imRNA: Prediction of Immunomodulatory RNAs

imrnapred: Prediction of Immunomodulatory Potential of RNA Sequences Welcome to the official documentation for imrnapred, a computational method developed for predicting the immunomodulatory potential of single-stranded RNA (ssRNA) sequences. This tool is designed to assist researchers in designing non-toxic siRNAs and potent RNA-based vaccine adjuvants by identifying whether an RNA sequence will activate or evade the innate immune system. Web Server: http://crdd.osdd.net/raghava/imornpred/(https://webs.iiitd.edu.in/raghava/imrna) Citation Chaudhary, K., Nagpal, G., Dhanda, S. K., & Raghava, G. P. S. (2016). Prediction of Immunomodulatory potential of an RNA sequence for designing non-toxic siRNAs and RNA-based vaccine adjuvants. Scientific Reports, 6, 20678. https://doi.org/10.1038/srep20678 GitHub:-https://github.com/Manish-IIITD-repository/imRNA About the Platform The innate immune system recognizes foreign RNA through pattern recognition receptors like Toll-like receptors (TLRs). While this effect is desirable for vaccine adjuvants and immunotherapy, it causes unwanted immunotoxicity in siRNA-based therapies. IMORNpred allows for the design of RNA sequences with specific immunomodulatory potentials to suit these different therapeutic needs. Dataset Overview The models were trained and validated on a high-quality dataset: Immunomodulatory Oligoribonucleotides (IMORNs): 602 experimentally verified ssRNA sequences (length 17–27 nucleotides). Non-immunomodulatory Sequences: 520 circulating miRNAs. Key Features Prediction and Design Potency Prediction: Predicts whether an ssRNA sequence is immunomodulatory or non-immunomodulatory. Mutant Generation: Helps users design RNA analogs with altered immunomodulatory effects by suggesting specific nucleotide substitutions. Feature Analysis: Utilizes various features including nucleotide composition, transition, and distribution to achieve high prediction accuracy. Performance Metrics The models were evaluated using five-fold cross-validation and external validation: Maximum Accuracy: Achieved a maximum accuracy of 83.21%. Matthews Correlation Coefficient (MCC): Achieved a maximum MCC of 0.66. Technical Overview IMORNpred leverages diverse RNA features and machine learning algorithms to model the immunomodulatory potential of sequences. Machine Learning: Developed using Support Vector Machines (SVM) and other classifiers like Random Forest and K-Nearest Neighbor. Input Features: Includes simple composition, binary profiles, and hybrid features combining multiple descriptors. Motif Discovery: Analysis of preferred motifs (e.g., "UGU", "GUGU") that contribute significantly to the immunomodulatory nature of RNA. Applications siRNA Therapeutics: Designing "stealth" siRNAs that avoid triggering an innate immune response to prevent toxicity. Vaccine Adjuvants: Identifying potent RNA sequences that can act as adjuvants to enhance the efficacy of vaccines. Immunotherapy: Discovering IMORNs that can be used to specifically modulate the immune system for treating various diseases. Contact & Authors Prof. Gajendra P. S. Raghava Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi), New Delhi, India. Email: [e-mail] / [e-mail] License This resource is open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License, permitting unrestricted use and distribution provided the original work is properly credited.