CavBench: A benchmark for protein cavity detection methods

English version

Autoři: Sérgio Dias ^aff001; Tiago Simões ^aff001; Francisco Fernandes ^aff003; Ana Mafalda Martins ^aff005; Alfredo Ferreira ^aff003; Joaquim Jorge ^aff003; Abel J. P. Gomes ^aff001
Působiště autorů: Instituto de Telecomunicações, Delegação da Covilhã, Covilhã, Portugal ^aff001; Universidade da Beira Interior, Departamento de Informática, Covilhã, Portugal ^aff002; INESC-ID, Lisboa, Portugal ^aff003; Universidade de Lisboa, IST, Lisboa, Portugal ^aff004; Universidade Europeia, Lisboa, Portugal ^aff005
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0223596

Souhrn

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.

Klíčová slova:

Database and informatics methods – Protein interactions – Statistical data – Drug discovery – Protein structure – Software design – Parsers – Drug design

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CavBench: A benchmark for protein cavity detection methods

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