Candidate antibacterials are usually identified on the basis of their in vitro activity. However, the apparent inhibitory activity of new leads can be misleading because most culture media do not reproduce an environment relevant to infection in vivo. In this study, while screening for novel anti-tuberculars, we uncovered how carbon metabolism can affect antimicrobial activity. Novel pyrimidine–imidazoles (PIs) were identified in a whole-cell screen against Mycobacterium tuberculosis. Lead optimization generated in vitro potent derivatives with desirable pharmacokinetic properties, yet without in vivo efficacy. Mechanism of action studies linked the PI activity to glycerol metabolism, which is not relevant for M. tuberculosis during infection. PIs induced self-poisoning of M. tuberculosis by promoting the accumulation of glycerol phosphate and rapid ATP depletion. This study underlines the importance of understanding central bacterial metabolism in vivo and of developing predictive in vitro culture conditions as a prerequisite for the rational discovery of new antibiotics.

Genome-wide gene essentiality data sets are becoming available for Escherichia coli, but these data sets have yet to be analyzed in the context of a genome scale model. Here, we present an integrative model-driven analysis of the Keio E. coli mutant collection screened in this study on glycerol-supplemented minimal medium. Out of 3,888 single-deletion mutants tested, 119 mutants were unable to grow on glycerol minimal medium. These conditionally essential genes were then evaluated using a genome scale metabolic and transcriptional-regulatory model of E. coli, and it was found that the model made the correct prediction in ϳ91% of the cases. The discrepancies between model predictions and experimental results were analyzed in detail to indicate where model improvements could be made or where the current literature lacks an explanation for the observed phenotypes. The identified set of essential genes and their model-based analysis indicates that our current understanding of the roles these essential genes play is relatively clear and complete. Furthermore, by analyzing the data set in terms of metabolic subsystems across multiple genomes, we can project which metabolic pathways are likely to play equally important roles in other organisms. Overall, this work establishes a paradigm that will drive model enhancement while simultaneously generating hypotheses that will ultimately lead to a better understanding of the organism.The advent of whole-genome sequencing and other highthroughput experimental technologies provides system level measurements that are driving efforts to develop computational models of the cell. The constraint-based reconstruction and analysis (COBRA) approach (36) has emerged in recent years as a successful approach to modeling systems on a genome scale. The COBRA approach begins with developing a metabolic network reconstruction based on the annotated genome sequence, known biochemistry, and other physiological data (38). Known constraints, such as enzymaticreaction reversibility and maximum flux capacity, are then imposed on the network reconstruction to generate a model that defines all attainable network states (36). A current metabolic and regulatory model of Escherichia coli contains 932 unique metabolic reactions and Boolean logic statements for how 104 transcription factors regulate the expression of 479 out of the 906 metabolic genes (6). COBRA methods are available to predict which metabolic and regulatory genes are required for growth under given environmental conditions (7,11,43,44).Knowledge of which genes in an organism are essential and under what conditions they are essential is of fundamental and practical importance. This knowledge provides us with a unique tool to refine the interpretation of cellular networks and to map critical points in these networks. Examples of applications in which this information may be useful include engineering industrial microbial strains, as well as developing novel anti-infective agents. The importance of this emerging field devoted to investigat...

Like thymidylate synthase (TS) in eukaryotes, the thymidylate synthase-complementing proteins (TSCPs) are mandatory for cell survival of many prokaryotes in the absence of external sources of thymidylate. Details of the mechanism of this novel family of enzymes are unknown. Here, we report the structural and functional analysis of a TSCP from Thermotoga maritima and its complexes with substrate, analogs, and cofactor. The structures presented here provide a basis for rationalizing the TSCP catalysis and reveal the possibility of the design of an inhibitor. We have identified a new helix-loop-strand FAD binding motif characteristic of the enzymes in the TSCP family. The presence of a hydrophobic core with residues conserved among the TSCP family suggests a common overall fold.

RNA pseudouridine synthase, TruB, catalyzes pseudouridine formation at U55 in tRNA. This posttranscriptional modification is almost universally conserved and occurs in the T arm of most tRNAs. We determined the crystal structure of Escherichia coli TruB apo enzyme, as well as the structure of Thermotoga maritima TruB in complex with RNA. Comparison of the RNA-free and -bound forms of TruB reveals that this enzyme undergoes significant conformational changes on binding to its substrate. These conformational changes include the ordering of the ''thumb loop,'' which binds right into the RNA hairpin loop, and a 10°hinge movement of the C-terminal domain. Along with the result of docking experiments performed on apo TruB, we conclude that TruB recognizes its RNA substrate through a combination of rigid docking and induced fit, with TruB first rigidly binding to its target and then maximizing the interaction by induced fit. RNA modification is a posttranscriptional process whereby certain nucleotides are altered after their initial incorporation into an RNA chain. Pseudouridine (⌿) is the most abundantly found modification in RNA (1). It is found in most RNAs, including transfer, ribosomal, transfer-messenger, small nuclear, and small nucleolar guide RNAs. Many ⌿ residues are highly conserved and appear to be confined to the functionally important part of RNA. For example, ⌿s are clustered within the peptidyl transferase center of the ribosome (2), are conserved within regions of small nuclear RNAs that are involved in RNA-RNA interactions (3), and have been implicated in spliceosome assembly (4).The most obvious structural effect of ⌿ formation is the creation of a new hydrogen bond donor N-H, located where C5 used to be. It has been shown that pseudouridylation has the effect of enhancing local RNA stacking in both single-stranded and duplex regions, resulting in increased conformational stability (5, 6). Certain genetic mutants lacking specific ⌿ residues in tRNA or rRNA exhibit difficulties in translation, display slow growth rates, and fail to compete effectively with wild-type strains in mixed culture (7-10). All of the evidence indicates that ⌿s play an important and critical role in RNA-mediated cellular processes. The precise role of this modification, however, remains unclear.⌿ synthases catalyze the isomerization of U to ⌿. The general mechanism for these enzymes requires a nucleophilic attack on C6 of the uracil ring in the target U by a universally conserved Asp residue, which leads to the breakage of the glycosidic bond, followed by a rotation of the uracil ring and reattachment of the C5 atom of the uracil to C1Ј of the ribose (Fig. 1A) (11). In prokaryotes, pseudouridylation is mediated by a set of enzymes that are site-or region-specific; each of these enzymes specifies the formation of just one or sometimes several ⌿s in RNA. Although the reaction catalyzed by each of these enzymes is the same, the substrate specificity varies from simple stem-loop structures to larger and more complex RNA.TruB catal...

A single base (U1939) within E. coli 23S ribosomal RNA is methylated by its dedicated enzyme, RumA. The structure of RumA/RNA/S-adenosylhomocysteine uncovers the mechanism for achieving unique selectivity. The single-stranded substrate is "refolded" on the enzyme into a compact conformation with six key intra-RNA interactions. The RNA substrate contributes directly to catalysis. In addition to the target base, a second base is "flipped out" from the core loop to stack against the adenine of the cofactor S-adenosylhomocysteine. Nucleotides in permuted sequence order are stacked into the site vacated by the everted target U1939 and compensate for the energetic penalty of base eversion. The 3' hairpin segment of the RNA binds distal to the active site and provides binding energy that contributes to enhanced catalytic efficiency. Active collaboration of RNA in catalysis leads us to conclude that RumA and its substrate RNA may reflect features from the earliest RNA-protein era.