In Silico Screening of Aptamers Configuration against Hepatitis B Surface Antigen

Prediction of aptamer affinity using an artificial intelligence approach

Aptamers are oligonucleotide sequences that can connect to particular target molecules, similar to monoclonal antibodies. They can be chosen by systematic evolution of ligands by exponential enrichment (SELEX), and are modifiable and can be synthesized. Even if the SELEX approach has been improved a lot, it is frequently challenging and time-consuming to identify aptamers experimentally. In particular, structure-based methods are the most used in computer-aided design and development of aptamers. For this purpose, numerous web-based platforms have been suggested for the purpose of forecasting the secondary structure and 3D configurations of RNAs and DNAs. Also, molecular docking and molecular dynamics (MD), which are commonly utilized in protein compound selection by structural information, are suitable for aptamer selection. On the other hand, from a large number of sequences, artificial intelligence (AI) may be able to quickly discover the possible aptamer candidates. Conversely, sophisticated machine and deep-learning (DL) models have demonstrated efficacy in forecasting the binding properties between ligands and targets during drug discovery; as such, they may provide a reliable and precise method for forecasting the binding of aptamers to targets. This research looks at advancements in AI pipelines and strategies for aptamer binding ability prediction, such as machine and deep learning, as well as structure-based approaches, molecular dynamics and molecular docking simulation methods.

Analysis of aptamer-target binding and molecular mechanisms by thermofluorimetric analysis and molecular dynamics simulation

Introduction: Aptamers are valuable for bioassays, but aptamer-target binding is susceptible to reaction conditions. In this study, we combined thermofluorimetric analysis (TFA) and molecular dynamics (MD) simulations to optimize aptamer-target binding, explore underlying mechanisms and select preferred aptamer. Methods: Alpha-fetoprotein (AFP) aptamer AP273 (as the model) was incubated with AFP under various experimental conditions, and melting curves were measured in a real-time PCR system to select the optimal binding conditions. The intermolecular interactions of AP273-AFP were analysed by MD simulations with these conditions to reveal the underlying mechanisms. A comparative study between AP273 and control aptamer AP-L3-4 was performed to validate the value of combined TFA and MD simulation in selecting preferred aptamers. Results: The optimal aptamer concentration and buffer system were easily determined from the dF/dT peak characteristics and the melting temperature (Tm) values on the melting curves of related TFA experiments, respectively. A high Tm value was found in TFA experiments performed in buffer systems with low metal ion strength. The molecular docking and MD simulation analyses revealed the underlying mechanisms of the TFA results, i.e., the binding force and stability of AP273 to AFP were affected by the number of binding sites, frequency and distance of hydrogen bonds, and binding free energies; these factors varied in different buffer and metal ion conditions. The comparative study showed that AP273 was superior to the homologous aptamer AP-L3-4. Conclusion: Combining TFA and MD simulation is efficient for optimizing the reaction conditions, exploring underlying mechanisms, and selecting aptamers in aptamer-target bioassays.

Design and Prediction of Aptamers Assisted by In Silico Methods

An aptamer is a single-stranded DNA or RNA that binds to a specific target with high binding affinity. Aptamers are developed through the process of systematic evolution of ligands by exponential enrichment (SELEX), which is repeated to increase the binding power and specificity. However, the SELEX process is time-consuming, and the characterization of aptamer candidates selected through it requires additional effort. Here, we describe in silico methods in order to suggest the most efficient way to develop aptamers and minimize the laborious effort required to screen and optimise aptamers. We investigated several methods for the estimation of aptamer-target molecule binding through conformational structure prediction, molecular docking, and molecular dynamic simulation. In addition, examples of machine learning and deep learning technologies used to predict the binding of targets and ligands in the development of new drugs are introduced. This review will be helpful in the development and application of in silico aptamer screening and characterization.

In silico approach to target PI3K/Akt/mTOR axis by selected Olea europaea phenols in PIK3CA mutant colorectal cancer

