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. Author manuscript; available in PMC: 2021 Jun 8.
Published in final edited form as: Methods Mol Biol. 2019;1903:1–21. doi: 10.1007/978-1-4939-8955-3_1

Table 3.

Representative online protein binding pocket prediction methods

Name Web server Features
DeepSite [114] http://www.playmolecule.org/deepsite Uses neural network to predict ligand binding pockets on proteins
AlloPred [115] http://www.sbg.bio.ic.ac.uk/allopred/home Investigates normal mode perturbation analysis and pocket features to predict allosteric pockets on proteins
PockDrug [116] http://pockdrug.rpbs.univparis-diderot.fr/cgi-bin/index.py Uses a combination of pocket estimation methods and pocket properties to predict pocket druggability
LIGSITEcsc [117] http://projects.biotec.tudresden.de/cgi-bin/index.php Identifies pockets on protein surface using Connolly surface and degree of conservation
MetaPocket [118] http://projects.biotec.tudresden.de/metapocket Combines the predicted binding sites from eight different methods to identify ligand binding sites on protein surface
POCASA [119] http://altair.sci.hokudai.ac.jp/g6/service/pocasa Predicts protein binding sites by rolling a sphere to detect pockets and cavities on protein surface