Artificial neural network prediction of antisense oligodeoxynucleotide activity.
An mRNA transcript contains many potential antisense oligodeoxynucleotide target sites. Identification of the most efficacious targets remains an important and challenging problem. Building on separate work that revealed a strong correlation between the inclusion of short sequence motifs and the activity level of an oligo, we have developed a predictive artificial neural network system for mapping tetranucleotide motif content to antisense oligo activity. Trained for high-specificity prediction, the system has been cross-validated against a database of 348 oligos from the literature and a larger proprietary database of 908 oligos. In cross- validation tests the system identified effective oligos (i.e. oligos capable of reducing target mRNA expression to <25% that of the control) with 53% accuracy, in contrast to the <10% success rates commonly reported for trial-and-error oligo selection, suggesting a possible 5-fold reduction in the in vivo screening required to find an active oligo. We have implemented a web interface to a trained neural network. Given an RNA transcript as input, the system identifies the most likely oligo targets and provides estimates of the probabilities that oligos targeted against these sites will be effective.
D001665:Binding Sites / Q000235:genetics; D016014:Linear Models; D016571:Neural Networks, Computer; D020319:Oligodeoxyribonucleotides, Antisense / Q000235:genetics* / Q000378:metabolism; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism
D020319:Oligodeoxyribonucleotides, Antisense; D012333:RNA, Messenger