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PRIP: A Protein-RNA Interface Predictor Based on Semantics of Sequences.

You Li1, Jianyi Lyu1, Yaoqun Wu2

  • 1School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China.

Life (Basel, Switzerland)
|February 25, 2022
PubMed
Summary

Predicting RNA-binding interfaces is crucial for understanding diseases. A new sequence semantics method, PRIP, accurately identifies these interfaces by learning hidden word relationships, outperforming existing techniques.

Keywords:
RNA-protein interactionsembeddingsemanticsword2vecxgboost

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Area of Science:

  • Molecular Biology
  • Bioinformatics

Background:

  • RNA-protein interactions are vital in biological processes.
  • Dysregulated RNA-protein interactions are linked to human diseases.
  • Accurate detection of RNA-binding sites is essential for functional and mechanistic studies.

Purpose of the Study:

  • To develop a novel sequence semantics-based method for predicting RNA-binding interfaces.
  • To improve the accuracy of RNA-binding site prediction.

Main Methods:

  • Extracted semantic embeddings using Word2vec pre-training.
  • Employed extreme gradient boosting for classifier training.
  • Utilized a sequence semantics-based approach named PRIP.

Main Results:

  • PRIP achieved a 0.73 SN in five-fold cross-validation.
  • PRIP obtained a 0.67 SN on an independent test set.
  • The method outperformed state-of-the-art RNA-binding interface prediction techniques.

Conclusions:

  • PRIP effectively learns hidden semantic relationships within sequence data.
  • Identified specific word semantics associated with RNA-binding interfaces.
  • Provides a novel semantic perspective for exploring RNA-protein interaction mechanisms.