Identification of four novel DC-SIGN ligands on Mycobacterium bovis BCG

Dendritic-cell-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN; CD209) has an important role in mediating adherence of Mycobacteria species, including M. tuberculosis and M. bovis BCG to human dendritic cells and macrophages, in which these bacteria can survive intracellularly. DC-SIGN is a C-type lectin, and interactions with mycobacterial cells are believed to occur via mannosylated structures on the mycobacterial surface. Recent studies suggest more varied modes of binding to multiple mycobacterial ligands. Here we identify, by affinity chromatography and mass-spectrometry, four novel ligands of M. bovis BCG that bind to DC-SIGN. The novel ligands are chaperone protein DnaK, 60 kDa chaperonin-1 (Cpn60.1), glyceraldehyde-3 phosphate dehydrogenase (GAPDH) and lipoprotein lprG. Other published work strongly suggests that these are on the cell surface. Of these ligands, lprG appears to bind DC-SIGN via typical protein-glycan interactions, but DnaK and Cpn60.1 binding do not show evidence of carbohydrate-dependent interactions. LprG was also identified as a ligand for DC-SIGNR (L-SIGN; CD299) and the M. tuberculosis orthologue of lprG has been found previously to interact with human toll-like receptor 2. Collectively, these findings offer new targets for combating mycobacterial adhesion and within-host survival, and reinforce the role of DC-SIGN as an important host ligand in mycobacterial infection.     

Essential residues for the enzyme activity of ATP-dependent MurE ligase from Mycobacterium tuberculosis

The emergence of total drug-resistant tuberculosis (TDR-TB) has made the discovery of new therapies for tuberculosis urgent. The cytoplasmic enzymes of peptidoglycan biosynthesis have generated renewed interest as attractive targets for the development of new antimycobacterials. One of the cytoplasmic enzymes, uridine diphosphate (UDP)-MurNAc-tripeptide ligase (MurE), catalyses the addition of meso-diaminopimelic acid (m-DAP) into peptidoglycan in Mycobacterium tuberculosis coupled to the hydrolysis of ATP. Mutants of M. tuberculosis MurE were generated by replacing K157, E220, D392, R451 with alanine and N449 with aspartate, and truncating the first 24 amino acid residues at the N-terminus of the enzyme. Analysis of the specific activity of these proteins suggested that apart from the 24 N-terminal residues, the other mutated residues are essential for catalysis. Variations in K_m values for one or more substrates were observed for all mutants, except the N-terminal truncation mutant, indicating that these residues are involved in binding substrates and form part of the active site structure. These mutant proteins were also tested for their specificity for a wide range of substrates. Interestingly, the mutations K157A, E220A and D392A showed hydrolysis of ATP uncoupled from catalysis. The ATP hydrolysis rate was enhanced by at least partial occupation of the uridine nucleotide dipeptide binding site. This study provides an insight into the residues essential for the catalytic activity and substrate binding of the ATP-dependent MurE ligase. Since ATP-dependent MurE ligase is a novel drug target, the understanding of its function may lead to development of novel inhibitors against resistant forms of M. tuberculosis.     

Structure analysis of the extracellular domain reveals disulfide bond forming-protein properties of Mycobacterium tuberculosis Rv2969c

Disulfide bond-forming (Dsb) protein is a bacterial periplasmic protein that is essential for the correct folding and disulfide bond formation of secreted or cell wallassociated proteins. DsbA introduces disulfide bonds into folding proteins, and is re-oxidized through interaction with its redox partner DsbB. Mycobacterium tuberculosis, a Gram-positive bacterium, expresses a DsbA-like protein ( Rv2969c), an extracellular protein that has its Nterminus anchored in the cell membrane. Since Rv2969c is an essential gene, crucial for disulfide bond formation, research of DsbA may provide a target of a new class of anti-bacterial drugs for treatment of M.tuberculosis infection. In the present work, the crystal structures of theextracellular region of Rv2969c (Mtb DsbA) were determined in both its reduced and oxidized states. The overall structure of Mtb DsbA can be divided into two domains: a classical thioredoxin-like domain with a typical CXXC active site, and an α-helical domain. It largely resembles its Escherichiacoli homologue EcDsbA, however, it possesses a truncated binding groove; in addition, its active site is surrounded by an acidic, rather than hydrophobic surface. In our oxidoreductase activity assay, Mtb DsbA exhibited a different substrate specificity when compared to EcDsbA. Moreover, structural analysis revealed a second disulfide bond in Mtb DsbA, which is rare in the previously reported DsbA structures, and is assumed to contribute to the overall stability of Mtb DsbA. To investigate the disulphide formation pathway in M.tuberculosis, we modeled Mtb Vitamin K epoxide reductase (Mtb VKOR), a binding partner of Mtb DsbA, to Mtb DsbA.     

