Implying Analytic Measures for Unravelling Rheumatoid Arthritis Significant Proteins Through Drug-Target Interaction.
Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease that mainly alters the synovial joints and ultimately leads to their destruction. The involvement of the immune system and its related cells is a basic trademark of autoimmune-associated diseases. The present work focuses on network analysis and its functional characterization to predict novel targets for RA. The interactive model called as rheumatoid arthritis drug-target-protein (RA-DTP) is built of 1727 nodes and 7954 edges followed the power-law distribution. RA-DTP comprised of 20 islands, 55 modules and 123 submodules. Good interactome coverage of target-protein was detected in island 2 (Q-Score 0.875) which includes 673 molecules with 20 modules and 68 submodules. The biological landscape of these modules was examined based on the participation molecules in specific cellular localization, molecular function and biological pathway with favourable p value. Functional characterization and pathway analysis through KEGG, Biocarta and Reactome also showed their involvement in relation to the immune system and inflammatory processes and biological processes such as cell signalling and communication, glucosamine metabolic process, renin-angiotensin system, BCR signals, galactose metabolism, MAPK signalling, complement and coagulation system and NGF signalling pathways. Traffic values and centrality parameters were applied as the selection criteria for identifying potential targets from the important hubs which resulted into FOS, KNG1, PTGDS, HSP90AA1, REN, POMC, FCER1G, IL6, ICAM1, SGK1, NOS3 and PLA2G4A. This approach provides an insight into experimental validation of these associations of potential targets for clinical value to find their effect on animal studies.
Department of Bioinformatics, School of Biotechnology and Health Sciences, Karunya University, Coimbatore, India. sachidanand@karunya.edu.|Department of Biotechnology, School of Biotechnology and Health Sciences, Karunya University, Coimbatore, India. sachidanand@karunya.edu.
Sachidanand
S
Singh
Department of Bioinformatics, School of Biotechnology and Health Sciences, Karunya University, Coimbatore, India.|Department of Biotechnology, School of Biotechnology and Health Sciences, Karunya University, Coimbatore, India.
J Jannet
JJ
Vennila
Department of Bioinformatics, School of Biotechnology and Health Sciences, Karunya University, Coimbatore, India.|Department of Biotechnology, School of Biotechnology and Health Sciences, Karunya University, Coimbatore, India.
V P
VP
Snijesh
Department of Bioinformatics, School of Biotechnology and Health Sciences, Karunya University, Coimbatore, India.|Department of Biotechnology, School of Biotechnology and Health Sciences, Karunya University, Coimbatore, India.
Gincy
G
George
Department of Bioinformatics, School of Biotechnology and Health Sciences, Karunya University, Coimbatore, India.|Department of Biotechnology, School of Biotechnology and Health Sciences, Karunya University, Coimbatore, India.
Chinnu
C
Sunny
D000818:Animals; D001172:Arthritis, Rheumatoid / Q000188:drug therapy* / Q000276:immunology / Q000378:metabolism*; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D011485:Protein Binding
Centrality parameters; Network analysis; Rheumatoid arthritis; Traffic value