JBPC  Vol.4 No.2 , May 2013
Selection of highly efficient small interference RNA (SiRNA) targeting mammalian genes
Abstract: RNAi is the method of silencing the expression of targeted genes. RNAi applications include gene function analysis and target validation. Designing highly efficient small interference RNA (siRNA) sequence with maximum target specificity for mammalian RNAi is one of important topics in recent years. In this work, a statistical analysis of the information for a large number (3734) of siRNA presented in the database available on the internet is done. This is to improve the design of efficient siRNA molecules. The (3734) siRNAs are classified according to their efficiency to three groups (high efficient, moderate efficient and low efficient). Thirteen properties (positional and thermodynamics) are identified in the high efficient group in the primary statistical study. In the final statistical study, the average weight of each identified property is calculated. A very good linear correlation was found between the average percentage efficiency and the weighted score of siRNA properties. It is found that the most important feature of highly efficient siRNA is the difference in binding energy between the 5’ end and the 3’ end of the anti-sense strand. The (RISC) activation step is a critical step in RNAi process where the efficiency of this process depends on the instability of the 5’ end of the anti-sense strand.
Cite this paper: El-lakkani, A. , Elgawad, W. and Sayed, E. (2013) Selection of highly efficient small interference RNA (SiRNA) targeting mammalian genes. Journal of Biophysical Chemistry, 4, 72-79. doi: 10.4236/jbpc.2013.42010.

[1]   Elbashir, S.M., Harborth, J., Lendeckel, W., Yalcin, A., Weber, K. and Tuschl, T. (2001) Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells. Nature, 411, 494-498. doi:10.1038/35078107

[2]   Martinez, J., Patkaniowska, A., Urlaub, H. Lührmann, R. and Tuschl, T. (2002) Single-stranded antisense siRNAs guide target RNA cleavage in RNAi. Cell, 110, 563-574. doi:10.1016/S0092-8674(02)00908-X

[3]   Hannon, G.J. and Rossi, J.J. (2004) Unlocking the potential of the human genome with RNA interference. Nature, 431, 371-378. doi:10.1038/nature02870

[4]   Hsieh, A.C., Bo, R., Manola, J., Vazquez, F., Bare, O., Khvorova, A., Scaringe, S. and Sellers, W.R. (2004) A library of siRNA duplexes targeting the phosphoinositide 3-kinase pathway: Determinants of gene silencing for use in cell-based screens. Nucleic Acids Research, 32, 893-901. doi:10.1093/nar/gkh238

[5]   Huesken, D., Lange, J., Mickanin, C., Weiler, J., Asselbergs, F., Warner, J., Meloon, B., Engel, S., Rosenberg, A., Cohen, D., Labow, M., Reinhardt, M., Natt, F. and Hall, J. (2005) Design of a genome-wide siRNA library using an artificial neural network. Nature Biotechnology, 23, 995-1001. doi:10.1038/nbt1005-1315a

[6]   Jagla, B., Aulner, N., Kelly, P.D., Song, D., Volchuk, A., Zatorski, A., Shum, D., Mayer, T., De Angelis, D.A., Ouerfelli, O., Rutishauser, U. and Rothman, J.E. (2005) Sequence characteristics of functional siRNAs. RNA, 11, 864-872. doi:10.1261/rna.7275905

[7]   Katoh, T. and Suzuki, T. (2007) Specific residues at every third position of siRNA shape its efficient RNAi activity. Nucleic Acids Research, 35, 27-40. doi:10.1093/nar/gkl1120

[8]   Khvorova, A., Reynolds, A. and Jayasena, S.D. (2003) Functional siRNAs and miRNAs exhibit strand bias. Cell, 115, 209-216. doi:10.1016/S0092-8674(03)00801-8

[9]   Shabalina, S. A., Spiridonov, A.N. and Ogurtsov, A.Y. (2006) Computational models with thermodynamic and composition features improve siRNA design. BMC Bioinformatics, 7, 65-80. doi:10.1186/1471-2105-7-65

[10]   Shah, J.K., Garner, H.R., White, M.A., Shames, D.S. and Minna, J.D. (2007) sIR: siRNA information resource, a web-based tool for siRNA sequence design and analysis and an open access siRNA database. BMC Bioinformatics, 8, 178. doi:10.1186/1471-2105-8-178

[11]   Reynolds, A., Leake, D., Boese, Q., Scaringe, S., Marshall, W.S. and Khvorova, A. (2004) Rational siRNA design for RNA interference. Nature Biotechnology, 22, 326-330. doi:10.1038/nbt936

[12]   Ui-Tei, K., Naito, Y., Takahashi, F., Haraguchi, T., OhkiHamazaki, H., Juni, A., Ueda, R. and Saigo, K. (2004) Guidelines for the selection ofhighly effective siRNA sequences for mammalian and chick RNA interference. Nucleic Acids Research, 32, 936-948. doi:10.1093/nar/gkh247

[13]   Chalk, A.M., Wahlestedt, C. and Sonnhammer, E.L. (2004) Improved and automated prediction of effective siRNA. Biochemicaland Biophysical Research Communications, 319, 264-274. doi:10.1016/j.bbrc.2004.04.181

[14]   Matveeva, O., Nechipurenko, Y., Rossi, L., Moore, B., Saetrom, P., Ogurtsov, A.Y., Atkins, J.F. and Naito, Y., Yamada, T., Ui-Tei, K., Morishita, S. and Saigo, K. (2004) siDi- rect: Highly effective, target-specific siRNA design software for mammalian RNA interference. Nucleic Acids Research, 32, W124-W129. doi:10.1093/nar/gkh442

[15]   Tilesi, F., Fradiani, P., Socci, V., Willems, D. and Ascenzioni, F. (2009) Design and validation of siRNAs and shRNAs. Molecular Therapeutics, 11, 156-164.