Drug resistance mechanisms and novel drug targets for tuberculosis therapy

Drug-resistant tuberculosis (TB) poses a significant challenge to the successful treatment and control of TB worldwide. Resistance to anti-TB drugs has existed since the beginning of the chemotherapy era. New insights into the resistant mechanisms of anti-TB drugs have been provided. Better understanding of drug resistance mechanisms helps in the development of new tools for the rapid diagnosis of drug-resistant TB. There is also a pressing need in the development of new drugs with novel targets to improve the current treatment of TB and to prevent the emergence of drug resistance in Mycobacterium tuberculosis. This review summarizes the anti-TB drug resistance mechanisms, furnishes some possible novel drug targets in the development of new agents for TB therapy and discusses the usefulness using known targets to develop new anti-TB drugs. Whole genome sequencing is currently an advanced technology to uncover drug resistance mechanisms in M. tuberculosis. However, further research is required to unravel the significance of some newly discovered gene mutations in their contribution to drug resistance.     

Current strategies for identifying and validating targets for new treatment-shortening drugs for TB

There is an urgent need for new drugs to treat tuberculosis. During the last forty years the only drugs to have been developed are variations on existing ones, but new drug candidates must offer improvements over existing agents. In particular, we require new drugs having novel mechanisms of action that are active against drug-resistant strains and also kill persistent bacilli, thus shortening the length of chemotherapy. Recent advances in our understanding of the biology of Mycobacterium tuberculosis, in particularly the availability of the genome sequence coupled with development of new genetic tools, have greatly contributed to the discovery of potential drug targets for new antituberculars. However, although many potential new drug targets have been identified, greater effort is required in target validation to show properly that they are essential for bacterial growth and survival. In this review, the current drug development pipeline and the strategies employed to identify and validate novel tuberculosis drug targets are presented.     

Purification and characterization of anthranilate synthase component I (TrpE) from Mycobacterium tuberculosis H37Rv

The emergence of multi-drug resistant (MDR) strains of Mycobacterium tuberculosis is the main reason why tuberculosis (TB) continues to be a major health problem worldwide. It is urgent to discover novel anti-mycobacterial agents based on new drug targets for the treatment of TB, especially MDR-TB. Tryptophan biosynthetic pathway, which is essential for the survival of M. tuberculosis and meanwhile absent in mammals, provides potential anti-TB drug targets. One of the promising drug targets in this pathway is anthranilate synthase component I (TrpE), whose role is to catalyze the conversion of chorismate to anthranilate using ammonia as amino source. In order to get a deep understanding of TrpE, a study on purification and characteristic identification of TrpE is required. In this work, the putative trpE gene of M. tuberculosis H37Rv was expressed as a fusion protein with a 6x His-tag on the N-terminal (His-TrpE) in Escherichia coli. The recombinant TrpE protein was successfully purified and then its enzymatic characteristics were analyzed. The native TrpE without His-tag was obtained by removal of the N-terminal fusion partner of His-TrpE using enterokinase. It was found that N-terminal fusion partner had little influence on TrpE catalytic activity. In addition, the key residues related to enzyme catalytic activity and that involved in l-tryptophan inhibition were predicted in the structure of M. tuberculosis H37Rv TrpE. These results would be beneficial to the designing of novel anti-TB drugs with high potency and selectivity.     

Mycobacterium tuberculosis cytochrome P450 enzymes: a cohort of novel TB drug targets

TB (tuberculosis) disease remains responsible for the death of over 1.5 million people each year. The alarming emergence of drug-resistant TB has sparked a critical need for new front-line TB drugs with a novel mode of action. In the present paper, we review recent genomic and biochemical evidence implicating Mycobacterium tuberculosis CYP (cytochrome P450) enzymes as exciting potential targets for new classes of anti-tuberculars. We also discuss HTS (high-throughput screening) and fragment-based drug-discovery campaigns that are being used to probe their potential druggability.     

