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

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...

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Main Authors: Gillian V Kupakuwana, James E Crill, Mark P McPike, Philip N Borer
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2011-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3098231?pdf=render
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spelling doaj-7935eb9998704ca0b46a47f8beac835b2020-11-25T02:31:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-0165e1939510.1371/journal.pone.0019395Acyclic identification of aptamers for human alpha-thrombin using over-represented libraries and deep sequencing.Gillian V KupakuwanaJames E CrillMark P McPikePhilip N BorerAptamers 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.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.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.http://europepmc.org/articles/PMC3098231?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Gillian V Kupakuwana
James E Crill
Mark P McPike
Philip N Borer
spellingShingle Gillian V Kupakuwana
James E Crill
Mark P McPike
Philip N Borer
Acyclic identification of aptamers for human alpha-thrombin using over-represented libraries and deep sequencing.
PLoS ONE
author_facet Gillian V Kupakuwana
James E Crill
Mark P McPike
Philip N Borer
author_sort Gillian V Kupakuwana
title Acyclic identification of aptamers for human alpha-thrombin using over-represented libraries and deep sequencing.
title_short Acyclic identification of aptamers for human alpha-thrombin using over-represented libraries and deep sequencing.
title_full Acyclic identification of aptamers for human alpha-thrombin using over-represented libraries and deep sequencing.
title_fullStr Acyclic identification of aptamers for human alpha-thrombin using over-represented libraries and deep sequencing.
title_full_unstemmed Acyclic identification of aptamers for human alpha-thrombin using over-represented libraries and deep sequencing.
title_sort acyclic identification of aptamers for human alpha-thrombin using over-represented libraries and deep sequencing.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2011-01-01
description 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.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.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.
url http://europepmc.org/articles/PMC3098231?pdf=render
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