article GUID: 24980894
Title:Integrative approach for detecting membrane proteins.
Authors:Alballa M,  Butler G
Link:https://www.ncbi.nlm.nih.gov/pubmed/33349234
DOI:10.1186/s12859-020-03891-x
Category:BMC Bioinformatics
PMID:33349234
Dept Affiliation: GENOMICS
1 Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada. m_alball@encs.concordia.ca.
2 College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. m_alball@encs.concordia.ca.
3 Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada.
4 Centre for Structural and Functional Genomics, Concordia University, Montreal, QC, 24105, Canada.

Description:

Integrative approach for detecting membrane proteins.

BMC Bioinformatics. 2020 Dec 21; 21(Suppl 19):575

Authors: Alballa M, Butler G

Abstract

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 proteins, they do not address surface-bound proteins. In this study, we focused on finding the best techniques for distinguishing all types of membrane proteins.

RESULTS: This research first demonstrates the shortcomings of merely using transmembrane topology prediction tools to detect all types of membrane proteins. Then, the performance of various feature extraction techniques in combination with different machine learning algorithms was explored. The experimental results obtained by cross-validation and independent testing suggest that applying an integrative approach that combines the results of transmembrane topology prediction and position-specific scoring matrix (Pse-PSSM) optimized evidence-theoretic k nearest neighbor (OET-KNN) predictors yields the best performance.

CONCLUSION: The integrative approach outperforms the state-of-the-art methods in terms of accuracy and MCC, where the accuracy reached a 92.51% in independent testing, compared to the 89.53% and 79.42% accuracies achieved by the state-of-the-art methods.

PMID: 33349234 [PubMed - in process]