Characterization of a triazole scaffold compound as an inhibitor of Mycobacterium tuberculosis imidazoleglycerol-phosphate dehydratase.

Mycobacterium tuberculosis (Mtb), the causative agent of human tuberculosis (TB), employs ten enzymes including imidazoleglycerol-phosphate dehydratase (IGPD) for de novo biosynthesis of histidine. The absence of histidine-biosynthesis in humans combined with its essentiality for Mtb makes the enzymes of this pathway major anti-TB drug targets. We explored the inhibitory potential of a small molecule ß-(1,2,4-Triazole-3-yl)-DL-alanine (DLA) against Mtb IGPD. DLA exhibits an in vitro inhibitory efficacy in the lower micromolar range. Higher-resolution crystal structures of native and substrate-bound Mtb IGPD provided additional structural features of this important drug target. Crystal structure of IGPD-DLA complex at a resolution of 1.75 Å, confirmed that DLA locks down the function of the enzyme by binding in the active site pocket of the IGPD mimicking the substrate-binding mode to a high degree. In our biochemical study, DLA showed an efficient inhibition of Mtb IGPD. Furthermore, DLA also showed bactericidal activity against Mtb and Mycobacterium smegmatis and inhibited their growth in respective culture medium. Importantly, owing to the favorable ADME and physicochemical properties, it serves as an important lead molecule for further derivatizations.

Identification and structural characterization of a histidinol phosphate phosphatase from Mycobacterium tuberculosis.

The absence of a histidine biosynthesis pathway in humans, coupled with histidine essentiality for survival of the important human pathogen Mycobacterium tuberculosis (Mtb), underscores the importance of the bacterial enzymes of this pathway as major antituberculosis drug targets. However, the identity of the mycobacterial enzyme that functions as the histidinol phosphate phosphatase (HolPase) of this pathway remains to be established. Here, we demonstrate that the enzyme encoded by the Rv3137 gene, belonging to the inositol monophosphatase (IMPase) family, functions as the Mtb HolPase and specifically dephosphorylates histidinol phosphate. The crystal structure of Rv3137 in apo form enabled us to dissect its distinct structural features. Furthermore, the holo-complex structure revealed that a unique cocatalytic multizinc-assisted mode of substrate binding and catalysis is the hallmark of Mtb HolPase. Interestingly, the enzyme-substrate complex structure unveiled that although monomers possess individual catalytic sites they share a common product-exit channel at the dimer interface. Furthermore, target-based screening against HolPase identified several small-molecule inhibitors of this enzyme. Taken together, our study unravels the missing enzyme link in the Mtb histidine biosynthesis pathway, augments our current mechanistic understanding of histidine production in Mtb, and has helped identify potential inhibitors of this bacterial pathway.

Characterization of a secretory hydrolase from Mycobacterium tuberculosis sheds critical insight into host lipid utilization by M. tuberculosis.

Mycobacterium tuberculosis causes tuberculosis in humans and predominantly infects alveolar macrophages. To survive inside host lesions and to evade immune surveillance, this pathogen has developed many strategies. For example, M. tuberculosis uses host-derived lipids/fatty acids as nutrients for prolonged persistence within hypoxic host microenvironments. M. tuberculosis imports these metabolites through its respective transporters, and in the case of host fatty acids, a pertinent question arises: does M. tuberculosis have the enzyme(s) for cleavage of fatty acids from host lipids? We show herein that a previously uncharacterized membrane-associated M. tuberculosis protein encoded by Rv2672 is conserved exclusively in actinomycetes, exhibits both lipase and protease activities, is secreted into macrophages, and catalyzes host lipid hydrolysis. In light of these functions, we annotated Rv2672 as mycobacterial secreted hydrolase 1 (Msh1). Furthermore, we found that this enzyme is up-regulated both in an in vitro model of hypoxic stress and in a mouse model of M. tuberculosis infection, suggesting that the pathogen requires Msh1 under hypoxic conditions. Silencing Msh1 expression compromised the ability of M. tuberculosis to proliferate inside lipid-rich foamy macrophages but not under regular culture conditions in vitro, underscoring Msh1's importance for M. tuberculosis persistence in lipid-rich microenvironments. Of note, this is the first report providing insight into the mechanism of host lipid catabolism by an M. tuberculosis enzyme, augmenting our current understanding of how M. tuberculosis meets its nutrient requirements under hypoxic conditions.

