in silico analyses of metabolic pathway and protein interaction network for identification of next gen therapeutic targets in Chlamydophila pneumoniae

Chlamydophila pneumoniae, the causative agent of chronic obstructive pulmonary disease (COPD), is presently the fifth mortality causing chronic disease in the world. The understanding of disease and treatment options are limited represents a severe concern and a need for better therapeutics. With the advancements in the field of complete genome sequencing and computational approaches development have lead to metabolic pathway analysis and protein-protein interaction network which provides vital evidence to the protein function and has been appropriate to the fields such as systems biology and drug discovery. Protein interaction network analysis allows us to predict the most potential drug targets among large number of the non-homologous proteins involved in the unique metabolic pathway. A computational comparative metabolic pathway analysis of the host H. sapiens and the pathogen C pneumoniae AR39 has been carried out at three level analyses. Firstly, metabolic pathway analysis was performed to identify unique metabolic pathways and non-homologous proteins were identified. Secondly, essentiality of the proteins was checked, where these proteins contribute to the growth and survival of the organism. Finally these proteins were further subjected to predict protein interaction networks. Among the total 65 pathways in the C pneumoniae AR39 genome 10 were identified as the unique metabolic pathways which were not found in the human host, 32 enzymes were predicted as essential and these proteins were considered for protein interaction analysis, later using various criteria''s we have narrowed down to prioritize ribonucleotide-diphosphate reductase subunit beta as a potential drug target which facilitate for the successful entry into drug designing.     

Potential therapeutic drug target identification in Community Acquired-Methicillin Resistant Staphylococcus aureus (CA-MRSA) using computational analysis

The emergence of multidrug-resistant strain of community-acquired methicillin resistant Staphylococcus aureus (CA-MRSA) strain has highlighted the urgent need for the alternative and effective therapeutic approach to combat the menace of this nosocomial pathogen. In the present work novel potential therapeutic drug targets have been identified through the metabolic pathways analysis. All the gene products involved in different metabolic pathways of CA-MRSA in KEGG database were searched against the proteome of Homo sapiens using the BLASTp program and the threshold of E-value was set to as 0.001. After database searching, 152 putative targets were identified. Among all 152 putative targets, 39 genes encoding for putative targets were identified as the essential genes from the DEG database which are indispensable for the survival of CA-MRSA. After extensive literature review, 7 targets were identified as potential therapeutic drug target. These targets are Fructose-bisphosphate aldolase, Phosphoglyceromutase, Purine nucleoside phosphorylase, Uridylate kinase, Tryptophan synthase subunit beta, Acetate kinase and UDP-N-acetylglucosamine 1-carboxyvinyltransferase. Except Uridylate kinase all the identified targets were involved in more than one metabolic pathways of CA-MRSA which underlines the importance of drug targets. These potential therapeutic drug targets can be exploited for the discovery of novel inhibitors for CA-MRSA using the structure based drug design (SBDD) strategy.     

Identification of potential targets in Staphylococcus aureus N315 using computer aided protein data analysis

Staphylococcus aureus is a gram positive bacterium, responsible for both community-acquired and hospital-acquired infection, resulting in a mortality rate of 39%. 43.2% resistance to methicilin and emerging resistance to Fluroquinolone and Oxazolidinone, have evoked the necessity of the establishment of alternative and effective therapeutic approach to treat this bacteria. In this computational study, various database and online software are used to determine some specific targets of Staphylococcus aureus N315 other than those used by Penicillin, Quinolone and Oxazolidinone. For this purpose, among 302 essential proteins, 101 nonhomologous proteins were accrued and 64 proteins which are unique in several metabolic pathways of S. aureus were isolated by using metabolic pathway analysis tools. Furthermore, 7 essentially unique enzymes involved in exclusive metabolic pathways were revealed by this research, which can be potential drug target. Along with these important enzymes, 15 non-homologous proteins located on membrane were identified, which can play a vital role as potential therapeutic targets for the future researchers.     

