ProMate: a structure based prediction program to identify the location of protein-protein binding sites.

By Hani Neuvirth, R. Raz, Gideon Schreiber. 2004

456 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

Exploiting sequence and structure homologs to identify protein–protein binding sites (2005, unknown): “Other methods identify the binding sites on the basis of the residue hydrophobicity, 27 the properties of surface patches, 28,29 and the charge distributions at protein interaction interfaces.”
Protein interface conservation across structure space (2010, result): “Results were compared to the top three programs [cons-PPISP (30), PINUP (31), and ProMate (32)] reported in a recent comparative study of interface prediction methods (33), which also performed best in a small-scale evaluation we carried out.”
CPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK (2011, unknown): “For PIER, WHISCY, ProMate, SPPIDER and cons-PPISP these numbers were 71, 58, 56, 52 and 49, respectively.”
Structural analysis of B-cell epitopes in antibody:protein complexes (2012, background): “The over-representation of loops is small but significant and in agreement with the perception that protein-protein binding sites are flexible regions (Neuvirth et al., 2004).”
Improved prediction of protein-protein binding sites using a support vector machines approach. (2005, methodology): “We also tested our method against the dataset of Neuvirth et al. (2004).”
HADDOCK versus HADDOCK: New features and performance of HADDOCK2.0 on the CAPRI targets (2007, methodology): “9–12 The active and passive residues that were defined during the docking and target specific modifications to the protocols when applicable are given in Supplementary Tables S1 and S2.”
ElliPro: a new structure-based tool for the prediction of antibody epitopes (2008, methodology): “ElliPro has been tested on a benchmark dataset of epitopes inferred from 3D structures of antibody-protein complexes [11] and compared with six structure-based methods, including the only two existing methods developed specifically for epitope prediction, CEP [12] and DiscoTope [13]; two protein-pro”
Virtual screening strategies in drug discovery: a critical review. (2013, unknown): “programs, WHISCY [189] and ProMate [190].”
Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein-protein interaction sites (2010, background): “…determined protein 3D structures indeed provide important clues ∗To whom correspondence should be addressed. to identifying interaction sites and understanding protein functions (Fariselli et al., 2002; Fernandez-Recio et al., 2005; Jones and Thornton, 1997a, 1997b; Neuvirth et al., 2004).”
Prediction‐based fingerprints of protein–protein interactions (2006, background): “25–27 In the latter case, the problem typically involves the identification of specific patches on the surface of an unbound protein structure with residues that are either evolutionarily conserved or have a propensity for interaction interfaces.”
Protein–Protein Interaction Hotspots Carved into Sequences (2007, methodology): “ProMate [26] is a structure-based method that extracts features from an unbound chain and uses them to predict the binding site.”
ISIS: interaction sites identified from sequence (2007, methodology): “A computational-method that reliably identiﬁes interface residues from sequence could, therefore, be extremely valuable.”
Recent progress and future directions in protein-protein docking. (2008, background): “…main challenges in protein docking today are to be able to generate reliably trial conformations which closely resemble those of the native complex, and to devise improved scoring functions which can correctly distinguish near-native docking orientations from a list of highly complementary decoys.”