Aims: To utilize in silico-based approach for investigating the ability of PEGylated rapamycin as a competitive inhibitor to Galectin-3 for curing various diseases or that may provide an attractive strategy for treatment of a wide variety of tumors.Background: Galectin-3 (Gal-3) signaling protein is a unique member of lectin family present at the cell surface, intracellularly in both the nucleus and cytoplasm and extracellularly in the general circulation. Circulating Gal-3 is present in both normal and cancer cells. High levels of circulating Gal-3 have been proven to be associated with inflammation and fibrosis in several acute and chronic conditions, which include neurological degeneration, inflammatory and immune responses, autoimmune diseases, diabetes, heart failure, atherosclerosis, response to infection, wound healing, liver, lung, and kidney disease. Gal-3 is known to regulate many biological activities including cell adhesion, angiogenesis, growth, apoptosis, migration, and metastasis. Rapamycin has been examined alone or in combination with other drugs for treatment of various cancers in clinical studies. Although it has shown promising therapeutic effects, its clinical development was interrupted by poor aqueous solubility and limited preferential distribution. To overcome these limitations, RA has been chemically modified to hydrophilic analogues, such as everolimus (EV). However, all these approaches can only partially increase the solubility, but has little effect on the blood distribution and pharmacokinetics. Therefore, it is necessary to explore other RA conjugates to improve aqueous solubility and tissue distribution profile. Recently we reported that RP-MPEG inhibits the growth of various cancer cell lines by acting on mammalian target of RP (mTOR) receptor site and it can be used for gastric cancer. Objective: To construct various molecular weight RP-MPEG by replacing MPEG chain in 40-O-(2- hydroxyethyl) position of the EV and analyze their binding affinity to Gal-3. Methods: The chemical structures of various molecular weight RP-MPEG were built using ChemSketch software. The molecular docking study was performed to find the best probable structure of RP-MPEG for competitive inhibition of the CRD, based on the interaction score. For that purpose, the 3D structures of RP and EV were obtained from NCBI PubChem compound database, where the structural protein-co-crystallized ligand complex of Gal-3 (TD2, as a native ligand) was retrieved from RCSB Protein Data Bank. All structures of the selected compounds, served as molecules for molecular modeling, were optimized through MOE.2014 software before docking. Hydrogen atoms and partial charges were added to the protein. Protein minimization was performed in gas solvation with the side chains, keeping it rigid and the ligand flexible. The selected site was isolated and minimized, followed by protonating the protein. The 3D ligands were minimized using MMFF94x with cutoffs of 10 to 12 Å. The hydrogens and charges were fixed, and the RMS gradient was set to 0.001 kcal/mol. The docking results were analyzed to identify and assess the binding affinity of these compounds to CRD using drug discovery software. Results: Our results indicated that RP-MPEG with MW 1178.51 g/mol has a logP value of 3.79 and has possessed the strongest binding affinity toward CRD of Gal-3 with a docking score of -6.87. Compared with TD2, there were additional close contacts for RP-MPEG (MW 1178.51 g/mol), coming from three hydrogen bonds with Asp148, Arg162, and Arg144 which suggest that this ligand is a strong competitive inhibitor among the other molecules for Gal-3. Conclusion: RP-MPEG with the MW 1178.51 g/mol could be a promising blocker for various biological action of Gal-3 includes profibrotic activity, modulation of immune responses and inflammatory responses to cancer that contributes to neoplastic transformation, angiogenesis and metastasis. Other: The 95% confidence intervals (CIs) of the binding affinity (according to their mean and standard errors) were estimated with 2.5 and 97.5 percentile as the lower and upper bounds.
[http://dx.doi.org/10.1023/B:GLYC.0000014084.01324.15] [PMID: 14758078]
[http://dx.doi.org/10.2174/156720181105140922124759] [PMID: 25268676]
[http://dx.doi.org/10.1111/j.1747-0285.2011.01283.x] [PMID: 22136701]