Literature DB >> 11342032

Inhibitors of dihydrodipicolinate reductase, a key enzyme of the diaminopimelate pathway of Mycobacterium tuberculosis.

A M Paiva1, D E Vanderwall, J S Blanchard, J W Kozarich, J M Williamson, T M Kelly.   

Abstract

Tuberculosis (TB) remains a leading cause of infectious disease in the world today and therapies developed over the last forty years are becoming increasingly ineffective against resistant strains of Mycobacterium tuberculosis. In an effort to explore new mechanisms for drug development, we have investigated the enzymes of the diaminopimelate biosynthetic pathway as potential targets. Specifically, dihydrodipicolinate reductase, the essential gene product of dapB, was screened for novel inhibitors. Inhibitors were identified both by a molecular modeling approach which utilized the available crystal structure of the enzyme with an inhibitor bound at the active site as well as by more conventional screening strategies. The resulting compounds contain a number of structural motifs and were all found to be competitive with respect to the DHDP substrate. The K(i) values for the inhibitors range from 10 to 90 microM. The molecular modeling approach was very effective in identifying novel inhibitors of the enzyme. These compounds were obtained at a higher frequency based on the number of compounds analyzed than those inhibitors discovered via conventional screening. However, conventional screening proved beneficial in identifying compounds with greater structural diversity.

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Year:  2001        PMID: 11342032     DOI: 10.1016/s0167-4838(00)00262-4

Source DB:  PubMed          Journal:  Biochim Biophys Acta        ISSN: 0006-3002


  23 in total

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Review 2.  Virtual screening of chemical libraries.

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Review 5.  Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach.

Authors:  I M Kapetanovic
Journal:  Chem Biol Interact       Date:  2006-12-16       Impact factor: 5.192

6.  Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection?

Authors:  Andrew C Good; Tudor I Oprea
Journal:  J Comput Aided Mol Des       Date:  2008-01-09       Impact factor: 3.686

Review 7.  Docking Screens for Novel Ligands Conferring New Biology.

Authors:  John J Irwin; Brian K Shoichet
Journal:  J Med Chem       Date:  2016-03-15       Impact factor: 7.446

8.  Multiscale modelling of relationships between protein classes and drug behavior across all diseases using the CANDO platform.

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Review 9.  Targeting xenobiotic receptors PXR and CAR in human diseases.

Authors:  Monimoy Banerjee; Delira Robbins; Taosheng Chen
Journal:  Drug Discov Today       Date:  2014-11-20       Impact factor: 7.851

10.  Probing molecular docking in a charged model binding site.

Authors:  Ruth Brenk; Stefan W Vetter; Sarah E Boyce; David B Goodin; Brian K Shoichet
Journal:  J Mol Biol       Date:  2006-02-02       Impact factor: 5.469

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