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Protein-RNA interaction prediction with deep learning: Structure matters
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TLDR
A thorough review of the protein-RNA interaction prediction field can be found in this paper, which surveys both the binding site and binding preference prediction problems and covers the commonly used datasets, features, and models.Abstract:
Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Due to the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features, and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RBP-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.read more
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References
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Capsule Network for Predicting RNA-Protein Binding Preferences Using Hybrid Feature
TL;DR: This study proposes an improved capsule network to predict RNA-protein binding preferences, which can use both RNA sequence features and structure features and shows that the proposed method iCapsule performs better than three baseline methods in this field.
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JAR3D Webserver: Scoring and aligning RNA loop sequences to known 3D motifs
TL;DR: JAR3D finds possible 3D geometries for hairpin and internal loops by matching loop sequences to motif groups from the RNA 3D Motif Atlas, by exact sequence match when possible, and by probabilistic scoring and edit distance for novel sequences.
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icSHAPE-pipe: A comprehensive toolkit for icSHAPE data analysis and evaluation.
TL;DR: iSHAPE-pipe provides a convenient workflow for researchers to analyze RNA structural data from icSHAPE sequencing experiments and calculates RNA structural information with higher accuracy and achieves higher coverage of the transcriptome.
Journal ArticleDOI
Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions
Amit Sagar,Bin Xue +1 more
TL;DR: In this review, the recent advances on RNA-protein interaction were summarized in three aspects, including prediction strategies, input features, and datasets.
Book ChapterDOI
Deep Learning for Protein-Protein Interaction Site Prediction.
TL;DR: In this paper, a deep learning approach is proposed to predict which residues in a protein are involved in forming a protein-protein interaction (PPI) site prediction, a task known as PPI site prediction.