Literature DB >> 10369784

SuperStar: a knowledge-based approach for identifying interaction sites in proteins.

M L Verdonk1, J C Cole, R Taylor.   

Abstract

An empirical method for identifying interaction sites in proteins is described and validated. The method is based entirely on experimental information about non-bonded interactions occurring in small-molecule crystal structures. These data are used in the form of scatterplots that show the experimentally observed distribution of one functional group (the "contact group" or "probe") around another. A template molecule (e.g. a protein binding site) is broken down into structure fragments and the scatterplots, showing the distribution of a chosen probe around these structure fragments, are superimposed on the corresponding parts of the template. The scatterplots are then translated into a three-dimensional map that shows the propensity of the probe at different positions around the template molecule. The method is illustrated for l -arabinose-binding protein, complexed with l -arabinose and with d -fucose, and for dihydrofolate reductase complexed with methotrexate. The method is validated on 122 X-ray structures of protein-ligand complexes. For all the binding sites of these proteins, propensity maps are generated for four different probes: a charged NH+3nitrogen, a carbonyl oxygen, a hydroxyl oxygen and a methyl carbon atom. Next, the maps are compared with the experimentally observed positions of ligand atoms of these types. For 74% of these ligand atoms (84% of the solvent-inaccessible ones) the calculated propensity of the matching probe at the experimental positions is higher than expected by chance. For 68% of the atoms (82% of the solvent-inaccessible ones) the propensity of the matching probe is higher than that of the other three probes. These results indicate that the approach generally gives good predictions for protein-ligand interactions. The potential applications of the propensity maps range from an aid in manual docking and structure-based drug design to their use in pharmacophore development. Copyright 1999 Academic Press.

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Year:  1999        PMID: 10369784     DOI: 10.1006/jmbi.1999.2809

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  34 in total

1.  Calculating the knowledge-based similarity of functional groups using crystallographic data.

Authors:  P Watson; P Willett; V J Gillet; M L Verdonk
Journal:  J Comput Aided Mol Des       Date:  2001-09       Impact factor: 3.686

2.  On the molecular discrimination between adenine and guanine by proteins.

Authors:  I Nobeli; R A Laskowski; W S Valdar; J M Thornton
Journal:  Nucleic Acids Res       Date:  2001-11-01       Impact factor: 16.971

3.  Flexible docking under pharmacophore type constraints.

Authors:  Sally A Hindle; Matthias Rarey; Christian Buning; Thomas Lengaue
Journal:  J Comput Aided Mol Des       Date:  2002-02       Impact factor: 3.686

4.  A Bayesian molecular interaction library.

Authors:  Ville-Veikko Rantanen; Mats Gyllenberg; Timo Koski; Mark S Johnson
Journal:  J Comput Aided Mol Des       Date:  2003-07       Impact factor: 3.686

Review 5.  The multi-copy simultaneous search methodology: a fundamental tool for structure-based drug design.

Authors:  Christian R Schubert; Collin M Stultz
Journal:  J Comput Aided Mol Des       Date:  2009-06-09       Impact factor: 3.686

6.  Challenges of fragment screening.

Authors:  Diane Joseph-McCarthy
Journal:  J Comput Aided Mol Des       Date:  2009-06-30       Impact factor: 3.686

7.  In Silico Studies Targeting G-protein Coupled Receptors for Drug Research Against Parkinson's Disease.

Authors:  Agostinho Lemos; Rita Melo; Antonio Jose Preto; Jose Guilherme Almeida; Irina Sousa Moreira; Maria Natalia Dias Soeiro Cordeiro
Journal:  Curr Neuropharmacol       Date:  2018       Impact factor: 7.363

8.  Structure of apo-phosphatidylinositol transfer protein alpha provides insight into membrane association.

Authors:  Arie Schouten; Bogos Agianian; Jan Westerman; Jan Kroon; Karel W A Wirtz; Piet Gros
Journal:  EMBO J       Date:  2002-05-01       Impact factor: 11.598

9.  Molecular mechanisms of ligand-receptor interactions in transmembrane domain V of the alpha2A-adrenoceptor.

Authors:  Juha M Peltonen; Tommi Nyrönen; Siegfried Wurster; Marjo Pihlavisto; Anna-Marja Hoffrén; Anne Marjamäki; Henri Xhaard; Liisa Kanerva; Juha-Matti Savola; Mark S Johnson; Mika Scheinin
Journal:  Br J Pharmacol       Date:  2003-08-18       Impact factor: 8.739

10.  Ligand-binding site prediction of proteins based on known fragment-fragment interactions.

Authors:  Kota Kasahara; Kengo Kinoshita; Toshihisa Takagi
Journal:  Bioinformatics       Date:  2010-05-13       Impact factor: 6.937

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