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Protein Networks02:26

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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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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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lncRNA - Long Non-coding RNAs02:39

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Capsule-LPI: a LncRNA-protein interaction predicting tool based on a capsule network.

Ying Li1, Hang Sun1, Shiyao Feng1

  • 1Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China.

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|May 14, 2021
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Summary

This study introduces Capsule-LPI, a novel deep learning tool for predicting long noncoding RNA-protein interactions (LPIs). By integrating multimodal features, Capsule-LPI enhances prediction accuracy, aiding in understanding lncRNA functions.

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Capsule networkLong noncoding RNAlncRNA–protein interaction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Long noncoding RNAs (lncRNAs) are crucial regulators in biological processes.
  • Understanding lncRNA-protein interactions (LPIs) is essential for elucidating lncRNA functions.
  • Accurate LPI prediction remains a significant challenge in bioinformatics.

Purpose of the Study:

  • To develop an advanced computational method for predicting LPIs.
  • To explore the integration of multimodal features for improved prediction accuracy.
  • To leverage deep learning architectures for enhanced LPI recognition.

Main Methods:

  • A novel multichannel capsule network framework, Capsule-LPI, was developed.
  • Integration of four types of multimodal features: sequence, motif, physicochemical, and secondary structure.
  • The framework comprises four feature-learning subnetworks and one capsule subnetwork.

Main Results:

  • Capsule-LPI significantly improves LPI prediction performance by integrating multimodal features and utilizing a multichannel capsule network architecture.
  • Experimental evaluations demonstrate superior performance compared to existing state-of-the-art tools.
  • Achieved a precision of 87.3% (1.7% improvement) and an F-value of 92.2% (1.4% improvement).

Conclusions:

  • Presents a novel and effective LPI prediction tool, Capsule-LPI.
  • Highlights the benefits of multimodal feature integration and capsule networks for LPI prediction.
  • A user-friendly webserver is available for accessing the prediction tool.