DDTRP: Database of Drug Targets for Resistant Pathogens

Emergence of drug resistance is a major threat to public health. Many pathogens have developed resistance to most of the existing antibiotics, and multidrug-resistant and extensively drug resistant strains are extremely difficult to treat. This has resulted in an urgent need for novel drugs. We describe a database called 'Database of Drug Targets for Resistant Pathogens' (DDTRP). The database contains information on drugs with reported resistance, their respective targets, metabolic pathways involving these targets, and a list of potential alternate targets for seven pathogens. The database can be accessed freely at http://bmi.icmr.org.in/DDTRP.     

Use of antisense oligonucleotides in functional genomics and target validation

With the completion of sequencing of the human genome, a great deal of interest has been shifted toward functional genomics-based research for identification of novel drug targets for treatment of various diseases. The major challenge facing the pharmaceutical industry is to identify disease-causing genes and elucidate additional roles for genes of known functions. Gene functionalization and target validation are probably the most important steps involved in identifying novel potential drug targets. This review focuses on recent advances in antisense technology and its use for rapid identification and validation of new drug targets. The significance and applicability of this technology as a beginning of the drug discovery process are underscored by relevant cell culture-based assays and positive correlation in specific animal disease models. Some of the antisense inhibitors used to validate gene targets are themselves being developed as drugs. The current clinical trials based on such leads that were identified in a very short time further substantiate the importance of antisense technology-based functional genomics as an integral part of target validation and drug target identification.     

Biotin biosynthesis in Mycobacterium tuberculosis: physiology,biochemistry and molecular intervention

Biotin is an important micronutrient that serves as an essential enzyme cofactor. Bacteria obtain biotin either through de novo synthesis or by active uptake from exogenous sources. Mycobacteria are unusual amongst bacteria in that their primary source of biotin is through de novo synthesis. Here we review the importance of biotin biosynthesis in the lifecycle of Mycobacteria. Genetic screens designed to identify key metabolic processes have highlighted a role for the biotin biosynthesis in bacilli growth, infection and survival during the latency phase. These studies help to establish the biotin biosynthetic pathway as a potential drug target for new anti-tuberculosis agents.     

A systematic analysis of FDA-approved anticancer drugs

Background

The discovery of novel anticancer drugs is critical for the pharmaceutical research and development, and patient treatment. Repurposing existing drugs that may have unanticipated effects as potential candidates is one way to meet this important goal. Systematic investigation of efficient anticancer drugs could provide valuable insights into trends in the discovery of anticancer drugs, which may contribute to the systematic discovery of new anticancer drugs.

Results

In this study, we collected and analyzed 150 anticancer drugs approved by the US Food and Drug Administration (FDA). Based on drug mechanism of action, these agents are divided into two groups: 61 cytotoxic-based drugs and 89 target-based drugs. We found that in the recent years, the proportion of targeted agents tended to be increasing, and the targeted drugs tended to be delivered as signal drugs. For 89 target-based drugs, we collected 102 effect-mediating drug targets in the human genome and found that most targets located on the plasma membrane and most of them belonged to the enzyme, especially tyrosine kinase. From above 150 drugs, we built a drug-cancer network, which contained 183 nodes (150 drugs and 33 cancer types) and 248 drug-cancer associations. The network indicated that the cytotoxic drugs tended to be used to treat more cancer types than targeted drugs. From 89 targeted drugs, we built a cancer-drug-target network, which contained 214 nodes (23 cancer types, 89 drugs, and 102 targets) and 313 edges (118 drug-cancer associations and 195 drug-target associations). Starting from the network, we discovered 133 novel drug-cancer associations among 52 drugs and 16 cancer types by applying the common target-based approach. Most novel drug-cancer associations (116, 87%) are supported by at least one clinical trial study.

Conclusions

In this study, we provided a comprehensive data source, including anticancer drugs and their targets and performed a detailed analysis in term of historical tendency and networks. Its application to identify novel drug-cancer associations demonstrated that the data collected in this study is promising to serve as a fundamental for anticancer drug repurposing and development.

     

Antimalarial drugs - host targets (re)visited

Every year, forty percent of the world population is at risk of contracting malaria. Hopes for the erradication of this disease during the 20th century were dashed by the ability of Plasmodium falciparum, its most deadly causative agent, to develop resistance to available drugs. Efforts to produce an effective vaccine have so far been unsuccessful, enhancing the need to develop novel antimalarial drugs. In this review, we summarize our knowledge concerning existing antimalarials, mechanisms of drug-resistance development, the use of drug combination strategies and the quest for novel anti-plasmodial compounds. We emphasize the potential role of host genes and molecules as novel targets for newly developed drugs. Recent results from our laboratory have shown Hepatocyte Growth Factor/MET signaling to be essential for the establishment of infection in hepatocytes. We discuss the potential use of this pathway in the prophylaxis of malaria infection.     