Identification of Phosphoribosyl-AMP cyclohydrolase,as drug target and its inhibitors in Brucella melitensis bv. 1 16M using metabolic pathway analysis

Brucella melitensis is a pathogenic Gram-negative bacterium which is known for causing zoonotic diseases (Brucellosis). The organism is highly contagious and has been reported to be used as bioterrorism agent against humans. Several antibiotics and vaccines have been developed but these antibiotics have exhibited the sign of antibiotic resistance or ineffective at lower concentrations, which imposes an urgent need to identify the novel drugs/drug targets against this organism. In this work, metabolic pathways analysis has been performed with different filters such as non-homology with humans, essentially of genes and choke point analysis, leading to identification of novel drug targets. A total of 18 potential drug target proteins were filtered out and used to develop the high confidence protein–protein interaction network The Phosphoribosyl-AMP cyclohydrolase (HisI) protein has been identified as potential drug target on the basis of topological parameters. Further, a homology model of (HisI) protein has been developed using Modeller with multiple template (1W6Q (48%), 1ZPS (55%), and 2ZKN (48%)) approach and validated using PROCHECK and Verify3D. The virtual high throughput screening (vHTS) using DockBlaster tool has been performed against 16,11,889 clean fragments from ZINC database. Top 500 molecules from DockBlaster were docked using Vina. The docking analysis resulted in ZINC04880153 showing the lowest binding energy (?9.1 kcal/mol) with the drug target. The molecular dynamics study of the complex HisI-ZINC04880153 was conducted to analyze the stability and fluctuation of ligand within the binding pocket of HisI. The identified ligand could be analyzed in the wet-lab based experiments for future drug discovery.     

Identification and Characterization of Potential Therapeutic Candidates in Emerging Human Pathogen Mycobacterium abscessus: A Novel Hierarchical In Silico Approach

Mycobacterium abscessus, a non-tuberculous rapidly growing mycobacterium, is recognized as an emerging human pathogen causing a variety of infections ranging from skin and soft tissue infections to severe pulmonary infections. Lack of an optimal treatment regimen and emergence of multi-drug resistance in clinical isolates necessitate the development of better/new drugs against this pathogen. The present study aims at identification and qualitative characterization of promising drug targets in M. abscessus using a novel hierarchical in silico approach, encompassing three phases of analyses. In phase I, five sets of proteins were mined through chokepoint, plasmid, pathway, virulence factors, and resistance genes and protein network analysis. These were filtered in phase II, in order to find out promising drug target candidates through subtractive channel of analysis. The analysis resulted in 40 therapeutic candidates which are likely to be essential for the survival of the pathogen and non-homologous to host, human anti-targets, and gut flora. Many of the identified targets were found to be involved in different metabolisms (viz., amino acid, energy, carbohydrate, fatty acid, and nucleotide), xenobiotics degradation, and bacterial pathogenicity. Finally, in phase III, the candidate targets were qualitatively characterized through cellular localization, broad spectrum, interactome, functionality, and druggability analysis. The study explained their subcellular location identifying drug/vaccine targets, possibility of being broad spectrum target candidate, functional association with metabolically interacting proteins, cellular function (if hypothetical), and finally, druggable property. Outcome of the present study could facilitate the identification of novel antibacterial agents for better treatment of M. abscesses infections.     

Strategic genome-scale prioritization of unique drug targets: A case study of Streptococcus gordonii

The current reach of genomics extends facilitated identification of microbial virulence factors, a primary objective for antimicrobial drug and vaccine design. Many putative proteins are yet to be identified which can act as potent drug targets. There is lack and limitation of methods which appropriately combine several omics ways for putative and new drug target identification. The study emphasizes a combined bioinformatic and theoretical method of screening unique and putative drug targets, lacking similarity with experimentally reported essential genes and drug targets. Synteny based comparison was carried out with 11 streptococci considering S. gordonii as reference genome. It revealed 534 non-homologous genes of which 334 were putative. Similarity search against host proteome, metabolic pathway annotation and subcellular localization predication identified 16 potent drug targets. This is a first attempt of several combinational approaches of similarity search with target protein structural features for screening drug targets, yielding a pipeline which can be substantiated to other human pathogens.     

