Keyword search (4,163 papers available)

"Butler G" Authored Publications:

Title Authors PubMed ID
1 Ion channel classification through machine learning and protein language model embeddings Ghazikhani H; Butler G; 39572876
ENCS
2 SPOT: A machine learning model that predicts specific substrates for transport proteins Kroll A; Niebuhr N; Butler G; Lercher MJ; 39325691
ENCS
3 Comparative genomic analysis of thermophilic fungi reveals convergent evolutionary adaptations and gene losses Steindorff AS; Aguilar-Pontes MV; Robinson AJ; Andreopoulos B; LaButti K; Kuo A; Mondo S; Riley R; Otillar R; Haridas S; Lipzen A; Grimwood J; Schmutz J; Clum A; Reid ID; Moisan MC; Butler G; Nguyen TTM; Dewar K; Conant G; Drula E; Henrissat B; Hansel C; Singer S; Hutchinson MI; de Vries RP; Natvig DO; Powell AJ; Tsang A; Grigoriev IV; 39266695
CSFG
4 Exploiting protein language models for the precise classification of ion channels and ion transporters Ghazikhani H; Butler G; 38656743
CSFG
5 Enhanced identification of membrane transport proteins: a hybrid approach combining ProtBERT-BFD and convolutional neural networks Ghazikhani H; Butler G; 37497772
ENCS
6 Integrative approach for detecting membrane proteins. Alballa M, Butler G 33349234
CSFG
7 BENIN: Biologically enhanced network inference. Wonkap SK, Butler G 32698722
ENCS
8 TooT-T: discrimination of transport proteins from non-transport proteins. Alballa M, Butler G 32321420
CSFG
9 TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information. Alballa M, Aplop F, Butler G 31935244
CSFG
10 Analytical and computational approaches to define the Aspergillus niger secretome. Tsang A, Butler G, Powlowski J, Panisko EA, Baker SE 19618504
BIOLOGY
11 SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models. Reid I, O'Toole N, Zabaneh O, Nourzadeh R, Dahdouli M, Abdellateef M, Gordon PM, Soh J, Butler G, Sensen CW, Tsang A 24980894
CSFG
12 Machine learning for biomedical literature triage. Almeida H, Meurs MJ, Kosseim L, Butler G, Tsang A 25551575
CSFG
13 mycoCLAP, the database for characterized lignocellulose-active proteins of fungal origin: resource and text mining curation support. Strasser K, McDonnell E, Nyaga C, Wu M, Wu S, Almeida H, Meurs MJ, Kosseim L, Powlowski J, Butler G, Tsang A 25754864
CSFG
14 An Adaptive Defect Weighted Sampling Algorithm to Design Pseudoknotted RNA Secondary Structures. Zandi K, Butler G, Kharma N 27499762
CSFG

 

Title:TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information.
Authors:Alballa MAplop FButler G
Link:https://www.ncbi.nlm.nih.gov/pubmed/31935244?dopt=Abstract
DOI:10.1371/journal.pone.0227683
Publication:PloS one
Keywords:
PMID:31935244 Category:PLoS One Date Added:2020-01-15
Dept Affiliation: CSFG
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]





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