An aptamer to the MAP kinase insert region

Therapeutic Potential of Aptamer-Protein Interactions

Aptamers are single-stranded oligonucleotides (RNA or DNA) with a typical length between 25 and 100 nucleotides which fold into three-dimensional structures capable of binding to target molecules. Specific aptamers can be isolated against a large variety of targets through efficient and relatively cheap methods, and they demonstrate target-binding affinities that sometimes surpass those of antibodies. Consequently, interest in aptamers has surged over the past three decades, and their application has shown promise in advancing knowledge in target analysis, designing therapeutic interventions, and bioengineering. With emphasis on their therapeutic applications, aptamers are emerging as a new innovative class of therapeutic agents with promising biochemical and biological properties. Aptamers have the potential of providing a feasible alternative to antibody- and small-molecule-based therapeutics given their binding specificity, stability, low toxicity, and apparent non-immunogenicity. This Review examines the general properties of aptamers and aptamer-protein interactions that help to understand their binding characteristics and make them important therapeutic candidates.

Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach

Oligonucleotide-based aptamers, which have a three-dimensional structure with a single-stranded fragment, feature various characteristics with respect to size, toxicity, and permeability. Accordingly, aptamers are advantageous in terms of diagnosis and treatment and are materials that can be produced through relatively simple experiments. Systematic evolution of ligands by exponential enrichment (SELEX) is one of the most widely used experimental methods for generating aptamers; however, it is highly expensive and time-consuming. To reduce the related costs, recent studies have used in silico approaches, such as aptamer-protein interaction (API) classifiers that use sequence patterns to determine the binding affinity between RNA aptamers and proteins. Some of these methods generate candidate RNA aptamer sequences that bind to a target protein, but they are limited to producing candidates of a specific size. In this study, we present a machine learning approach for selecting candidate sequences of various sizes that have a high binding affinity for a specific sequence of a target protein. We applied the Monte Carlo tree search (MCTS) algorithm for generating the candidate sequences using a score function based on an API classifier. The tree structure that we designed with MCTS enables nucleotide sequence sampling, and the obtained sequences are potential aptamer candidates. We performed a quality assessment using the scores of docking simulations. Our validation datasets revealed that our model showed similar or better docking scores in ZDOCK docking simulations than the known aptamers. We expect that our method, which is size-independent and easy to use, can provide insights into searching for an appropriate aptamer sequence for a target protein during the simulation step of SELEX.

Developing and Characterization of Chemically Modified RNA Aptamers for Targeting Wild Type and Mutated c-KIT Receptor Tyrosine Kinases

The c-KIT receptor represents an attractive target for cancer therapy. Aptamers are emerging as a new promising class of nucleic acid therapeutics. In this study, a conventional SELEX approach was applied against the kinase domain of a group of c-KIT proteins (c-KIT^WT, c-KIT^D816V, and c-KIT^D816H) to select aptamers from a random RNA pool that can bind to the kinase domain of each target with high affinity and can selectively interfere with their kinase activities. Interestingly, our data indicated that one candidate aptamer, called V15, can specifically inhibit the in vitro kinase activity of mutant c-KIT^D816V with an IC₅₀ value that is 9-fold more potent than the sunitinib drug tested under the same conditions. Another aptamer, named as H5/V36, showed the potential to distinguish between the c-KIT kinases by modulating the phosphorylation activity of each in a distinct mechanism of action and in a different potency.

Robotic assisted generation of 2'-deoxy-2'-fluoro-modifed RNA aptamers - High performance enabling strategies in aptamer selection

Aptamer selection is a laborious procedure, requiring expertise and significant resources. These characteristics limit the accessibility of researchers to these molecular tools. We describe a selection procedure, making use of a robotic system that allows the fully automated selection of RNA and 2'deoxy-2'-fluoro pyrimidine RNA aptamers. The platform offers a rapid access to aptamers for basic research and development, therefore opening the path to aptamer-based systemic analysis of proteomes in biological settings.

Aptamers as Valuable Molecular Tools in Neurosciences

Aptamers are short nucleic acids that interact with a variety of targets with high affinity and specificity. They have been shown to inhibit biological functions of cognate target proteins, and they are identifiable by an in vitro selection process, also termed SELEX (Systematic Evolution of Ligands by EXponential enrichment). Being nucleic acids, aptamers can be synthesized chemically or enzymatically. The latter renders RNA aptamers compatible with the cell's own transcription machinery and, thus, expressable inside cells. The synthesis of aptamers by chemical approaches opens up the possibility of producing aptamers on a large scale and enables a straightforward access to introduce modifications in a site-specific manner (e.g., fluorophores or photo-labile groups). These characteristics make aptamers broadly applicable (e.g., as an analytical, diagnostic, or separation tool). In this TechSight, we provide a brief overview on aptamer technology and the potential of aptamers as valuable research tools in neurosciences.