Selection of antisense oligonucleotides based on multiple predicted target mRNA structures.
BACKGROUND
Local structures of target mRNAs play a significant role in determining the efficacies of antisense oligonucleotides (ODNs), but some structure-based target site selection methods are limited by uncertainties in RNA secondary structure prediction. If all the predicted structures of a given mRNA within a certain energy limit could be used simultaneously, target site selection would obviously be improved in both reliability and efficiency. In this study, some key problems in ODN target selection on the basis of multiple predicted target mRNA structures are systematically discussed.


RESULTS
Two methods were considered for merging topologically different RNA structures into integrated representations. Several parameters were derived to characterize local target site structures. Statistical analysis on a dataset with 448 ODNs against 28 different mRNAs revealed 9 features quantitatively associated with efficacy. Features of structural consistency seemed to be more highly correlated with efficacy than indices of the proportion of bases in single-stranded or double-stranded regions. The local structures of the target site 5' and 3' termini were also shown to be important in target selection. Neural network efficacy predictors using these features, defined on integrated structures as inputs, performed well in "minus-one-gene" cross-validation experiments.


CONCLUSIONS
Topologically different target mRNA structures can be merged into integrated representations and then used in computer-aided ODN design. The results of this paper imply that some features characterizing multiple predicted target site structures can be used to predict ODN efficacy.
Bo
Xiaochen
X
Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, P R China. boxc@bmi.ac.cn
Lou
Shaoke
S
Sun
Daochun
D
Shu
Wenjie
W
Yang
Jing
J
Wang
Shengqi
S
D000465:Algorithms; D016375:Antisense Elements (Genetics) / Q000235:genetics*; D001483:Base Sequence; D003198:Computer Simulation; D017076:Computer-Aided Design; D018390:Gene Targeting / Q000379:methods; D008957:Models, Genetic; D008969:Molecular Sequence Data; D020320:Oligoribonucleotides, Antisense / Q000235:genetics*; D012333:RNA, Messenger / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*
D016375:Antisense Elements (Genetics); D020320:Oligoribonucleotides, Antisense; D012333:RNA, Messenger