Influence of mRNA self-structure on hybridization: computational tools for antisense sequence selection.
Antisense targeting, an innovative technology based on preventing biosynthesis through sequence-specific hybridization of mRNA to synthetic oligodeoxynucleotides (ODNs), is used to selectively and transiently downregulate the expression of any gene product. Its potential applications are both investigative (neurobiology and related disciplines) and therapeutic (oncology, virology, genetic diseases), and several antisense-based drugs are currently undergoing clinical trials. However, the reported efficiencies vary and there is still a lack of clarity in the underlying mechanisms of action. A critical factor of antisense efficiency is the issue of target site selection, as both mRNA and ODN molecules exhibit a significant amount of highly heterogeneous self-structure and the region selected for targeting may well be sterically or energetically inaccessible. Because of the prohibitively large chain length, mRNA structural information is not accessible by X-ray crystallography or NMR, making a modeling approach indispensable. I outline how the latest molecular modeling techniques can be employed to establish the secondary (2D) and tertiary (3D) structures into which a given mRNA folds during and after transcription. The aim should be to integrate 2D prediction results achieved by (a) free-energy minimization, (b) kinetic folding simulations, (c) iterative population breeding by genetic algorithms, and (d) phylogenetic comparison techniques. These results can form the input of a 3D structure prediction paradigm based on constraint-satisfying programming, governed by experimental molecular mechanical constraints, and refined by molecular dynamics simulations. Finally, the automated docking (by simulated annealing) of ODN molecules to the mRNA structure can provide information about the accessibility of target mRNA regions for hybridization. To date, the great majority of studies that employ antisense as a tool select their target sequences more or less randomly. Although the wealth of molecular interactions that take place within a cell makes complete predictability unrealistic, the kind of information that can be extracted from such techniques is of relevance to every application of antisense technology, both investigative and therapeutic.
Toschi
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Department of Behavioral Neuroendocrinology, MaxPlanck Institute of Psychiatry, Kraepelinstrasse 2-10, Munich, D-80804, Germany. toschi@mpipsykl.mpg.de
D000465:Algorithms; D000818:Animals; D007700:Kinetics; D008957:Models, Genetic; D008958:Models, Molecular; D009690:Nucleic Acid Conformation; D009693:Nucleic Acid Hybridization; D020319:Oligodeoxyribonucleotides, Antisense / Q000235:genetics*; D010802:Phylogeny; D012333:RNA, Messenger / Q000737:chemistry* / Q000235:genetics*; D051381:Rats; D018019:Receptors, Corticotropin-Releasing Hormone / Q000235:genetics; D013816:Thermodynamics
D020319:Oligodeoxyribonucleotides, Antisense; D012333:RNA, Messenger; D018019:Receptors, Corticotropin-Releasing Hormone