In silico selection of an aptamer to estrogen receptor alpha using computational docking employing estrogen response elements as aptamer-alike molecules

Ahirwar, Rajesh ; Nahar, Smita ; Aggarwal, Shikha ; Ramachandran, Srinivasan ; Maiti, Souvik ; Nahar, Pradip (2016) In silico selection of an aptamer to estrogen receptor alpha using computational docking employing estrogen response elements as aptamer-alike molecules Scientific Reports, 6 . Article ID 21285, 11 pages. ISSN 2045-2322

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Official URL: http://www.nature.com/articles/srep21285

Related URL: http://dx.doi.org/10.1038/srep21285

Abstract

Aptamers, the chemical-antibody substitute to conventional antibodies, are primarily discovered through SELEX technology involving multi-round selections and enrichment. Circumventing conventional methodology, here we report an in silico selection of aptamers to estrogen receptor alpha (ERα) using RNA analogs of human estrogen response elements (EREs). The inverted repeat nature of ERE and the ability to form stable hairpins were used as criteria to obtain aptamer-alike sequences. Near-native RNA analogs of selected single stranded EREs were modelled and their likelihood to emerge as ERα aptamer was examined using AutoDock Vina, HADDOCK and PatchDock docking. These in silico predictions were validated by measuring the thermodynamic parameters of ERα -RNA interactions using isothermal titration calorimetry. Based on the in silico and in vitro results, we selected a candidate RNA (ERaptR4; 5′-GGGGUCAAGGUGACCCC-3′) having a binding constant (Ka) of 1.02 ± 0.1 × 108 M−1 as an ERα-aptamer. Target-specificity of the selected ERaptR4 aptamer was confirmed through cytochemistry and solid-phase immunoassays. Furthermore, stability analyses identified ERaptR4 resistant to serum and RNase A degradation in presence of ERα. Taken together, an efficient ERα-RNA aptamer is identified using a non-SELEX procedure of aptamer selection. The high-affinity and specificity can be utilized in detection of ERα in breast cancer and related diseases.

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ID Code:103628
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