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Related Concept Videos

Protein Networks02:26

Protein Networks

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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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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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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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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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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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Related Experiment Video

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Capsule Network for Predicting RNA-Protein Binding Preferences Using Hybrid Feature.

Zhen Shen, Su-Ping Deng, De-Shuang Huang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 29, 2019
    PubMed
    Summary

    This study introduces iCapsule, an improved capsule network for predicting RNA-Protein binding preferences. iCapsule outperforms existing methods by effectively integrating both RNA sequence and structure features for enhanced accuracy.

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    Area of Science:

    • Molecular Biology
    • Bioinformatics
    • Computational Biology

    Background:

    • RNA-Protein interactions are crucial for numerous biological processes.
    • Advancements in data generation have spurred the development of computational methods for predicting these interactions.
    • Current prediction methods often show suboptimal performance, necessitating improved approaches.

    Purpose of the Study:

    • To develop an enhanced computational model for predicting RNA-Protein binding preferences.
    • To leverage both RNA sequence and structure features for improved prediction accuracy.
    • To evaluate the efficacy of the proposed iCapsule model against existing baseline methods.

    Main Methods:

    • Development of an improved capsule network architecture, termed iCapsule.
    • Integration of both RNA sequence and RNA structure features into the prediction model.
    • Systematic evaluation of the model's performance by varying primary capsule layer configurations, number of convolution layers, and kernel sizes.

    Main Results:

    • The proposed iCapsule method demonstrated superior performance compared to three established baseline methods.
    • Analysis confirmed the significant contribution of integrating both RNA sequence and structure features.
    • Investigating architectural variations provided insights into optimizing model performance.

    Conclusions:

    • The iCapsule model offers a more accurate and robust approach for predicting RNA-Protein binding preferences.
    • The study highlights the importance of utilizing multi-modal features (sequence and structure) in computational prediction tasks.
    • Further research can build upon this architecture to refine RNA-Protein interaction predictions.