ReviewInternational journal of molecular sciences2020
Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type.
Review in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- Empirical Assessment of Sequence-Based Predictions of Intrinsically Disordered Regions Involved in Phase Separation.Biomolecules · 2025Article
- DescribePROT Database of Residue-Level Protein Structure and Function Annotations.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Structural comparison of homologous protein-RNA interfaces reveals widespread overall conservation contrasted with versatility in polar contacts.PLoS computational biology · 2024Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- Structure-based prediction of protein-nucleic acid binding using graph neural networks.Biophysical reviews · 2024Review
- DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options.Nucleic acids research · 2024Article
- HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins.Nucleic acids research · 2023Article
- Computational prediction of disordered binding regions.Computational and structural biotechnology journal · 2023Review
- qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids.Protein science : a publication of the Protein Society · 2023Article
- Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions.Biomolecules · 2022Article
- Complementarity of the residue-level protein function and structure predictions in human proteins.Computational and structural biotechnology journal · 2022Article
- Deep learning in prediction of intrinsic disorder in proteins.Computational and structural biotechnology journal · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 3 countries.
Funding
Abstract
With close to 30 sequence-based predictors of RNA-binding residues (RBRs), this comparative survey aims to help with understanding and selection of the appropriate tools. We discuss past reviews on this topic, survey a comprehensive collection of predictors, and comparatively assess six representative methods. We provide a novel and well-designed benchmark dataset and we are the first to report and compare protein-level and datasets-level results, and to contextualize performance to specific types of RNAs. The methods considered here are well-cited and rely on machine learning algorithms on occasion combined with homology-based prediction. Empirical tests reveal that they provide relatively accurate predictions. Virtually all methods perform well for the proteins that interact with rRNAs, some generate accurate predictions for mRNAs, snRNA, SRP and IRES, while proteins that bind tRNAs are predicted poorly. Moreover, except for DRNApred, they confuse DNA and RNA-binding residues. None of the six methods consistently outperforms the others when tested on individual proteins. This variable and complementary protein-level performance suggests that users should not rely on applying just the single best dataset-level predictor. We recommend that future work should focus on the development of approaches that facilitate protein-level selection of accurate predictors and the consensus-based prediction of RBRs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.