Corpus ID: 236428816

Protein-RNA interaction prediction with deep learning: Structure matters

@article{Wei2021ProteinRNAIP,
  title={Protein-RNA interaction prediction with deep learning: Structure matters},
  author={Junkang Wei and Siyuan Chen and Li Zong and Xin Gao and Yu Li},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.12243}
}
  • Junkang Wei, Siyuan Chen, +2 authors Yu Li
  • Published 26 July 2021
  • Biology, Computer Science
  • ArXiv
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… Expand

Figures and Tables from this paper

A Max-Margin Model for Predicting Residue—Base Contacts in Protein–RNA Interactions
Protein–RNA interactions (PRIs) are essential for many biological processes, so understanding aspects of the sequences and structures involved in PRIs is important for unraveling such processes.Expand
USPNet: unbiased organism-agnostic signal peptide predictor with deep protein language model
  • Shenyang Chen, Qingxiong Tan, Jing-Chao Li, Yu Li
  • Biology
  • 2021
TLDR
Unbiased Organism-agnostic Signal Peptide Network (USPNet) is presented, a signal peptide prediction and cleavage site prediction model based on deep protein language model that uses label distribution-aware margin (LDAM) loss and evolutionary scale modeling (ESM) embedding to handle data imbalance and object-dependence problems. Expand
HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides
  • Qinze Yu, Zhihang Dong, Xingyu Fan, Li Zong, Yu Li
  • Computer Science, Biology
  • ArXiv
  • 2021
TLDR
An end-to-end hierarchical multi-label deep forest framework, HMD-AMP, to annotate AMP comprehensively and suggest that this framework outperforms state-of-the-art models in both the binary classification task and the multi- label classification task, especially on the minor classes. Expand
CLMB: deep contrastive learning for robust metagenomic binning
  • Pengfei Zhang, Zhengyuan Jiang, Yixuan Wang, Yu Li
  • Computer Science, Biology
  • ArXiv
  • 2021
TLDR
A deep Contrastive Learning framework for Metagenome Binning (CLMB), which can efficiently eliminate the disturbance of noise and produce more stable and robust results, and improves the performance of bin refinement. Expand

References

SHOWING 1-10 OF 178 REFERENCES
Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions.
TLDR
In this review, the recent advances on RNA-protein interaction were summarized in three aspects, including prediction strategies, input features, and datasets. Expand
Recent methodology progress of deep learning for RNA–protein interaction prediction
TLDR
An overview of the successful implementation of various deep learning approaches for predicting RNA– protein interactions, mainly focusing on the prediction of RNA–protein interaction pairs and RBP‐binding sites on RNAs is provided. Expand
Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions
TLDR
RPI-Net learns and exploits a graph representation of RNA molecules, yielding significant performance gains over existing state-of-the-art approaches, and introduces an approach to rectify particular type of sequence bias present in many CLIP-Seq data sets. Expand
A deep learning framework to predict binding preference of RNA constituents on protein surface
TLDR
NucleicNet can serve to provide quantitative fitness of RNA sequences for given binding pockets or to predict potential binding pockets and binding RNAs for previously unknown RNA binding proteins on a diverse set of challenging RNA-binding proteins. Expand
A Review About RNA–Protein-Binding Sites Prediction Based on Deep Learning
TLDR
This review discusses machine learning and deep learning approaches, mainly focusing on the prediction of RNA and proteins binding sites on RNAs by deep learning, and recommends some promising future directions of deep learning models in the study of RBP-binding sites onRNAs, especially the embedding, generative adversarial net, and attention model. Expand
aPRBind: protein-RNA interface prediction by combining sequence and I-TASSER model-based structural features learned with convolutional neural networks
TLDR
A convolutional neural network (CNN)-based ab-initio method for RNA-binding residue prediction with a marginal dependence on the accuracy of the structure model, which allows aPRBind to be applied to the RNA- binding site prediction for the modeled or unbound structures. Expand
Computational approaches for the analysis of RNA–protein interactions: A primer for biologists
TLDR
This review discusses statistical inference and machine-learning approaches and tools relevant for the study of RBPs and the analysis of large-scale RNA–protein interaction datasets, and begins with the demystification of regression models, as used in theAnalysis of next-generation sequencing data. Expand
Deep Learning for Protein-Protein Interaction Site Prediction.
TLDR
Details of developing a deep learning approach to predicting which residues in a protein are involved in forming a PPI-a task known as PPI site prediction-are outlined and the key decisions to be made in defining a supervised machine learning project in this domain are highlighted. Expand
RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach
TLDR
A deep learning-based framework by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs, which can achieve promising performance and easily capture interpretable binding motifs. Expand
Capsule Network for Predicting RNA-Protein Binding Preferences Using Hybrid Feature
TLDR
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. Expand
...
1
2
3
4
5
...