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Multi-resBind: a residual network-based multi-label classifier for in vivo RNA binding prediction and preference

Shitao Zhao1, Michiaki Hamada2,3,4

  • 1Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo Shinjuku-ku, Tokyo, 169-8555, Japan. shitao.zhao@aoni.waseda.jp.

BMC Bioinformatics
|November 16, 2021
PubMed
Summary

Multi-resBind, a novel deep-learning method, accurately predicts protein-RNA binding sites in vivo. This approach improves upon existing models by offering better prediction power and biological insights into RNA-binding protein interactions.

Keywords:
Integrated gradientsMulti-label classificationPhotoactivatable ribonucleoside enhanced cross-linking and immunoprecipitationRNA-binding proteinResidual network

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Protein-RNA interactions are crucial for gene expression regulation.
  • Ultraviolet cross-linking and immunoprecipitation (CLIP) methods identify RNA-binding protein (RBP) binding sites in vivo.
  • Inferring RNA binding preferences and predicting binding sites from large-scale CLIP data presents a significant challenge, with existing deep-learning models often exhibiting experimental bias.

Purpose of the Study:

  • To develop a novel deep-learning approach for inferring protein-RNA binding preferences.
  • To predict novel RNA-binding protein (RBP) interactions and binding sites in vivo.
  • To overcome limitations of existing methods, such as experimental bias and weak neural network architectures.

Main Methods:

  • Proposed Multi-resBind, a multi-label deep-learning framework for protein-RNA interaction prediction.
  • Utilized large-scale PAR-CLIP datasets for training and evaluation.
  • Conducted extensive experiments to assess the impact of input data types and loss functions on prediction accuracy.
  • Employed a modified integrated gradient method for generating attribution maps to understand model predictions.

Main Results:

  • Multi-resBind demonstrated substantial improvements over the DeepRiPe method in predicting protein-RNA binding sites.
  • Achieved higher area under the receiver operating characteristic curve and average precision.
  • Attribution maps provided biological insights into the mechanisms of protein-RNA interactions.

Conclusions:

  • Multi-resBind is a promising multi-label deep-learning tool for predicting unknown in vivo protein-RNA binding sites.
  • The method offers enhanced prediction power and aids in understanding the basis of neural network predictions.
  • Provides valuable biological insights into protein-RNA interaction mechanisms.