Differential genome analyses of metabolic enzymes in Pseudomonas aeruginosa for drug target identification

Complete genome sequences of several pathogenic bacteria have been determined, and many more such projects are currently under way. While these data potentially contain all the determinants of host-pathogen interactions and possible drug targets, computational tools for selecting suitable candidates for further experimental analyses are currently limited. Detection of bacterial genes that are non-homologous to human genes, and are essential for the survival of the pathogen represents a promising means of identifying novel drug targets. We used a differential pathway analyses approach (based on KEGG data) to identify essential genes from Pseudomonas aeruginosa. Our approach identified 214 unique enzymes in P. aeruginosa that may be potential drug targets and can be considered for rational drug design. About 40% of these putative targets have been reported as essential by transposon mutagenesis data elsewhere. Homology model for one of the proteins (LpxC) is presented as a case study and can be explored for in silico docking with suitable inhibitors. This approach is a step towards facilitating the search for new antibiotics.     

T-iDT : tool for identification of drug target in bacteria and validation by Mycobacterium tuberculosis

With the completion of the Human Genome Project in 2003, many new projects to sequence bacterial genomes were started and soon many complete bacterial genome sequences were available. The sequenced genomes of pathogenic bacteria provide useful information for understanding host-pathogen interactions. These data prove to be a new weapon in fighting against pathogenic bacteria by providing information about potential drug targets. But the limitation of computational tools for finding potential drug targets has hindered the process and further experimental analysis. There are many in silico approaches proposed for finding drug targets but only few have been automated. One such approach finds essential genes in bacterial genomes with no human homologue and predicts these as potential drug targets. The same approach is used in our tool. T-iDT, a tool for the identification of drug targets, finds essential genes by comparing a bacterial gene set against DEG (Database of Essential Genes) and excludes homologue genes by comparing against a human protein database. The tool predicts both the set of essential genes as well as potential target genes for the given genome. The tool was tested with Mycobacterium tuberculosis and results were validated. With default parameters, the tool predicted 236 essential genes and 52 genes to encode potential drug targets. A pathway-based approach was used to validate these potential drug target genes. The pathway in which the products of these genes are involved was determined. Our analysis shows that almost all these pathways are very essential for the bacterial survival and hence these genes encode possible drug targets. Our tool provides a fast method for finding possible drug targets in bacterial genomes with varying stringency level. The tool will be helpful in finding possible drug targets in various pathogenic organisms and can be used for further analysis in novel therapeutic drug development. The tool can be downloaded from http://www.milser.co.in/research.htm and http://www.srmbioinformatics.edu.in/ forum.htm.     

In silico identification of potential therapeutic targets in the human pathogen Helicobacter pylori

Availability of genome sequences of pathogens has provided a tremendous amount of information that can be useful in drug target and vaccine target identification. One of the recently adopted strategies is based on a subtractive genomics approach, in which the subtraction dataset between the host and pathogen genome provides information for a set of genes that are likely to be essential to the pathogen but absent in the host. This approach has been used successfully in recent times to identify essential genes in Pseudomonas aeruginosa. We have used the same methodology to analyse the whole genome sequence of the human gastric pathogen Helicobacter pylori. Our analysis revealed that out of the 1590 coding sequences of the pathogen, 40 represent essential genes that have no human homolog. We have further analysed these 40 genes by the protein sequence databases to list some 10 genes whose products are possibly exposed on the pathogen surface. This preliminary work reported here identifies a small subset of the Helicobacter proteome that might be investigated further for identifying potential drug and vaccine targets in this pathogen.     

Comparative Genomics Analysis of Mycobacterium ulcerans for the Identification of Putative Essential Genes and Therapeutic Candidates

Mycobacterium ulcerans, the causative agent of Buruli ulcer, is the third most common mycobacterial disease after tuberculosis and leprosy. The present treatment options are limited and emergence of treatment resistant isolates represents a serious concern and a need for better therapeutics. Conventional drug discovery methods are time consuming and labor-intensive. Unfortunately, the slow growing nature of M. ulcerans in experimental conditions is also a barrier for drug discovery and development. In contrast, recent advancements in complete genome sequencing, in combination with cheminformatics and computational biology, represent an attractive alternative approach for the identification of therapeutic candidates worthy of experimental research. A computational, comparative genomics workflow was defined for the identification of novel therapeutic candidates against M. ulcerans, with the aim that a selected target should be essential to the pathogen, and have no homology in the human host. Initially, a total of 424 genes were predicted as essential from the M. ulcerans genome, via homology searching of essential genome content from 20 different bacteria. Metabolic pathway analysis showed that the most essential genes are associated with carbohydrate and amino acid metabolism. Among these, 236 proteins were identified as non-host and essential, and could serve as potential drug and vaccine candidates. Several drug target prioritization parameters including druggability were also calculated. Enzymes from several pathways are discussed as potential drug targets, including those from cell wall synthesis, thiamine biosynthesis, protein biosynthesis, and histidine biosynthesis. It is expected that our data will facilitate selection of M. ulcerans proteins for successful entry into drug design pipelines.     

