Literature DB >> 23432621

Fragment-based drug discovery using a multidomain, parallel MD-MM/PBSA screening protocol.

Tian Zhu1, Hyun Lee, Hao Lei, Christopher Jones, Kavankumar Patel, Michael E Johnson, Kirk E Hevener.   

Abstract

We have developed a rigorous computational screening protocol to identify novel fragment-like inhibitors of N(5)-CAIR mutase (PurE), a key enzyme involved in de novo purine synthesis that represents a novel target for the design of antibacterial agents. This computational screening protocol utilizes molecular docking, graphics processing unit (GPU)-accelerated molecular dynamics, and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) free energy estimations to investigate the binding modes and energies of fragments in the active sites of PurE. PurE is a functional octamer comprised of identical subunits. The octameric structure, with its eight active sites, provided a distinct advantage in these studies because, for a given simulation length, we were able to place eight separate fragment compounds in the active sites to increase the throughput of the MM/PBSA analysis. To validate this protocol, we have screened an in-house fragment library consisting of 352 compounds. The theoretical results were then compared with the results of two experimental fragment screens, Nuclear Magnetic Resonance (NMR) and Surface Plasmon Resonance (SPR) binding analyses. In these validation studies, the protocol was able to effectively identify the competitive binders that had been independently identified by experimental testing, suggesting the potential utility of this method for the identification of novel fragments for future development as PurE inhibitors.

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Year:  2013        PMID: 23432621      PMCID: PMC3752004          DOI: 10.1021/ci300502h

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  57 in total

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8.  N5-CAIR mutase: role of a CO2 binding site and substrate movement in catalysis.

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Review 5.  Mathematical and computational modeling in biology at multiple scales.

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