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Nucleic Acids Research Advance Access published online on May 21, 2009

Nucleic Acids Research, doi:10.1093/nar/gkp408
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Published by Oxford University Press 2009
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


Methods Online

In silico selection of RNA aptamers

Yaroslav Chushak1,* and Morley O. Stone2

1Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702 and 2Human Effectiveness Directorate, 711 Human Performance Wing, Air Force Research Laboratory, Wright-Patterson AFB, OH 45433, USA

*To whom correspondence should be addressed. Tel: +1 937 904 9542; Fax: +1 937 255 1474; Email: yaroslav.chushak{at}wpafb.af.mil

Received February 11, 2009. Revised April 30, 2009. Accepted May 2, 2009.

In vitro selection of RNA aptamers that bind to a specific ligand usually begins with a random pool of RNA sequences. We propose a computational approach for designing a starting pool of RNA sequences for the selection of RNA aptamers for specific analyte binding. Our approach consists of three steps: (i) selection of RNA sequences based on their secondary structure, (ii) generating a library of three-dimensional (3D) structures of RNA molecules and (iii) high-throughput virtual screening of this library to select aptamers with binding affinity to a desired small molecule. We developed a set of criteria that allows one to select a sequence with potential binding affinity from a pool of random sequences and developed a protocol for RNA 3D structure prediction. As verification, we tested the performance of in silico selection on a set of six known aptamer–ligand complexes. The structures of the native sequences for the ligands in the testing set were among the top 5% of the selected structures. The proposed approach reduces the RNA sequences search space by four to five orders of magnitude—significantly accelerating the experimental screening and selection of high-affinity aptamers.


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