ArticleRNA (New York, N.Y.)2006
Prediction of RNA binding sites in proteins from amino acid sequence.
Article in RNA (New York, N.Y.), 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed, 180 citations in OpenAlex.
- DGSite: A Well-Calibrated Deep Learning Framework for Protein-RNA Binding Site Prediction.ACS omega · 2026Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- Predicting nuclear G-quadruplex RNA-binding proteins with roles in transcription and phase separation.Nature communications · 2024Article
- A high-throughput search for intracellular factors that affect RNA folding identifiesRNA biology · 2024Article
- PRIP: A Protein-RNA Interface Predictor Based on Semantics of Sequences.Life (Basel, Switzerland) · 2022Article
- Amino Acid Composition in Various Types of Nucleic Acid-Binding Proteins.International journal of molecular sciences · 2021Review
- Mapping of Functional Subdomains in theFrontiers in plant science · 2021Article
- A Tale of Loops and Tails: The Role of Intrinsically Disordered Protein Regions in R-Loop Recognition and Phase Separation.Frontiers in molecular biosciences · 2021Article
- Concurrent binding to DNA and RNA facilitates the pluripotency reprogramming activity of Sox2.Nucleic acids research · 2020Article
- Deciphering the three-domain architecture in schlafens and the structures and roles of human schlafen12 and serpinB12 in transcriptional regulation.Journal of molecular graphics & modelling · 2019Article
- Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins.Frontiers in genetics · 2019Article
- RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence.Molecules (Basel, Switzerland) · 2018Article
- A boosting approach for prediction of protein-RNA binding residues.BMC bioinformatics · 2017Article
- EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation.BMC bioinformatics · 2017Article
- DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues.Nucleic acids research · 2017Article
- Selecting high-quality negative samples for effectively predicting protein-RNA interactions.BMC systems biology · 2017Article
- Sequence-Based Prediction of RNA-Binding Residues in Proteins.Methods in molecular biology (Clifton, N.J.) · 2017Article
- A Graph Approach to Mining Biological Patterns in the Binding Interfaces.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- PDNAsite: Identification of DNA-binding Site from Protein Sequence by Incorporating Spatial and Sequence Context.Scientific reports · 2016Article
- Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.BMC bioinformatics · 2016Article
21 more citing papers are in PubMed but not listed here.
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 1 country.
Funding
Abstract
RNA-protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA-protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA-protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located in the RNA-protein interface. In leave-one-out cross-validation experiments, RNABindR identifies interface residues with >85% overall accuracy. It can be calibrated by the user to obtain either high specificity or high sensitivity for interface residues. RNABindR, implementing a Naive Bayes classifier, performs as well as a more complex neural network classifier (to our knowledge, the only previously published sequence-based method for RNA binding site prediction) and offers the advantages of speed, simplicity and interpretability of results. RNABindR predictions on the human telomerase protein hTERT are in good agreement with experimental data. The availability of computational tools for predicting which residues in an RNA binding protein are likely to contact RNA should facilitate design of experiments to directly test RNA binding function and contribute to our understanding of the diversity, mechanisms, and regulation of RNA-protein complexes in biological systems. (RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.).
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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.