Fast prediction and visualization of protein binding pockets with PASS

By Brady Gp, P. Stouten. 2000

544 citations recorded by the source corpus.

Subfields: Cell Biology, Materials Chemistry, Molecular Biology

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Citations in the corpus

Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites (2005, methodology): “Other pocket detection methods include Cavity Search (Ho and Marshall, 1990), POCKET (Levitt and Banaszak, 1992), CAST (Liang et al., 1998), VOIDOO (Kleywegt, 1994), PASS (Brady and Stouten, 2000), LigandFit (Venkatachalam et al., 2003), APROPOS (Peters et al., 1996) and the methods of Delaney (1992”
PointSite: a point cloud segmentation tool for identification of protein ligand binding atoms (2019, methodology): “, PASS [3]), alpha shape (e.”
DeepSite: protein‐binding site predictor using 3D‐convolutional neural networks (2017, background): “PASS (Brady and Stouten, 2000) computes a coating of probe spheres with protein as substrate, with additional layers of probes accreted onto previously found probe spheres, keeping in the end low solvent exposure spheres at the point when an accretion layer no longer produces new probe spheres.”
LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation (2006, methodology): “Alternatively, the success rate of predictions can be measured by computing the distance between the ligand and a single point representing the pocket [9].”
Molecular docking: a powerful approach for structure-based drug discovery. (2011, background): “GRID[20, 21], POCKET [22], SurfNet [23, 24], PASS [25] and MMC [26] can be utilized to identify putative active sites within proteins.”
MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories (2011, unknown): “Only few tools have been reported to address the identiﬁcation algorithm (Brady and Stouten, 2000), EPOS BP deﬁnes pocket volumes via active site points and neighbouring probe positions identiﬁed by PASS.”
Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes* (2005, unknown): “PASS ( 17 ) identifies the “active site points” by coating the protein surface with a layer of spherical probes and then filtering out From the ‡Department of Molecular Biology, The Scripps Research”
MOLE 2.0: advanced approach for analysis of biomacromolecular channels (2013, methodology): “This method is implemented in PASS [32] and SURFNET [33].”
Toward prediction of functional protein pockets using blind docking and pocket search algorithms (2011, methodology): “The second binding pocket (L 1b ) of Propofol was identified as a 3rd rank by AutoDock4, and two PS methods (Q-SiteFinder and Pass) also placed it in the top three ranks using the holo target form (Fig.”
Fpocket: An open source platform for ligand pocket detection (2009, background): “Some have been released as binaries (e.g. [14]), and for instance only the recently released PocketPicker [23] and LigSite(csc) [19] are available as open source softwares.”
Druggable Protein Interaction Sites Are More Predisposed to Surface Pocket Formation than the Rest of the Protein Surface (2013, result): “This observation is consistent with results generated using other pocket detection methods [29–33], although those other studies were not focused on inhibitors of protein-protein interactions.”
PocketPicker: analysis of ligand binding-sites with shape descriptors (2007, methodology): “To assess the quality of PocketPicker's binding-site predictions we refer to an evaluation method already applied in previous studies [14,17].”
MetaPocket: A Meta Approach to Improve Protein Ligand Binding Site Prediction (2009, unknown): “, 1998), PASS (Brady and Stouten, 2000), and PocketPicker (Weisel et al.”