Flux balance analysis of mycolic acid pathway: targets for anti-tubercular drugs

Mycobacterium tuberculosis is the focus of several investigations for design of newer drugs, as tuberculosis remains a major epidemic despite the availability of several drugs and a vaccine. Mycobacteria owe many of their unique qualities to mycolic acids, which are known to be important for their growth, survival, and pathogenicity. Mycolic acid biosynthesis has therefore been the focus of a number of biochemical and genetic studies. It also turns out to be the pathway inhibited by front-line anti-tubercular drugs such as isoniazid and ethionamide. Recent years have seen the emergence of systems-based methodologies that can be used to study microbial metabolism. Here, we seek to apply insights from flux balance analyses of the mycolic acid pathway (MAP) for the identification of anti-tubercular drug targets. We present a comprehensive model of mycolic acid synthesis in the pathogen M. tuberculosis involving 197 metabolites participating in 219 reactions catalysed by 28 proteins. Flux balance analysis (FBA) has been performed on the MAP model, which has provided insights into the metabolic capabilities of the pathway. In silico systematic gene deletions and inhibition of InhA by isoniazid, studied here, provide clues about proteins essential for the pathway and hence lead to a rational identification of possible drug targets. Feasibility studies using sequence analysis of the M. tuberculosis H37Rv and human proteomes indicate that, apart from the known InhA, potential targets for anti-tubercular drug design are AccD3, Fas, FabH, Pks13, DesA1/2, and DesA3. Proteins identified as essential by FBA correlate well with those previously identified experimentally through transposon site hybridisation mutagenesis. This study demonstrates the application of FBA for rational identification of potential anti-tubercular drug targets, which can indeed be a general strategy in drug design. The targets, chosen based on the critical points in the pathway, form a ready shortlist for experimental testing.     

UniDrug-target: a computational tool to identify unique drug targets in pathogenic bacteria

Background

Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed.

Methods

A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein''s critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed.

Results

The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets.

Conclusions/Significance

UniDrug-Target is expected to accelerate pathogen-specific drug targets identification which will increase their success and durability as drugs developed against them have less chance to develop resistances and adverse impact on environment. The server is freely available at http://117.211.115.67/UDT/main.html. The standalone application (source codes) is available at http://www.bioinformatics.org/ftp/pub/bioinfojuit/UDT.rar.     

Core proteome mediated therapeutic target mining and multi-epitope vaccine design for Helicobacter pylori

Helicobacter pylori is a Gram-negative spiral-shaped bacterium that infects half of the human population worldwide and causes chronic inflammation. In the present study, we used the art of computational biology for therapeutic drug targets identification and a multi-epitope vaccine against multi-strains of H. pylori. For drug target identification, we used different tools and softwares to identify human non-homologous but pathogen essential proteins, with virulent properties and involved in unique metabolic pathways of H. pylori. For this purpose, the core proteome of 84 strains of H. pylori was retrieved from EDGAR 2.3 database. There were 59,808 proteins sequences in these strains. Duplicates and paralogous protein sequence removal was followed by human non-homologous protein miningPathogen essential and virulent proteins were subjected to pathway analysis Subcellular localization of the virulent proteins was predicted and druggability was also checked, leading to 30 druggable targets based on their similarity with the approved drug targets in Drugbank. For immunoinformatics analysis, we selected two outer membrane proteins (HPAKL86_RS06305 and HPSNT_RS00950) and subjected to determined immunogenic B and T-Cell epitopes. The B and T-Cell overlapped epitopes were selected to design 9 different vaccine constructs by using linkers and adjuvants. Least allergenic and most antigenic construct (C-8) was selected as a promiscuous vaccine to elicit host immune response. Cloning and in silico expression of the constructed vaccine (C-8) was done to produce a clone having the desired (gene) vaccine construct. In conclusion, the prioritized therapeutic targets for 84 strains of H.pylori will be useful for future therapy design. Vaccine design may also prove useful in the quest for targeting multi-strains of H. pylori in patients.     

