Thioredoxin system: a model for determining novel lead molecules for breast cancer chemotherapy.

Background: Thioredoxin reductase 1 (TXNRD1) and thioredoxin interacting protein (TXNIP) also known as thioredoxin binding protein 2 or vitamin D3-upregulated protein 1 are key players in oxidative stress control. Thioredoxin (TRX) is one of the major components of the thiol reducing system and plays multiple roles in cellular processes. Computational analyses of TXNRD1, TXNIP and TRX expressions have not been analyzed in relation to prognosis of breast cancer. High expression of TXNRD1 and low expression of TXNIP are associated with worst prognosis in breast cancer.

Methods: Using bioinformatics applications we studied sequence analysis, molecular modeling, template and fold recognition, docking and scoring of thioredoxin as a target.

Results: The resultant model obtained was validated based on the templates from I-TASSER server and binding site residues were predicted. The predicted model was used for Threading and Fold recognition and was optimized using GROMACS. The generated model was validated using programs such as Procheck, Ramachandran plot, verify-3d and Errat value from Saves server, and the results show that the model is reliable. Next we obtained small molecules from pubchem and chembank which are databases for selecting suitable ligands for our modeled target. These molecules were screened for docking, using GOLD and scoring was obtained using Chemscore as a scoring function.

Conclusion: This study predicted the ligand interaction of four molecules with the minimized protein modeled structure and the best ligand with top scores from about 500 molecules screened. These were 3-hydroxy-2,3-diphenylbutanoic acid, 4-amino-3-pentadecylphenol, 3-(hydroxyimino)-2,4-diphenylbutanenitrile and 2-ethyl-1,2-diphenylbutyl carbamate, which are proposed as possible hit molecules for the drug discovery and development process.