Computational Selection and Experimental Validation of Allosteric Ribozymes That Sense a Specific Sequence of Human Telomerase Reverse Transcriptase mRNAs as Universal Anticancer Therapy Agents

Abstract

High expression levels of telomerase reverse transcriptase messenger RNAs in differentiated cells can be used as a common marker for cancer development. In this paper, we describe a novel computational method for selection of allosteric ribozymes that sense a specific sequence of human telomerase reverse transcriptase mRNAs. The in silico selection employed is based on computing secondary structures of RNA using the partition function in combination with a random search algorithm. We selected one of the ribozymes for experimental validation. The obtained results demonstrate that the tested ribozyme has a high-speed (∼1.8 per minute) of self-cleavage and is very selective. It can distinguish well between perfectly matching effector and the closest expressed RNA sequence in the human cell with 10 mismatches, with a ∼300-fold difference under physiologically relevant conditions. The presented algorithm is universal since the allosteric ribozymes can be designed to sense any specific RNA or DNA sequence of interest. Such designer ribozymes may be used for monitoring the expression of mRNAs in the cell and for developing novel anticancer gene therapies.