Cheminformatics Approach to Gene Silencing: Z Descriptors of Nucleotides and SVM Regression Afford Predictive Models...

Short interfering RNA mediated gene silencing technology has been through tremendous development over the past decade, and has found broad applications in both basic biomedical research and pharmaceutical development. Critical to the effective use of this technology is the development of reliable algorithms to predict the potency and selectivity of siRNAs under study. Existing algorithms are mostly built upon sequence information of siRNAs and then employ statistical pattern recognition or machine learning techniques to derive rules or models. However, sequence-based features have limited ability to characterize siRNAs, especially chemically modified ones. In this study, we proposed a cheminformatics approach to describe siRNAs. Principal component scores (z1, z2, z3, z4) have been derived for each of the 5 nucleotides (A, U, G, C, T) from the descriptor matrix computed by the MOE program. Descriptors of a given siRNA sequence are simply the concatenation of the z values of its composing nucleotides. Thus, for each of the 2431 siRNA sequences in the Huesken dataset, 76 descriptors were generated for the 19-NT representation, and 84 descriptors were generated for the 21-NT representation of siRNAs. Support Vector Machine regression (SVMR) was employed to develop predictive models. In all cases, the models achieved Pearson correlation coefficient r and R about 0.84 and 0.65 for the training sets and test sets, respectively. A minimum of 25 % of the whole dataset was needed to obtain predictive models that could accurately predict 75 % of the remaining siRNAs. Thus, for the first time, a cheminformatics approach has been developed to successfully model the structure-potency relationship in siRNA-based gene silencing data, which has laid a solid foundation for quantitative modeling of chemically modified siRNAs.