Core Proteomic Analysis of Unique Metabolic Pathways of Salmonella enterica for the Identification of Potential Drug Targets

Background

Infections caused by Salmonella enterica, a Gram-negative facultative anaerobic bacteria belonging to the family of Enterobacteriaceae, are major threats to the health of humans and animals. The recent availability of complete genome data of pathogenic strains of the S. enterica gives new avenues for the identification of drug targets and drug candidates. We have used the genomic and metabolic pathway data to identify pathways and proteins essential to the pathogen and absent from the host.

Methods

We took the whole proteome sequence data of 42 strains of S. enterica and Homo sapiens along with KEGG-annotated metabolic pathway data, clustered proteins sequences using CD-HIT, identified essential genes using DEG database and discarded S. enterica homologs of human proteins in unique metabolic pathways (UMPs) and characterized hypothetical proteins with SVM-prot and InterProScan. Through this core proteomic analysis we have identified enzymes essential to the pathogen.

Results

The identification of 73 enzymes common in 42 strains of S. enterica is the real strength of the current study. We proposed all 73 unexplored enzymes as potential drug targets against the infections caused by the S. enterica. The study is comprehensive around S. enterica and simultaneously considered every possible pathogenic strain of S. enterica. This comprehensiveness turned the current study significant since, to the best of our knowledge it is the first subtractive core proteomic analysis of the unique metabolic pathways applied to any pathogen for the identification of drug targets. We applied extensive computational methods to shortlist few potential drug targets considering the druggability criteria e.g. Non-homologous to the human host, essential to the pathogen and playing significant role in essential metabolic pathways of the pathogen (i.e. S. enterica). In the current study, the subtractive proteomics through a novel approach was applied i.e. by considering only proteins of the unique metabolic pathways of the pathogens and mining the proteomic data of all completely sequenced strains of the pathogen, thus improving the quality and application of the results. We believe that the sharing of the knowledge from this study would eventually lead to bring about novel and unique therapeutic regimens against the infections caused by the S. enterica.     

PINAT1.0: Protein interaction network analysis tool

Cellular processes are regulated by interaction of various proteins i.e. multiprotein complexes and absences of these interactions are often the cause of disorder or disease. Such type of protein interactions are of great interest for drug designing. In host­parasite diseases like Tuberculosis, non-homologous proteins as drug target are first preference. Most potent drug target can be identifying among large number of non-homologous protein through protein interaction network analysis. Drug target should be those non-homologous protein which is associated with maximum number of functional proteins i.e. has highest number of interactants, so that maximum harm can be caused to pathogen only. In present work, Protein Interaction Network Analysis Tool (PINAT) has been developed to identification of potential protein interaction for drug target identification. PINAT is standalone, GUI application software made for protein-protein interaction (PPI) analysis and network building by using co­evolutionary profile. PINAT is very useful for large data PPI study with easiest handling among available softwares. PINAT provides excellent facilities for the assembly of data for network building with visual presentation of the results and interaction score. The software is written in JAVA and provides reliability through transparency with user.

Availability

PINAT is available at www.manit.ac.in/pinat     

TiD: Standalone software for mining putative drug targets from bacterial proteome

TiD is a standalone application, which relies on basic assumption that a protein must be essential for pathogens survival and non-homologous with host to qualify as putative target. With an input bacterial proteome, TiD removes paralogous proteins, picks essential ones, and excludes proteins homologous with host organisms. The targets illustrate non-homology with at least 40 out of 84 gut microbes, considered safe for human. TiD classifies proposed targets as known, novel and virulent. Users can perform pathway analysis, choke point analysis, interactome analysis, subcellular localization and functional annotations through web servers cross-referenced with the application. Drug targets identified by TiD for Listeria monocytogenes, Bacillus anthracis and Pseudomonas aeruginosa have revealed significant overlaps with previous studies. TiD takes < 2 h to scan putative targets from a bacterial proteome with ~ 5000 proteins; hence, we propose it as a useful tool for rational drug design. TiD is available at http://bmicnip.in/TiD/.     

