In-silico Functional and Structural Annotation of Rheumatoid arthritis linked Gene and Protein

Main Article Content

Fazal Shan
Awais Rahat
Arbaz Khan
Muhammad Khalid Khan
Sajid Ali
Mehik Ikram


Background: Rheumatoid arthritis (RA) is an autoimmune disease marked by chronic inflammation and immune system dysregulation. The Ankyrin repeat domain 55 (ANKRD55) gene, located on chromosome 5q11.2, has been associated with RA susceptibility. Understanding the gene and protein characteristics could illuminate pathways involved in RA and aid in the development of targeted therapies.

Objective: To conduct a detailed analysis of the ANKRD55 gene and protein, focusing on its structure, expression profile, functional domains, and polymorphisms, thereby establishing its role in RA and potential as a therapeutic target.

Methods: Using bioinformatics tools, we analyzed ANKRD55's genomic localization (NCBI), peptide sequences (Uniprot), physicochemical properties (Protparam Server), signal peptides (SignalP v4.1), transmembrane helices (TMHMM), and glycosylation sites (NetNGlyc 1.0). Polymorphism and methylation patterns were examined using PolyPhen and MethyCancer, respectively. Splice variants were identified, and protein-protein interaction networks were assessed (STRING database). Protein domains were characterized, including the prediction of RhoGAP domains. Structural modeling was conducted to identify potential drug-binding pockets with the DoGSite Scorer server.

Results: ANKRD55's expression was predominantly observed in the testis (RPKM 1.8) and to a lesser extent in lymph nodes (0.8), appendix (0.6), and brain (0.2). It encodes a 614-amino-acid protein with a molecular weight of approximately 68.4 kDa and a pI of 6.72. Six splice variants were identified, enriching the understanding of its potential isoform diversity. No significant N-glycosylation sites were predicted, and methylation analysis suggested a nuclear localization without DNA methylation sites. Structural analysis revealed drug-binding pockets with volumes ranging from 233.35 to 5389.58 A^3, with drug scores between 0.78 and 0.82.

Conclusion: The study concludes that while several factors contribute to student satisfaction in public sector medical colleges, infrastructure and facilities, along with quality instructional materials and clinical exposure, are key drivers. Enhancing these areas could lead to a more positive educational experience for students.

Article Details

How to Cite
Shan, F., Rahat, A., Khan , A., Khan , M. K., Ali , S., & Ikram, M. (2024). In-silico Functional and Structural Annotation of Rheumatoid arthritis linked Gene and Protein. Journal of Health and Rehabilitation Research, 4(1), 1261–1267.
Author Biographies

Fazal Shan, Khyber Medical University Dir Lower

Department of Medical Lab Technology, Institute of Health Sciences, Khyber Medical University Dir Lower

Awais Rahat, Khyber Medical University

Department of Medical lab technology, Khyber Medical University

Arbaz Khan , Khyber Medical University

Department of Medical Lab Technology, Khyber Medical University

Muhammad Khalid Khan , Khyber Medical University

Department of Medical Lab Technology, Khyber Medical University

Sajid Ali , Khyber Medical University Dir Lower

Department of Medical lab technology, Institute of Health Sciences, Khyber Medical University Dir Lower

Mehik Ikram, Primer Institute of Health Sciences Peshawar

Department of Medical lab technology, Primer Institute of Health Sciences Peshawar


Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014 Feb;506(7488).

Chang M, Rowland CM, Garcia VE, Schrodi SJ, Catanese JJ, van der Helm-van Mil AH, et al. A large-scale rheumatoid arthritis genetic study identifies association at chromosome 9q33.2. PLoS Genet. 2008 Jun;4(6):e1000107.

Kurreeman FAS, Padyukov L, Marques RB, Schrodi SJ, Seddighzadeh M, Stoeken-Rijsbergen G, et al. A candidate gene approach identifies the TRAF1/C5 region as a risk factor for rheumatoid arthritis. PLoS Med. 2007 Sep;4(9):e278.

Raychaudhuri S, Sandor C, Stahl EA, Freudenberg J, Lee H-S, Jia X, et al. Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat Genet. 2012 Mar;44(3):291-6.

Li J, Mahajan A, Tsai M-D. Ankyrin repeat: a unique motif mediating protein-protein interactions. Biochemistry. 2006 Dec;45(51):15168-78.

McWilliam H, Li W, Uludag M, Squizzato S, Park YM, Buso N, et al. Analysis tool web services from the EMBL-EBI. Nucleic Acids Res. 2013 Jul;41(W1):W597-W600.

Garg VK, Avashthi H, Tiwari A, Jain PA, Ramkete PW, Kayastha AM, et al. MFPPI–Multi FASTA ProtParam Interface. Bioinformation. 2016;12(2):74-7.

Emanuelsson O, Brunak S, von Heijne G, Nielsen H. Locating proteins in the cell using TargetP, SignalP and related tools. Nat Protoc. 2007;2(4):953-71.

He X, Chang S, Zhang J, Zhao Q, Xiang H, Kusonmano K, et al. MethyCancer: the database of human DNA methylation and cancer. Nucleic Acids Res. 2008 Jan;36(suppl_1):D836-D41.

Julenius K. NetCGlyc 1.0: prediction of mammalian C-mannosylation sites. Glycobiology. 2007 Aug;17(8):868-76.

Gupta R, Jung E, Brunak S. Prediction of N-glycosylation sites in human proteins. In Preparation 2004.

Steentoft C, Vakhrushev SY, Joshi HJ, Kong Y, Vester-Christensen MB, Schjoldager KTB, et al. Precision mapping of the human O-GalNAc glycoproteome through SimpleCell technology. EMBO J. 2013 May;32(10):1478-88.

Blom N, Sicheritz-Pontén T, Gupta R, Gammeltoft S, Brunak S. Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics. 2004 Jun;4(6):1633-49.

Duckert P, Brunak S, Blom N. Prediction of proprotein convertase cleavage sites. Protein Eng Des Sel. 2004 Jan;17(1):107-12.

Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic acids research. 2010;39(suppl_1):D561-D8.

Bashir M, Mateen W, Khurshid S, Malik JM, Agha Z, Khan F, Ajmal M, Ali SH. A common missense variant rs874881 of PADI4 gene and rheumatoid arthritis: Genetic association study and in-silico analysis. Gene. 2023 Feb 20;854:147123.

Deshpande SH, Bagewadi ZK, Khan TY, Mahnashi MH, Shaikh IA, Alshehery S, Khan AA, Patil VS, Roy S. Exploring the potential of phytocompounds for targeting epigenetic mechanisms in rheumatoid arthritis: An in silico study using similarity indexing. Molecules. 2023 Mar 7;28(6):2430.

Nandi A, Das A, Dey YN, Roy KK. The abundant phytocannabinoids in rheumatoid arthritis: Therapeutic targets and molecular processes identified using integrated bioinformatics and network pharmacology. Life. 2023 Mar 5;13(3):700.

Singh N, Rai MK, Agarwal V, Agarwal V. In Silico Prediction of NOS2, NOS3 and Arginase 1 Genes Targeting by Micro RNAs Upregulated in Systemic Sclerosis. Indian Journal of Rheumatology. 2024 Feb 23:09733698241229803.

Liu B, Fu X, Du Y, Feng Z, Liu X, Li Z, Yu F, Zhou G, Ba Y. In silico analysis of ferroptosis-related genes and its implication in drug prediction against fluorosis. International Journal of Molecular Sciences. 2023 Feb 20;24(4):4221.