A text feature-based approach for literature mining of lncRNA–protein interactions

Background: Tremor is one of the most noticeable features, which occurs during the early stages of Parkinson’s disease (PD). It is one of the major pathological hallmarks and does not have any interpreted mechanism. In this study we have framed a hypothesis and deciphered protein-protein interactions between the proteins involved in impairment in sodium and calcium ion channels and thus cause synaptic plasticity leading to a tremor. Methods: Literature mining for retrieval of proteins was done using Science Direct, PubMed Central, SciELO and JSTOR databases. A well thought approach was used and a list of differentially expressed proteins in PD was collected from different sources. A total of 71 proteins were retrieved and a protein interaction network was constructed between them by using Cytoscape.v.3.7. The network was further analysed using BiNGO plugin for retrieval of overrepresented biological processes in Tremor-PD datasets. Hub nodes were also generated in the network. Results: The Tremor-PD pathway was deciphered which demonstrates the cascade of protein interactions that might lead to tremors in PD. Major proteins involved were LRRK2, TUBA1A, TRAF6, HSPA5, ADORA2A, DRD1, DRD2, SNCA, ADCY5, TH etc. Conclusion: In the current study it is predicted that ADORA2A and DRD1/DRD2 are equally contributing to the progression of disease by inhibiting the activity of adenylyl cyclase and thereby increases the permeability of the blood brain barrier causing an influx of neurotransmitters and together they alter the level of dopamine in the brain which eventually leads to tremor.

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LCK as a Potential Therapeutic Target for Acute Rejection after Kidney Transplantation: A Bioinformatics Clue

Journal of Immunology Research ◽

10.1155/2018/6451298 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Linpei Jia ◽

Rufu Jia ◽

Yinping Li ◽

Xiaoxia Li ◽

Qiang Jia ◽

...

Keyword(s):

Kidney Transplantation ◽

T Cell ◽

Acute Rejection ◽

T Cell Receptor ◽

Protein Interactions ◽

Therapeutic Target ◽

Cell Receptor ◽

Functional Enrichment ◽

Literature Mining ◽

Potential Therapeutic Target

Objectives. We aim to identify the key biomarker of acute rejection (AR) after kidney transplantation via bioinformatics methods. Methods. The gene expression data GSE75693 of 30 samples with stable kidney transplantation recipients and 15 AR samples were downloaded and analyzed by the limma package to identify differentially expressed genes (DEGs). Then, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were done to explore the biological functions and potential important pathways of DEGs. Finally, protein-protein interactions (PPIs) and literature mining were applied to construct the cocitation network and to select the hub protein. Results. A total of 437 upregulated genes and 353 downregulated genes were selected according to P<0.01 and log2fold change>1.0. DEGs of AR are mainly located on membranes and impact the activation of receptors in immune responses. In the PPI network, Src kinase, lymphocyte kinase (LCK), CD3G, B2M, interferon-γ, CD3D, tumor necrosis factor, VAV1, and CD3E in the T cell receptor signaling pathway were selected as important factors, and LCK was identified as the hub protein. Conclusion. LCK, via acting on T-cell receptor, might be a potential therapeutic target for AR after kidney transplantation.

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Feature‐based classification of native and non‐native protein–protein interactions: Comparing supervised and semi‐supervised learning approaches

PROTEOMICS ◽

10.1002/pmic.201100217 ◽

2011 ◽

Vol 11 (22) ◽

pp. 4321-4330 ◽

Cited By ~ 8

Author(s):

Nan Zhao ◽

Bin Pang ◽

Chi‐Ren Shyu ◽

Dmitry Korkin

Keyword(s):

Supervised Learning ◽

Protein Interactions ◽

Learning Approaches ◽

Native Protein ◽

Protein Protein Interactions ◽

Feature Based ◽

Feature Based Classification

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Deep Proteome Profiling Enabled Functional Annotation and Data-Independent Quantification of Proline Hydroxylation Targets

10.1101/2022.01.12.474993 ◽

2022 ◽

Author(s):

Yao Gong ◽

Gaurav Behera ◽

Luke Erber ◽

Ang Luo ◽

Yue Chen

Keyword(s):

Protein Interactions ◽

Critical Role ◽

Network Connectivity ◽

Integrated Analysis ◽

Literature Mining ◽

Structure Stability ◽

Proteome Profiling ◽

Specific Distribution ◽

Functional Features ◽

Proline Hydroxylation

Proline hydroxylation (Hyp) regulates protein structure, stability and protein-protein interaction and is widely involved in diverse metabolic and physiological pathways in cells and diseases. To reveal functional features of the proline hydroxylation proteome, we integrated various data sources for deep proteome profiling of proline hydroxylation proteome in human and developed HypDB (https://www.HypDB.site), an annotated database and web server for proline hydroxylation proteome. HypDB provides site-specific evidence of modification based on extensive LC-MS analysis and literature mining with 15319 non-redundant Hyp sites and 8226 sites with high confidence on human proteins. Annotation analysis revealed significant enrichment of proline hydroxylation on key functional domains and tissue-specific distribution of Hyp abundance across 26 types of human organs and fluids and 6 cell lines. The network connectivity analysis further revealed a critical role of proline hydroxylation in mediating protein-protein interactions. Moreover, the spectral library generated by HypDB enabled data-independent analysis (DIA) of clinical tissues and the identification of novel Hyp biomarkers in lung cancer and kidney cancer. Taken together, our integrated analysis of human proteome with publicly accessible HypDB revealed functional diversity of Hyp substrates and provides a quantitative data source to characterize proline hydroxylation in pathways and diseases.

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