Worldwide disease burden of colorectal cancer (CRC) increasing alarmingly, but a suitable therapeutic strategy is not available yet. Abnormal activation of the PI3K/Akt/mTOR signalling because of mutation in the PIK3CA gene is a driving force behind CRC development. Therefore, this study aimed to comprehensively characterise the potential of phenolic compounds from Olea europaea against the PI3K/Akt/mTOR axis by using in silico methodologies. Molecular docking was utilised to study key interactions between phenolic compounds of O. europaea and target proteins PI3K, Akt, mTOR with reference to known inhibitor of target. Drug likeness and ADME/T properties of selected phenols were explored by online tools. Dynamic properties and binding free energy of target-ligand interactions were studied by molecular dynamic simulation and MM-PBSA method respectively. Molecular docking revealed apigenin, luteolin, pinoresinol, oleuropein, and oleuropein aglycone as the top five phenolic compounds which showed comparable/better binding affinity than the known inhibitor of the respective target protein. Drug likeness and ADME/T properties were employed to select the top three phenols namely, apigenin, luteolin, and pinoresinol which shown to bind stably to the catalytic cleft of target proteins as confirmed by molecular dynamics simulations. Therefore, Apigenin, luteolin, and pinoresinol have the potential to be used as the non-toxic alternative to synthetic chemical inhibitors generally used in CRC treatment as they can target PI3K/Akt/mTOR axis. Particularly, pinoresinol showed great potential as dual PI3K/mTOR inhibitor. However, this study needs to be complemented with future in vitro and in vivo studies to provide an alternative way of CRC treatment. Communicated by Ramaswamy H. Sarma.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Enhancing Electrochemical Biosensor Performance for 17β-Estradiol Determination with Short Split-Aptamers

Chronic exposure of 17β-estradiol (E2) even at low concentration can disorganize the endocrine system and lead to undesirable health problems in the long run. An electrochemical biosensor for rapid detection of E2 in water samples was successfully developed. The biosensor was based on split DNA aptamers attached onto poly (methacrylic acid-co-n butyl acrylate-succinimide) microspheres deposited on polypyrrole nanowires coated electrode (PPY/PMAA-NBA). The sandwich paired of split DNA aptamers used were truncated from 75 mer parent aptamers. These two strands of 12-mer and 14-mer split DNA aptamers were then immobilized on the PMAA-NBA microspheres. In the presence of E2, the split DNA aptamers formed an apt12-E2-apt14 complex, where the binding reaction on the electrode surface led to the detection of E2 by differential pulse voltammetry using ferrocyanide as a redox indicator. Under optimum conditions, the aptasensor detected E2 concentrations in the range of 1 × 10-4 M to 1 × 10-12 M (R2 = 0.9772) with a detection limit of 4.8 × 10-13 M. E2, which were successfully measured in a real sample with 97-104% recovery and showed a good correlation (R2 = 0.9999) with the established method, such as high-performance liquid chromatography. Interactions between short and sandwich-type aptamers (split aptamers) demonstrated improvement in aptasensor performance, especially the selectivity towards several potential interferents.

A Novel ssDNA Aptamer Targeting Carcinoembryonic Antigen: Selection and Characterization

One of the major causes of a drastically shorter life expectancy and one of the most prevalent diseases in the world today is cancer. Given the data on the rise in cancer cases throughout the world, it is obvious that, despite the diagnostic techniques currently being used, there is a pressing need to develop precise and sensitive techniques for early diagnosis of the disease. A high degree of affinity and specificity towards particular targets is maintained by the short nucleic acid molecules known as aptamers. Aptamers outperform antibodies due to their unique benefits, such as their simplicity in synthesis and modification, lack of toxicity, and long-term stability. Utilizing an accurate recognition element and a robust signal transduction mechanism, molecular diagnostics can be extremely sensitive and specific. In this study, development of new single-stranded DNA aptamers against CEA for use in cancer diagnostics was accomplished using SELEX and NGS methods. As a result of 12 iterative SELEX rounds, nine aptamer candidates against CEA were developed. NGS comparative analysis revealed that round twelve had an enriched number of aptamers that were specifically bound, as opposed to round eight. Among the selected nine sequences characterized by bioinformatics analysis and ELONA, an aptamer sequence with the highest specificity and affinity for the target protein was identified and further examined. Aptamer sequence (6) was screened in a concentration-dependent assay, specificity analysis was performed, and its potential secondary and tertiary structures were predicted, which enabled us to test one of the possible putative interactions with CEA. Finally, aptamer sequence (6) labelled with a Cy5 fluorescent tag was used in confocal microscopy to observe its binding towards the CEA expressed in HT-29 human colon adenocarcinoma cell line.

