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Nucleic Acids Research Advance Access published online on September 20, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm699
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© 2007 The Author(s)
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

Thermodynamic instability of siRNA duplex is a prerequisite for dependable prediction of siRNA activities

Masatoshi Ichihara1,2, Yoshiki Murakumo2, Akio Masuda3, Toru Matsuura3, Naoya Asai2, Mayumi Jijiwa2, Maki Ishida2, Jun Shinmi3, Hiroshi Yatsuya4, Shanlou Qiao1, Masahide Takahashi2,5 and Kinji Ohno3,*

1Department of Biomedical Sciences, College of Life and Health Sciences, Chubu University, 1200 Matsumoto, Kasugai 487-8501, 2Department of Pathology, 3Division of Neurogenetics and Bioinformatics, Center for Neurological Diseases and Cancer, 4Department of Public Health/Health Information Dynamics, Field of Social Life Science, Program in Health and Community Medicine and 5Division of Molecular Pathology, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, 65 Tsurumai, Showa-ku, Nagoya 466-8550, Japan

*To whom correspondence should be addressed. Tel: +81 52 744 2446; Fax: +81 52 744 2449; Email: ohnok{at}med.nagoya-u.ac.jp

Received July 27, 2007. Revised August 22, 2007. Accepted August 22, 2007.

We developed a simple algorithm, i-Score (inhibitory-Score), to predict active siRNAs by applying a linear regression model to 2431 siRNAs. Our algorithm is exclusively comprised of nucleotide (nt) preferences at each position, and no other parameters are taken into account. Using a validation dataset comprised of 419 siRNAs, we found that the prediction accuracy of i-Score is as good as those of s-Biopredsi, ThermoComposition21 and DSIR, which employ a neural network model or more parameters in a linear regression model. Reynolds and Katoh also predict active siRNAs efficiently, but the numbers of siRNAs predicted to be active are less than one-eighth of that of i-Score. We additionally found that exclusion of thermostable siRNAs, whose whole stacking energy ({Delta}G) is less than –34.6 kcal/mol, improves the prediction accuracy in i-Score, s-Biopredsi, ThermoComposition21 and DSIR. We also developed a universal target vector, pSELL, with which we can assay an siRNA activity of any sequence in either the sense or antisense direction. We assayed 86 siRNAs in HEK293 cells using pSELL, and validated applicability of i-Score and the whole {Delta}G value in designing siRNAs.


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Z. J. Lu and D. H. Mathews
Fundamental differences in the equilibrium considerations for siRNA and antisense oligodeoxynucleotide design
Nucleic Acids Res., June 1, 2008; 36(11): 3738 - 3745.
[Abstract] [Full Text] [PDF]


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