Enhancing the prediction of IDC breast cancer staging from gene expression profiles using hybrid feature selection methods and deep learning architecture.

Prediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do not capture molecular events that cause cancer progression. Gene-expression data-based analyses are able to identify these events, allowing RNA-sequence and microarray cancer data to be used for cancer analyses. Breast cancer is the most common cancer worldwide, and is classified into four stages - stages 1, 2, 3, and 4 [2]. While machine learning models have previously been explored to perform stage classification with limited success, multi-class stage classification has not had significant progress. There is a need for improved multi-class classification models, such as by investigating deep learning models. Gene-expression-based cancer data is characterised by the small size of available datasets, class imbalance, and high dimensionality. Class balancing methods must be applied to the dataset. Since all the genes are not necessary for stage prediction, retaining only the necessary genes can improve classification accuracy. The breast cancer samples are to be classified into 4 classes of stages 1 to 4. Invasive ductal carcinoma breast cancer samples are obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets and combined. Two class balancing techniques are explored, synthetic minority oversampling technique (SMOTE) and SMOTE followed by random undersampling. A hybrid feature selection pipeline is proposed, with three pipelines explored involving combinations of filter and embedded feature selection methods: Pipeline 1 - minimum-redundancy maximum-relevancy (mRMR) and correlation feature selection (CFS), Pipeline 2 - mRMR, mutual information (MI) and CFS, and Pipeline 3 - mRMR and support vector machine-recursive feature elimination (SVM-RFE). The classification is done using deep learning models, namely deep neural network, convolutional neural network, recurrent neural network, a modified deep neural network, and an AutoKeras generated model. Classification performance post class-balancing and various feature selection techniques show marked improvement over classification prior to feature selection. The best multiclass classification was found to be by a deep neural network post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with a Cohen-Kappa score of 0.303 and a classification accuracy of 53.1%. For binary classification into early and late-stage cancer, the best performance is obtained by a modified deep neural network (DNN) post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with an accuracy of 81.0% and a Cohen-Kappa score (CKS) of 0.280. This pipeline also showed improved multiclass classification performance on neuroblastoma cancer data, with a best area under the receiver operating characteristic (auROC) curve score of 0.872, as compared to 0.71 obtained in previous work, an improvement of 22.81%. The results and analysis reveal that feature selection techniques play a vital role in gene-expression data-based classification, and the proposed hybrid feature selection pipeline improves classification performance. Multi-class classification is possible using deep learning models, though further improvement particularly in late-stage classification is necessary and should be explored further.

Structural snapshots of Mycobacterium tuberculosis enolase reveal dual mode of 2PG binding and its implication in enzyme catalysis.

Enolase, a ubiquitous enzyme, catalyzes the reversible conversion of 2-phosphoglycerate (2PG) to phosphoenolpyruvate (PEP) in the glycolytic pathway of organisms of all three domains of life. The underlying mechanism of the 2PG to PEP conversion has been studied in great detail in previous work, however that of the reverse reaction remains to be explored. Here we present structural snapshots of Mycobacterium tuberculosis (Mtb) enolase in apo, PEP-bound and two 2PG-bound forms as it catalyzes the conversion of PEP to 2PG. The two 2PG-bound complex structures differed in the conformation of the bound product (2PG) viz the widely reported canonical conformation and a novel binding pose, which we refer to here as the alternate conformation. Notably, we observed two major differences compared with the forward reaction: the presence of MgB is non-obligatory for the reaction and 2PG assumes an alternate conformation that is likely to facilitate its dissociation from the active site. Molecular dynamics studies and binding free energy calculations further substantiate that the alternate conformation of 2PG causes distortions in both metal ion coordination and hydrogen-bonding interactions, resulting in an increased flexibility of the active-site loops and aiding product release. Taken together, this study presents a probable mechanism involved in PEP to 2PG catalysis that is likely to be mediated by the conformational change of 2PG at the active site.