Genome subtraction for novel target definition in Salmonella typhi

Large genomic sequencing projects of pathogens as well as human genome leads to immense genomic and proteomic data which would be very beneficial for the novel target identification in pathogens. Subtractive genomic approach is one of the most useful strategies helpful in identification of potential targets. The approach works by subtracting the genes or proteins homologous to both host and the pathogen and identify those set of gene or proteins which are essential for the pathogen and are exclusively present in the pathogen. Subtractive genomic approach is employed to identify novel target in salmonella typhi. The pathogen has 4718 proteins out of which 300 are found to be essential (“ indispensable to support cellular life”) in the pathogen with no human homolog. Metabolic pathway analyses of these 300 essential proteins revealed that 149 proteins are exclusively involved in several metabolic pathway of S. typhi. 8 metabolic pathways are found to be present exclusively in the pathogen comprising of 27 enzymes unique to the pathogen. Thus, these 27 proteins may serve as prospective drug targets. Sub-cellular localization prediction of the 300 essential proteins was done which reveals that 11 proteins lie on the outer membrane of the pathogen which could be probable vaccine candidates.     

In silico analysis of Burkholderia pseudomallei genome sequence for potential drug targets

Recent advances in DNA sequencing technology have enabled elucidation of whole genome information from a plethora of organisms. In parallel with this technology, various bioinformatics tools have driven the comparative analysis of the genome sequences between species and within isolates. While drawing meaningful conclusions from a large amount of raw material, computer-aided identification of suitable targets for further experimental analysis and characterization, has also led to the prediction of non-human homologous essential genes in bacteria as promising candidates for novel drug discovery. Here, we present a comparative genomic analysis to identify essential genes in Burkholderia pseudomallei. Our in silico prediction has identified 312 essential genes which could also be potential drug candidates. These genes encode essential proteins to support the survival of B. pseudomallei including outer-inner membrane and surface structures, regulators, proteins involved in pathogenenicity, adaptation, chaperones as well as degradation of small and macromolecules, energy metabolism, information transfer, central/intermediate/miscellaneous metabolism pathways and some conserved hypothetical proteins of unknown function. Therefore, our in silico approach has enabled rapid screening and identification of potential drug targets for further characterization in the laboratory.     

Identification and modeling of a drug target for Clostridium perfringens SM101

In the present study, comparative genome analysis between Clostridium perfringens and the human genome was carried out to identify genes that are essential for the pathogen's survival, and non-homologous to the genes of human host, that can be used as potential drug targets. The study resulted in the identification of 426 such genes. The number of these potential drug targets thus identified is significantly lower than the genome's protein coding capacity (2558 protein coding genes). The 426 genes of C. perfringens were further analyzed for overall similarities with the essential genes of 14 different bacterial species present in Database of Essential Genes (DEG). Our results show that there are only 5 essential genes of C. perfringens that exhibit similarity with 12 species of the 14 different bacterial species present in DEG database. Of these, 1 gene was similar in 12 species and 4 genes were similar in 11 species. Thus, the study opens a new avenue for the development of potential drugs against the highly pathogenic bacterium. Further, by selecting these essential genes of C. perfringens, which are common and essential for other pathogenic microbial species, a broad spectrum anti-microbial drug can be developed. As a case study, we have built a homology model of one of the potential drug targets, ABC transporter-ATP binding protein, which can be employed for in silico docking studies by suitable inhibitors.     

In silico prediction of drug targets in <Emphasis Type="Italic">Vibrio cholerae</Emphasis>

Identification of potential drug targets is the first step in the process of modern drug discovery, subjected to their validation and drug development. Whole genome sequences of a number of organisms allow prediction of potential drug targets using sequence comparison approaches. Here, we present a subtractive approach exploiting the knowledge of global gene expression along with sequence comparisons to predict the potential drug targets more efficiently. Based on the knowledge of 155 known virulence and their coexpressed genes mined from microarray database in the public domain, 357 coexpressed probable virulence genes for Vibrio cholerae were predicted. Based on screening of Database of Essential Genes using blastn, a total of 102 genes out of these 357 were enlisted as vitally essential genes, and hence good putative drug targets. As the effective drug target is a protein which is only present in the pathogen, similarity search of these 102 essential genes against human genome sequence led to subtraction of 66 genes, thus leaving behind a subset of 36 genes whose products have been called as potential drug targets. The gene ontology analysis using Blast2GO of these 36 genes revealed their roles in important metabolic pathways of V. cholerae or on the surface of the pathogen. Thus, we propose that the products of these genes be evaluated as target sites of drugs against V. cholerae in future investigations.     

