Acyclic identification of aptamers for human alpha-thrombin using over-represented libraries and deep sequencing

Background: Aptamers are oligonucleotides that bind proteins and other targets with high affinity and selectivity. Twenty years ago elements of natural selection were adapted to in vitro selection in order to distinguish aptamers among randomized sequence libraries. The primary bottleneck in traditional aptamer discovery is multiple cycles of in vitro evolution. Methodology/Principal Findings: We show that over-representation of sequences in aptamer libraries and deep sequencing enables acyclic identification of aptamers. We demonstrated this by isolating a known family of aptamers for human α-thrombin. Aptamers were found within a library containing an average of 56,000 copies of each possible randomized 15mer segment. The high affinity sequences were counted many times above the background in 2-6 million reads. Clustering analysis of sequences with more than 10 counts distinguished two sequence motifs with candidates at high abundance. Motif I contained the previously observed consensus 15mer, Thb1 (46,000 counts), and related variants with mostly G/T substitutions; secondary analysis showed that affinity for thrombin correlated with abundance (K_d = 12 nM for Thb1). The signal-to-noise ratio for this experiment was roughly 10,000:1 for Thb1. Motif II was unrelated to Thb1 with the leading candidate (29,000 counts) being a novel aptamer against hexose sugars in the storage and elution buffers for Concanavilin A (K_d = 0.5 μM for α-methyl-mannoside); ConA was used to immobilize α-thrombin. Conclusions/Significance: Over-representation together with deep sequencing can dramatically shorten the discovery process, distinguish aptamers having a wide range of affinity for the target, allow an exhaustive search of the sequence space within a simplified library, reduce the quantity of the target required, eliminate cycling artifacts, and should allow multiplexing of sequencing experiments and targets.