ArticleNucleic acids research2016
Probing binding hot spots at protein-RNA recognition sites.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 48 citations in OpenAlex.
- Hierarchical breakdown of RNA structure prediction in CASP16: from reliable local helices to speculative multimer assembly.Bioinformatics (Oxford, England) · 2026Article
- Precise prediction of hotspot residues in protein-RNA complexes using graph attention networks and pretrained protein language models.Bioinformatics (Oxford, England) · 2025Article
- DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning.Nucleic acids research · 2025Article
- ASR gene family: a case of tandem-drive evolution.Frontiers in molecular biosciences · 2025Article
- PRITrans: A Transformer-Based Approach for the Prediction of the Effects of Missense Mutation on Protein-RNA Interactions.International journal of molecular sciences · 2024Article
- Probing RNA structures and functions by solvent accessibility: an overview from experimental and computational perspectives.Briefings in bioinformatics · 2022Review
- Article
- Recent advances in user-friendly computational tools to engineer protein function.Briefings in bioinformatics · 2021Review
- Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions.PLoS computational biology · 2021Article
- PremPRI: Predicting the Effects of Missense Mutations on Protein-RNA Interactions.International journal of molecular sciences · 2020Article
- iPNHOT: a knowledge-based approach for identifying protein-nucleic acid interaction hot spots.BMC bioinformatics · 2020Article
- rMAPS2: an update of the RNA map analysis and plotting server for alternative splicing regulation.Nucleic acids research · 2020Article
- Analyses on clustering of the conserved residues at protein-RNA interfaces and its application in binding site identification.BMC bioinformatics · 2020Article
- A structure-based model for the prediction of protein-RNA binding affinity.RNA (New York, N.Y.) · 2019Article
- XGBPRH: Prediction of Binding Hot Spots at Protein⁻RNA Interfaces Utilizing Extreme Gradient Boosting.Genes · 2019Article
- PredPRBA: Prediction of Protein-RNA Binding Affinity Using Gradient Boosted Regression Trees.Frontiers in genetics · 2019Article
- Protein-RNA interactions: structural characteristics and hotspot amino acids.RNA (New York, N.Y.) · 2018Article
- Review
- An account of solvent accessibility in protein-RNA recognition.Scientific reports · 2018Article
- Recent advances in automated protein design and its future challenges.Expert opinion on drug discovery · 2018Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We use evolutionary conservation derived from structure alignment of polypeptide sequences along with structural and physicochemical attributes of protein-RNA interfaces to probe the binding hot spots at protein-RNA recognition sites. We find that the degree of conservation varies across the RNA binding proteins; some evolve rapidly compared to others. Additionally, irrespective of the structural class of the complexes, residues at the RNA binding sites are evolutionary better conserved than those at the solvent exposed surfaces. For recognitions involving duplex RNA, residues interacting with the major groove are better conserved than those interacting with the minor groove. We identify multi-interface residues participating simultaneously in protein-protein and protein-RNA interfaces in complexes where more than one polypeptide is involved in RNA recognition, and show that they are better conserved compared to any other RNA binding residues. We find that the residues at water preservation site are better conserved than those at hydrated or at dehydrated sites. Finally, we develop a Random Forests model using structural and physicochemical attributes for predicting binding hot spots. The model accurately predicts 80% of the instances of experimental ΔΔG values in a particular class, and provides a stepping-stone towards the engineering of protein-RNA recognition sites with desired affinity.
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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.