Target-Pathogen: a structural bioinformatic approach to prioritize drug targets in pathogens.
Ontology highlight
ABSTRACT:
SUBMITTER: Sosa EJ
PROVIDER: S-EPMC5753371 | biostudies-literature | 2018 Jan
REPOSITORIES: biostudies-literature
Publications
Target-Pathogen: a structural bioinformatic approach to prioritize drug targets in pathogens.
Sosa Ezequiel J EJ Burguener Germán G Lanzarotti Esteban E Defelipe Lucas L Radusky Leandro L Pardo Agustín M AM Marti Marcelo M Turjanski Adrián G AG Fernández Do Porto Darío D
Nucleic acids research 20180101 D1
Available genomic data for pathogens has created new opportunities for drug discovery and development to fight them, including new resistant and multiresistant strains. In particular structural data must be integrated with both, gene information and experimental results. In this sense, there is a lack of an online resource that allows genome wide-based data consolidation from diverse sources together with thorough bioinformatic analysis that allows easy filtering and scoring for fast target sele ...[more]
Similar Datasets
Project description:BackgroundIn spite of the implementations of several strategies, tuberculosis (TB) is overwhelmingly a serious global public health problem causing millions of infections and deaths every year. This is mainly due to the emergence of drug-resistance varieties of TB. The current treatment strategies for the drug-resistance TB are of longer duration, more expensive and have side effects. This highlights the importance of identification and prioritization of targets for new drugs. This study has been carried out to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv based on their flow to resistance genes.MethodsThe weighted proteome interaction network of the pathogen was constructed using a dataset from STRING database. Only a subset of the dataset with interactions that have a combined score value ≥770 was considered. Maximum flow approach has been used to prioritize potential drug targets. The potential drug targets were obtained through comparative genome and network centrality analysis. The curated set of resistance genes was retrieved from literatures. Detail literature review and additional assessment of the method were also carried out for validation.ResultsA list of 537 proteins which are essential to the pathogen and non-homologous with human was obtained from the comparative genome analysis. Through network centrality measures, 131 of them were found within the close neighborhood of the centre of gravity of the proteome network. These proteins were further prioritized based on their maximum flow value to resistance genes and they are proposed as reliable drug targets of the pathogen. Proteins which interact with the host were also identified in order to understand the infection mechanism.ConclusionPotential drug targets of Mycobacterium tuberculosis H37Rv were successfully prioritized based on their flow to resistance genes of existing drugs which is believed to increase the druggability of the targets since inhibition of a protein that has a maximum flow to resistance genes is more likely to disrupt the communication to these genes. Purposely selected literature review of the top 14 proteins showed that many of them in this list were proposed as drug targets of the pathogen.
| S-EPMC4467812 | biostudies-literature
Project description:BackgroundThe increased sequencing of pathogen genomes and the subsequent availability of genome-scale functional datasets are expected to guide the experimental work necessary for target-based drug discovery. However, a major bottleneck in this has been the difficulty of capturing and integrating relevant information in an easily accessible format for identifying and prioritizing potential targets. The open-access resource TDRtargets.org facilitates drug target prioritization for major tropical disease pathogens such as the mycobacteria Mycobacterium leprae and Mycobacterium tuberculosis; the kinetoplastid protozoans Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi; the apicomplexan protozoans Plasmodium falciparum, Plasmodium vivax, and Toxoplasma gondii; and the helminths Brugia malayi and Schistosoma mansoni.Methodology/principal findingsHere we present strategies to prioritize pathogen proteins based on whether their properties meet criteria considered desirable in a drug target. These criteria are based upon both sequence-derived information (e.g., molecular mass) and functional data on expression, essentiality, phenotypes, metabolic pathways, assayability, and druggability. This approach also highlights the fact that data for many relevant criteria are lacking in less-studied pathogens (e.g., helminths), and we demonstrate how this can be partially overcome by mapping data from homologous genes in well-studied organisms. We also show how individual users can easily upload external datasets and integrate them with existing data in TDRtargets.org to generate highly customized ranked lists of potential targets.Conclusions/significanceUsing the datasets and the tools available in TDRtargets.org, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens. While these lists are broadly consistent with the research community's current interest in certain specific proteins, and suggest novel target candidates that may merit further study, the lists can easily be modified in a user-specific manner, either by adjusting the weights for chosen criteria or by changing the criteria that are included.
| S-EPMC2927427 | biostudies-literature
Project description: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.
| S-EPMC3174044 | biostudies-literature
Project description:BackgroundRecent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions.MethodsWe have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication.ResultsAs a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.
