Keyword search (4,164 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:Analytical and computational approaches to define the Aspergillus niger secretome.
Authors:Tsang A,  Butler G,  Powlowski J,  Panisko EA,  Baker SE
Link:https://www.ncbi.nlm.nih.gov/pubmed/19618504?dopt=Abstract
DOI:10.1016/j.fgb.2008.07.014
Publication:Fungal genetics and biology : FG & B
Keywords:
PMID:19618504 Category:Fungal Genet Biol Date Added:2019-06-20
Dept Affiliation: BIOLOGY
1 Centre for Structural and Functional Genomics, Concordia University, 7141 Sherbrooke West, Montréal, Québec, Canada H4B 1R6, Department of Biology, Concordia University, Montréal, Québec, Canada. tsang@gene.concordia.ca

Description:

Analytical and computational approaches to define the Aspergillus niger secretome.

Fungal Genet Biol. 2009 Mar;46 Suppl 1:S153-S160

Authors: Tsang A, Butler G, Powlowski J, Panisko EA, Baker SE

Abstract

We used computational and mass spectrometric approaches to characterize the Aspergillus niger secretome.The 11,200 gene models predicted in the genome of A. niger strain ATCC 1015 were the data source for the analysis. Depending on the computational methods used, 691 to 881 proteins were predicted to be secreted proteins. We cultured A. niger in six different media and analyzed the extracellular proteins produced using mass spectrometry. A total of 222 proteins were identified, with 39 proteins expressed under all six conditions and 74 proteins expressed under only one condition. The secreted proteins identified by mass spectrometry were used to guide the correction of about 20 gene models. Additional analysis focused on extracellular enzymes of interest for biomass processing. Of the 63 glycoside hydrolases predicted to be capable of hydrolyzing cellulose, hemicellulose or pectin, 94% of the exo-acting enzymes and only 18% of the endo-acting enzymes were experimentally detected.

PMID: 19618504 [PubMed - indexed for MEDLINE]




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