Assessing drug target association using semantic linked data

The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level) are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a [Formula: see text] score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap.     

Genomes2Drugs: Identifies Target Proteins and Lead Drugs from Proteome Data

Background

Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials.

Methodology/Principal Findings

To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i) homologous to previously crystallized proteins or (ii) targets of known drugs, but are (iii) not homologous to human proteins. When tested using the Plasmodium falciparum malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins.

Conclusions/Significance

Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines. This free online resource helps in the identification of potential drug targets. Importantly, the program further highlights proteins that are likely to be inhibited by FDA-approved drugs. These drugs can then be rapidly moved into Phase IV clinical studies under ‘change-of-application’ patents.     

Structural view of the regulatory subunit of aspartate kinase from Mycobacterium tuberculosis

The aspartate kinase (AK) from Mycobacterium tuberculosis (Mtb) catalyzes the biosynthesis of aspartate family amino acids, including lysine, threonine, isoleucine and methionine. We determined the crystal structures of the regulatory subunit of aspartate kinase from Mtb alone (referred to as MtbAKβ) and in complex with threonine (referred to as MtbAKβ-Thr) at resolutions of 2.6 ? and 2.0 ?, respectively. MtbAKβ is composed of two perpendicular non-equivalent ACT domains [aspartate kinase, chorismate mutase, and TyrA (prephenate dehydrogenase)] per monomer. Each ACT domain contains two α helices and four antiparallel β strands. The structure of MtbAKβ shares high similarity with the regulatory subunit of the aspartate kinase from Corynebacterium glutamicum (referred to as CgAKβ), suggesting similar regulatory mechanisms. Biochemical assays in our study showed that MtbAK is inhibited by threonine. Based on crystal structure analysis, we discuss the regulatory mechanism of MtbAK.     

Identifying Novel Drug Indications through Automated Reasoning

Background

With the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process.

Methodology

In this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications.

Conclusion/Significance

To evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.     

Multiple Drug Targeting Potential of Novel Ligands Against Virulent Proteins of Candida albicans

Systemic candidiasis is one of the most common nosocomial systemic infections causing high mortality and morbidity. Although infections caused by other Candida species are increasing the majority of candidiasis is commonly caused by Candida albicans which accounts for 40–60% of the reported cases. Current antifungal treatment regime is feeble due to persistent multiple drug resistance. Moreover, antifungal agents are mostly synthetic drugs that cause toxic side effects on immune-compromised patients. So, not only finding novel drug candidates from natural sources is necessary but to identify such compounds that have multiple targeting potential is essential. Hence, in this study, we propose to screen and identify bioactive natural compounds based on flavonoids, terpenes, and alkaloids for their potential antifungal activity. This in silico study was carried out by targeting 16 major virulent protein targets of C. albicans with 91 ligands. We have reported few ligands having multi-targeting potential and further analyzed theirs in silico pharmacokinetic profile. In silico ADMET analysis and protein–protein interaction can not only help in refining better drug candidates but also shortlist ideal protein for drug targeting.

     

Network Neighbors of Drug Targets Contribute to Drug Side-Effect Similarity

In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects.     

扩增子测序法筛选结核分枝杆菌的耐药突变

结核病是严重的公共健康问题之一,而耐药结核病的增加是控制结核病流行的难点之一。快速、准确的诊断是提高结核患者治愈率和降低死亡率的关键因素。本研究建立了基于二代测序技术的扩增子测序方法,对5种一线抗结核药物的17个耐药基因进行检测。在26个临床耐药结核菌株中共鉴定出65个突变,包括33个热点突变,9个稀有突变和23个新突变。对18个新发现的错义突变进行了蛋白质序列保守性和蛋白质局部结构的分析。结果表明,14个新的错义突变在9种分枝杆菌中显示出高度保守性,并且导致了该蛋白质局部结构的改变。根据本研究检测和分析结果,推测这些新发现的突变可能是潜在的耐药突变。在本研究中,构建了扩增子测序的检测方法,可同时检测10株临床结核菌株的17个耐药基因,是一种快速、准确并且全面的检测耐药结核分枝杆菌一线治疗药物耐药突变的方法,该方法不仅能检测热点突变和稀有突变,还能发现一些未报道过的新突变。该检测方法或可用于临床诊断和基础研究。