Evidence map›Paper›PMID 39312145›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

A Computational Approach for Designing Synthetic Riboswitches for Next-Generation RNA Therapeutics.

Sumit Mukherjee, Sunanda Biswas Mukherjee, Danny Barash

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Sumit Mukherjee *Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, USA. sumit.mukherjee@nih.gov.
Sunanda Biswas Mukherjee *Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.
Danny BarashDepartment of Computer Science, Ben-Gurion University, Beer-Sheva, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Riboswitches are naturally occurring regulatory segments of RNA molecules that modulate gene expression in response to specific ligand binding. They serve as a molecular 'switch' that controls the RNA's structure and function, typically influencing the synthesis of proteins. Riboswitches are unique because they directly interact with metabolites without the need for proteins, making them attractive tools in synthetic biology and RNA-based therapeutics. In synthetic biology, riboswitches are harnessed to create biosensors and genetic circuits. Their ability to respond to specific molecular signals allows for the design of precise control mechanisms in genetic engineering. This specificity is particularly useful in therapeutic applications, where riboswitches can be synthetically designed to respond to disease-specific metabolites, thereby enabling targeted drug delivery or gene therapy. Advancements in designing synthetic riboswitches for RNA-based therapeutics hinge on sophisticated computational techniques, which are described in this chapter. The chapter concludes by underscoring the potential of computational strategies in revolutionizing the design and application of synthetic riboswitches, paving the way for advanced RNA-based therapeutic solutions.

Indexed as

Computational BiologyRiboswitchSynthetic BiologyAptamers, NucleotideGenetic EngineeringHumansLigandsNucleic Acid ConformationRNAAptamers, NucleotideLigandsRiboswitchRNAInverse RNA FoldingRiboswitchRibozymeRNA-based therapeuticsSynthetic biology

Identifiers

PMID39312145

What OpenQuestion holds

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Registered trials

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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.