Comparative Molecular docking analysis of DNA Gyrase subunit A in Pseudomonas aeruginosaPAO1

Pseudomonas aeruginosa is an opportunistic bacterium known for causing chronic infections in cystic fibrosis and chronic obstructive pulmonary disease (COPD) patients. Recently, several drug targets in Pseudomonas aeruginosa PAO1 have been reported using network biology approaches on the basis of essentiality and topology and further ranked on network measures viz. degree and centrality. Till date no drug/ligand molecule has been reported against this targets.In our work we have identified the ligand /drug molecules, through Orthologous gene mapping against Bacillus subtilis subsp. subtilis str. 168 and performed modelling and docking analysis. From the predicted drug targets in PA PAO1, we selected those drug targets which show statistically significant orthology with a model organism and whose orthologs are present in all the selected drug targets of PA PAO1.Modeling of their structure has been done using I-Tasser web server. Orthologous gene mapping has been performed using Cluster of Orthologs (COGs) and based on orthology; drugs available for Bacillus sp. have been docked with PA PAO1 protein drug targets using MoleGro virtual docker version 4.0.2.Orthologous gene for PA3168 gyrA is BS gyrAfound in Bacillus subtilis subsp. subtilis str. 168. The drugs cited for Bacillus sp. have been docked with PA genes and energy analyses have been made. Based on Orthologous gene mapping andin-silico studies, Nalidixic acid is reported as an effective drug against PA3168 gyrA for the treatment of CF and COPD.     

Identification of new benzamide inhibitor against α-subunit of tryptophan synthase from Mycobacterium tuberculosis through structure-based virtual screening,anti-tuberculosis activity and molecular dynamics simulations

Multi-drug-resistant tuberculosis and extensively drug-resistant tuberculosis has emerged as global health threat, causing millions of deaths worldwide. Identification of new drug candidates for tuberculosis (TB) by targeting novel and less explored protein targets will be invaluable for antituberculosis drug discovery. We performed structure-based virtual screening of eMolecules database against a homology model of relatively unexplored protein target: the α-subunit of tryptophan synthase (α-TRPS) from Mycobacterium tuberculosis essential for bacterial survival. Based on physiochemical properties analysis and molecular docking, the seven candidate compounds were selected and evaluated through whole cell-based activity against the H37Rv strain of M. tuberculosis. A new Benzamide inhibitor against α-subunit of tryptophan synthase (α-TRPS) from M. tuberculosis has been identified causing 100% growth inhibition at 25 μg/ml and visible bactericidal activity at 6 μg/ml. This benzamide inhibitor displayed a good predicted binding score (?48.24 kcal/mol) with the α-TRPS binding pocket and has logP value (2.95) comparable to Rifampicin. Further refinement of docking results and evaluation of inhibitor-protein complex stability were investigated through Molecular dynamic (MD) simulations studies. Following MD simulations, Root mean square deviation, Root mean square fluctuation and secondary structure analysis confirmed that protein did not unfold and ligand stayed inside the active pocket of protein during the explored time scale. This identified benzamide inhibitor against the α-subunit of TRPS from M. tuberculosis could be considered as candidate for drug discovery against TB and will be further evaluated for enzyme-based inhibition in future studies.     

