ArticleProteins2014
Assessment of ligand binding site predictions in CASP10.
Article in Proteins, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Multivalent ion binding site identification with structure-based deep learning.Communications biology · 2026Article
- Endowing protein language models with structural knowledge.Bioinformatics (Oxford, England) · 2025Article
- DescribePROT Database of Residue-Level Protein Structure and Function Annotations.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Article
- New prediction categories in CASP15.Proteins · 2023Article
- PDBspheres: a method for finding 3D similarities in local regions in proteins.NAR genomics and bioinformatics · 2022Article
- A multilayer dynamic perturbation analysis method for predicting ligand-protein interactions.BMC bioinformatics · 2022Article
- DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.International journal of molecular sciences · 2021Article
- Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods with a Focus on FunFOLD3.Methods in molecular biology (Clifton, N.J.) · 2021Article
- 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
- FINDSITEJournal of chemical information and modeling · 2018Article
- P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure.Journal of cheminformatics · 2018Article
- Sequence-based prediction of physicochemical interactions at protein functional sites using a function-and-interaction-annotated domain profile database.BMC bioinformatics · 2018Article
- Protein ligand-specific binding residue predictions by an ensemble classifier.BMC bioinformatics · 2016Article
- Article
- Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms.PloS one · 2016Article
- Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.International journal of molecular sciences · 2015Review
- IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences.Nucleic acids research · 2015Article
- Template-based prediction of protein function.Current opinion in structural biology · 2015Review
- Review
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3 authors.
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Abstract
The identification of amino acid residues in proteins involved in binding small molecule ligands is an important step for their functional characterization, as the function of a protein often depends on specific interactions with other molecules. The accuracy of computational methods aiming to predict such binding residues was evaluated within the "function prediction (prediction of binding sites, FN)" category of the critical assessment of protein structure prediction (CASP) experiment. In the last edition of the experiment (CASP10), 17 research groups participated in this category, and their predictions were evaluated on 13 prediction targets containing biologically relevant ligands. The results of this experiment indicate that several methods achieved an overall good performance, showing the usefulness of such methods in predicting ligand binding residues. As in previous years, methods based on a homology transfer approach were dominating. In comparison to CASP9, a larger fraction of the top predictors are automated servers. However, due to the small number of targets and the characteristics of the prediction format, the differences observed among the first ten methods were not statistically significant and it was also not possible to analyze differences in accuracy for different ligand types or overall structure, difficulty. To overcome these limitations and to allow for a more detailed evaluation, in future editions of CASP, methods in the FN category will no longer be evaluated on the "normal" CASP targets, but assessed continuously by CAMEO (continuous automated model evaluation) based on weekly prereleased sequences from the PDB.
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