Protein interaction network analysis—Approach for potential drug target identification in Mycobacterium tuberculosis

In host-parasite diseases like tuberculosis, non-homologous proteins (enzymes) as drug target are first preference. Most potent drug target can be identified among large number of non-homologous protein through protein interaction network analysis. In this study, the entire promising dimension has been explored for identification of potential drug target. A comparative metabolic pathway analysis of the host Homo sapiens and the pathogen M. tuberculosis H37Rv has been performed with three level of analysis. In first level, the unique metabolic pathways of M. tuberculosis have been identified through its comparative study with H. sapiens and identification of non-homologous proteins has been done through BLAST similarity search. In second level, choke-point analysis has been performed with identified non-homologous proteins of metabolic pathways. In third level, two type of analysis have been performed through protein interaction network. First analysis has been done to find out the most potential metabolic functional associations among all identified choke point proteins whereas second analysis has been performed to find out the functional association of high metabolic interacting proteins to pathogenesis causing proteins. Most interactive metabolic proteins which have highest number of functional association with pathogenesis causing proteins have been considered as potential drug target. A list of 18 potential drug targets has been proposed which are various stages of progress at the TBSGC and proposed drug targets are also studied for other pathogenic strains.As a case study, we have built a homology model of identified drug targets histidinol-phosphate aminotransferase (HisC1) using MODELLER software and various information have been generated through molecular dynamics which will be useful in wetlab structure determination. The generated model could be further explored for insilico docking studies with suitable inhibitors.     

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.     

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.     

A novel genomics approach for the identification of drug targets in pathogens, with special reference to Pseudomonas aeruginosa

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 have used three-way genome comparisons to identify essential genes from Pseudomonas aeruginosa. Our approach identified 306 essential genes that may be considered as potential drug targets. The resultant analyses are in good agreement with the results of systematic gene deletion experiments. This approach enables rapid potential drug target identification, thereby greatly facilitating the search for new antibiotics. These results underscore the utility of large genomic databases for in silico systematic drug target identification in the post-genomic era.     

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.     

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.     

Functional cell-based uHTS in chemical genomic drug discovery

The availability of genomic information significantly increases the number of potential targets available for drug discovery, although the function of many targets and their relationship to disease is unknown. In a chemical genomic research approach, ultra-high throughput screening (uHTS) of genomic targets takes place early in the drug discovery process, before target validation. Target-selective modulators then provide drug leads and pharmacological research tools to validate target function. Effective implementation of a chemical genomic strategy requires assays that can perform uHTS for large numbers of genomic targets. Cell-based functional assays are capable of the uHTS throughput required for chemical genomic research, and their functional nature provides distinct advantages over ligand-binding assays in the identification of target-selective modulators.     

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.     

A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism

Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the “effect space” comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism.     

Predict potential drug targets from the ion channel proteins based on SVM

The identification of molecular targets is a critical step in the drug discovery and development process. Ion channel proteins represent highly attractive drug targets implicated in a diverse range of disorders, in particular in the cardiovascular and central nervous systems. Due to the limits of experimental technique and low-throughput nature of patch-clamp electrophysiology, they remain a target class waiting to be exploited. In our study, we combined three types of protein features, primary sequence, secondary structure and subcellular localization to predict potential drug targets from ion channel proteins applying classical support vector machine (SVM) method. In addition, our prediction comprised two stages. In stage 1, we predicted ion channel target proteins based on whole-genome target protein characteristics. Firstly, we performed feature selection by Mann-Whitney U test, then made predictions to identify potential ion channel targets by SVM and designed a new evaluating indicator Q to prioritize results. In stage 2, we made a prediction based on known ion channel target protein characteristics. Genetic algorithm was used to select features and SVM was used to predict ion channel targets. Then, we integrated results of two stages, and found that five ion channel proteins appeared in both prediction results including CGMP-gated cation channel beta subunit and Gamma-aminobutyric acid receptor subunit alpha-5, etc., and four of which were relative to some nerve diseases. It suggests that these five proteins are potential targets for drug discovery and our prediction strategies are effective.     

Properties of Protein Drug Target Classes

Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein’s sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein’s sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins’ hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme.     

