ReviewInternational journal of molecular sciences2015
Computational Prediction of RNA-Binding Proteins and Binding Sites.
Review in International journal of molecular sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 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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Who cites it
44 citing papers in PubMed, 76 citations in OpenAlex.
- Intron retention in health and amyotrophic lateral sclerosis.Brain : a journal of neurology · 2026Review
- RNA-binding proteins in aging and age-related diseases: roles, mechanisms, and a large language model analysis.Biogerontology · 2026Review
- Interpretable prediction of nucleic acid-binding proteins using a protein language model.Bioinformatics advances · 2026Article
- Article
- Research on Plant RNA-Binding Protein Prediction Method Based on Improved Ensemble Learning.Biology · 2025Article
- DescribePROT Database of Residue-Level Protein Structure and Function Annotations.Methods in molecular biology (Clifton, N.J.) · 2025Article
- RNA-protein interaction prediction without high-throughput data: An overview and benchmark ofComputational and structural biotechnology journal · 2024Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options.Nucleic acids research · 2024Article
- In silico protein function prediction: the rise of machine learning-based approaches.Medical review (2021) · 2023Review
- Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet.Nature communications · 2023Article
- HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins.Nucleic acids research · 2023Article
- Availability of web servers significantly boosts citations rates of bioinformatics methods for protein function and disorder prediction.Bioinformatics advances · 2023Article
- 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
- RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins.Briefings in bioinformatics · 2022Article
- Ribonomics Approaches to Identify RBPome in Plants and Other Eukaryotes: Current Progress and Future Prospects.International journal of molecular sciences · 2022Review
- ENNGene: an Easy Neural Network model building tool for Genomics.BMC genomics · 2022Article
- Protein-RNA interaction prediction with deep learning: structure matters.Briefings in bioinformatics · 2022Article
- Complementarity of the residue-level protein function and structure predictions in human proteins.Computational and structural biotechnology journal · 2022Article
- DescribePROT: database of amino acid-level protein structure and function predictions.Nucleic acids research · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proteins and RNA interaction have vital roles in many cellular processes such as protein synthesis, sequence encoding, RNA transfer, and gene regulation at the transcriptional and post-transcriptional levels. Approximately 6%-8% of all proteins are RNA-binding proteins (RBPs). Distinguishing these RBPs or their binding residues is a major aim of structural biology. Previously, a number of experimental methods were developed for the determination of protein-RNA interactions. However, these experimental methods are expensive, time-consuming, and labor-intensive. Alternatively, researchers have developed many computational approaches to predict RBPs and protein-RNA binding sites, by combining various machine learning methods and abundant sequence and/or structural features. There are three kinds of computational approaches, which are prediction from protein sequence, prediction from protein structure, and protein-RNA docking. In this paper, we review all existing studies of predictions of RNA-binding sites and RBPs and complexes, including data sets used in different approaches, sequence and structural features used in several predictors, prediction method classifications, performance comparisons, evaluation methods, and future directions.
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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.