ArticleBMC bioinformatics2018
Comparative assessment of strategies to identify similar ligand-binding pockets in proteins.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- Edge-aware GAT-based protein binding sites prediction.PLoS computational biology · 2026Article
- Prioritising search for virtual screening via preliminary interpretable low-feature likelihood-based rankings of drug-target activity measures.BMC bioinformatics · 2026Article
- Systematic Exploration of Small-Molecule Binding via a Large Language Model Trained on Textualized Protein-Ligand Interactions.Molecules (Basel, Switzerland) · 2025Article
- Review
- Comprehensive detection and characterization of human druggable pockets through binding site descriptors.Nature communications · 2024Article
- Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention.Journal of chemical information and modeling · 2024Article
- Low Molecular Weight Inhibitors Targeting the RNA-Binding Protein HuR.International journal of molecular sciences · 2023Article
- An Intriguing Purview on the Design of Macrocyclic Inhibitors for Unexplored Protein Kinases through Their Binding Site Comparison.Pharmaceuticals (Basel, Switzerland) · 2023Review
- PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity.Journal of cheminformatics · 2023Article
- TWN-RENCOD: A novel method for protein binding site comparison.Computational and structural biotechnology journal · 2023Article
- Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches.Frontiers in molecular biosciences · 2023Article
- PDBspheres: a method for finding 3D similarities in local regions in proteins.NAR genomics and bioinformatics · 2022Article
- Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network.Journal of chemical information and modeling · 2022Article
- Estimating the Similarity between Protein Pockets.International journal of molecular sciences · 2022Review
- What Makes GPCRs from Different Families Bind to the Same Ligand?Biomolecules · 2022Article
- Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design.Frontiers in pharmacology · 2022Article
- Comprehensive Survey of Recent Drug Discovery Using Deep Learning.International journal of molecular sciences · 2021Review
- CHARMM-GUIJournal of chemical information and modeling · 2021Article
- Article
- Ligand-Binding-Site Refinement to Generate Reliable Holo Protein Structure Conformations from Apo Structures.Journal of chemical information and modeling · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundDetecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges.
resultsWe evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA. Our assessment considers not only the capabilities to identify similar pockets and to construct accurate local alignments, but also the dependence of these alignments on the sequence order. We point out certain drawbacks of previously compiled datasets, such as the inclusion of structurally similar proteins, leading to an overestimated performance. To address these issues, a rigorous procedure to prepare unbiased, high-quality benchmarking sets is proposed. Further, we conduct a comparative assessment of techniques directly aligning binding pockets to indirect strategies employing structure-based virtual screening with AutoDock Vina and rDock.
conclusionsThorough benchmarks reveal that G-LoSA offers a fairly robust overall performance, whereas the accuracy of APoc and SiteEngine is satisfactory only against easy datasets. Moreover, combining various algorithms into a meta-predictor improves the performance of existing methods to detect similar binding sites in unrelated proteins by 5-10%. All data reported in this paper are freely available at https://osf.io/6ngbs/ .
Indexed as
Identifiers
What OpenQuestion holds
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.