Tertiary structure prediction and identification of druggable pocket in the cancer biomarker – Osteopontin-c

Abstract

BackgroundOsteopontin (Eta, secreted sialoprotein 1, opn) is secreted from different cell types including cancer cells. Three splice variant forms namely osteopontin-a, osteopontin-b and osteopontin-c have been identified. The main astonishing feature is that osteopontin-c is found to be elevated in almost all types of cancer cells. This was the vital point to consider it for sequence analysis and structure predictions which provide ample chances for prognostic, therapeutic and preventive cancer research.MethodsOsteopontin-c gene sequence was determined from Breast Cancer sample and was translated to protein sequence. It was then analyzed using various software and web tools for binding pockets, docking and druggability analysis. Due to the lack of homological templates, tertiary structure was predicted using ab-initio method server – I-TASSER and was evaluated after refinement using web tools. Refined structure was compared with known bone sialoprotein electron microscopic structure and docked with CD44 for binding analysis and binding pockets were identified for drug designing.ResultsSignal sequence of about sixteen amino acid residues was identified using signal sequence prediction servers. Due to the absence of known structures of similar proteins, three dimensional structure of osteopontin-c was predicted using I-TASSER server. The predicted structure was refined with the help of SUMMA server and was validated using SAVES server. Molecular dynamic analysis was carried out using GROMACS software. The final model was built and was used for docking with CD44. Druggable pockets were identified using pocket energies.ConclusionsThe tertiary structure of osteopontin-c was predicted successfully using the ab-initio method and the predictions showed that osteopontin-c is of fibrous nature comparable to firbronectin. Docking studies showed the significant similarities of QSAET motif in the interaction of CD44 and osteopontins between the normal and splice variant forms of osteopontins and binding pockets analyses revealed several pockets which paved the way to the identification of a druggable pocket.

Extracellular proteasome-osteopontin circuit regulates cell migration with implications in multiple sclerosis

“…The c-score (value between -5 and 2, where higher c-scores indicate higher confidence in the predicted structure) of the best model predicted by I-TASSER is −3.33, which is comparable to the confidence obtained by predicting the structure of OPN-C37. Pymol is used for graphic visualization (The PyMOL Molecular Graphics System, Version 1.7.4 Schrödinger, LLC).…”

Development of an electrochemical RNA-aptasensor to detect human osteopontin

“…The rhOPN and THR structures are not similar. Nonetheless, the rhOPN contains several conserved domains including THR cleavage domain RSK site, leading to the breakdown of the protein into two fragments (Beausoleil et al, 2011;Sivakumar and Niranjali Devaraj, 2014). Thus, during the process of RNA aptamer selection, the cleavage of OPN might have occurred, and therefore the RNA aptamer could hold a site that is able to recognize THR.…”

PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins

“…During PockDrug model construction, different pocket estimation methods were considered: (i) estimation method guided by the ligand position information corresponds to the extraction of protein atoms localized within two fixed distance thresholds of 4 and 5.5 Å from a binding ligand, respectively known as prox4 and prox5.5; (ii) the two considered pocket estimation methods, not guided by the ligand position, are fpocket ( 14 ) and DoGSite ( 15 ), recently used ( 16 – 19 ) and proposing their own druggability prediction methods: pocket druggability score ( 7 ) and DoGSiteScorer ( 9 ). NRDLD pocket set was estimated using these four different pocket estimation methods.…”