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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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Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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A Protocol for Computer-Based Protein Structure and Function Prediction
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SAWRPI: A Stacking Ensemble Framework With Adaptive Weight for Predicting ncRNA-Protein Interactions Using Sequence

Zhong-Hao Ren1, Chang-Qing Yu1, Li-Ping Li1

  • 1School of Information Engineering, Xijing University, Xi'an, China.

Frontiers in Genetics
|April 1, 2022
PubMed
Summary

A new computational method, SAWRPI, predicts non-coding RNA-protein interactions using sequence data. It offers a robust and efficient alternative to experimental methods for understanding RNA binding protein functions.

Keywords:
ensemble learningnatural language processingncRNAncRNA-protein interactionssequence analysis

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Non-coding RNAs (ncRNAs) play crucial roles in gene regulation and biological processes.
  • Interactions between ncRNAs and RNA binding proteins (RBPs) are key to ncRNA function.
  • Existing experimental methods for identifying ncRNA-RBP interactions are time-consuming and labor-intensive.

Purpose of the Study:

  • To develop a computational method for predicting ncRNA-protein interactions using sequence information.
  • To address limitations of existing computational methods, such as applicability to novel RNAs/proteins and handling long sequences.

Main Methods:

  • Feature extraction from ncRNA and protein sequences using k-mer sparse matrix with SVD reduction and natural language processing.
  • Application of Hilbert Transformation to map features to a new space for improved classification.
  • Utilization of a stacking ensemble strategy for automated learning of high-level features and final prediction.

Main Results:

  • The proposed method, SAWRPI, demonstrated high performance across three diverse datasets.
  • SAWRPI achieved superior results compared to state-of-the-art methods and alternative feature extraction strategies.
  • The method proved robust and stable in predicting interactions, including lncRNA-protein interactions.

Conclusions:

  • SAWRPI is a trustworthy, robust, and simple computational tool for predicting ncRNA-protein interactions.
  • The method can serve as a valuable supplement to existing approaches for studying ncRNA function.
  • SAWRPI offers an efficient alternative for identifying ncRNA-RBP interactions, particularly for novel sequences.