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Riboswitches01:56

Riboswitches

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Riboswitches are non-coding mRNA domains that regulate the transcription and translation of downstream genes without the help of proteins. Riboswitches bind directly to a metabolite and can form unique stem-loop or hairpin structures in response to the amount of the metabolite present. They have two distinct regions – a metabolite-binding aptamer and an expression platform.
The aptamer has high specificity for a particular metabolite which allows riboswitches to specifically regulate...
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Types of RNA01:23

Types of RNA

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Overview
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA...
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Translational Regulation01:29

Translational Regulation

91
Translational regulation in prokaryotes ensures efficient protein synthesis by controlling ribosome access to mRNA. This regulation is mediated by secondary RNA structures, including translational riboswitches, RNA thermometers, and small RNAs (sRNAs), which respond to intracellular and environmental signals to modulate gene expression.Translational RiboswitchesRiboswitches in the leader region of mRNAs can regulate translation by altering the accessibility of the Shine-Dalgarno (SD) sequence,...
91
RNA Interference01:23

RNA Interference

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RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
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RNA-seq03:21

RNA-seq

10.4K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Experimental RNAi02:15

Experimental RNAi

6.2K
RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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Video Experimental Relacionado

Updated: Sep 8, 2025

An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA
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An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA

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Toehold-VISTA: Un enfoque de aprendizaje automático para descifrar las interacciones programables entre el sensor de

James M Robson1,2, Alexander A Green1,2,3

  • 1Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.

bioRxiv : the preprint server for biology
|August 20, 2025
PubMed
Resumen

Desarrollamos un marco de aprendizaje automático, VISTA, para diseñar rápidamente biosensores de ARN de alto rendimiento. Este enfoque acelera la ingeniería de sensores de ARN para la biología sintética y el diagnóstico, incluso para la detección de SARS-CoV-2.

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Área de la Ciencia:

  • Biología sintética
  • Diagnóstico molecular
  • Biología computacional

Sus antecedentes:

  • Los biosensores basados en ARN son cruciales para la biología sintética y el diagnóstico, pero su diseño consume mucho tiempo.
  • Comprender las interacciones ARN-ARN y las relaciones estructura-función es clave para mejorar el rendimiento del sensor.
  • Los métodos actuales para el diseño de sensores de ARN son lentos y carecen de poder predictivo.

Objetivo del estudio:

  • Presentar un marco guiado por el aprendizaje automático, VISTA, para un análisis rápido y versátil de orientación de ARN en silicio.
  • Acelerar el diseño y la ingeniería de biosensores de ARN de alto rendimiento.
  • Mejorar la función del sensor de ARN a través de una estrategia de diseño consciente del objetivo.

Principales métodos:

  • VISTA integra el modelado biofísico de sensores y ARN objetivo con el análisis discriminante de mínimos cuadrados parciales (PLS-DA).
  • Las mediciones experimentales de alto rendimiento y la extracción de características de la estructura de secuencias se utilizaron para entrenar modelos predictivos.
  • Los interruptores de toehold se utilizaron como un sistema de sensores de ARN modelo para validar el marco VISTA.

Principales resultados:

  • VISTA capturó con éxito los determinantes clave del rendimiento del sensor de ARN.
  • Toehold-VISTA demostró la capacidad de diseñar sensores de ARN con una función mejorada contra el ARN del SARS-CoV-2.
  • El marco permite un diseño rápido y consciente del objetivo de los sensores de ARN.

Conclusiones:

  • VISTA proporciona una estrategia ampliamente aplicable para acelerar la ingeniería de los sensores de ARN.
  • Este enfoque de aprendizaje automático mejora el desarrollo de herramientas basadas en ARN para biotecnología y diagnóstico.
  • El estudio establece una base para un diseño más eficiente de los biosensores de ARN.