Computational analysis and optimization of carcinoembryonic antigen aptamers and experimental evaluation.

Carcinoembryonic antigen (CEA), a highly glycosylated protein, overexpresses in many cancers. In this study, computational methods were used to optimize CEA aptamers. Experimental evaluvation of selected aptamers were conducted through electrochemical impedance spectroscopy. After two and three-dimensional structure modeling, the complexes of twelve reported aptamers against CEA were simulated using the ZDOCK server. Based on docking scores, two aptamer sequences (CSR59 and CSR57.1) were selected and used to create a new library. This ssDNA aptamer library consisting of 91 sequences was created using diverse in silico mutational methods. We obtained seventeen sequences having higher binding scores than reported sequences. Based on ZDOCK scores, the interaction domain of CEA, and steric hindrance due to glycosylation, two aptamer sequences (G3S1.5 and G2S2.2) were selected. An impedimetric aptasensor was designed, and selected aptamers were used as biorecognition elements. Resistance to charge transfer (Rct) quantities confirmed the bioinformatic approach and molecular docking scores. The result showed that the interaction ability of selected aptamers was about 13.5 fold higher than the control. It can be concluded that the selected aptamers have good potential for detection of carcinoembryonic antigen biomarker.