Identification of drug target candidates of the swine pathogen <Emphasis Type="Italic">Actinobacillus pleuropneumoniae</Emphasis> by construction of protein–protein interaction network

Porcine pleuropneumonia caused by Actinobacillus pleuropneumoniae has led to severe economic losses in the pig industry worldwide. A. pleuropneumoniae displays various levels of antimicrobial resistance, leading to the dire need to identify new drug targets. Protein–protein interaction (PPI) network can aid the identification of drug targets by discovering essential proteins during the life of bacteria. The aim of this study is to identify drug target candidates of A. pleuropneumoniae from essential proteins in PPI network. The homologous protein mapping method (HPM) was utilized to construct A. pleuropneumoniae PPI network. Afterwards, the subnetwork centered with H-NS was selected to verify the PPI network using bacterial two-hybrid assays. Drug target candidates were identified from the hub proteins by analyzing the topology of the network using interaction degree and homologous comparison with the pig proteome. An A. pleuropneumoniae PPI network containing 2737 non-redundant interaction pairs among 533 proteins was constructed. These proteins were distributed in 21 COG functional categories and 28 KEGG metabolic pathways. The A. pleuropneumoniae PPI network was scale free and the similar topological tendencies were found when compared with other bacteria PPI network. Furthermore, 56.3% of the H-NS subnetwork interactions were validated. 57 highly connected proteins (hub proteins) were identified from the A. pleuropneumoniae PPI network. Finally, 9 potential drug targets were identified from the hub proteins, with no homologs in swine. This study provides drug target candidates, which are promising for further investigations to explore lead compounds against A. pleuropneumoniae.     

Target network analysis of adiponectin,a multifaceted adipokine

Application of network analysis to dissect the potential molecular mechanisms of biological processes and complicated diseases has been the new trend in biology and medicine in recent years. Among which, the protein–protein interactions (PPI) networks attract interests of most researchers. Adiponectin, a cytokine secreted from adipose tissue, participates in a number of metabolic processes, including glucose regulation and fatty acid metabolism and involves in a series of complicated diseases from head to toe. Hundreds of proteins including many identified and potential drug targets have been reported to be involved in adiponectin related signaling pathways, which comprised a complicated regulation network. Therapeutic target database (TTD) provides extensive information about the known and explored therapeutic protein targets and the signaling pathway information. In this study, adiponectin associated drug targets based PPI was constructed and its topological properties were analyzed, which might provide some insight into the dissection of adiponectin action mechanisms and promote adiponectin signaling based drug target identification and drug discovery. J. Cell. Biochem. 114: 1145–1152, 2013. © 2012 Wiley Periodicals, Inc.     

Comparative genome-scale modelling of the pathogenic Flavobacteriaceae species Riemerella anatipestifer in China

Riemerella anatipestifer (RA) is a gram-negative bacterium that has a high potential to infect waterfowl. Although more and more genomes of RA have been generated comparaed to genomic analysis of RA still remains at the level of individual species. In this study, we analysed the pan-genome of 27 RA virulent isolates to reveal the intraspecies genomic diversity from various aspects. The multi-locus sequence typing (MLST) analysis suggests that the geographic origin of R. anatipestifer is Guangdong province, China. Results of pan-genome analysis revealed an open pan-genome for all 27 species with the sizes of 2967 genes. We identified 387 genes among 555 unique genes originated by horizontal gene transfer. Further studies showed 204 strain-specific HGT genes were predicted as virulent proteins. Screening the 1113 core genes in RA through subtractive genomic approach, 70 putative vaccine targets out of 125 non-cytoplasmic proteins have been predicted. Further analysis of these non A. platyrhynchos homologous proteins predicted that 56 essential proteins as drug target with more interaction partners were involved in unique metabolic pathways of RA. In conclusion, the present study indicated the essence and the diversity of RA and also provides useful information for identification of vaccine and drugs candidates in future.     