Target Inhibition Networks: Predicting Selective Combinations of Druggable Targets to Block Cancer Survival Pathways

A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of non-additive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced prediction accuracies in cross validation as well as significant reduction in computation times. Such cost-effective computational-experimental design strategies have the potential to greatly speed-up the drug testing efforts by prioritizing those interventions and interactions warranting further study in individual cancer cases.     

Metabolic pathway analysis in trypanosomes and malaria parasites

Identification of novel drug targets is required for the development of new classes of drugs to overcome drug resistance and replace less efficacious treatments. In theory, knowledge of the entire genome of a pathogen identifies every potential drug target in any given microbe. In practice, the sheer complexity and the inadequate or inaccurate annotation of genomic information makes target identification and selection somewhat more difficult. Analysis of metabolic pathways provides a useful conceptual framework for the identification of potential drug targets and also for improving our understanding of microbial responses to nutritional, chemical and other environmental stresses. A number of metabolic databases are available as tools for such analyses. The strengths and weaknesses of this approach are discussed.     

Chemogenomics: drug discovery's panacea?

Chemogenomics aims towards the systematic identification of small molecules that interact with the products of the genome and modulate their biological function. This Opinion article summarizes the different knowledge-based chemogenomics strategies that are followed and outlines the challenges and opportunities that will impact drug discovery. Chemogenomics aims towards the systematic identification of small molecules that interact with the products of the genome and modulate their biological function. While historically the approach is based on efforts that systematically explore target gene families like kinases, today additional knowledge-based systematization principles are followed within early drug discovery projects which aim to biologically validate the targets and to identify starting points for chemical lead optimization. While the expectations of chemogenomics are very high, the reality of drug discovery is quite sobering with very high project attrition rates. This article summarizes the different knowledge-based chemogenomics strategies that are followed and outlines the challenges and potential opportunities that will impact drug discovery.     

Large-scale essential gene identification in Candida albicans and applications to antifungal drug discovery

Candida albicans is the primary fungal pathogen of humans. Despite the need for novel drugs to combat fungal infections [Sobel, J.D. (2000) Clin Infectious Dis 30: 652], antifungal drug discovery is currently limited by both the availability of suitable drug targets and assays to screen corresponding targets. A functional genomics approach based on the diploid C. albicans genome sequence, termed GRACETM (gene replacement and conditional expression), was used to assess gene essentiality through a combination of gene replacement and conditional gene expression. In a systematic application of this approach, we identify 567 essential genes in C. albicans. Interestingly, evaluating the conditional phenotype of all identifiable C. albicans homologues of the Saccharomyces cerevisiae essential gene set [Giaever, G., Chu, A.M., Ni, L., Connelly, C., Riles, L., Veronneau, S., et al. (2002) Nature 418: 387-391] by GRACE revealed only 61% to be essential in C. albicans, emphasizing the importance of performing such studies directly within the pathogen. Construction of this conditional mutant strain collection facilitates large-scale examination of terminal phenotypes of essential genes. This information enables preferred drug targets to be selected from the C. albicans essential gene set by phenotypic information derived both in vitro, such as cidal versus static terminal phenotypes, as well as in vivo through virulence studies using conditional strains in an animal model of infection. In addition, the combination of phenotypic and bioinformatic analyses further improves drug target selection from the C. albicans essential gene set, and their respective conditional mutant strains may be directly used as sensitive whole-cell assays for drug screening.     

Microbial genomics: rhetoric or reality?

The availability of complete genome sequences of many bacterial species is facilitating numerous computational approaches for understanding bacterial genomes. One of the major incentives behind the genome sequencing of many pathogenic bacteria is the desire to better understand their diversity and to develop new approaches for controlling human diseases caused by these microorganisms. This task has become even more urgent with the rapid evolution of antibiotic resistance among many bacterial pathogens. Novel drug targets are required in order to design new antimicrobials against antibiotic-resistant pathogens. The complete genome sequences of an ever increasing number of pathogenic microbes constitute an invaluable resource and provide lead information on potential drug targets. This review focuses on in silico analyses of microbial genomes, their host-specific adaptations, with specific reference to genome architecture, design, evolution, and trends in computational identification of microbial drug targets. These trends underscore the utility of genomic data for systematic in silico drug target identification in the post-genomic era.     

