ArticlePloS one2019
CavBench: A benchmark for protein cavity detection methods.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- pyCAST, a Python package for the detection of cavities on surface proteins.Computational and structural biotechnology journal · 2025Article
- PDBspheres: a method for finding 3D similarities in local regions in proteins.NAR genomics and bioinformatics · 2022Article
- In Silico Analysis of Peptide Macrocycle -Protein Interactions.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Predicting binding sites from unbound versus bound protein structures.Scientific reports · 2020Article
- A Comprehensive Mapping of the Druggable Cavities within the SARS-CoV-2 Therapeutically Relevant Proteins by Combining Pocket and Docking Searches as Implemented in Pockets 2.0.International journal of molecular sciences · 2020Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extensive research has been applied to discover new techniques and methods to model protein-ligand interactions. In particular, considerable efforts focused on identifying candidate binding sites, which quite often are active sites that correspond to protein pockets or cavities. Thus, these cavities play an important role in molecular docking. However, there is no established benchmark to assess the accuracy of new cavity detection methods. In practice, each new technique is evaluated using a small set of proteins with known binding sites as ground-truth. However, studies supported by large datasets of known cavities and/or binding sites and statistical classification (i.e., false positives, false negatives, true positives, and true negatives) would yield much stronger and reliable assessments. To this end, we propose CavBench, a generic and extensible benchmark to compare different cavity detection methods relative to diverse ground truth datasets (e.g., PDBsum) using statistical classification methods.
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