Nagpal, Gandharva and Chaudhary, Kumardeep and Dhanda, Sandeep Kumar and Raghava, G.P.S. (2017) Computational Prediction of the Immunomodulatory Potential of RNA Sequences. Methods Molecular Biology, 1632. pp. 75-90. ISSN 1064-3745
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Advances in the knowledge of various roles played by non-coding RNAs have stimulated the application of RNA molecules as therapeutics. Among these molecules, miRNA, siRNA, and CRISPR-Cas9 associated gRNA have been identified as the most potent RNA molecule classes with diverse therapeutic applications. One of the major limitations of RNA-based therapeutics is immunotoxicity of RNA molecules as it may induce the innate immune system. In contrast, RNA molecules that are potent immunostimulators are strong candidates for use in vaccine adjuvants. Thus, it is important to understand the immunotoxic or immunostimulatory potential of these RNA molecules. The experimental techniques for determining immunostimulatory potential of siRNAs are time- and resource-consuming. To overcome this limitation, recently our group has developed a web-based server "imRNA" for predicting the immunomodulatory potential of RNA sequences. This server integrates a number of modules that allow users to perform various tasks including (1) generation of RNA analogs with reduced immunotoxicity, (2) identification of highly immunostimulatory regions in RNA sequence, and (3) virtual screening. This server may also assist users in the identification of minimum mutations required in a given RNA sequence to minimize its immunomodulatory potential that is required for designing RNA-based therapeutics. Besides, the server can be used for designing RNA-based vaccine adjuvants as it may assist users in the identification of mutations required for increasing immunomodulatory potential of a given RNA sequence. In summary, this chapter describes major applications of the "imRNA" server in designing RNA-based therapeutics and vaccine adjuvants (http://www.imtech.res.in/raghava/imrna/).
| Item Type: | Article |
|---|---|
| Additional Information: | Copyright of this article belongs to Springer |
| Uncontrolled Keywords: | Adjuvant; Immunomodulatory RNA; Machine learning; Prediction; RNA immunotoxicity; SVM; TLR 7; imRNA |
| Subjects: | Q Science > QR Microbiology |
| Depositing User: | Dr. K.P.S.Sengar |
| Date Deposited: | 27 Mar 2018 10:32 |
| Last Modified: | 27 Mar 2018 10:32 |
| URI: | http://crdd.osdd.net/open/id/eprint/2029 |
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