A deep learning model for characterizing protein-RNA interactions from sequences at single-base resolution

Protein-RNA interactions play pivotal roles in regulating transcription, translation, and RNA metabolism. Characterizing these interactions offers key insights into RNA dysregulation mechanisms. Here, we introduce Reformer, a deep learning model that predicts protein-RNA binding affinity from sequence data. Trained on 225 enhanced cross-linking and immunoprecipitation sequencing (eCLIP-seq) datasets encompassing 155 RNA-binding proteins across three cell lines, Reformer achieves high accuracy in predicting binding affinity at single-base resolution. The model uncovers binding motifs that are often undetectable through traditional eCLIP-seq methods. Notably, the motifs learned by Reformer are shown to correlate with RNA processing functions. Validation via electrophoretic mobility shift assays confirms the model's precision in quantifying the impact of mutations on RNA regulation. In summary, Reformer improves the resolution of RNA-protein interaction predictions and aids in prioritizing mutations that influence RNA regulation.

Authors

Institutions

Publication Details

Journal
Patterns
Published
2025-01-01
DOI
https://doi.org/10.1016/j.patter.2024.101150
Citations
3
Primary Topic
RNA and protein synthesis mechanisms
Type
article
Field-Weighted Citation Impact
2.42

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A deep learning model for characterizing protein-RNA interactions from sequences at single-base resolution

Xueer Wang, Jilei Liu, Yichen Yang, Xilin Shen et al.
3 citations
Patterns
RNA and protein synthesis mechanisms
2.42
article

A deep learning model for characterizing protein-RNA interactions from sequences at single-base resolution

Xueer Wang, Jilei Liu, Yichen Yang, Xilin Shen, Lei Shi, Ya‐Ming Hou, Hongru Shen, Chunyong Zhang, Xiangchun Li, Wei Wang, Jin Zhang, Yang Li, Kexin Chen, Yang Meng, Yan Sun
article en
3 citations

Abstract

Protein-RNA interactions play pivotal roles in regulating transcription, translation, and RNA metabolism. Characterizing these interactions offers key insights into RNA dysregulation mechanisms. Here, we introduce Reformer, a deep learning model that predicts protein-RNA binding affinity from sequence data. Trained on 225 enhanced cross-linking and immunoprecipitation sequencing (eCLIP-seq) datasets encompassing 155 RNA-binding proteins across three cell lines, Reformer achieves high accuracy in predicting binding affinity at single-base resolution. The model uncovers binding motifs that are often undetectable through traditional eCLIP-seq methods. Notably, the motifs learned by Reformer are shown to correlate with RNA processing functions. Validation via electrophoretic mobility shift assays confirms the model's precision in quantifying the impact of mutations on RNA regulation. In summary, Reformer improves the resolution of RNA-protein interaction predictions and aids in prioritizing mutations that influence RNA regulation.

PatternsVol. 6(1)
Tianjin Medical University Cancer Institute and Hospital (CN), First Affiliated Hospital of Xi'an Jiaotong University (CN)
National Natural Science Foundation of China, Tianjin Science and Technology Committee, Program for Changjiang Scholars and Innovative Research Team in University
Openalex Percentile: Top 14%
RNA and protein synthesis mechanisms
2.42
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.