Identification of Motifs in Aptamers Using MEME Analysis to aid design of Aptasensors

Aptamer-based biosensors (Aptasensors) have gained significant interest over the last decade for higher specificity to their targets, lower cost, and longer shelf life compared to their antibodies-based counterparts. However, not much is known about the binding characteristic of aptamers to their targets, i.e molecular recognition properties as this is a relatively unexplored area of research. Therefore, there is a need to better understand how aptamers bind to their targets because it can help the identification of aptamers for specific targets and in turn, lower the cost of aptamer-based biosensors design. In this work, we performed MEME analysis to detect recurring, fixed length patterns (better known as motifs) in our aptamer database. We focused on previously published aptamers that target different steroids. We also conducted another round of MEME on the four aptamer sequence types combined to further understanding of aptamers. We obtained the most likely motif for each steroid group using two accuracy metrics called E-value and p-value. This lays the foundation for exploring different ways on how aptamers bind to a myriad of targets as well as understanding their binding precision.