2,387 research outputs found

    Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

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    The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

    Synthesis, In Silico Studies, Antiprotozoal and Cytotoxic Activities of Quinoline‐Biphenyl Hybrids

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    This is the pre-peer reviewed version of the following article: Synthesis, In Silico Studies, Antiprotozoal and Cytotoxic Activities of Quinoline‐Biphenyl Hybrids, which has been published in final form at https://doi.org/10.1002/slct.201903835. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived VersionsThe synthesis, in silico studies, antiprotozoal and cytotoxic activities of eleven quinoline‐biphenyl hybrids are described herein. The structure of the synthesized products was elucidated by a combination of spectrometric analyses. The synthesized compounds were evaluated against Plasmodium falciparum, and amastigotes forms both Leishmania (V) panamensis and Trypanosoma cruzi. Cytotoxicity was evaluated against human U‐937 macrophages. 8‐phenylquinoline (4 a) showed similar activity than meglumine antimoniate and 4‐(quinolin‐8‐yl)phenol (4 b) exhibited an activity similar to that of benznidazole. 8‐(3,4‐dimethoxyphenyl) quinoline (4 k) showed the best activity against P. falciparum. Although these compounds were toxic for mammalian U‐937 cells, however they may still have potential to be considered as candidates for drug development because of their antiparasite activity. Molecular docking was used to determine the in silico inhibition of some of the designed compounds against PfLDH and cruzipain, two important pharmacological targets involved in antiparasitic diseases. All hybrids were docked to the three‐dimensional structures of PfLDH and T. cruzi cruzipain as enzymes using AutoDock Vina. Notably, the docking results showed that the most active compounds 4‐(quinolin‐8‐yl)phenol (4 b, CE50: 11.33 Όg/mL for T. cruzi) and 8‐(3,4‐dimethoxyphenyl) quinoline (4 k, CE50: 8.84 Όg/mL for P. falciparum) exhibited the highest scoring pose (−7.5 and −7.7 kcal/mol, respectively). This result shows a good correlation between the predicted scores with the experimental data profile, suggesting that these ligands could act as competitive inhibitors of PfLDH or T. cruzi cruzipain enzymes, respectively. Finally, in silico ADME studies of the quinoline hybrids showed that these novel compounds have suitable drug‐like properties, making them potentially promising agents for antiprotozoal therapy

    A multilayer network approach for guiding drug repositioning in neglected diseases

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    Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature.Fil: Berenstein, Ariel JosĂ©. FundaciĂłn Instituto Leloir; Argentina. Universidad de Buenos Aires. Facultad de IngenierĂ­a. Departamento de FĂ­sica; ArgentinaFil: Magariños, MarĂ­a Paula. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Centro CientĂ­fico TecnolĂłgico Conicet - La Plata. Instituto de Investigaciones BiotecnolĂłgicas. Instituto de Investigaciones BiotecnolĂłgicas "Dr. RaĂșl AlfonsĂ­n" (sede ChascomĂșs). Universidad Nacional de San MartĂ­n. Instituto de Investigaciones BiotecnolĂłgicas. Instituto de Investigaciones BiotecnolĂłgicas "Dr. RaĂșl AlfonsĂ­n" (sede ChascomĂșs); ArgentinaFil: Chernomoretz, Ariel. FundaciĂłn Instituto Leloir; Argentina. Universidad de Buenos Aires. Facultad de IngenierĂ­a. Departamento de FĂ­sica; ArgentinaFil: Fernandez Aguero, Maria Jose. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas. Centro CientĂ­fico TecnolĂłgico Conicet - La Plata. Instituto de Investigaciones BiotecnolĂłgicas. Instituto de Investigaciones BiotecnolĂłgicas "Dr. RaĂșl AlfonsĂ­n" (sede ChascomĂșs). Universidad Nacional de San MartĂ­n. Instituto de Investigaciones BiotecnolĂłgicas. Instituto de Investigaciones BiotecnolĂłgicas "Dr. RaĂșl AlfonsĂ­n" (sede ChascomĂșs); Argentin

    Large–scale data–driven network analysis of human–plasmodium falciparum interactome: extracting essential targets and processes for malaria drug discovery

