imrna

imrna designs single-stranded RNA (ssRNA) sequences with specified immunomodulatory potential to support development of RNA-based therapeutics, immunotherapy, and vaccine adjuvants.

Key Features:

• Design of immunomodulatory ssRNA sequences: Designs ssRNA sequences predicted to elicit specific immunomodulatory effects.
• siRNA toxicity mitigation: Designs ssRNA sequences to minimize unintended immunostimulatory or immunotoxic effects in siRNA-based therapies.
• Predictive modeling: Predictive models trained using 602 experimentally verified immunomodulatory oligoribonucleotides (IMORNs) and 520 circulating miRNAs as non-immunomodulatory controls.
• Feature types: Models incorporate composition-based features, binary profiles, selected features, and hybrid features.
• Model evaluation: Models were evaluated using five-fold cross-validation and external validation, achieving a maximum mean Matthews Correlation Coefficient (MCC) of 0.86 and accuracy of 93%.
• Motif identification: Identifies motifs using MERCI software and reports an abundance of adenine (A) in identified immunomodulatory motifs.

Scientific Applications:

• RNA-based therapeutics: Assists design of ssRNA sequences for therapeutic modulation of immune responses.
• Vaccine adjuvant development: Enables selection of RNA sequences with adjuvant-like immunostimulatory properties.
• siRNA therapy optimization: Helps refine siRNA sequences to reduce immunotoxicity in siRNA therapeutics.

Methodology:

Models were developed from a dataset of 602 experimentally verified IMORNs and 520 circulating miRNAs labeled non-immunomodulatory, using composition-based, binary profile, selected and hybrid features; motifs were identified with MERCI (adenine-rich motifs noted); models were evaluated by five-fold cross-validation and external validation yielding a maximum mean MCC of 0.86 and accuracy of 93%.