Dataset Information

The dystrobrevin-binding protein 1 gene: features and networks.

ABSTRACT: The dystrobrevin-binding protein 1 (DTNBP1) gene has been one of the most studied and promising schizophrenia susceptibility genes since it was first reported to be associated with schizophrenia in the Irish Study of High Density Schizophrenia Families (ISHDSF). Although many studies have been performed both at the functional level and in association with psychiatric disorders, there has been no systematic review of the features of the DTNBP1 gene, protein or the relationship between function and phenotype. Using a bioinformatics approach, we identified the DTNBP1 gene in 13 vertebrate species. The comparison of these genes revealed a conserved gene structure, protein-coding sequence and dysbindin domain, but a diverse noncoding sequence. The molecular evolutionary analysis suggests the DTNBP1 gene probably originated in chordates and matured in vertebrates. No signature of recent positive selection was seen in any primate lineage. The DTNBP1 gene likely has many more alternative transcripts than the current three major isoforms annotated in the NCBI database. Our examination of risk haplotypes revealed that, although the frequency of a single nucleotide polymorphism (SNP) or haplotype might be significantly different in cases from controls, difference between major geographic populations was even larger. Finally, we constructed the first DTNBP1 interactome and explored its network features. Besides the biogenesis of lysosome-related organelles complex 1 and dystrophin-associated protein complex, several molecules in the DTNBP1 network likely provide insight into the role of DTNBP1 in biological systems: retinoic acid, beta-estradiol, calmodulin and tumour necrosis factor. Studies of these subnetworks and pathways may provide opportunities to deepen our understanding of the mechanisms of action of DTNBP1 variants.

SUBMITTER: Guo AY

PROVIDER: S-EPMC2859304 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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The dystrobrevin-binding protein 1 gene: features and networks.

Guo A Y AY Sun J J Riley B P BP Thiselton D L DL Kendler K S KS Zhao Z Z

Molecular psychiatry 20080729 1

The dystrobrevin-binding protein 1 (DTNBP1) gene has been one of the most studied and promising schizophrenia susceptibility genes since it was first reported to be associated with schizophrenia in the Irish Study of High Density Schizophrenia Families (ISHDSF). Although many studies have been performed both at the functional level and in association with psychiatric disorders, there has been no systematic review of the features of the DTNBP1 gene, protein or the relationship between function an ...[more]

PMID: 18663367

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Project description:Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.

Dataset Information

The dystrobrevin-binding protein 1 gene: features and networks.

Publications

The dystrobrevin-binding protein 1 gene: features and networks.

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