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Identifying glycan motifs using a novel subtree mining approach.

ABSTRACT: BACKGROUND: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. RESULTS: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. CONCLUSIONS: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.

SUBMITTER: Coff L

PROVIDER: S-EPMC7001330 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Identifying glycan motifs using a novel subtree mining approach.

Coff Lachlan L Chan Jeffrey J Ramsland Paul A PA Guy Andrew J AJ

BMC bioinformatics 20200204 1

<h4>Background</h4>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 frequ ...[more]

PMID: 32019496

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Project description:Carbohydrates, or glycans, are one of the most abundant and structurally diverse biopolymers constitute the third major class of biomolecules, following DNA and proteins. However, the study of carbohydrate sugar chains has lagged behind compared to that of DNA and proteins, mainly due to their inherent structural complexity. However, their analysis is important because they serve various important roles in biological processes, including signaling transduction and cellular recognition. In order to glean some light into glycan function based on carbohydrate structure, kernel methods have been developed in the past, in particular to extract potential glycan biomarkers by classifying glycan structures found in different tissue samples. The recently developed weighted qgram method (LK-method) exhibits good performance on glycan structure classification while having limitations in feature selection. That is, it was unable to extract biologically meaningful features from the data. Therefore, we propose a biochemicallyweighted tree kernel (BioLK-method) which is based on a glycan similarity matrix and also incorporates biochemical information of individual q-grams in constructing the kernel matrix. We further applied our new method for the classification and recognition of motifs on publicly available glycan data. Our novel tree kernel (BioLK-method) using a Support Vector Machine (SVM) is capable of detecting biologically important motifs accurately while LK-method failed to do so. It was tested on three glycan data sets from the Consortium for Functional Glycomics (CFG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) GLYCAN and showed that the results are consistent with the literature. The newly developed BioLK-method also maintains comparable classification performance with the LK-method. Our results obtained here indicate that the incorporation of biochemical information of q-grams further shows the flexibility and capability of the novel kernel in feature extraction, which may aid in the prediction of glycan biomarkers.

Project description:ObjectiveIrritability, which is strongly associated with impairment and negative outcomes, is a common reason for referral to mental health services but is a nosological and treatment challenge. A major issue is how irritability should be conceptualized. The authors used a developmental approach to test the hypothesis that there are several forms of irritability, including a "neurodevelopmental/ADHD-like" type, with onset in childhood, and a "depression/mood" type, with onset in adolescence.MethodsData were analyzed from the Avon Longitudinal Study of Parents and Children, a prospective U.K. population-based cohort. Irritability trajectory classes were estimated for 7,924 individuals with data at multiple time points across childhood and adolescence (four possible time points from approximately ages 7 to 15). Psychiatric diagnoses were assessed at approximately ages 7 and 15. Psychiatric genetic risk was indexed by polygenic risk scores (PRSs) for attention deficit hyperactivity disorder (ADHD) and depression, derived using large genome-wide association study results.ResultsFive irritability trajectory classes were identified: low (81.2%), decreasing (5.6%), increasing (5.5%), late-childhood limited (5.2%), and high-persistent (2.4%). The early-onset high-persistent trajectory was associated with male preponderance, childhood ADHD (odds ratio=108.64, 95% CI=57.45-204.41), and ADHD PRS (odds ratio=1.31, 95% CI=1.09-1.58). The adolescent-onset increasing trajectory was associated with female preponderance, adolescent depression (odds ratio=5.14, 95% CI=2.47-10.73), and depression PRS (odds ratio=1.20, 95% CI=1.05-1.38). Both the early-onset high-persistent and adolescent-onset increasing trajectory classes were associated with adolescent depression diagnosis and ADHD PRS.ConclusionsThe developmental context of irritability may be important in its conceptualization: early-onset persistent irritability may be more neurodevelopmental/ADHD-like and later-onset irritability more depression/mood-like. These findings have implications for treatment as well as nosology.

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Identifying glycan motifs using a novel subtree mining approach.

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Identifying glycan motifs using a novel subtree mining approach.

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