The Crystal Structure of MCAT from Mycobacterium tuberculosis Reveals Three New Catalytic Models

Li, Zexuan; Huang, Yishu; Ge, Jing; Fan, Hang; Zhou, Xiao Hong; Li, Shentao; Bartlam, Mark; Wang, Honghai; Rao, Zihe

doi:10.1016/j.jmb.2007.06.004

Cited by 31 publications

(32 citation statements)

References 33 publications

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“…S1). Crystal structures of bacterial FabDs (19)(20)(21)(22) and their mammalian orthologs are available (23). We identified 15 potential inhibitors of FabD (FDi1-15) through virtual screening.…”

Section: Virtual Screening Of Small-molecule Compounds Against Enzymementioning

confidence: 99%

Blueprint for antimicrobial hit discovery targeting metabolic networks

Shen

Liu

Estiú

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

102

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Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strainspecific antiinfective therapy.antibiotics | flux balance analysis | virtual screening T he deciphering of genomes from a variety of organisms holds the promise of significantly increasing the speed and efficiency of drug discovery. The challenge to find "druggable" targets (1) that will have an effect in the complex interaction network of a living organism has been only partially met by genomics and functional genomics approaches (2). For example, systematic gene deletion studies have been widely used to identify essential genes whose protein product could serve as potential antibiotic targets in a given bacterium. This approach yields only growthcondition-specific results, as a molecular target that is found to be essential in one specific environment may not be essential in others. To overcome these limitations and to couple target identification to the identification of small molecules that can affect them, cellular network analysis and computational chemistry approaches may prove highly efficient and scalable methods. By using entirely computational methods in the initial steps of drug discovery, the more expensive and time-consuming experimental methods could be applied in a more focused fashion to smaller sets of targets and hits. Although the potential time and resource savings of this strategy are widely acknowledged and have already yielded interesting results in individual aspects of drug discovery (3, 4), so far there have been no successful examples where computational methods were integrated seamlessly to develop hits toward targets identified and validated by using genomic and systems-level methods.Antibiotic drug resistance significantly eroded the effectiveness of currently available antimicrobial drugs toward disease-causing bacteria (5). As a consequence, today the yearly mortality rate in the United States due to multidrug-resistant Staphylococcus aureus infections is higher than that due to AIDS (6). Here, we provide a proof-of-principle demonstration that the combined use of bacterial metabolic network analysis with virtual screening and subsequent experimental verification is an effective method for the simu...

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Section: Virtual Screening Of Small-molecule Compounds Against Enzymementioning

confidence: 99%

Blueprint for antimicrobial hit discovery targeting metabolic networks

Shen

Liu

Estiú

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

102

View full text Add to dashboard Cite

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“…However, the activity of Q10A mutant declined to 26.6%, whereas that was observed to 10% or even lower from Streptomyces and Mycobaterium [10]. Detailed analysis of the tertiary structure showed that the side chain of Gln10 directed upwards of the backbone and flipped 60 comparing with MCAT from other species (Fig.…”

Section: Mutagenesis Analysis Of Spmcatmentioning

confidence: 91%

“…3 in which Ser88-His188 conducts proton transfer. The second one considered the active serine hydroxyl turning towards the neighboring histidine, hence the corresponding His87 substituting His188 to participate in the deprotonation represented in M. tuberculosis [10]. The third one is described in S. coelicolor, in which as an alternative mechanism, especially when Ser88-His188 interaction was inhibited, His87 acts as a nucleophilic candidate instead of Ser88 and cooperates with Asn152 by forming hydrogen bond to initialize the reaction [9].…”

Section: Mutagenesis Analysis Of Spmcatmentioning

confidence: 99%

Structural and biochemical characterization of MCAT from photosynthetic microorganism Synechocystis sp. PCC 6803 reveal its stepwise catalytic mechanism

Liu

Feng

Wang

et al. 2015

Biochemical and Biophysical Research Communications

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“…Several MCAT crystal structures from different organisms have been published (Natarajan et al, 2012;Keatinge-Clay et al, 2003;Li et al, 2007;Oefner et al, 2006;Serre et al, 1995;Zhang et al, 2007). However, the structure of an MCAT from a cyanobacterium has not yet been reported.…”

Section: Introductionmentioning

confidence: 99%

“…Sequence alignment indicated that the MCAT from Synechocystis sp. PCC 6803 has sequence identities of 40% to the MCAT from Escherichia coli (PDB entry 1mla; Serre et al, 1995), 36% to MCAT from Staphylococcus aureus (PDB entry 3im9; Hong, Kim, Park et al, 2010), 32% to MCAT from Mycobacterium tuberculosis (PDB entry 2qc3; Li et al, 2007) and 30% to MCAT from Helicobacter pylori (PDB entry 2h1y; Zhang et al, 2007). In order to determine the cyanobacterial MCAT structure, thereby enriching the structural information for the entire MCAT enzyme family, here we report the cloning, expression, purification, crystallization and preliminary X-ray crystallographic analysis of the MCAT encoded by fabD (slr2023) from Synechocystis sp.…”

Section: Introductionmentioning

confidence: 99%

Cloning, purification, crystallization and preliminary X-ray crystallographic analysis of MCAT fromSynechocystissp. PCC 6803

Liu

Zhang²,

Cao

et al. 2013

Acta Cryst Sect F

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Malonyl-coenzymeA:acyl-carrier protein transacylase (MCAT), which catalyzes the transfer of the malonyl group from malonyl-CoA to acyl-carrier protein (ACP), is an essential enzyme in type II fatty-acid synthesis. The enzyme MCAT from Synechocystis sp. PCC 6803 (spMCAT), the first MCAT counterpart from a cyanobacterium, was cloned, purified and crystallized in order to determine its three-dimensional crystal structure. A higher-quality crystal with better diffraction was obtained by crystallization optimization. The crystal diffracted to 1.8 Å resolution and belonged to the orthorhombic space group P2 1 2 1 2, with unit-cell parameters a = 43.22, b = 149.21, c = 40.59 Å . Matthews coefficient calculations indicated that the crystal contained one spMCAT molecule in the asymmetric unit with a Matthews coefficient of 2.18 Å 3 Da À1 and a solvent content of 43.65%.

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The Crystal Structure of MCAT from Mycobacterium tuberculosis Reveals Three New Catalytic Models

Cited by 31 publications

References 33 publications

Blueprint for antimicrobial hit discovery targeting metabolic networks

Blueprint for antimicrobial hit discovery targeting metabolic networks

Structural and biochemical characterization of MCAT from photosynthetic microorganism Synechocystis sp. PCC 6803 reveal its stepwise catalytic mechanism

Cloning, purification, crystallization and preliminary X-ray crystallographic analysis of MCAT fromSynechocystissp. PCC 6803

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