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    Background: Plasmodium falciparum malaria is an infectious disease considered to have great impact on public health due to its associated high mortality rates especially in sub Saharan Africa. Falciparum drugresistant strains, notably, to chloroquine and sulfadoxine-pyrimethamine in Africa is traced mainly to Southeast Asia where artemisinin resistance rate is increasing. Although careful surveillance to monitor the emergence and spread of artemisinin-resistant parasite strains in Africa is on-going, research into new drugs, particularly, for African populations, is critical since there is no replaceable drug for artemisinin combination therapies (ACTs) yet. Objective: The overall objective of this study is to identify potential protein targets through host–pathogen protein–protein functional interaction network analysis to understand the underlying mechanisms of drug failure and identify those essential targets that can play their role in predicting potential drug candidates specific to the African populations through a protein-based approach of both host and Plasmodium falciparum genomic analysis. Methods: We leveraged malaria-specific genome wide association study summary statistics data obtained from Gambia, Kenya and Malawi populations, Plasmodium falciparum selective pressure variants and functional datasets (protein sequences, interologs, host-pathogen intra-organism and host-pathogen inter-organism protein-protein interactions (PPIs)) from various sources (STRING, Reactome, HPID, Uniprot, IntAct and literature) to construct overlapping functional network for both host and pathogen. Developed algorithms and a large-scale data-driven computational framework were used in this study to analyze the datasets and the constructed networks to identify densely connected subnetworks or hubs essential for network stability and integrity. The host-pathogen network was analyzed to elucidate the influence of parasite candidate key proteins within the network and predict possible resistant pathways due to host-pathogen candidate key protein interactions. We performed biological and pathway enrichment analysis on critical proteins identified to elucidate their functions. In order to leverage disease-target-drug relationships to identify potential repurposable already approved drug candidates that could be used to treat malaria, pharmaceutical datasets from drug bank were explored using semantic similarity approach based of target–associated biological processes Results: About 600,000 significant SNPs (p-value< 0.05) from the summary statistics data were mapped to their associated genes, and we identified 79 human-associated malaria genes. The assembled parasite network comprised of 8 clusters containing 799 functional interactions between 155 reviewed proteins of which 5 clusters contained 43 key proteins (selective variants) and 2 clusters contained 2 candidate key proteins(key proteins characterized by high centrality measure), C6KTB7 and C6KTD2. The human network comprised of 32 clusters containing 4,133,136 interactions between 20,329 unique reviewed proteins of which 7 clusters contained 760 key proteins and 2 clusters contained 6 significant human malaria-associated candidate key proteins or genes P22301 (IL10), P05362 (ICAM1), P01375 (TNF), P30480 (HLA-B), P16284 (PECAM1), O00206 (TLR4). The generated host-pathogen network comprised of 31,512 functional interactions between 8,023 host and pathogen proteins. We also explored the association of pfk13 gene within the host-pathogen. We observed that pfk13 cluster with host kelch–like proteins and other regulatory genes but no direct association with our identified host candidate key malaria targets. We implemented semantic similarity based approach complemented by Kappa and Jaccard statistical measure to identify 115 malaria–similar diseases and 26 potential repurposable drug hits that can be 3 appropriated experimentally for malaria treatment. Conclusion: In this study, we reviewed existing antimalarial drugs and resistance–associated variants contributing to the diminished sensitivity of antimalarials, especially chloroquine, sulfadoxine-pyrimethamine and artemisinin combination therapy within the African population. We also described various computational techniques implemented in predicting drug targets and leads in drug research. In our data analysis, we showed that possible mechanisms of resistance to artemisinin in Africa may arise from the combinatorial effects of many resistant genes to chloroquine and sulfadoxine–pyrimethamine. We investigated the role of pfk13 within the host–pathogen network. We predicted key targets that have been proposed to be essential for malaria drug and vaccine development through structural and functional analysis of host and pathogen function networks. Based on our analysis, we propose these targets as essential co-targets for combinatorial malaria drug discovery

    WISDOM: A Grid-Enabled Drug Discovery Initiative Against Malaria

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    The goal of this chapter is to present the WISDOM initiative, which is one of the main accomplishments in the use of grids for biomedical sciences achieved on grid infrastructures in Europe. Researchers in life sciences are among the most active scientifi c communities on the EGEE infrastructure. As a consequence, the biomedical virtual organization stands fourth in terms of resources consumed in 2007, with an average of 7000 jobs submitted every day to the grid and more than 4 million hours of CPU consumed in the last 12 months. Only three experiments on the CERN Large Hadron Collider have used more resources. Compared to particle physics, the use of resources is much less centralized as about 40 different scientifi c applications are now currently deployed on EGEE. Each of them requires an amount of CPU which ranges from a few to a few hundred CPU years. Thanks to the 20,000 processors available to the users of the biomedical virtual organization, crunching factors in the hundreds are witnessed routinely. Such performances were already achieved on supercomputers but at the cost of reservation and long delays in the access to resources. On the contrary, grid infrastructures are constantly open to the user communities. Such changes in the scale of the computing resources made continuously available to the researchers in biomedical sciences open opportunities for exploring new fi elds or changing the approach to existing challenges. In this chapter, we would like to show the potential impact of grids in the fi eld of drug discovery through the example of the WISDOM initiative

    Discovery: an interactive resource for the rational selection and comparison of putative drug target proteins in malaria

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    <p>Abstract</p> <p>Background</p> <p>Up to half a billion human clinical cases of malaria are reported each year, resulting in about 2.7 million deaths, most of which occur in sub-Saharan Africa. Due to the over-and misuse of anti-malarials, widespread resistance to all the known drugs is increasing at an alarming rate. Rational methods to select new drug target proteins and lead compounds are urgently needed. The Discovery system provides data mining functionality on extensive annotations of five malaria species together with the human and mosquito hosts, enabling the selection of new targets based on multiple protein and ligand properties.</p> <p>Methods</p> <p>A web-based system was developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein features used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions and host-pathogen interactions among others. Searching by chemical structure is also available.</p> <p>Results</p> <p>An <it>in silico</it> system for the selection of putative drug targets and lead compounds is presented, together with an example study on the bifunctional DHFR-TS from <it>Plasmodium falciparum</it>.</p> <p>Conclusion</p> <p>The Discovery system allows for the identification of putative drug targets and lead compounds in Plasmodium species based on the filtering of protein and chemical properties.</p

    tackling malaria

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    Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.publishersversionpublishe
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