Potential drug targets on insomnia and intervention effects of Jujuboside A through metabolic pathway analysis as revealed by UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition approach

Potential metabolites from the metabolic pathways could be therapeutic targets and useful for the discovery of broad spectrum drugs. UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition methods including PCA, PLS-DA, OPLS-DA and Heatmap were integrated to examine the global metabolic signature of insomnia and intervention effects of Jujuboside A (JuA). Six unique pathways of the insomnia were identified using Ingenuity Pathway Analysis (IPA) software. The VIP-value threshold cutoff of the metabolites was set to 10, above this threshold, were filtered out as potential target biomarkers. Sixteen distinct metabolites were identified from these pathways, and 6 of them can be considered for rational drug design. It was further experimental validation that the changes in metabolic profiling were restored to their baseline values after JuA treatment according to the multivariate data analysis. Potential metabolite network of the insomnia was preliminarily predicted JuA-target interaction networks, and could be further explored for in silico docking studies with suitable drugs. Thus, our method is an efficient procedure for drug target identification through metabolic analysis. It can guide testable predictions, provide insights into drug action mechanisms and enable us to increase research productivity toward metabolomic drug discovery.     

Can medical genetics and evolutionary biology inspire drug target identification?

New strategies for target identification are urgently needed to tackle the current productivity challenges in drug discovery. By examining successful human drug targets, it can be seen that approximately 50% are associated with genetic disorders. Further analysis shows that these successfully targeted genes share some common evolutionary features, which strongly suggests that evolutionary information can help identify drug targets with the greatest potential for therapeutic development.     

Proteomic methods for drug target discovery

The field of drug target discovery is currently very popular with a great potential for advancing biomedical research and chemical genomics. Innovative strategies have been developed to aid the process of target identification, either by elucidating the primary mechanism-of-action of a drug, by understanding side effects involving unanticipated 'off-target' interactions, or by finding new potential therapeutic value for an established drug. Several promising proteomic methods have been introduced for directly isolating and identifying the protein targets of interest that are bound by active small molecules or for visualizing enzyme activities affected by drug treatment. Significant progress has been made in this rapidly advancing field, speeding the clinical validation of drug candidates and the discovery of the novel targets for lead compounds developed using cell-based phenotypic screens. Using these proteomic methods, further insight into drug activity and toxicity can be ascertained.     

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.     

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.     

基于生物信息学方法发现潜在药物靶标

药物靶点通常是在代谢或信号通路中与特定疾病或病理状态有关的关键分子.通过绑定到特定活动区域抑制这个关键分子进行药物设计.确定特定疾病有关的靶标分子是现代新药开发的基础.在药物靶标发现的过程中,生物信息学方法发挥了不可替代的重要的作用,尤其适用于大规模多组学数据的分析.目前,已涌现了许多与疾病相关的数据库资源,基于生物网络特征、多基因芯片、蛋白质组、代谢组数据等建立了多种生物信息学方法发现潜在的药物靶标,并预测靶标可药性和药物副作用.     

Drug target identification from protein dynamics using quantitative pathway analysis

Dynamic proteomics promises to greatly facilitate identification of target proteins for drug molecules. Cohen et al. [Science, 2008, 322 (5907), 1511-1516] illustrated this potential, with the responses of 812 fluorescently tagged proteins to camptothecin administration monitored over 48 h. Directly from this data, one can restrict the list of candidate targets to 52 proteins. However, this approach has numerous limitations: equipment, labor (tagging and analyzing ≥1 colony/protein), and data analysis (aggregating individual cell data into population-relevant data sets). Furthermore, analytical success requires both explicit knowledge of drug target time-course evolution and, most importantly, monitoring of the target, itself. To address these issues, we developed a quantitative pathway analysis (qPA) technique, which employs well-annotated signaling pathways and elucidates putative drug targets and other molecules of interest. qPA, using more general assumptions and only 3 out of 144 available time points, identified the single known camptothecin target, TOPI, among only a handful of putative targets. Importantly, identification was possible without containing TOPI within the input data. These results demonstrate the potential of qPA in drug target discovery and highlight the importance of systems biology approaches for analysis of proteomics data.     

Drugs against superbugs: private lessons from bacteriophages

Bacterial genomics has provided a plethora of potential targets for antibacterial drug discovery, however, success in the hunt for new antibiotics will hinge on selecting targets with the highest potential. A recent paper by Liu and coworkers describes a new approach to target selection that uncovers strategies used by bacteriophage to disable bacteria. The method uses key phage proteins to identify and validate vulnerable targets and exploits them further in the identification of new antibacterial leads.