PRIME-3D2D is a 3D2D model to predict binding sites of protein–RNA interaction

Xie, Juan; Zheng, Jie; Xu, Hong; Tong, Xinming; Liu, Shiyong

doi:10.1038/s42003-020-1114-y

Cited by 15 publications

(13 citation statements)

References 72 publications

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“…RPI-BIND (Luo et al, 2017) analyses solved RNA-protein structural complexes and employs protein local conformations (PLCs) and 12 classes of RNA local conformations (RLCs), to train a model and predict such interactions. PRIME-3D2D (Xie et al, 2020) is another tool that can predict protein-RNA complex structure and is amenable to genome-scale binding site prediction of proteins on RNA. It uses an alignment-based approach involving TMalign (Zhang and Skolnick, 2005) and LocARNA (multiple alignment of RNA) (Will et al, 2007), to model the protein-RNA complex from which interactions are inferred.…”

Section: Prediction Using Both Rna and Protein Structures As Inputmentioning

confidence: 99%

Computational tools to study RNA-protein complexes

Bheemireddy

Sandhya²,

Srinivasan³

et al. 2022

Front. Mol. Biosci.

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RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein–RNA interactions are still poorly derstood in contrast to protein–protein and protein–DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.

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Section: Prediction Using Both Rna and Protein Structures As Inputmentioning

confidence: 99%

Computational tools to study RNA-protein complexes

Bheemireddy

Sandhya²,

Srinivasan³

et al. 2022

Front. Mol. Biosci.

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“…Instead, people believe a large amount of training RNA sequences can describe the target protein implicitly. However, recent studies [ 17 , 31 ] show that the protein information can be used directly to predict the interaction preference, even without the high-throughput assay data.…”

Section: Computational Problems For Protein–rna Interactionmentioning

confidence: 99%

Protein–RNA interaction prediction with deep learning: structure matters

Wei

Chen

Zong

et al. 2021

Briefings in Bioinformatics

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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.

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“…Instead, people believe a large amount of training RNA sequences can describe the target protein implicitly. However, recent studies (Lam et al, 2019;Xie et al, 2020a) show that the protein information can be used directly to predict the interaction preference, even without the high-throughput assay data.…”

Section: Binding Preference Predictionmentioning

confidence: 99%

“…They can be divided into the following categories. Firstly, based on the assumption that similar structures may have similar function, people have used the template-based method to predict the binding sites Chen et al, 2014;Wu et al, 2018;Xie et al, 2020a) and the binding preference (Zheng et al, 2016). Although such methods can perform well on queries with homologs, they have difficulty in handling new sequences without homologs (Senior et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Protein-RNA interaction prediction with deep learning: Structure matters

Wei¹,

Chen²,

Zong³

et al. 2021

Preprint

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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.

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PRIME-3D2D is a 3D2D model to predict binding sites of protein–RNA interaction

Cited by 15 publications

References 72 publications

Computational tools to study RNA-protein complexes

Computational tools to study RNA-protein complexes

Protein–RNA interaction prediction with deep learning: structure matters

Protein-RNA interaction prediction with deep learning: Structure matters

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