ArticleBMC bioinformatics2014
LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Article
- In Silico Analysis of CMolecules (Basel, Switzerland) · 2025Article
- Hybrid protein-ligand binding residue prediction with protein language models: does the structure matter?Bioinformatics (Oxford, England) · 2025Article
- Machine Learning in Enhancing Protein Binding Sites Predictions - What Has Changed Since Then?Combinatorial chemistry & high throughput screening · 2025Review
- Recent trends in machine learning and deep learning-based prediction of G-protein coupled receptor-ligand binding affinities.Frontiers in bioinformatics · 2025Review
- Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites.Briefings in bioinformatics · 2023Article
- Prediction of Protein Ion-Ligand Binding Sites with ELECTRA.Molecules (Basel, Switzerland) · 2023Article
- A multilayer dynamic perturbation analysis method for predicting ligand-protein interactions.BMC bioinformatics · 2022Article
- Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.Briefings in bioinformatics · 2022Review
- QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs.Genomics, proteomics & bioinformatics · 2021Article
- The Anti-Inflammatory Protein TNIP1 Is Intrinsically Disordered with Structural Flexibility Contributed by Its AHD1-UBAN Domain.Biomolecules · 2020Article
- Spatiotemporal identification of druggable binding sites using deep learning.Communications biology · 2020Article
- Recognizing Ion Ligand-Binding Residues by Random Forest Algorithm Based on Optimized Dihedral Angle.Frontiers in bioengineering and biotechnology · 2020Article
- Exploring the computational methods for protein-ligand binding site prediction.Computational and structural biotechnology journal · 2020Review
- Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.Journal of computer-aided molecular design · 2019Article
- The choice of sequence homologs included in multiple sequence alignments has a dramatic impact on evolutionary conservation analysis.Bioinformatics (Oxford, England) · 2019Article
- P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure.Journal of cheminformatics · 2018Article
- DEEPre: sequence-based enzyme EC number prediction by deep learning.Bioinformatics (Oxford, England) · 2018Article
- Prediction of Protein Hotspots from Whole Protein Sequences by a Random Projection Ensemble System.International journal of molecular sciences · 2017Article
- Protein ligand-specific binding residue predictions by an ensemble classifier.BMC bioinformatics · 2016Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundProtein-ligand binding is important for some proteins to perform their functions. Protein-ligand binding sites are the residues of proteins that physically bind to ligands. Despite of the recent advances in computational prediction for protein-ligand binding sites, the state-of-the-art methods search for similar, known structures of the query and predict the binding sites based on the solved structures. However, such structural information is not commonly available.
resultsIn this paper, we propose a sequence-based approach to identify protein-ligand binding residues. We propose a combination technique to reduce the effects of different sliding residue windows in the process of encoding input feature vectors. Moreover, due to the highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we construct several balanced data sets, for each of which a random forest (RF)-based classifier is trained. The ensemble of these RF classifiers forms a sequence-based protein-ligand binding site predictor.
conclusionsExperimental results on CASP9 and CASP8 data sets demonstrate that our method compares favorably with the state-of-the-art protein-ligand binding site prediction methods.
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