ArticleNucleic acids research2014
Quantifying sequence and structural features of protein-RNA interactions.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.
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Who cites it
49 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- TripleBind: a generalizable deep learning framework for protein-nucleic acid and protein-ligand binding sites prediction based on pre-trained protein language models.Molecular diversity · 2026Article
- DeepPANB: integrating protein language model with PaiNN equivariant graph neural networks for prediction of protein-nucleic acid binding sites.Briefings in bioinformatics · 2026Article
- Protein-nucleic acid binding site prediction using interpretable Kolmogorov-Arnold networks with hypergraph representation learning.Bioinformatics (Oxford, England) · 2026Article
- A protocol for single-sequence protein-RNA complex structure prediction using ProRNA3D-single.STAR protocols · 2026Article
- Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models.Cell systems · 2025Article
- Predicting nucleic acid binding sites by attention map-guided graph convolutional network with protein language embeddings and physicochemical information.Briefings in bioinformatics · 2025Article
- CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces.Proteins · 2025Article
- Advances in Language-Model-Informed Protein-Nucleic Acid Binding Site Prediction.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
- pyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data.Life science alliance · 2024Article
- GPSFun: geometry-aware protein sequence function predictions with language models.Nucleic acids research · 2024Article
- SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues.Communications biology · 2024Article
- EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins.The protein journal · 2024Article
- EGPDI: identifying protein-DNA binding sites based on multi-view graph embedding fusion.Briefings in bioinformatics · 2024Article
- Genome-scale annotation of protein binding sites via language model and geometric deep learning.eLife · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks.Nucleic acids research · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasing awareness of the importance of protein-RNA interactions has motivated many approaches to predict residue-level RNA binding sites in proteins based on sequence or structural characteristics. Sequence-based predictors are usually high in sensitivity but low in specificity; conversely structure-based predictors tend to have high specificity, but lower sensitivity. Here we quantified the contribution of both sequence- and structure-based features as indicators of RNA-binding propensity using a machine-learning approach. In order to capture structural information for proteins without a known structure, we used homology modeling to extract the relevant structural features. Several novel and modified features enhanced the accuracy of residue-level RNA-binding propensity beyond what has been reported previously, including by meta-prediction servers. These features include: hidden Markov model-based evolutionary conservation, surface deformations based on the Laplacian norm formalism, and relative solvent accessibility partitioned into backbone and side chain contributions. We constructed a web server called aaRNA that implements the proposed method and demonstrate its use in identifying putative RNA binding sites.
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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.