Protein-RNA Interaction Prediction with Deep Learning: Structure Matters

Overview

Journal Brief Bioinform

Publisher Oxford University Press

Specialty Biology

Date 2021 Dec 20

PMID 34929730

Citations 23

Authors

Junkang Wei

Siyuan Chen

Licheng Zong

Xin Gao

Yu Li

Affiliations

Soon will be listed here.

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. Because of 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 RNA-binding protein-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.

Citing Articles

RNA-protein interaction prediction without high-throughput data: An overview and benchmark of tools.

Krautwurst S, Lamkiewicz K Comput Struct Biotechnol J. 2024; 23:4036-4046.

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The regulatory landscape of interacting RNA and protein pools in cellular homeostasis and cancer.

Gallardo-Dodd C, Kutter C Hum Genomics. 2024; 18(1):109.

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ProNet DB: a proteome-wise database for protein surface property representations and RNA-binding profiles.

Wei J, Xiao J, Chen S, Zong L, Gao X, Li Y Database (Oxford). 2024; 2024.

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Classifying protein kinase conformations with machine learning.

Reveguk I, Simonson T Protein Sci. 2024; 33(4):e4918.

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Prediction of protein-ligand binding affinity via deep learning models.

Wang H Brief Bioinform. 2024; 25(2).

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