67. AptmR: A High Throughput Rational Aptamer Truncation Algorithm

Abstract

Aptamer development as both a therapeutic moiety and as a delivery tool is a rapidly growing field, with many aptamers in the clinical pipeline and one FDA-approved aptamer, Macugen. Aptamers are synthetic, single-stranded DNA or RNA oligonucleotides that recognize targets with similar specificity and affinity as antibody/antigen interactions. Current development of new aptamers uses a selection process (SELEX) coupled with high-throughput sequencing (HTS) and bioinformatics, followed by optimization and validation steps. Bioinformatics algorithms such as clustering algorithms have been applied to narrow down the millions of sequences attained by HTS to a handful that can be tested empirically. However, an important optimization step in aptamer development for clinical use is the identification of the minimum sequence required for target binding. This is necessary in order to increase the efficiency and reduce the cost of large-scale chemical synthesis.Herein we report the development of a truncation algorithm designed to identify the minimum aptamer sequence that retains functionality. Using published sequences of full-length aptamers and functional truncations, we first established a set of rules to identify the optimal site for truncation, with an accuracy of ~60% within 1-2 nucleotides and ~90% accuracy within 10 nucleotides. Based on these rules, we then created an algorithm that could be applied to individual aptamer sequences to identify truncations. The algorithm was expanded to analyze HTS Illumina HiSeq reads from multiple rounds of aptamer selection. We next used our previously reported HTS aptamer clustering algorithms (Thiel, et al., PLoS ONE 2012) to identify conserved aptamer truncation domains in a selection. Specifically, truncation sequences were clustered based on sequence similarity (edit distance) or structural similarity (tree distance). Ongoing studies are determining the effects of truncation on aptamer functionality. In future studies, we will use the truncation algorithm to enable the direct identification of shortened aptamer sequences from HTS aptamer data, potentially bypassing the need to empirically test full-length sequences. The truncation algorithm will be integrated into an open-access online aptamer bioinformatics platform that also includes the clustering algorithms. Such tools have the potential to accelerate aptamer development and thereby improve the efficiency of translating aptamers to clinic application.