Corrigendum to “Design and Evaluation of RNA Aptamer–Mediated Delivery of C/EBPβ siRNA for Oncological Therapy”

Interest in the design and manufacture of RNA and DNA aptamers as apta-biosensors for the early diagnosis of blood infections and other inflammatory conditions has increased considerably in recent years. The practical utility of these aptamers depends on the detailed knowledge about the putative interactions with their target proteins. Therefore, understanding the aptamer-protein interactions at the atomic scale can offer significant insights into the optimal apta-biosensor design. In this study, we consider one RNA and one DNA aptamer that were previously used as apta-biosensors for detecting the infection biomarker protein TNF-α, as an example of a novel computational workflow for selecting the aptamer candidate with the highest binding strength to a target. We combine information from the binding free energy calculations, molecular docking, and molecular dynamics simulations to investigate the interactions of both aptamers with TNF-α. The results reveal that the RNA aptamer has a more stable structure relative to the DNA aptamer. Interaction of aptamers with TNF-α does not have any negative effect on its structure. The results of molecular docking and molecular dynamics simulations suggest that the RNA aptamer has a stronger interaction with the protein. Also, these findings illustrate that basic residues of TNF-α establish more atomic contacts with the aptamers compared to acidic or pH-neutral ones. Furthermore, binding energy calculations show that the interaction of the RNA aptamer with TNF-α is thermodynamically more favorable. In total, the findings of this study indicate that the RNA aptamer is a more suitable candidate for using as an apta-biosensor of TNF-α and, therefore, of greater potential use for the diagnosis of blood infections. Also, this study provides more information about aptamer-protein interactions and increases our understanding of this phenomenon.

RNAs represent a class of programmable biomolecules capable of performing diverse biological functions. Recent studies have developed accurate RNA three-dimensional structure prediction methods, which may enable new RNAs to be designed in a structure-guided manner. Here, we develop a structure-to-sequence deep learning platform for the de novo generative design of RNA aptamers. We show that our approach can design RNA aptamers that are predicted to be structurally similar, yet sequence dissimilar, to known light-up aptamers that fluoresce in the presence of small molecules. We experimentally validate several generated RNA aptamers to have fluorescent activity, show that these aptamers can be optimized for activity in silico, and find that they exhibit a mechanism of fluorescence similar to that of known light-up aptamers. Our results demonstrate how structural predictions can guide the targeted and resource-efficient design of new RNA sequences.

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Potential applications for functional RNAs are rapidly expanding, not only to address functions based on primary nucleotide sequences, but also by RNA aptamer, which can suppress the activity of any target molecule. Aptamers are short DNA or RNA folded molecules that can be selected in vitro on the basis of their high affinity for a target molecule. Here, we demonstrate the ability of RNA aptamers to recognize--and bind to--human IgG with high specificity and affinity. An optimized 23-nucleotide aptamer, Apt8-2, was prepared, and was shown to bind to the Fc domain of human IgG, but not to other IgG's, with high affinity. Apt8-2 was observed to compete with protein A, but not with the Fcgamma receptor, for IgG binding. NMR chemical-shift analyses localized the aptamer-binding sites on the Fc subdomain, which partially overlaps the protein A binding site but not the Fcgamma receptor binding site. The tertiary structures of the predicted recognition sites on the Fc domain differ significantly between human IgG and other species of IgGs; this, in part, accounts for the high specificity of the selected aptamer. Apt8-2 can therefore be used as a protein A alternative for affinity purification of human IgG and therapeutic antibodies. Using Apt8-2 would have several potential advantages, raising the possibility of developing new applications based on aptamer design.

RNA aptamers are single-stranded nucleic acids of 20–100 nucleotides, with high sensitivity and specificity against particular molecular targets. In vitro production and selection of aptamers can be performed using the SELEX method. However, this procedure requires considerable time and cost. In this sense, bioinformatics tools play an important role in reducing the time and cost associated with development and production of aptamers. In this article, we propose bioinformatics strategies for modeling and analysis of the interaction with molecular targets for two RNA aptamers: ATP binding RNA aptamer and iSpinach aptamer. For this purpose, molecular modeling of the tertiary structure of the aptamers was performed with two servers (SimRNA and RNAComposer); and AutoDock Vina and rDock programs were used to dock their respective ligands. The predictions developed with these methods could be used for in silico design of RNA aptamers, through a simple and accessible methodology. Supplemental data for this article is available online at https://doi.org/10.1080/15257770.2021.1951754 .

RNAapt3D: RNA aptamer 3D-structural modeling database

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.

