In the development of RNA interference (RNAi) therapeutics merely selecting short interfering RNA (siRNA) sequences that are complementary to the messenger RNA (mRNA) target does not guarantee target silencing. only on these two asymmetry guidelines. The algorithm uses end sequence nucleotide preferences and expected thermodynamic stabilities each weighted based on teaching data from your literature to rank the probability that an siRNA sequence will have high or low activity. The algorithm successfully predicts weakly- and highly-active sequences for enhanced green fluorescent protein (EGFP) and protein kinase R (PKR). Use of these two guidelines in combination enhances the prediction of siRNA activity over current methods for predicting asymmetry. Going forward we anticipate that this approach to siRNA asymmetry prediction will become incorporated into the next generation of siRNA selection algorithms.  the proteins Dicer and TAR RNA Binding Protein (TRBP) are important for RLC/RISC activity [21-23]. Additional proteins such as the protein activator of PKR (PACT) [24 25 and component 3 promoter of RISC (C3PO)  may also have important but as yet undefined functional functions in the RNAi process. One essential process executed from the pathway proteins is the recognition and loading of the siRNA lead strand into the RLC/RISC and the concomitant damage of the passenger strand [2 27 28 The likelihood of one siRNA strand becoming the lead strand relative to the additional strand is definitely termed asymmetry [27 29 There are currently multiple proteins thought to participate in sensing the asymmetry of siRNA duplexes [18 30 When the living of siRNA asymmetry was first identified MBX-2982 it was proposed the relative hybridization stability of the two ends of the siRNA sequence was the principal means by which asymmetry was sensed from the pathway proteins . Since that time nearly all algorithms for selecting highly-active siRNAs have used a thermodynamic calculation for asymmetry among additional guidelines [29 33 More recently evidence has begun to accumulate the terminal nucleotides on each 5’-end of the siRNA may be useful for predicting the activity of an siRNA [18 30 38 in particular when classified according to the sixteen possible mixtures of nucleotides. When terminal nucleotide classification is definitely combined with relative hybridization stability the accuracy of predicting siRNA activity enhances markedly . With this work we wanted to forecast siRNA activities centered only on the two asymmetry characteristics terminal nucleotide classification and relative thermodynamic stability and set up experimentally their relative importance in determining the activity of an siRNA. Using a logistic regression model we successfully predicted active and inactive sequences MBX-2982 for the exogenous protein enhanced green fluorescent protein (EGFP) as well as the endogenous protein protein kinase R (PKR). In addition the combination of both end sequence and thermodynamic stability features offered improved correlation to siRNA activity when compared to either feature separately. These results demonstrate further that asymmetry may be determined by more factors than just relative stability and algorithms for prediction of siRNA activity should also account for terminal nucleotide MBX-2982 sequence classification in asymmetry calculations. Results Rating and Selection of EGFP-targeting siRNAs Our rating algorithm MBX-2982 was initially tested on siRNAs to target the EGFP mRNA. From your cDNA sequence (see Supporting Info) there were 824 possible siRNA sequences which were ranked based on the difference between the algorithm’s predicted probability of high and low activity. For assessment commercial algorithms (Dharmacon and Ambion) were also used. These selection algorithms were chosen because their predictions are centered solely within the characteristics of the siRNA and not on other factors used in some selection algorithms such as target mRNA structure which would make it hard to directly compare Rabbit Polyclonal to HAND1. the accuracy of our asymmetry-based predictions with predictions from more detailed selection methods. The commercial ratings MBX-2982 only included sequences expected to have high activity as opposed to the entire range of possible siRNA sequences. While this is adequate for those needing effective siRNA sequences it does not provide adequate data to compare the characteristics of high activity and low activity siRNAs. The Dharmacon algorithm rated the recommended siRNAs whereas for Ambion there were no distinctions among the top 35 candidate sequences. Interestingly there was no overlap between the lists of recommended sequences provided by the.