ArticleScientific reports2016
Prediction of Immunomodulatory potential of an RNA sequence for designing non-toxic siRNAs and RNA-based vaccine adjuvants.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 26 citations in OpenAlex.
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- Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants.Journal of translational medicine · 2018Article
- Prediction of Antimicrobial Potential of a Chemically Modified Peptide From Its Tertiary Structure.Frontiers in microbiology · 2018Article
- PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.Frontiers in microbiology · 2018Article
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Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our innate immune system recognizes a foreign RNA sequence of a pathogen and activates the immune system to eliminate the pathogen from our body. This immunomodulatory potential of RNA can be used to design RNA-based immunotherapy and vaccine adjuvants. In case of siRNA-based therapy, the immunomodulatory effect of an RNA sequence is unwanted as it may cause immunotoxicity. Thus, we developed a method for designing a single-stranded RNA (ssRNA) sequence with desired immunomodulatory potentials, for designing RNA-based therapeutics, immunotherapy and vaccine adjuvants. The dataset used for training and testing our models consists of 602 experimentally verified immunomodulatory oligoribonucleotides (IMORNs) that are ssRNA sequences of length 17 to 27 nucleotides and 520 circulating miRNAs as non-immunomodulatory sequences. We developed prediction models using various features that include composition-based features, binary profile, selected features, and hybrid features. All models were evaluated using five-fold cross-validation and external validation techniques; achieving a maximum mean Matthews Correlation Coefficient (MCC) of 0.86 with 93% accuracy. We identified motifs using MERCI software and observed the abundance of adenine (A) in motifs. Based on the above study, we developed a web server, imRNA, comprising of various modules important for designing RNA-based therapeutics (http://crdd.osdd.net/raghava/imrna/).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.