| S-EPMC10544355 | biostudies-literature
Project description:An important feature associated with Candida albicans pathogenicity is its ability to switch between yeast and hyphal forms, a process in which CaRas1 plays a key role. CaRas1 is activated by the guanine nucleotide exchange factor (GEF) CaCdc25, triggering hyphal growth-related signaling pathways through its conserved GTP-binding (G)-domain. An important function in hyphal growth has also been proposed for the long hypervariable region downstream the G-domain, whose unusual content of polyglutamine stretches and Q/N repeats make CaRas1 unique within Ras proteins. Despite its biological importance, both the structure of CaRas1 and the molecular basis of its activation by CaCdc25 remain unexplored. Here, we show that CaRas1 has an elongated shape and limited conformational flexibility and that its hypervariable region contains helical structural elements, likely forming an intramolecular coiled-coil. Functional assays disclosed that CaRas1-activation by CaCdc25 is highly efficient, with activities up to 2,000-fold higher than reported for human GEFs. The crystal structure of the CaCdc25 catalytic region revealed an active conformation for the α-helical hairpin, critical for CaRas1-activation, unveiling a specific region exclusive to CTG-clade species. Structural studies on CaRas1/CaCdc25 complexes also revealed an interaction surface clearly distinct from that of homologous human complexes. Furthermore, we identified an inhibitory synthetic peptide, prompting the proposal of a key regulatory mechanism for CaCdc25. To our knowledge, this is the first report of specific inhibition of the CaRas1-activation via targeting its GEF. This, together with their unique pathogen-structural features, disclose a set of novel strategies to specifically block this important virulence-related mechanism. IMPORTANCE Candida albicans is the main causative agent of candidiasis, the commonest fungal infection in humans. The eukaryotic nature of C. albicans and the rapid emergence of antifungal resistance raise the challenge of identifying novel drug targets to battle this prevalent and life-threatening disease. CaRas1 and CaCdc25 are key players in the activation of signaling pathways triggering multiple virulence traits, including the yeast-to-hypha interconversion. The structural similarity of the conserved G-domain of CaRas1 to those of human homologs and the lack of structural information on CaCdc25 has impeded progress in targeting these proteins. The unique structural and functional features for CaRas1 and CaCdc25 presented here, together with the identification of a synthetic peptide capable of specifically inhibiting the GEF activity of CaCdc25, open new possibilities to uncover new antifungal drug targets against C. albicans virulence.
| S-EPMC10470544 | biostudies-literature
Project description:Cholangiocarcinoma (CCA) has a complex immune microenvironment architecture, thus possessing challenges in its characterization and treatment. This study aimed to repurpose FDA-approved drugs for cholangiocarcinoma by transcriptomic-driven bioinformatic approach. Cox-proportional univariate regression was applied to 3017 immune-related genes known a priori to identify a list of mortality-associated genes, so-called immune-oncogenic gene signature, in CCA tumor-derived RNA-seq profiles of two independent cohorts. Unsupervised clustering stratified CCA tumors into two groups according to the immune-oncogenic gene signature expression, which then confirmed its clinical relevance by Kaplan-Meier curve. Molecularly guided drug repurposing was performed by an integrative connectivity map-prioritized drug-gene network analysis. The immune-oncogenic gene signature consists of 26 mortality-associated immune-related genes. Patients with high-expression signature had a poorer overall survival (log-rank p < 0.001), while gene enrichment analysis revealed cell-cycle checkpoint regulation and inflammatory-immune response signaling pathways affected this high-risk group. The integrative drug-gene network identified eight FDA-approved drugs as promising candidates, including Dasatinib a multi-kinase inhibitor currently investigated for advanced CCA with isocitrate-dehydrogenase mutations. This study proposes the use of the immune-oncogenic gene signature to identify high-risk CCA patients. Future preclinical and clinical studies are required to elucidate the therapeutic efficacy of the molecularly guided drugs as the adjunct therapy, aiming to improve the survival outcome.
| S-EPMC8871819 | biostudies-literature
Project description:BackgroundMedical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery.MethodsWe started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases.ResultsOur scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs.ConclusionsWe present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.