Identification and structural analysis of novel malathion-specific DNA aptameric sensors designed for food testing

Malathion is an organophosphate chemical (OPC) and a toxic contaminant that adversely impacts food quality, human health, biodiversity, and the environment. Due to its small size and unavailability of sensitive sensors, detection of malathion remains a challenging task. Often chromatographic methods employed to analyze OPCs suffer from several shortcomings, including cost, immobility, laboriousness, and unsuitability for point-of-care settings. Hence, developing a specific and sensitive diagnostic sensor for quick and inexpensive food testing is essential. We discovered four unique malathion-specific ssDNA aptamers; designed two independent sensing strategies using fluorescence labeling and Thioflavin T (ThT) displacement. Selected aptamers formed the G4-quadruplex-like (G4Q) structure, which helped develop a label-free detection approach with a 2.01 ppb limit of detection. Additionally, 3D structures of aptamers were generated and validated using a series of computational modeling programs. Furthermore, we explored structural features using CD spectroscopy and molecular docking, probing ligands' binding mode, and revealed vital intermolecular interactions with aptamers. Subsequently, the novel sensors were optimized to detect malathion from food samples. The novel sensors could be further developed to meet the demands of sensing and quantifying toxic contaminants from real food samples in field conditions.

In-Silico Selection of Aptamer Targeting SARS-CoV-2 Spike Protein

Aptamers are single-stranded, short DNA or RNA oligonucleotides that can specifically bind to various target molecules. To diagnose the infected cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in time, numerous conventional methods are applied for viral detection via the amplification and quantification of DNA or antibodies specific to antigens on the virus. Herein, we generated a large number of mutated aptamer sequences, derived from a known sequence of receptor-binding domain (RBD)-1C aptamer, specific to the RBD of SARS-CoV-2 spike protein (S protein). Structural similarity, molecular docking, and molecular dynamics (MD) were utilized to screen aptamers and characterize the detailed interactions between the selected aptamers and the S protein. We identified two mutated aptamers, namely, RBD-1CM1 and RBD-1CM2, which presented better docking results against the S protein compared with the RBD-1C aptamer. Through the MD simulation, we further confirmed that the RBD-1CM1 aptamer can form the most stable complex with the S protein based on the number of hydrogen bonds formed between the two biomolecules. Based on the experimental data of quartz crystal microbalance (QCM), the RBD-1CM1 aptamer could produce larger signals in mass change and exhibit an improved binding affinity to the S protein. Therefore, the RBD-1CM1 aptamer, which was selected from 1431 mutants, was the best potential candidate for the detection of SARS-CoV-2. The RBD-1CM1 aptamer can be an alternative biological element for the development of SARS-CoV-2 diagnostic testing.

Computational Screening for the Anticancer Potential of Seed-Derived Antioxidant Peptides: A Cheminformatic Approach

Some seed-derived antioxidant peptides are known to regulate cellular modulators of ROS production, including those proposed to be promising targets of anticancer therapy. Nevertheless, research in this direction is relatively slow owing to the inevitable time-consuming nature of wet-lab experimentations. To help expedite such explorations, we performed structure-based virtual screening on seed-derived antioxidant peptides in the literature for anticancer potential. The ability of the peptides to interact with myeloperoxidase, xanthine oxidase, Keap1, and p47^phox was examined. We generated a virtual library of 677 peptides based on a database and literature search. Screening for anticancer potential, non-toxicity, non-allergenicity, non-hemolyticity narrowed down the collection to five candidates. Molecular docking found LYSPH as the most promising in targeting myeloperoxidase, xanthine oxidase, and Keap1, whereas PSYLNTPLL was the best candidate to bind stably to key residues in p47^phox. Stability of the four peptide-target complexes was supported by molecular dynamics simulation. LYSPH and PSYLNTPLL were predicted to have cell- and blood-brain barrier penetrating potential, although intolerant to gastrointestinal digestion. Computational alanine scanning found tyrosine residues in both peptides as crucial to stable binding to the targets. Overall, LYSPH and PSYLNTPLL are two potential anticancer peptides that deserve deeper exploration in future.

Identification and Affinity Determination of Protein-Antibody and Protein-Aptamer Epitopes by Biosensor-Mass Spectrometry Combination

