ArticleFrontiers in genetics2019
PredPRBA: Prediction of Protein-RNA Binding Affinity Using Gradient Boosted Regression Trees.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- ProRB: a structure-free unified framework for joint prediction and design of protein-RNA interactions.Nucleic acids research · 2026Article
- M2-PRNet: multi-scale and multi-modal learning for protein-RNA binding affinity prediction.Bioinformatics (Oxford, England) · 2026Article
- PANTHER Score: Protein-Affinity for Nucleic Target-binding, Hybridization, and Energy Regression.RNA (New York, N.Y.) · 2026Article
- CAP: Commutative algebra prediction of protein-nucleic acid binding affinities.Machine learning: science and technology · 2025Article
- Bioinformatics Approaches for Understanding the Binding Affinity of Protein-Nucleic Acid Complexes.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
- PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features.Methods (San Diego, Calif.) · 2023Article
- Determinants of DNMT2/TRDMT1 preference for substrates tRNA and DNA during the evolution.RNA biology · 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
- In silico optimization of RNA-protein interactions for CRISPR-Cas13-based antimicrobials.Biology direct · 2022Article
- Characterization of Sequence-Specific Binding of LARP6 to the 5' Stem-Loop of Type I Collagen mRNAs and Implications for Rational Design of Antifibrotic Drugs.Journal of molecular biology · 2022Article
- ProNAB: database for binding affinities of protein-nucleic acid complexes and their mutants.Nucleic acids research · 2022Article
- Introduction to Bioinformatics Resources for Post-transcriptional Regulation of Gene Expression.Methods in molecular biology (Clifton, N.J.) · 2022Article
- LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA-protein interaction identification.BMC bioinformatics · 2021Article
- A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure.Scientific reports · 2021Article
- Novel and versatile artificial intelligence algorithms for investigating possible GHSR1α and DRD1 agonists for Alzheimer's disease.RSC advances · 2021Article
- Identification of Biomarkers for Cervical Cancer Radiotherapy Resistance Based on RNA Sequencing Data.Frontiers in cell and developmental biology · 2021Article
- How RNA-Binding Proteins Interact with RNA: Molecules and Mechanisms.Molecular cell · 2020Review
- PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity.Scientific reports · 2020Article
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3 authors.
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Abstract
Protein-RNA interactions play essential roles in many biological aspects. Quantifying the binding affinity of protein-RNA complexes is helpful to the understanding of protein-RNA recognition mechanisms and identification of strong binding partners. Due to experimentally measured protein-RNA binding affinity data available is still limited to date, there is a pressing demand for accurate and reliable computational approaches. In this paper, we propose a computational approach, PredPRBA, which can effectively predict protein-RNA binding affinity using gradient boosted regression trees. We build a dataset of protein-RNA binding affinity that includes 103 protein-RNA complex structures manually collected from related literature. Then, we generate 37 kinds of sequence and structural features and explore the relationship between the features and protein-RNA binding affinity. We find that the binding affinity mainly depends on the structure of RNA molecules. According to the type of RNA associated with proteins composed of the protein-RNA complex, we split the 103 protein-RNA complexes into six categories. For each category, we build a gradient boosted regression tree (GBRT) model based on the generated features. We perform a comprehensive evaluation for the proposed method on the binding affinity dataset using leave-one-out cross-validation. We show that PredPRBA achieves correlations ranging from 0.723 to 0.897 among six categories, which is significantly better than other typical regression methods and the pioneer protein-RNA binding affinity predictor SPOT-Seq-RNA. In addition, a user-friendly web server has been developed to predict the binding affinity of protein-RNA complexes. The PredPRBA webserver is freely available at http://PredPRBA.denglab.org/.
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