Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites

By Alasdair T. R. Laurie, Richard M. Jackson. 2005

285 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Biology & Bioinformatics, Computational Chemistry & Drug Discovery

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Citations in the corpus

A position-specific distance-dependent statistical potential for protein structure and functional study (2012, background): “…Levitt, 1992; Simons et al., 1997; Wu et al., 2007a; Zhao et al., 2008, 2010), docking (Zhang et al., 1997), binding (Kortemme and Baker, 2002; Laurie and Jackson, 2005), mutation study (Gilis and Rooman, 1996, 1997), decoy ranking (Bauer and Beyer, 1994; Casari and Sippl, 1992; Gatchell et…”
Fpocket: An open source platform for ligand pocket detection (2009, background): “Roughly, some are based on pure geometric analysis of the surface of the protein [4-15, 18 , 20, 22-26 22-26 ], whereas some others involve energy calculations [ 16 , 17].”
The FTMap family of web servers for determining and characterizing ligand binding hot spots of proteins (2015, methodology): “Q-SiteFinder and SITEHOUND both use a methyl probe, whereas SiteMap uses a water molecule as the probe.”
PocketPicker: analysis of ligand binding-sites with shape descriptors (2007, methodology): “It has been observed that the active site volume scales with the protein size whereas there is little correlation between protein volume and ligand volume [26].”
Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement (2012, unknown): “There are other methods that use calculated interaction energies (Goodford, 1985; Laurie and Jackson, 2005; Wade et al., 1993) or protein structure dynamics (Landon et al.”
Selectivity by Small-Molecule Inhibitors of Protein Interactions Can Be Driven by Protein Surface Fluctuations (2015, unknown): “Only those with pocket size (evaluated by Q-SiteFinder (41)) matching the protein interaction site (Figure S3) were included in Figures 2 and 3).”
Assessment of ligand binding site predictions in CASP10 (2014, unknown): “Several computational approaches for predicting ligand binding sites have been developed, which differ with respect to the information they are based on: (1) only the sequence of the protein; (2) its structural properties; (3) both sequence and structure; (4) homologue proteins.”
PointSite: a point cloud segmentation tool for identification of protein ligand binding atoms (2019, methodology): “MetaPocket2 [18, 47] [47, 18] is a meta server to identify LBS based on the identification results from the eight popular pocket-centric methods: LIGSITEcsc [19], PASS [3], Q-SiteFinder [26], SURFNET [25], Fpocket (version 1) [28], GHECOM [22], ConCavity [5] and POCASA [45].”
LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation (2006, methodology): “To this end, we use 210 bound structures from the Protein Ligand Database (PLD) [17] and 48 bound/unbound structures from [16] and [9].”
Machine-learning methods for ligand-protein molecular docking. (2021, methodology): “Commonly, this search is done either by a geometrical search like FPocket [39] or by looking for the most interesting zones regarding the free energy of binding, like in Q-SiteFinder, which used a − CH 3 probe to detect them [40].”
FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins (2011, methodology): “The structure-based methods can be further subdivided into the geometric methods utilised by FINDSITE [11] and SiteHunter (CASP8 - FN163) [12]; energetic methods utilised by SITEHOUND [13] and Q-SiteFinder [14] and miscellaneous methods that utilise information from; homology modelling, such as 3D L”
Fragment-based identification of druggable 'hot spots' of proteins using Fourier domain correlation techniques (2009, background): “A number of methods identify potential binding sites (An et al. , 2005; Glaser et al. , 2006; Laurie and Jackson, 2005).”
Toward prediction of functional protein pockets using blind docking and pocket search algorithms (2011, methodology): “These methods are citation-classics (Q-SiteFinder 24 and Pass 26 ), and a novel, promising program Sitehound 23 is also included.”
MetaPocket: A Meta Approach to Improve Protein Ligand Binding Site Prediction (2009, unknown): “Pocket-Finder is another implementation of LIGSITE (Laurie and Jackson, 2005).”