| S-EPMC9074288 | biostudies-literature
Project description:The discovery of new drugs is required in the time of global aging and increasing populations. Traditional drug development strategies are expensive, time-consuming, and have high risks. Thus, drug repurposing, which treats new/other diseases using existing drugs, has become a very admired tactic. It can also be referred to as the re-investigation of the existing drugs that failed to indicate the usefulness for the new diseases. Previously published literature used maximum flow approaches to identify new drug targets for drug-resistant infectious diseases but not for drug repurposing. Therefore, we are proposing a maximum flow-based protein-protein interactions (PPIs) network analysis approach to identify new drug targets (proteins) from the targets of the FDA (Food and Drug Administration) drugs and their associated drugs for chronic diseases (such as breast cancer, inflammatory bowel disease (IBD), and chronic obstructive pulmonary disease (COPD)) treatment. Experimental results showed that we have successfully turned the drug repurposing into a maximum flow problem. Our top candidates of drug repurposing, Guanidine, Dasatinib, and Phenethyl Isothiocyanate for breast cancer, IBD, and COPD were experimentally validated by other independent research as the potential candidate drugs for these diseases, respectively. This shows the usefulness of the proposed maximum flow approach for drug repurposing.
| S-EPMC8625622 | biostudies-literature
Project description:Genomic epidemiology uses pathogens' whole-genome sequences to understand and manage the spread of infectious diseases. Whole-genome data can be used to monitor outbreaks and cluster formation, identify cross-community transmissions, and characterize drug resistance and immune evasion. Typically, bacteria are cultured from clinical samples to obtain DNA for sequencing to generate whole-genome data. However, culture-independent diagnostic methods are utilized for some fastidious bacteria for better diagnostic yield and rapid pathogen genomics. Whole-genome enrichment (WGE) using targeted DNA sequencing enables direct sequencing of clinical samples without having to culture pathogens. However, the cost of capture probes ("baits") limits the utility of this method for large-scale genomic epidemiology. We developed a cost-effective method named Circular Nucleic acid Enrichment Reagent synthesis (CNERs) to generate whole-genome enrichment probes. We demonstrated the method by producing probes for Mycobacterium tuberculosis, which we used to enrich M. tuberculosis DNA that had been spiked at concentrations as low as 0.01% and 100 genome copies against a human DNA background to 1,225-fold and 4,636-fold. Furthermore, we enriched DNA from different M. tuberculosis lineages and M. bovis and demonstrated the utility of the WGE-CNERs data for lineage identification and drug-resistance characterization using an established pipeline. The CNERs method for whole-genome enrichment will be a valuable tool for the genomic epidemiology of emerging and difficult-to-grow pathogens. IMPORTANCE Emerging infectious diseases require continuous pathogen monitoring. Rapid clinical diagnosis by nucleic acid amplification is limited to a small number of targets and may miss target detection due to new mutations in clinical isolates. Whole-genome sequencing (WGS) identifies genome-wide variations that may be used to determine a pathogen's drug resistance patterns and phylogenetically characterize isolates to track disease origin and transmission. WGS is typically performed using DNA isolated from cultured clinical isolates. Culturing clinical specimens increases turn-around time and may not be possible for fastidious bacteria. To overcome some of these limitations, direct sequencing of clinical specimens has been attempted using expensive capture probes to enrich the entire genomes of target pathogens. We present a method to produce a cost-effective, time-efficient, and large-scale synthesis of probes for whole-genome enrichment. We envision that our method can be used for direct clinical sequencing of a wide range of microbial pathogens for genomic epidemiology.
| S-EPMC10653935 | biostudies-literature
Project description:We have developed a software program that weights and integrates specific properties on the genes in a pathogen so that they may be ranked as drug targets. We applied this software to produce three prioritized drug target lists for Mycobacterium tuberculosis, the causative agent of tuberculosis, a disease for which a new drug is desperately needed. Each list is based on an individual criterion. The first list prioritizes metabolic drug targets by the uniqueness of their roles in the M. tuberculosis metabolome ("metabolic chokepoints") and their similarity to known "druggable" protein classes (i.e., classes whose activity has previously been shown to be modulated by binding a small molecule). The second list prioritizes targets that would specifically impair M. tuberculosis, by weighting heavily those that are closely conserved within the Actinobacteria class but lack close homology to the host and gut flora. M. tuberculosis can survive asymptomatically in its host for many years by adapting to a dormant state referred to as "persistence." The final list aims to prioritize potential targets involved in maintaining persistence in M. tuberculosis. The rankings of current, candidate, and proposed drug targets are highlighted with respect to these lists. Some features were found to be more accurate than others in prioritizing studied targets. It can also be shown that targets can be prioritized by using evolutionary programming to optimize the weights of each desired property. We demonstrate this approach in prioritizing persistence targets.
| S-EPMC1475714 | biostudies-literature