Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/22573
Title: Identifying glycan motifs using a novel subtree mining approach.
Authors: Coff, Lachlan;Chan, Jeffrey;Ramsland, Paul A;Guy, Andrew J
Affiliation: Department of Surgery, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia
School of Science, College of Science, Engineering and Health, RMIT University, 3000, Melbourne, Australia
Department of Immunology, Monash University, 3004, Melbourne, Australia
School of Science, College of Science, Engineering and Health, RMIT University, 3000, Melbourne, Australia.
Issue Date: 4-Feb-2020
EDate: 2020
Citation: BMC bioinformatics 2020; 21(1): 42
Abstract: Glycans are complex sugar chains, crucial to many biological processes. By participating in binding interactions with proteins, glycans often play key roles in host-pathogen interactions. The specificities of glycan-binding proteins, such as lectins and antibodies, are governed by motifs within larger glycan structures, and improved characterisations of these determinants would aid research into human diseases. Identification of motifs has previously been approached as a frequent subtree mining problem, and we extend these approaches with a glycan notation that allows recognition of terminal motifs. In this work, we customised a frequent subtree mining approach by altering the glycan notation to include information on terminal connections. This allows specific identification of terminal residues as potential motifs, better capturing the complexity of glycan-binding interactions. We achieved this by including additional nodes in a graph representation of the glycan structure to indicate the presence or absence of a linkage at particular backbone carbon positions. Combining this frequent subtree mining approach with a state-of-the-art feature selection algorithm termed minimum-redundancy, maximum-relevance (mRMR), we have generated a classification pipeline that is trained on data from a glycan microarray. When applied to a set of commonly used lectins, the identified motifs were consistent with known binding determinants. Furthermore, logistic regression classifiers trained using these motifs performed well across most lectins examined, with a median AUC value of 0.89. We present here a new subtree mining approach for the classification of glycan binding and identification of potential binding motifs. The Carbohydrate Classification Accounting for Restricted Linkages (CCARL) method will assist in the interpretation of glycan microarray experiments and will aid in the discovery of novel binding motifs for further experimental characterisation.
URI: http://ahro.austin.org.au/austinjspui/handle/1/22573
DOI: 10.1186/s12859-020-3374-4
PubMed URL: 32019496
Type: Journal Article
Subjects: Carbohydrate
Frequent subtree mining
Glycan
Glycobiology
Machine learning
Microarray
Motif
Appears in Collections:Journal articles

Files in This Item:
There are no files associated with this item.


Items in AHRO are protected by copyright, with all rights reserved, unless otherwise indicated.