The ability of small interfering RNA (siRNA) to do post-transcriptional gene regulation by knocking down targeted genes is an important research topic in functional genomics, biomedical research as well as cancer therapeutics. Even though many models have been developed to design exogenous siRNA with high experimental inhibition, the design of effective siRNA sequences is still a challenging task because the target mRNA sites must be selected in such a way that their corresponding siRNAs are likely to be efficient against that target and are also unlikely to accidentally silence other transcripts due to sequence similarity.

OpsiD, our siRNA designer model, incorporates a new artificial neural-network (ANN) model which uses scores from pre-existing siRNA design models as well as the whole stacking energy (whole ΔG) to enable the identification of the efficacy (inhibition capacity) of siRNAs against target genes. The model lists all possible siRNAs against a particular mRNA with their inhibition efficacy and also the number of matches or sequence similarity of that siRNA with other genes in the database.

We could achieve an excellent performance of Pearson correlation coefficient (R=0.74) when the threshold of whole-stacking energy (ΔG) is ≥ -34.6 kcal/mol. To the best of the authors' knowledge, this is one of the best score achieved so far while considering the combined ‘efficacy and off- target possibility prediction‘ of siRNAs for silencing a gene.The proposed model shall be useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in the area of bioinformatics.

The publication details are .