VaccineDA: Prediction, design and genome-wide screening of oligodeoxynucleotide-based vaccine adjuvants

VaccineDA: Prediction, Design and Genome-Wide Screening of Oligodeoxynucleotide-Based Vaccine Adjuvants VaccineDA is a computational web-based resource developed for the prediction, design, and genome-wide screening of oligodeoxynucleotide-based vaccine adjuvants. The tool is designed to identify immunomodulatory oligodeoxynucleotides, also known as IMODNs, which can activate the innate immune system through Toll-like receptor 9. VaccineDA helps researchers design DNA-based vaccine adjuvants and screen oligonucleotide sequences for immunomodulatory potential. Web Server: https://webs.iiitd.edu.in/raghava/vaccineda/ Citation Nagpal, G., Gupta, S., Chaudhary, K., Dhanda, S. K., Prakash, S., and Raghava, G. P. S. VaccineDA: Prediction, design and genome-wide screening of oligodeoxynucleotide-based vaccine adjuvants. Scientific Reports, 5, 12478, 2015. https://doi.org/10.1038/srep12478 About the Research Vaccine adjuvants are important components that enhance immune responses during vaccination. Oligodeoxynucleotides containing immunomodulatory sequence patterns can act as vaccine adjuvants by activating the innate immune system through Toll-like receptor 9. Immunomodulatory oligodeoxynucleotides, or IMODNs, are short DNA sequences that can stimulate immune responses. Many of these sequences contain unmethylated CpG motifs, which are recognized by TLR9. VaccineDA was developed as an in silico platform to predict and design IMODN-based vaccine adjuvants using sequence-based machine learning models. Data Compilation: The study collected 2193 experimentally validated IMODNs from literature, mainly from patents. Non-IMODNs were generated from random CpG island fragments of the human genome. Methodology: VaccineDA uses Support Vector Machine-based models trained on nucleotide composition features and motif information. The study analyzed mono-, di-, tri-, tetra-, and pentanucleotide compositions, along with immunomodulatory motifs identified using MERCI.