Evidence map›Paper›PMID 24206267›Full record

ArticleNucleic acid therapeutics2013

Computational selection and experimental validation of allosteric ribozymes that sense a specific sequence of human telomerase reverse transcriptase mRNAs as universal anticancer therapy agents.

Robert Penchovsky, Gergana T Kostova

Open access · greenAbstract read
In one paragraph

Article in Nucleic acid therapeutics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 17 citations in OpenAlex.

  1. Review
  2. Article
  3. Computational Design of Allosteric Ribozymes via Genetic Algorithms.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Robert PenchovskyDepartment of Genetics, Faculty of Biology, Sofia University St. Kliment Ohridski , Sofia, Bulgaria .
Gergana T Kostova
Sofia University "St. Kliment Ohridski" · BG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Allosteric RegulationAntineoplastic AgentsBase SequenceDrug DesignHumansKineticsMolecular Sequence DataNucleic Acid ConformationRNA, CatalyticRNA CleavageRNA, MessengerSubstrate SpecificityTelomeraseAntineoplastic AgentsRNA, CatalyticRNA, MessengerTelomeraseTERT protein, human

Identifiers

PMID24206267
PMCPMC3868306
OpenAlexW2016771901

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.