Prediction of Antisense Oligonucleotide Efficacy Using Local and Global Structure Information with Support Vector Machines

Designing antisense oligonucleotides with high efficacy is of great interest both for its usefulness to the study of gene regulation and for its potential therapeutic effects. The high cost associated with experimental approaches has motivated the development of computational methods to assist in their design. Essentially, these computational methods rely on various sequential and structural features to differentiate the high efficacy antisense oligonucleotides from the low efficacy. By far, however, most of the features used are either local motifs present in primary sequences or in secondary structures. We proposed a novel approach to profiling antisense oligonucleotides and the target RNA to reflect some of the global structural features such as hairpin structures. Such profiles are then utilized for classification and prediction of high efficacy oligonucleotides using support vector machines. The method was tested on a set of 348 antisense oligonucleotides of 19 RNA targets with known activity. The performance was evaluated by cross validation and ROC scores. It was shown that the prediction accuracy was significantly enhanced