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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Related Experiment Video

Updated: Oct 5, 2025

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
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Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

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Challenges for machine learning in RNA-protein interaction prediction.

Viplove Arora1, Guido Sanguinetti1

  • 1Data Science, Department of Physics, International School for Advanced Studies (SISSA), Trieste 34136, Italy.

Statistical Applications in Genetics and Molecular Biology
|January 24, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning can predict RNA-protein interactions from sequencing data. This note discusses challenges in applying machine learning to computational RNA biology and predicting these crucial gene expression regulators.

Keywords:
RNA-protein interactionsgraph neural networksgraphshigher-order interactionsnoisy data

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Area of Science:

  • Computational RNA biology
  • Gene expression regulation
  • Bioinformatics

Background:

  • RNA-protein interactions are vital for regulating gene expression.
  • Advances in experimental techniques have generated large-scale RNA-protein interaction datasets.
  • These datasets present opportunities and challenges for machine learning applications.

Purpose of the Study:

  • To identify and discuss key challenges in applying machine learning to computational RNA biology.
  • To focus specifically on the prediction of RNA-protein interactions using next-generation sequencing data.

Main Methods:

  • Review of current machine learning methodologies in computational RNA biology.
  • Analysis of limitations in utilizing next-generation sequencing data for RNA-protein interaction prediction.

Main Results:

  • Identification of significant stumbling blocks in the machine learning pipeline for RNA-protein interaction prediction.
  • Highlighting data-specific challenges inherent in next-generation sequencing datasets.

Conclusions:

  • Addressing these challenges is crucial for advancing machine learning in computational RNA biology.
  • Improved methods are needed for accurate prediction of RNA-protein interactions from large-scale sequencing data.