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

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 (ER?) 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 ER? aptamer was examined using AutoDock Vina, HADDOCK and PatchDock docking. These in silico predictions were validated by measuring the thermodynamic parameters of ER? -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?×?10(8) M(-1) as an ER?-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 ER?. Taken together, an efficient ER?-RNA aptamer is identified using a non-SELEX procedure of aptamer selection. The high-affinity and specificity can be utilized in detection of ER? in breast cancer and related diseases.

SUBMITTER: Ahirwar R

PROVIDER: S-EPMC4761961 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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

Ahirwar Rajesh R Nahar Smita S Aggarwal Shikha S Ramachandran Srinivasan S Maiti Souvik S Nahar Pradip P

Scientific reports 20160222

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 RN ...[more]

PMID: 26899418

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Project description:Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals in widespread use that have been shown to be toxic to wildlife and humans. Human serum albumin (HSA) is a known transport protein that binds PFAS at various sites, leading to bioaccumulation and long-term toxicity. In silico tools like quantitative structure-activity relationship (QSAR), read-across, and quantitative read-across structure-property relationship (q-RASPR) are proven techniques for modeling chemical toxicity based on experimental data which can be used to predict the toxicity of untested and new chemicals, while at the same time, help to identify the major features responsible for toxicity. Classification-based and regression-based QSAR models are employed in the present study to predict the binding affinities of 24 PFAS to HSA. Regression-based QSAR models revealed that the packing density index (PDI) and quantitative estimation of drug-likeness (QED) descriptors were both positively correlated with higher binding affinity, while the classification-based QSAR model showed the average connectivity index of order 4 (X4A) descriptor was inversely correlated with binding affinity. Whereas molecular docking studies suggested that PFAS with the highest binding affinity to HSA create hydrogen bonds with Arg348 and salt bridges with Arg348 and Arg485, PFAS with lower binding affinity either showed no interactions with either amino acid or only interactions with Arg348. Among the studied PFAS, perfluoroalkyl acids (PFAA) with large carbon chain length (>C10) have one of the lowest binding affinities, compared to PFAA with carbon chain length ranging from 7 to 9, which showed the highest affinity to HSA. Generalized Read-Across (GenRA) was used to predict toxicity outcomes for the top five highest binding affinity PFAS based on 10 structural analogs for each and found that all are predicted as being chronic to sub-chronically toxic to HSA. The developed in silico models presented in this work can provide a framework for designing PFAS alternatives, screening compounds currently in use, and for the study of PFAS mixture toxicity, which is an area of intense research.

Dataset Information

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

Publications

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

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