VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses

Abstract

BackgroundSelection of effective viral siRNA is an indispensable step in the development of siRNA based antiviral therapeutics. Despite immense potential, a viral siRNA efficacy prediction algorithm is still not available. Moreover, performances of the existing general mammalian siRNA efficacy predictors are not satisfactory for viral siRNAs. Therefore, we have developed “VIRsiRNApred” a support vector machine (SVM) based method for predicting the efficacy of viral siRNA.MethodsIn the present study, we have employed a new dataset of 1725 viral siRNAs with experimentally verified quantitative efficacies tested under heterogeneous experimental conditions and targeting as many as 37 important human viruses including HIV, Influenza, HCV, HBV, SARS etc. These siRNAs were divided into training (T1380) and validation (V345) datasets. Important siRNA sequence features including mono to penta nucleotide frequencies, binary pattern, thermodynamic properties and secondary structure were employed for model development.ResultsDuring 10-fold cross validation on T1380 using hybrid approach, we achieved a maximum Pearson Correlation Coefficient (PCC) of 0.55 between predicted and actual efficacy of viral siRNAs. On V345 independent dataset, our best model achieved a maximum correlation of 0.50 while existing general siRNA prediction methods showed PCC from 0.05 to 0.18. However, using leave one out cross validation PCC was improved to 0.58 and 0.55 on training and validation datasets respectively. SVM performed better than other machine learning techniques used like ANN, KNN and REP Tree.ConclusionVIRsiRNApred is the first algorithm for predicting inhibition efficacy of viral siRNAs which is developed using experimentally verified viral siRNAs. We hope this algorithm would be useful in predicting highly potent viral siRNA to aid siRNA based antiviral therapeutics development. The web server is freely available at http://crdd.osdd.net/servers/virsirnapred/.

SMEpred workbench: A web server for predicting efficacy of chemicallymodified siRNAs

“…MNC C BIN-SS 24; (model 14 of Table 1) showed PCC value of 0.58. The performance of final models used in terms of PCC values of our algorithm is equivalent or better to other prediction algorithms of unmodified siRNA 23 (Table 2). With Homo-T 1900 same pattern in PCC value was seen as 0.86 for MNC (Supplementary Table S1).…”

“…Next, we chose to include the MNC of both the strands and various BIN features for the development of models. 23 These hybrid models using BIN with MNC 23 incorporated information based on the composition as well as position of modifications ( Table 1). …”

“…Working of the BIN model is based on the position of the particular nucleotide in the siRNA sequence. 23 …”

“…These were used to check the performance of the prediction method 23 based on different criteria. For example, MNC and BIN were analyzed on the basis of different lengths as well as either or both siRNA strands.…”

“…18 Additionally, many web servers are existing to predict the efficacy of siRNAs e.g. siPRED 19 BIOPREDsi 20 MysiRNA, 21 desiRm, 22 VIRsiRNApred 23 etc. However, none of these methods predict efficacy for the chemically modified siRNAs (cm-siRNAs).…”