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CavBench: A benchmark for protein cavity detection methods

Author

Listed:
  • Sérgio Dias
  • Tiago Simões
  • Francisco Fernandes
  • Ana Mafalda Martins
  • Alfredo Ferreira
  • Joaquim Jorge
  • Abel J P Gomes

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.

Suggested Citation

  • Sérgio Dias & Tiago Simões & Francisco Fernandes & Ana Mafalda Martins & Alfredo Ferreira & Joaquim Jorge & Abel J P Gomes, 2019. "CavBench: A benchmark for protein cavity detection methods," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-16, October.
    • Handle: RePEc:plo:pone00:0223596
    DOI: 10.1371/journal.pone.0223596
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    References listed on IDEAS

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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. John A Capra & Roman A Laskowski & Janet M Thornton & Mona Singh & Thomas A Funkhouser, 2009. "Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-18, December.
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