Analytical methods for molecular characterization of diagnostic or therapeutic targets have recently gained high interest. This review summarizes the combination of mass spectrometry and surface plasmon resonance (SPR) biosensor analysis for identification and affinity determination of protein interactions with antibodies and DNA-aptamers. The binding constant (KD) of a protein-antibody complex is first determined by immobilizing an antibody or DNA-aptamer on an SPR chip. A proteolytic peptide mixture is then applied to the chip, and following removal of unbound material by washing, the epitope(s) peptide(s) are eluted and identified by MALDI-MS. The SPR-MS combination was applied to a wide range of affinity pairs. Distinct epitope peptides were identified for the cardiac biomarker myoglobin (MG) both from monoclonal and polyclonal antibodies, and binding constants determined for equine and human MG provided molecular assessment of cross immunoreactivities. Mass spectrometric epitope identifications were obtained for linear, as well as for assembled ("conformational") antibody epitopes, e.g., for the polypeptide chemokine Interleukin-8. Immobilization using protein G substantially improved surface fixation and antibody stabilities for epitope identification and affinity determination. Moreover, epitopes were successfully determined for polyclonal antibodies from biological material, such as from patient antisera upon enzyme replacement therapy of lysosomal diseases. The SPR-MS combination was also successfully applied to identify linear and assembled epitopes for DNA-aptamer interaction complexes of the tumor diagnostic protein C-Met. In summary, the SPR-MS combination has been established as a powerful molecular tool for identification of protein interaction epitopes.

Search for RNA aptamers against non-structural protein of SARS-CoV-2: Design using molecular dynamics approach

Background

Recent outbreak of deadly Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) urges the scientist to identify the potential vaccine or drug to control the disease. SARS-CoV-2 with its single stranded RNA genome (length ~ 30 kb) is enveloped with active spike proteins. The genome is non-segmental with 5'-cap and 3'-poly tail and acts as a mRNA for the synthesis of replicase polyproteins. The replicase gene lying downstream to 5'-end encodes for non-structural protein, which in turn pose multiple functions ranging from envelope to nucleocapsid development. This study aims to identify the highly stable, effective and less toxic single strand RNA-based aptamers against non-structural protein 10 (NSP10). NSP10 is the significant activator of methyltransferase enzymes (NSP14 and NSP16) in SARS-CoV-2. Inhibiting the activation of methyltransferase leads to partial viral RNA capping or lack of capping, which makes the virus particles susceptible to host defence system.

Results

In this study, we focused on designing RNA aptamers through computational approach, docking of protein-aptamer followed by molecular dynamics simulation to perceive the binding stability of complex. Docking study reveals the high binding affinity of three aptamers namely RNA-053, 001, 010 to NSP10 with the HADDOCK score of - 88.5 ± 7.0, - 87.7 ± 11.5, - 86.1 ± 12 respectively. Molecular Dynamics suggests high conformational stability between the aptamer and the protein. Among the screened aptamers two aptamers maintained at least 3-4 intermolecular H-bonds throughout the simulation period.

Conclusions

The study identifies the potential aptamer candidate against less investigated but significant antiviral target i.e., NSP10/NSP16 interface complex.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43088-021-00152-5.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Determination of minimal sequence for zearalenone aptamer by computational docking and application on an indirect competitive electrochemical aptasensor

Aptamers are short single-stranded oligonucleotides (either DNA or RNA) that can fold into well-defined three-dimensional (3D) spatial structures which enable them to capture their specific target by complementary shape interactions. Aptamers are selected from large random libraries through the SELEX process and only a small fraction of the sequence is involved in direct docking with the target. In this paper, we describe the possible truncation variants of zearalenone (ZEA) aptamer which might be an effective binding region for the target. The originally selected zearalenone (ZEA) aptamer was 80-mer in length and shown to bind the target with a high affinity (K_d = 41 ± 5 nM). Herein, computational docking simulation was performed with 15 truncated variants to determine the predicted binding energy and responsible binding site of the aptamer-analyte complex. The results revealed that 5 truncated variants had binding energy lower than - 7.0 kcal/mol. Circular dichroism analysis was performed on the shortlisted aptamer and the conformational change of aptamers was observed with the presence of an analyte. Aptamer Z3IN (29-mer) was chosen as the most enhanced affinity for its target with a dissociation constant of 11.77 ± 1.44 nM. The aptamer was further applied in the electrochemical aptasensor of ZEA based on an indirect competitive format. The results demonstrated that the truncated aptamer leads to an enhancement of the sensitivity of the biosensor.

Artificial Intelligence in Aptamer-Target Binding Prediction

Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer-target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer-target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.

