Literature DB >> 9777490

Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs.

H J Böhm1.   

Abstract

A dataset of 82 protein-ligand complexes of known 3D structure and binding constant Ki was analysed to elucidate the important factors that determine the strength of protein-ligand interactions. The following parameters were investigated: the number and geometry of hydrogen bonds and ionic interactions between the protein and the ligand, the size of the lipophilic contact surface, the flexibility of the ligand, the electrostatic potential in the binding site, water molecules in the binding site, cavities along the protein-ligand interface and specific interactions between aromatic rings. Based on these parameters, a new empirical scoring function is presented that estimates the free energy of binding for a protein-ligand complex of known 3D structure. The function distinguishes between buried and solvent accessible hydrogen bonds. It tolerates deviations in the hydrogen bond geometry of up to 0.25 A in the length and up to 30 degrees in the hydrogen bond angle without penalizing the score. The new energy function reproduces the binding constants (ranging from 3.7 x 10(-2) M to 1 x 10(-14) M, corresponding to binding energies between -8 and -80 kJ/mol) of the dataset with a standard deviation of 7.3 kJ/mol corresponding to 1.3 orders of magnitude in binding affinity. The function can be evaluated very fast and is therefore also suitable for the application in a 3D database search or de novo ligand design program such as LUDI. The physical significance of the individual contributions is discussed.

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Year:  1998        PMID: 9777490     DOI: 10.1023/a:1007999920146

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  103 in total

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Authors:  A Morton; B W Matthews
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8.  Three-dimensional structure of a fluorescein-Fab complex crystallized in 2-methyl-2,4-pentanediol.

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Journal:  Proteins       Date:  1989

9.  Design and synthesis of potent and highly selective thrombin inhibitors.

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Journal:  J Med Chem       Date:  1994-11-11       Impact factor: 7.446

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  86 in total

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Authors:  G Schneider; M L Lee; M Stahl; P Schneider
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4.  Receptor-based 3D QSAR analysis of estrogen receptor ligands--merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods.

Authors:  W Sippl
Journal:  J Comput Aided Mol Des       Date:  2000-08       Impact factor: 3.686

5.  Computer-aided design and activity prediction of leucine aminopeptidase inhibitors.

Authors:  J Grembecka; W A Sokalski; P Kafarski
Journal:  J Comput Aided Mol Des       Date:  2000-08       Impact factor: 3.686

6.  Protein ligand docking based on empirical method for binding affinity estimation.

Authors:  P Tao; L Lai
Journal:  J Comput Aided Mol Des       Date:  2001-05       Impact factor: 3.686

7.  Filtering databases and chemical libraries.

Authors:  Paul S Charifson; W Patrick Walters
Journal:  J Comput Aided Mol Des       Date:  2002 May-Jun       Impact factor: 3.686

8.  Induction of intracellular calcium concentration by environmental benzo(a)pyrene involves a β2-adrenergic receptor/adenylyl cyclase/Epac-1/inositol 1,4,5-trisphosphate pathway in endothelial cells.

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Journal:  J Biol Chem       Date:  2011-12-13       Impact factor: 5.157

9.  Can we separate active from inactive conformations?

Authors:  David J Diller; Kenneth M Merz
Journal:  J Comput Aided Mol Des       Date:  2002-02       Impact factor: 3.686

10.  Further development and validation of empirical scoring functions for structure-based binding affinity prediction.

Authors:  Renxiao Wang; Luhua Lai; Shaomeng Wang
Journal:  J Comput Aided Mol Des       Date:  2002-01       Impact factor: 3.686

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