# Protein-RNA interaction prediction with deep learning: Structure matters

arXiv: https://arxiv.org/abs/2107.12243 · HTML: https://mlsanity.com/paper/2107.12243/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 164 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model](https://mlsanity.com/paper/1609.00680/) (2016)
- [RNA Secondary Structure Prediction By Learning Unrolled Algorithms](https://mlsanity.com/paper/2002.05810/) (2020)
- [Deep learning in bioinformatics: introduction, application, and perspective in big data era](https://mlsanity.com/paper/1903.00342/) (2019)
- [Message Passing Neural Processes](https://mlsanity.com/paper/2009.13895/) (2020)
- [DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins](https://mlsanity.com/paper/1611.05777/) (2016)
- [Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding Specificities](https://mlsanity.com/paper/1901.10526/) (2019)

## Built on by (most cited first, 6 of 6)

- [A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges](https://mlsanity.com/paper/2210.12089/) (2022)
- [Multi-modal Transfer Learning between Biological Foundation Models](https://mlsanity.com/paper/2406.14150/) (2024)
- [HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides](https://mlsanity.com/paper/2111.06023/) (2021)
- [Graph Representation Learning for Interactive Biomolecule Systems](https://mlsanity.com/paper/2304.02656/) (2023)
- [Unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model](https://mlsanity.com/paper/2312.08987/) (2023)
- [CLMB: deep contrastive learning for robust metagenomic binning](https://mlsanity.com/paper/2111.09656/) (2021)

## Abstract (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 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.