In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm

Aptamers can be regarded as efficient substitutes for monoclonal antibodies in many diagnostic and therapeutic applications. Due to the tedious and prohibitive nature of SELEX (systematic evolution of ligands by exponential enrichment), the in silico methods have been developed to improve the enrichment processes rate. However, the majority of these methods did not show any effort in designing novel aptamers. Moreover, some target proteins may have not any binding RNA candidates in nature and a reductive mechanism is needed to generate novel aptamer pools among enormous possible combinations of nucleotide acids to be examined in vitro. We have applied a genetic algorithm (GA) with an embedded binding predictor fitness function to in silico design of RNA aptamers. As a case study of this research, all steps were accomplished to generate an aptamer pool against aminopeptidase N (CD13) biomarker. First, the model was developed based on sequential and structural features of known RNA-protein complexes. Then, utilizing RNA sequences involved in complexes with positive prediction results, as the first-generation, novel aptamers were designed and top-ranked sequences were selected. A 76-mer aptamer was identified with the highest fitness value with a 3 to 6 time higher score than parent oligonucleotides. The reliability of obtained sequences was confirmed utilizing docking and molecular dynamic simulation. The proposed method provides an important simplified contribution to the oligonucleotide-aptamer design process. Also, it can be an underlying ground to design novel aptamers against a wide range of biomarkers.

Methods and Applications of In Silico Aptamer Design and Modeling

Aptamers are nucleic acid analogues of antibodies with high affinity to different targets, such as cells, viruses, proteins, inorganic materials, and coenzymes. Empirical approaches allow the design of in vitro aptamers that bind particularly to a target molecule with high affinity and selectivity. Theoretical methods allow significant expansion of the possibilities of aptamer design. In this study, we review theoretical and joint theoretical-experimental studies dedicated to aptamer design and modeling. We consider aptamers with different targets, such as proteins, antibiotics, organophosphates, nucleobases, amino acids, and drugs. During nucleic acid modeling and in silico design, a full set of in silico methods can be applied, such as docking, molecular dynamics (MD), and statistical analysis. The typical modeling workflow starts with structure prediction. Then, docking of target and aptamer is performed. Next, MD simulations are performed, which allows for an evaluation of the stability of aptamer/ligand complexes and determination of the binding energies with higher accuracy. Then, aptamer/ligand interactions are analyzed, and mutations of studied aptamers made. Subsequently, the whole procedure of molecular modeling can be reiterated. Thus, the interactions between aptamers and their ligands are complex and difficult to understand using only experimental approaches. Docking and MD are irreplaceable when aptamers are studied in silico.

In silico molecular docking in DNA aptamer development

Aptamers are single-stranded DNA or RNA oligonucleotides generated by SELEX that exhibit binding affinity and specificity against a wide variety of target molecules. Compared to RNA aptamers, DNA aptamers are much more stable and therefore are widely adopted in a number of applications especially in diagnostics. The tediousness and rigor associated with certain steps of the SELEX intensify the efforts to adopt in silico molecular docking approaches together with in vitro SELEX procedures in developing DNA aptamers. Inspired by these endeavors, we carry out an overview of the in silico molecular docking approaches in DNA aptamer generation, by detailing the stepwise procedures as well as shedding some light on the various softwares used. The in silico maturation strategy and the limitations of the in silico approaches are also underscored.

Designing of RNA aptamer against DNA binding domain of the glucocorticoid receptor: A response element-based in-silico approach

Virtual screening, a conventional in-silico approach to design an RNA aptamer against target proteins require huge RNA library containing 10¹⁰ to 10¹⁵ combination of RNA oligomers and high-performance computing systems. However, in the case of nuclear receptor proteins, screening can be narrowed down by using response element sequences rather than random RNA oligomer library. In this study, we used a novel method to design RNA aptamer against the DNA binding domain of the glucocorticoid receptor α (GRα). GRα plays a vital role in cancer metastasis such as colon, cervical and breast cancer by activating the S100A8 calcium-binding protein, which makes it a potential drug target for those cancers. We started the screening of 24 RNA aptamers (16 nucleotides long), all of which are glucocorticoid response elements (GRE) of S100A8. Among the aptamers screened, Apt-2, Apt-5, Apt-6 and Apt-15 are found to be most suitable by molecular docking and dynamic studies. The stability and compactness of the aptamer-protein complexes were assessed by GROMACS. The binding energies were rescored using the MM-PBSA method, which were -3679.581, -3690.892, -8246.052 and -3412.802 KJ/mol, respectively for Apt-2, Apt- 5, Apt-6 and Apt-15. The designed RNA aptamer may directly bind to the DNA binding domain of GR and prevent the trans-activation of the S100A8 gene by blocking the binding of GR to its response element. Thus, this novel approach of design the response elements-based RNA aptamer against GRα like nuclear receptor proteins will help to generate target-specific RNA aptamers with minimal efforts and cost.Communicated by Ramaswamy H. Sarma.