Literature DB >> 12820253

Application and limitations of X-ray crystallographic data in structure-based ligand and drug design.

Andrew M Davis1, Simon J Teague, Gerard J Kleywegt.   

Abstract

Structure-based design usually focuses upon the optimization of ligand affinity. However, successful drug design also requires the optimization of many other properties. The primary source of structural information for protein-ligand complexes is X-ray crystallography. The uncertainties introduced during the derivation of an atomic model from the experimentally observed electron density data are not always appreciated. Uncertainties in the atomic model can have significant consequences when this model is subsequently used as the basis of manual design, docking, scoring, and virtual screening efforts. Docking and scoring algorithms are currently imperfect. A good correlation between observed and calculated binding affinities is usually only observed only when very large ranges of affinity are considered. Errors in the correlation often exceed the range of affinities commonly encountered during lead optimization. Some structure-based design approaches now involve screening libraries by using technologies based on NMR spectroscopy and X-ray crystallography to discover small polar templates, which are used for further optimization. Such compounds are defined as leadlike and are also sought by more traditional high-throughput screening technologies. Structure-based design and HTS technologies show important complementarity and a degree of convergence.

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Year:  2003        PMID: 12820253     DOI: 10.1002/anie.200200539

Source DB:  PubMed          Journal:  Angew Chem Int Ed Engl        ISSN: 1433-7851            Impact factor:   15.336


  71 in total

1.  The effect of tightly bound water molecules on the structural interpretation of ligand-derived pharmacophore models.

Authors:  David G Lloyd; Alfonso T García-Sosa; Ian L Alberts; Nikolay P Todorov; Ricardo L Manceral
Journal:  J Comput Aided Mol Des       Date:  2004-02       Impact factor: 3.686

2.  Frozen out: molecular modeling in the age of cryocrystallography.

Authors:  Yvonne C Martin; Steven W Muchmore
Journal:  J Comput Aided Mol Des       Date:  2011-12-24       Impact factor: 3.686

3.  Challenges in the determination of the binding modes of non-standard ligands in X-ray crystal complexes.

Authors:  Alpeshkumar K Malde; Alan E Mark
Journal:  J Comput Aided Mol Des       Date:  2010-11-04       Impact factor: 3.686

4.  PDB ligand conformational energies calculated quantum-mechanically.

Authors:  Markus Sitzmann; Iwona E Weidlich; Igor V Filippov; Chenzhong Liao; Megan L Peach; Wolf-Dietrich Ihlenfeldt; Rajeshri G Karki; Yulia V Borodina; Raul E Cachau; Marc C Nicklaus
Journal:  J Chem Inf Model       Date:  2012-02-21       Impact factor: 4.956

5.  Methanethiol Binding Strengths and Deprotonation Energies in Zn(II)-Imidazole Complexes from M05-2X and MP2 Theories: Coordination Number and Geometry Influences Relevant to Zinc Enzymes.

Authors:  Douglas P Linder; Kenton R Rodgers
Journal:  J Phys Chem B       Date:  2015-09-04       Impact factor: 2.991

Review 6.  Elucidation of the molecular structures of components of the phycobilisome: reconstructing a giant.

Authors:  Noam Adir
Journal:  Photosynth Res       Date:  2005       Impact factor: 3.573

7.  Improving the Resistance Profile of Hepatitis C NS3/4A Inhibitors: Dynamic Substrate Envelope Guided Design.

Authors:  Ayşegül Ozen; Woody Sherman; Celia A Schiffer
Journal:  J Chem Theory Comput       Date:  2013-12-10       Impact factor: 6.006

8.  Single-Molecule Fluorescence Detection of the Epidermal Growth Factor Receptor in Membrane Discs.

Authors:  Steven D Quinn; Shwetha Srinivasan; Jesse B Gordon; Wei He; Kermit L Carraway; Matthew A Coleman; Gabriela S Schlau-Cohen
Journal:  Biochemistry       Date:  2018-04-06       Impact factor: 3.162

9.  Recipes for the selection of experimental protein conformations for virtual screening.

Authors:  Manuel Rueda; Giovanni Bottegoni; Ruben Abagyan
Journal:  J Chem Inf Model       Date:  2010-01       Impact factor: 4.956

10.  PIK3CA somatic mutations in breast cancer: Mechanistic insights from Langevin dynamics simulations.

Authors:  Parminder K Mankoo; Saraswati Sukumar; Rachel Karchin
Journal:  Proteins       Date:  2009-05-01
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