Nucleic acid aptamers hold promise for clinical applications, yet understanding their molecular binding mechanisms to target proteins and efficiently optimizing their binding affinities remain challenging. Here, we present CAAMO (Computer-Aided Aptamer Modeling and Optimization), which integrates in silico aptamer design with experimental validation to accelerate the development of aptamer-based RNA therapeutics. Starting from the sequence information of a reported RNA aptamer, Ta, for the SARS-CoV-2 spike protein, our CAAMO method first determines its binding mode with the spike protein’s receptor binding domain (RBD) through a multi-strategy computational approach. We then optimize its binding affinity via structure-based rational design. Among the six designed candidates, five were experimentally verified and exhibited enhanced binding affinities compared to the original Ta sequence. Furthermore, we directly compared the binding properties of the RNA aptamers to neutralizing antibodies, and found that the designed aptamer TaG34C demonstrated a comparable binding affinity to the RBD compared to all tested neutralizing antibodies. This highlights its potential as an alternative to existing COVID-19 antibodies. Our work provides a robust approach for the efficient design of a relatively large number of high-affinity aptamers with complicated topologies. This approach paves the way for the development of aptamer-based RNA diagnostics and therapeutics.

Nucleic acid aptamers hold promise for clinical applications, yet understanding their molecular binding mechanisms to target proteins and efficiently optimizing their binding affinities remain challenging. Here, we present CAAMO (Computer-Aided Aptamer Modeling and Optimization), which integrates in silico aptamer design with experimental validation to accelerate the development of aptamer-based RNA therapeutics. Starting from the sequence information of a reported RNA aptamer, Ta, for the SARS-CoV-2 spike protein, our CAAMO method first determines its binding mode with the spike protein’s receptor binding domain (RBD) through a multi-strategy computational approach. We then optimize its binding affinity via structure-based rational design. Among the six designed candidates, five were experimentally verified and exhibited enhanced binding affinities compared to the original Ta sequence. Furthermore, we directly compared the binding properties of the RNA aptamers to neutralizing antibodies, and found that the designed aptamer TaG34C demonstrated a comparable binding affinity to the RBD compared to all tested neutralizing antibodies. This highlights its potential as an alternative to existing COVID-19 antibodies. Our work provides a robust approach for the efficient design of a relatively large number of high-affinity aptamers with complicated topologies. This approach paves the way for the development of aptamer-based RNA diagnostics and therapeutics.

The concept of in vitro selection of nucleic acid aptamers emerged 25 years ago, and since then tremendous progress has been achieved in the development of different aptamers and their applications for various bioanalytical and therapeutic purposes. Among other protein targets of aptamers, immune system proteins are of particular interest both as diagnostic markers and therapeutic targets. The present review summarizes up-to-date articles concerning the selection and design of DNA and RNA aptamers against immunologic targets such as antibodies, cytokines, and T-cell and B-cell receptors. We also discuss the prospects of employing aptamers as recognizing modules of diagnostic aptasensors, potential therapeutic candidates for the treatment of autoimmune diseases and cancer, and specific tools for functional studies of immune system proteins.

Individual RNA aptamers are often used to modulate the function of their target proteins, and multi-valent aptamers have been constructed to enhance their activity. To expand the utility of aptamers in manipulating and controlling biological processes, here we advance a general method for the design and construction of composite aptamers. The resulting molecular constructs resemble proteins in that they can form specific interactions with three or more different partners and be readily integrated into existing protein regulatory networks. As the first embodiment of this method, we created a tetra-valent aptamer that simultaneously binds to two molecules of the Drosophila protein B52 and two copies of streptavidin, thus mimicking the function of an antibody in immunochemical assays. We demonstrated that the performance of this ‘aptabody’ rivals that of a monoclonal antibody against B52 in these assays. While this study was performed in vitro and the composite aptamer we made was intended to mimic an existing protein, the same method can be used to accommodate arbitrary combinations of individual aptamers in composite molecular contexts, and these constructs can be delivered into living cells, where they are able to utilize existing cellular infrastructure for their production and processing.

Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of RNA secondary structures; however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to RNA secondary structures, such as RNA-protein interactions, has also been enhanced, making a substantial contribution to the field of RNA biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of RNA-small molecule interactions for RNA-targeted drug discovery and in the design of RNA aptamers, where RNA serves as its own ligand. This review will highlight recent trends in the prediction of RNA secondary structure, RNA aptamers and RNA drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of RNA informatics.

Structural characterization and molecular interaction studies of a novel RNA aptamer-MC1R macromolecular system using integrated computational and experimental approaches.

RNA aptamers targeting bacterial repressors show significant promise in synthetic biology. We determined the crystal structure of the SELEX-derived aptamer Dap1 in complex with its target, the GntR/HutC repressor family member DasR. In the complex, two monomers of Dap1 individually sequester the two DNA-binding domains (DBDs) of the dimeric repressor. The DBDs of DasR interact with Dap1 in a highly similar fashion to what is observed in GntR–dsDNA complexes. This also holds true at the level of single amino acids, and identical arginine residues play crucial roles in both RNA and DNA recognition. The structure shows how DasR binding to Dap1 competes with DasR binding to its operator dre sites and, hence, how Dap1 is able to induce DasR-controlled gene transcription in vivo. A structure-informed Dap1 aptamer pool randomized at only four positions allowed for the identification of the novel aptamer Dap1m1 that binds and de-represses the DasR homologous protein NagR instead of DasR. This shows that the complex structure can serve as a blueprint for the design of novel aptamer–protein interaction pairs, opening up new venues for the generation of aptamers directed against other GntR/HutC family members and possibly also against other repressor classes.

Harnessing deep learning to accelerate the development of antibodies and aptamers