This figure depicts different ways that aptamer sequencing data can be analyzed to determine aptamer candidates for binding affinity testing. There are three main methods for analysis that are being covered in the figure: Secondary structure, Clustering, and Motif finding. The information that we can receive from the sequencing data will be one of the final steps in sequencing before the best aptamer candidates can be tested through binding affinity.
Enrichment can show the increased frequency of specific sequencing through multiple rounds of selection.
Motifs are short, recurring patterns that have often indicate sequence specific binding sites for proteins. Motifs can be present in a large amount of the aptamers that shows a significant sequence similarity in the single stranded regions.
There are many secondary structures that can be present in aptamers, which can affect how well they bind to the target molecule. For example, there are hairpins, G-quadruplex, and bulge loops. There is software that exists to predict secondary structure, such as aptaSUITE that will be explained in the methods section. Mfold is a software that is available to predict secondary structures, more information can be found on our research page.
In this paper, the authors discuss how they are integrating microfluidic selection with high-throughput DNA sequencing technology to rapidly discover nucleic acid aptamers. They are using a sequencing method that is tracking the copies and enrichment through the many rounds of selection, which will lead to the identification of high-affinity aptamers. The data is then put through analysis methods, such as clustering and fluorescecne binding affinity to ensure specificity to the target protein.
This paper discusses the use of the identification of aptamers for the thrombin protein, which are then compared using sequencing and motif finding methods. This knowledge has the potential to simplify the selection of aptamers by avoiding multiple rounds of enzymatic transcription and amplificaition.