Fpocket: An open source platform for ligand pocket detection

By V. Guilloux, Peter Schmidtke, Pierre Tufféry. BMC Bioinformatics, 2009

1,462 citations recorded by the source corpus.

BackgroundVirtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces.ResultsFpocket is an open source pocket detection package based on Voronoi tessellation and alpha spheres built on top of the publicly available package Qhull. The modular source code is organised around a central library of functions, a basis for three main programs: (i) Fpocket, to perform pocket identification, (ii) Tpocket, to organise pocket detection benchmarking on a set of known protein-ligand complexes, and (iii) Dpocket, to collect pocket descriptor values on a set of proteins. Fpocket is written in the C programming language, which makes it a platform well suited for the scientific community willing to develop new scoring functions and extract various pocket descriptors on a large scale level. Fpocket 1.0, relying on a simple scoring function, is able to detect 94% and 92% of the pockets within the best three ranked pockets from the holo and apo proteins respectively, outperforming the standards of the field, while being faster.ConclusionFpocket provides a rapid, open source and stable basis for further developments related to protein pocket detection, efficient pocket descriptor extraction, or drugablity prediction purposes. Fpocket is freely available under the GNU GPL license at http://fpocket.sourceforge.net.

Subfields: Biophysics, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d9ca92602df178bf86f6d427d99de3edf03f51c8

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Citations in the corpus

Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances (2014, methodology): “Examples of such an approach include SiteMap [29], FTMap [30], Fpocket [31], MDpocket [32], QsiteFinder [33], MED-SUMO [34] and SiteHoundweb [35].”
Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures (2022, methodology): “FPocket [31] was used to compute the active site volumes, where we used the -r option to specify the ligand and have a better approximation of the binding site (BS) volume.”
Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement (2012, neutral): “…lock'' (Fischer, 1894); hence shape and physiochemical complementarity are often used to detect concave pockets on proteins surface (Brady and Stouten, 2000; Hendlich et al., 1997; Huang and Schroeder, 2006; Laskowski, 1995; Le Guilloux et al., 2009; Levitt and Banaszak, 1992; Weisel et al., 2007).”
COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information (2017, methodology): “enrichment factor of binding site prediction by COFACTOR at different CscoreLBS over a ‘naı̈ve pocket’ approach in which ligand-binding sites are simply assigned by the largest cavities in the protein structure, as identified using Fpocket (32).”
COSMIC: the Catalogue Of Somatic Mutations In Cancer (2018, methodology): “Furthermore, COSMIC-3D allows the juxtaposition of cancer mutation data with known small-molecule binding sites in the wwPDB, and with druggable binding sites as predicted using fPocket (14).”
Selectivity by Small-Molecule Inhibitors of Protein Interactions Can Be Driven by Protein Surface Fluctuations (2015, neutral): “A number of related approaches have been described for identifying potential small molecule binding sites on protein surfaces in this way (51, 52, 80-84); the primary difference between these methods relative to the method described here is our use of “deep” pocket volume, which leads to improved di”
Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network (2022, methodology): “As a result, we took all proteins from our previous training set and generated training labels based on local changes in LIGSITE pocket volume and fpocket druggability scores 47 (see Methods).”
Uni-Mol: A Universal 3D Molecular Representation Learning Framework (2023, methodology): “To extract candidate pockets, we first clean the data by adding the missing side chains and hydrogen atoms; then we use Fpocket [45] to detect possible binding pockets of the proteins; and finally, we filter pockets by the number of residues in contact with and retains water molecules in the pocket.”
SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features (2014, positive): “Fpocket was used to find surfacepockets [38].”