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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\u2013RNA 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\u2013RNA interaction field in the past and foresees its future development in the post-AlphaFold era.<\/jats:p>","DOI":"10.1093\/bib\/bbab540","type":"journal-article","created":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T20:11:44Z","timestamp":1638216704000},"source":"Crossref","is-referenced-by-count":84,"title":["Protein\u2013RNA interaction prediction with deep learning: structure matters"],"prefix":"10.1093","volume":"23","author":[{"given":"Junkang","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), 999077, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi 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pairwise residue distance map","volume":"60","author":"Chen","year":"2019","journal-title":"J Chem Inf Model"},{"issue":"12","key":"2022012000334027000_ref112","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1002\/bip.360221211","article-title":"Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features","volume":"22","author":"Kabsch","year":"1983","journal-title":"Biopolymers: Original Research on Biomolecules"},{"key":"2022012000334027000_ref113","volume-title":"Naccess","author":"Ding","year":"2006"},{"issue":"11","key":"2022012000334027000_ref114","doi-asserted-by":"crossref","first-page":"3170","DOI":"10.1002\/prot.24682","article-title":"Accurate single-sequence prediction of solvent accessible surface area using local and global features","volume":"82","author":"Faraggi","year":"2014","journal-title":"Proteins: Structure, Function, and 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memory with convolutional neural networks","volume":"34","author":"Hanson","year":"2018","journal-title":"Bioinformatics"},{"issue":"1","key":"2022012000334027000_ref118","first-page":"1","article-title":"Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map","volume":"13","author":"Chen","year":"2021","journal-title":"J Chem"},{"issue":"2","key":"2022012000334027000_ref119","first-page":"1","article-title":"The feature framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications","volume":"9","author":"Halperin","year":"2008","journal-title":"BMC Genomics"},{"issue":"5","key":"2022012000334027000_ref120","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1038\/nprot.2016.051","article-title":"Computational protein\u2013ligand docking and virtual drug screening with the autodock 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module for boosting the power of graph neural networks in molecular graph analysis","author":"Ishiguro","year":"2019"},{"issue":"1","key":"2022012000334027000_ref125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-23303-9","article-title":"Structure-based protein function prediction using graph convolutional networks","volume":"12","author":"Gligorijevi\u0107","year":"2021","journal-title":"Nat Commun"},{"issue":"11","key":"2022012000334027000_ref126","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1000567","article-title":"A threading-based method for the prediction of dna-binding proteins with application to the human genome","volume":"5","author":"Gao","year":"2009","journal-title":"PLoS Comput Biol"},{"issue":"5","key":"2022012000334027000_ref127","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0097725","article-title":"Rnabindrplus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted rna-binding residues in proteins","volume":"9","author":"Walia","year":"2014","journal-title":"PloS one"},{"issue":"10","key":"2022012000334027000_ref128","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1002\/prot.24610","article-title":"Rbrdetector: Improved prediction of binding residues on rna-binding protein structures using complementary feature-and template-based strategies","volume":"82","author":"Yang","year":"2014","journal-title":"Proteins: Structure, Function, and Bioinformatics"},{"key":"2022012000334027000_ref129","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/BIBM.2016.7822515","article-title":"Deeperbind: Enhancing prediction of sequence specificities of dna binding proteins","volume-title":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Hassanzadeh","year":"2016"},{"issue":"17","key":"2022012000334027000_ref130","doi-asserted-by":"crossref","first-page":"i638","DOI":"10.1093\/bioinformatics\/bty600","article-title":"A deep neural network approach for learning intrinsic protein-rna binding preferences","volume":"34","author":"Ben-Bassat","year":"2018","journal-title":"Bioinformatics"},{"key":"2022012000334027000_ref131","first-page":"7132","article-title":"Squeeze-and-excitation networks","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Jie","year":"2018"},{"issue":"Suppl 2","key":"2022012000334027000_ref132","doi-asserted-by":"crossref","first-page":"S2","DOI":"10.1186\/1471-2164-9-S2-S2","article-title":"The feature framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications","volume":"9","author":"Halperin","year":"2008","journal-title":"BMC Genomics"},{"key":"2022012000334027000_ref133","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkab044","article-title":"Graphbind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues","author":"Xia","year":"2021","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"2022012000334027000_ref134","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1093\/bioinformatics\/btg432","article-title":"Analysis and prediction of dna-binding proteins and their binding residues based on composition, sequence and structural information","volume":"20","author":"Ahmad","year":"2004","journal-title":"Bioinformatics"},{"issue":"15","key":"2022012000334027000_ref135","doi-asserted-by":"crossref","first-page":"10086","DOI":"10.1093\/nar\/gku681","article-title":"Quantifying sequence and structural features of protein\u2013rna interactions","volume":"42","author":"Li","year":"2014","journal-title":"Nucleic Acids 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Res"},{"issue":"16","key":"2022012000334027000_ref142","doi-asserted-by":"crossref","first-page":"2730","DOI":"10.1093\/bioinformatics\/bty1068","article-title":"Promoter analysis and prediction in the human genome using sequence-based deep learning models","volume":"35","author":"Umarov","year":"2019","journal-title":"Bioinformatics"},{"issue":"1","key":"2022012000334027000_ref143","first-page":"1","article-title":"Hmd-arg: hierarchical multi-task deep learning for annotating antibiotic resistance genes","volume":"9","author":"Yu","year":"2021","journal-title":"Microbiome"},{"issue":"6558","key":"2022012000334027000_ref144","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1126\/science.abe5650","article-title":"Geometric deep learning of rna 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