Implying Analytic Measures for Unravelling Rheumatoid Arthritis Significant Proteins Through Drug–Target Interaction

Singh, Sachidanand; Vennila, J. Jannet; Snijesh, V. P.; George, Gincy; Sunny, Chinnu

doi:10.1007/s12539-015-0108-9

Cited by 14 publications

(8 citation statements)

References 36 publications

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“…In a recent study, HSP90AA1 was suggested as a pathogenesis‐related protein of RA by network analysis and its functional characterization method , which is first validated in this study by the real‐world clinical subjects that the prevalence of anti‐HSP90AA1 antibodies in patients with RA was higher than that in healthy controls, further proving that network approaches combined with valid functional information interpretation can be a powerful tool to predict new biomarker candidates of diseases.…”

Section: Discussionsupporting

confidence: 52%

Bioinformatics‐Driven New Immune Target Discovery in Disease

et al. 2016

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Biomolecular network analysis has been widely applied in the discovery of cancer driver genes and molecular mechanism anatomization of many diseases on the genetic level. However, the application of such approach in the potential antigen discovery of autoimmune diseases remains largely unexplored. Here, we describe a previously uncharacterized region, with disease-associated autoantigens, to build antigen networks with three bioinformatics tools, namely NetworkAnalyst, GeneMANIA and ToppGene. First, we identified histone H2AX as an antigen of systemic lupus erythematosus by comparing highly ranked genes from all the built network-derived gene lists, and then a new potential biomarker for Behcet's disease, heat shock protein HSP 90-alpha (HSP90AA1), was further screened out. Moreover, 130 confirmed patients were enrolled and a corresponding enzymelinked immunosorbent assay, mass spectrum analysis and immunoprecipitation were performed to further confirm the bioinformatics results with real-world clinical samples in succession. Our findings demonstrate that the combination of multiple molecular network approaches is a promising tool to discover new immune targets in diseases.

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Section: Discussionsupporting

confidence: 52%

Bioinformatics‐Driven New Immune Target Discovery in Disease

et al. 2016

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“…A previous study found that KNG1 overexpression exacerbated oxidative stress and mitochondrial damage ( 62 ). Singh et al ( 63 ) predicted that KNG1 may be the potential target of RA by bioinformatics, which requires further experimental verification. The level of GPNMB is low in normal hepatocytes but higher in hepatocellular carcinoma cells according to research ( 64 , 65 ).…”

Section: Discussionmentioning

confidence: 99%

Analysis of Hepatic Lipid Metabolism and Immune Function During the Development of Collagen-Induced Arthritis

et al. 2022

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The liver is essential for metabolic and immune functions and has been linked to systemic inflammatory diseases. However, the role of the liver is still elusive during the development of rheumatoid arthritis (RA), although there have been indeed some reports. We used label-free quantitative proteomics and experimental verification in this study to reveal the hepatic lipid metabolism and immune function during collagen-induced arthritis (CIA) development. The proteomics results revealed that the role of the liver differs in different phases of CIA rats. In terms of specific performance, hepatic lipid metabolism, which is primarily concerned with cholesterol, triacylglycerol, and phospholipid, was significantly influenced in the CIA induction phase, whereas the immune function, which includes binding of granulocytes, adhesion of immune cells, etc., was affected considerably at the peak phase of CIA rats compared to normal rats. Finally, the hepatic dynamic changes in CIA rats were further confirmed using targeted metabolomics and ELISA. We found that most fatty acids of the liver in the CIA induction phase were significantly decreased, and proteins related to complement activation and migration or adhesion of immune cells including C3, MMP-8, CTSZ, and S100A9 were significantly increased in the liver of CIA rats in the peak phase. Our findings indicated that the lipid metabolism and immune function of the liver were influenced in CIA rats. Thus, the conditions of the liver during RA development should be considered in therapeutic and nutritional interventions.

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“…In recent times, there has been a tremendous increase in the in-silico structure prediction of protein using different software and algorithms. 12 International Journal of Pharmaceutical Investigation, Vol 9, Issue 2, Apr-Jun, 2019 But the accuracy of the structure prediction, the magnitude of errors in the fold assignment, modelling of side chains and loops still need a considerable body shift. Aquaporin-9s are a family of proteins encoded by AQP9 gene in humans.…”

Section: Discussionmentioning

confidence: 99%

Comparative Modeling, Characterization and Energy Minimization Studies of Aquaporin 9: An Exclusive Target Protein for Rheumatoid Arthritis

Das

Johri²,

Sharma

et al. 2019

IJPI

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Background: In the absence of the experimentally determined structure, computer aided protein structure prediction, evaluation and their energetically stable structure identification is the only way out of the problem. The main objective of the study was to perform the structure prediction of Aquaporin 9 (APQ9) the most targeted protein for rheumatoid arthritis, using in-silico methodology and validate the generated models. Methods: Secondary structure prediction of AQP9 was performed using GOR4, SOPMA and CFSSP algorithm. This was followed by the three-dimensional structure identification from MODELLER, LOMETS and MUSTER server. Many models were built and the best amongst them was identified on the basis of their DOPE score. RAMPAGE was used to validate these models and finally the selected model was energetically stabilized. Results: Amongst the 4 predicted models, model predicted using MODELLER software with 1FX8 PDB template (MODELER MODEL 2) was selected as the best. This model showed the best results in Ramachandran plot validation. In the Ramachandran Plot, 223 residues (95.7%) were found to be in the favored region, 9 residues (3.9%) in the Allowed region and the rest 1 residue (0.4%) in the Outlier region. Energy minimization calculations were also done for the four models using SPBDV software and Modeler Model 2 model showed the least energy (E= 3484.038 KJ/mol). Conclusion:The accurate three-dimensional structure prediction of proteins is a grand challenge now. Massive amount of sequence and structural data is available now with low cost. The choice of one or other method depends solely on the type of protein sequence and the quality of the predicted structure. The accurate structure prediction, fold recognition, energy calculation, side chain modeling and target template identification are the crucial edges of the molecular modeling process which need to be scrutinized for the best predicted model.

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Implying Analytic Measures for Unravelling Rheumatoid Arthritis Significant Proteins Through Drug–Target Interaction

Cited by 14 publications

References 36 publications

Bioinformatics‐Driven New Immune Target Discovery in Disease

Bioinformatics‐Driven New Immune Target Discovery in Disease

Analysis of Hepatic Lipid Metabolism and Immune Function During the Development of Collagen-Induced Arthritis

Comparative Modeling, Characterization and Energy Minimization Studies of Aquaporin 9: An Exclusive Target Protein for Rheumatoid Arthritis

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