Cloning and targeted disruption of two lipopolysaccharide biosynthesis genes, kdsA and waaG, of Pseudomonas aeruginosa PAO1 by site-directed mutagenesis

The emergence of antibiotic resistance in bacterial pathogens poses a great challenge to public health and emphasizes the need for new antimicrobial targets. The recent development of microbial genomics and the availability of genome sequences allows for the identification of essential genes which could be novel and potential targets for antibacterial drugs. However, these predicted targets need experimental validation to confirm essentiality. Here, we report on experimental validation of a two potential targets in the lipopolysaccharide (LPS) biosynthesis pathway of the pathogen Pseudomonas aeruginosa PAO1 using insertion duplication. Two genes, kdsA and waaG, from LPS encoding proteins 2-dehydro-3-deoxyphosphooctonate aldolase and UDP-glucose (heptosyl) LPS α-1,3-glucosyltransferase were selected as putative target candidates for the gene disruption experiments using plasmid insertion mutagenesis to determine essentiality. The introduction of a selectable ampicillin and kanamycin resistance marker into the chromosome resulted in lack of recovery of antibiotic-resistant colonies suggesting the essentiality of these genes for the survival of P. aeruginosa. Several molecular analyses were carried out in order to confirm the essentiality of these genes. We propose that the above two validated drug targets are essential and can be screened for functional inhibitors for the discovery of novel therapeutic compounds against antibiotic-resistant opportunistic pathogen P. aeruginosa.     

Identification of 113 conserved essential genes using a high-throughput gene disruption system in Streptococcus pneumoniae

The recent availability of bacterial genome sequence information permits the identification of conserved genes that are potential targets for novel antibiotic drug discovery. Using a coupled bioinformatic/experimental approach, a list of candidate conserved genes was generated using a Microbial Concordance bioinformatics tool followed by a targeted disruption campaign. Pneumococcal sequence data allowed for the design of precise PCR primers to clone the desired gene target fragments into the pEVP3 ‘suicide vector’. An insertion–duplication approach was employed that used the pEVP3 constructs and resulted in the introduction of a selectable chloramphenicol resistance marker into the chromosome. In the case of non-essential genes, cells can survive the disruption and form chloramphenicol-resistant colonies. A total of 347 candidate reading frames were subjected to disruption analysis, with 113 presumed to be essential due to lack of recovery of antibiotic-resistant colonies. In addition to essentiality determination, the same high-throughput methodology was used to overexpress gene products and to examine possible polarity effects for all essential genes.     

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.     

In silico identification of common putative drug targets in <Emphasis Type="Italic">Leptospira interrogans</Emphasis>

Infectious diseases are the leading causes of death worldwide. Hence, there is a need to develop new antimicrobial agents. Traditional method of drug discovery is time consuming and yields a few drug targets with little intracellular information for guiding target selection. Thus, focus in drug development has been shifted to computational comparative genomics for identifying novel drug targets. Leptospirosis is a worldwide zoonosis of global concern caused by Leptospira interrogans. Availability of L. interrogans serovars and human genome sequences facilitated to search for novel drug targets using bioinformatics tools. The genome sequence of L. interrogans serovar Copenhageni has 5,124 genes while that of serovar Lai has 4,727 genes. Through subtractive genomic approach 218 genes in serovar Copenhageni and 158 genes in serovar Lai have been identified as putative drug targets. Comparative genomic approach had revealed that 88 drug targets were common to both the serovars. Pathway analysis using the Kyoto Encyclopaedia of Genes and Genomes revealed that 66 targets are enzymes and 22 are non-enzymes. Sixty two common drug targets were predicted to be localized in cytoplasm and 16 were surface proteins. The identified potential drug targets form a platform for further investigation in discovery of novel therapeutic compounds against Leptospira.     

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.     

A novel in silico approach to identify potential therapeutic targets in human bacterial pathogens

In recent years, genome-sequencing projects of pathogens and humans have revolutionized microbial drug target identification. Of the several known genomic strategies, subtractive genomics has been successfully utilized for identifying microbial drug targets. The present work demonstrates a novel genomics approach in which codon adaptation index (CAI), a measure used to predict the translational efficiency of a gene based on synonymous codon usage, is coupled with subtractive genomics approach for mining potential drug targets. The strategy adopted is demonstrated using respiratory pathogens, namely, Streptococcus pneumoniae and Haemophilus influenzae as examples. Our approach identified 8 potent target genes (Streptococcus pneumoniae?C2, H. influenzae?C6), which are functionally significant and also play key role in host-pathogen interactions. This approach facilitates swift identification of potential drug targets, thereby enabling the search for new inhibitors. These results underscore the utility of CAI for enhanced in silico drug target identification.