De novo histidine biosynthesis protects Mycobacterium tuberculosis from host IFN-γ mediated histidine starvation.

Intracellular pathogens including Mycobacterium tuberculosis (Mtb) have evolved with strategies to uptake amino acids from host cells to fulfil their metabolic requirements. However, Mtb also possesses de novo biosynthesis pathways for all the amino acids. This raises a pertinent question- how does Mtb meet its histidine requirements within an in vivo infection setting? Here, we present a mechanism in which the host, by up-regulating its histidine catabolizing enzymes through interferon gamma (IFN-γ) mediated signalling, exerts an immune response directed at starving the bacillus of intracellular free histidine. However, the wild-type Mtb evades this host immune response by biosynthesizing histidine de novo, whereas a histidine auxotroph fails to multiply. Notably, in an IFN-γ-/- mouse model, the auxotroph exhibits a similar extent of virulence as that of the wild-type. The results augment the current understanding of host-Mtb interactions and highlight the essentiality of Mtb histidine biosynthesis for its pathogenesis.

Structural insights into the substrate specificity of SP_0149, the substrate-binding protein of a methionine ABC transporter from Streptococcus pneumoniae.

Successful pathogenesis is a cumulative effect of the virulence factors of a pathogen and its capability to efficiently utilize the available nutrients from the host. Streptococcus pneumoniae, a Gram-positive opportunistic pathogen, may either reside asymptomatically as a nasopharyngeal commensal inside the human host or cause lethal diseases, including pneumonia, meningitis and sepsis. S. pneumoniae is known to acquire methionine (Met) from its host through a Met importer. Here, the crystal structure of the substrate-binding protein (SBP; SP_0149) of an ABC importer with Met bound is reported at a resolution of 1.95 Å. The three-dimensional structure of SBP shows that it is composed of two distinct domains, each consisting of a mixed ß-sheet flanked by helices. The substrate, Met, is bound in the central part of the interface between the two domains. The overall structure of SP_0149 resembles those of SBPs from other reported bacterial Met and Gly-Met dipeptide transporters. However, a detailed analysis of these structures shows notable variations in the amino-acid composition of the substrate-binding pockets of the SP_0149-Met and GmpC-Gly-Met structures. In particular, SP_0149 harbors Thr212 and Tyr114, whereas the corresponding residues in GmpC are Gly and Val. This difference is likely to be the underlying basis for their differential substrate specificity. In summary, the structure of the SP_0149-Met complex provides insights into the transport function of SP_0149 and its interactions with methionine. It opens up avenues for the rational design of inhibitors of SP_0149 through a structure-mediated approach.

Serendipitous crystallization and structure determination of bacterioferritin from Achromobacter.

Bacterioferritins (Bfrs) are ferritin-like molecules with a hollow spherical 24-mer complex design that are unique to bacterial and archaeal species. They play a critical role in storing iron(III) within the complex at concentrations much higher than the feasible solubility limits of iron(III), thus maintaining iron homeostasis within cells. Here, the crystal structure of bacterioferritin from Achromobacter (Ach Bfr) that crystallized serendipitously during a crystallization attempt of an unrelated mycobacterial protein is reported at 1.95 Šresolution. Notably, Fe atoms were bound to the structure along with a porphyrin ring sandwiched between the subunits of a dimer. Furthermore, the dinuclear ferroxidase center of Ach Bfr has only a single iron bound, in contrast to the two Fe atoms in other Bfrs. The structure of Ach Bfr clearly demonstrates the substitution of a glutamate residue, which is involved in the interaction with the second Fe atom, by a threonine and the consequent absence of another Fe atom there. The iron at the dinuclear center has a tetravalent coordination, while a second iron with a hexavalent coordination was found within the porphyrin ring, generating a heme moiety. Achromobacter spp. are known opportunistic pathogens; this structure enhances the current understanding of their iron metabolism and regulation, and importantly will be useful in the design of small-molecule inhibitors against this protein through a structure-guided approach.