Structural genomics of Mycobacterium tuberculosis: a preliminary report of progress at UCLA

The growing list of fully sequenced genomes, combined with innovations in the fields of structural biology and bioinformatics, provides a synergy for the discovery of new drug targets. With this background, the TB Structural Genomics Consortium has been formed. This international consortium is comprised of laboratories from 31 universities and institutes in 13 countries. The goal of the consortium is to determine the structures of over 400 potential drug targets from the genome of Mycobacterium tuberculosis and analyze their structures in the context of functional information. We summarize the efforts of the UCLA consortium members. Potential drug targets were selected using a variety of bioinformatics methods and screened for certain physical and species-specific properties to yield a starting group of protein targets for structure determination. Target determination methods include protein phylogenetic profiles and Rosetta Stone methods, and the use of related biochemical pathways to select genes linked to essential prokaryotic genes. Criteria imposed on target selection included potential protein solubility, protein or domain size, and targets that lack homologs in eukaryotic organisms. In addition, some protein targets were chosen that are specific to M. tuberculosis, such as PE and PPE domains. Thus far, the UCLA group has cloned 263 targets, expressed 171 proteins and purified 40 proteins, which are currently in crystallization trials. Our efforts have yielded 13 crystals and eight structures. Seven structures are summarized here. Four of the structures are secreted proteins: antigen 85B; MPT 63, which is one of the three major secreted proteins of M. tuberculosis; a thioredoxin derivative Rv2878c; and potentially secreted glutamate synthetase. We also report the structures of three proteins that are potentially essential to the survival of M. tuberculosis: a protein involved in the folate biosynthetic pathway (Rv3607c); a protein involved in the biosynthesis of vitamin B5 (Rv3602c); and a pyrophosphatase, Rv2697c. Our approach to the M. tuberculosis structural genomics project will yield information for drug design and vaccine production against tuberculosis. In addition, this study will provide further insights into the mechanisms of mycobacterial pathogenesis.     

Methylerythritol phosphate pathway to isoprenoids: Kinetic modeling and in silico enzyme inhibitions in Plasmodium falciparum

The methylerythritol phosphate (MEP) pathway of Plasmodium falciparum (P. falciparum) has become an attractive target for anti-malarial drug discovery. This study describes a kinetic model of this pathway, its use in validating 1-deoxy-d-xylulose 5-phosphate reductoisomerase (DXR) as drug target from the systemic perspective, and additional target identification, using metabolic control analysis and in silico inhibition studies. In addition to DXR, 1-deoxy-d-xylulose 5-phosphate synthase (DXS) can be targeted because it is the first enzyme of the pathway and has the highest flux control coefficient followed by that of DXR. In silico inhibition of both enzymes caused large decrement in the pathway flux. An added advantage of targeting DXS is its influence on vitamin B1 and B6 biosynthesis. Two more potential targets, 2-C-methyl-d-erythritol 2,4-cyclodiphosphate synthase and 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate synthase, were also identified. Their inhibition caused large accumulation of their substrates causing instability of the system.     

The 14-3-3 proteins in regulation of cellular metabolism

Thirty years ago, it was discovered that 14-3-3 proteins could activate enzymes involved in amino acid metabolism. In the following decades, 14-3-3s have been shown to be involved in many different signaling pathways that modulate cellular and whole body energy and nutrient homeostasis. Large scale screening for cellular binding partners of 14-3-3 has identified numerous proteins that participate in regulation of metabolic pathways, although only a minority of these targets have yet been subject to detailed studies. Because of the wide distribution of potential 14-3-3 targets and the resurging interest in metabolic pathway control in diseases like cancer, diabetes, obesity and cardiovascular disease, we review the role of 14-3-3 proteins in the regulation of core and specialized cellular metabolic functions. We cite illustrative examples of 14-3-3 action through their direct modulation of individual enzymes and through regulation of master switches in cellular pathways, such as insulin signaling, mTOR- and AMP dependent kinase signaling pathways, as well as regulation of autophagy. We further illustrate the quantitative impact of 14-3-3 association on signal response at the target protein level and we discuss implications of recent findings showing 14-3-3 protein membrane binding of target proteins.