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1.
Elife ; 102021 08 03.
Artigo em Inglês | MEDLINE | ID: mdl-34340747

RESUMO

The discovery of a drug requires over a decade of intensive research and financial investments - and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.


Assuntos
Desenho de Fármacos , Descoberta de Drogas/métodos , Reposicionamento de Medicamentos , Preparações Farmacêuticas/metabolismo , Animais , Antimetabólitos Antineoplásicos/química , Antimetabólitos Antineoplásicos/metabolismo , Antivirais/química , Antivirais/farmacologia , COVID-19/tratamento farmacológico , Bases de Dados de Produtos Farmacêuticos , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/etiologia , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/metabolismo , Fluoruracila/química , Fluoruracila/metabolismo , Humanos , Preparações Farmacêuticas/química , Fluxo de Trabalho
2.
Curr Opin Microbiol ; 63: 259-266, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34461385

RESUMO

Genome scale metabolic models (GEMs) offer a powerful means of integrating genome and biochemical information on an organism to make testable predictions of metabolic functions at different conditions and to systematically predict essential genes that may be targeted by drugs. This review describes how Plasmodium GEMs have become increasingly more accurate through the integration of omics and experimental genetic data. We also discuss how GEMs contribute to our increasing understanding of how Plasmodium metabolism is reprogrammed between life cycle stages.

5.
NPJ Syst Biol Appl ; 6(1): 1, 2020 01 07.
Artigo em Inglês | MEDLINE | ID: mdl-32001719

RESUMO

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.


Assuntos
Adaptação Biológica/fisiologia , Biologia Computacional/métodos , Adaptação Biológica/genética , Bactérias/genética , Bactérias/metabolismo , Fenômenos Bioquímicos , Biodegradação Ambiental , Genoma , Redes e Vias Metabólicas/genética , Modelos Biológicos , Pseudomonas/genética , Pseudomonas/metabolismo , Análise de Sistemas
6.
Cell ; 180(4): 688-702.e13, 2020 02 20.
Artigo em Inglês | MEDLINE | ID: mdl-32084340

RESUMO

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.


Assuntos
Antibacterianos/farmacologia , Descoberta de Drogas/métodos , Aprendizado de Máquina , Acinetobacter baumannii/efeitos dos fármacos , Animais , Antibacterianos/química , Quimioinformática/métodos , Clostridioides difficile/efeitos dos fármacos , Bases de Dados de Compostos Químicos , Camundongos , Camundongos Endogâmicos BALB C , Camundongos Endogâmicos C57BL , Mycobacterium tuberculosis/efeitos dos fármacos , Bibliotecas de Moléculas Pequenas/química , Bibliotecas de Moléculas Pequenas/farmacologia
7.
Cell Host Microbe ; 27(2): 290-306.e11, 2020 02 12.
Artigo em Inglês | MEDLINE | ID: mdl-31991093

RESUMO

To survive and proliferate in diverse host environments with varying nutrient availability, the obligate intracellular parasite Toxoplasma gondii reprograms its metabolism. We have generated and curated a genome-scale metabolic model (iTgo) for the fast-replicating tachyzoite stage, harmonized with experimentally observed phenotypes. To validate the importance of four metabolic pathways predicted by the model, we have performed in-depth in vitro and in vivo phenotyping of mutant parasites including targeted metabolomics and CRISPR-Cas9 fitness screening of all known metabolic genes. This led to unexpected insights into the remarkable flexibility of the parasite, addressing the dependency on biosynthesis or salvage of fatty acids (FAs), purine nucleotides (AMP and GMP), a vitamin (pyridoxal-5P), and a cofactor (heme) in both the acute and latent stages of infection. Taken together, our experimentally validated metabolic network leads to a deeper understanding of the parasite's biology, opening avenues for the development of therapeutic intervention against apicomplexans.


Assuntos
Ácidos Graxos/metabolismo , Heme/metabolismo , Toxoplasma/metabolismo , Vitamina B 6/metabolismo , Animais , Repetições Palindrômicas Curtas Agrupadas e Regularmente Espaçadas , Biologia Computacional , Desenvolvimento de Medicamentos/tendências , Genômica , Estágios do Ciclo de Vida/fisiologia , Redes e Vias Metabólicas , Metabolômica , Camundongos , Fenótipo , Toxoplasma/genética
8.
Cell ; 179(5): 1112-1128.e26, 2019 11 14.
Artigo em Inglês | MEDLINE | ID: mdl-31730853

RESUMO

Plasmodium gene functions in mosquito and liver stages remain poorly characterized due to limitations in the throughput of phenotyping at these stages. To fill this gap, we followed more than 1,300 barcoded P. berghei mutants through the life cycle. We discover 461 genes required for efficient parasite transmission to mosquitoes through the liver stage and back into the bloodstream of mice. We analyze the screen in the context of genomic, transcriptomic, and metabolomic data by building a thermodynamic model of P. berghei liver-stage metabolism, which shows a major reprogramming of parasite metabolism to achieve rapid growth in the liver. We identify seven metabolic subsystems that become essential at the liver stages compared with asexual blood stages: type II fatty acid synthesis and elongation (FAE), tricarboxylic acid, amino sugar, heme, lipoate, and shikimate metabolism. Selected predictions from the model are individually validated in single mutants to provide future targets for drug development.


Assuntos
Genoma de Protozoário , Estágios do Ciclo de Vida/genética , Fígado/metabolismo , Fígado/parasitologia , Plasmodium berghei/crescimento & desenvolvimento , Plasmodium berghei/genética , Alelos , Amino Açúcares/biossíntese , Animais , Culicidae/parasitologia , Eritrócitos/parasitologia , Ácido Graxo Sintases/metabolismo , Ácidos Graxos/metabolismo , Técnicas de Inativação de Genes , Genótipo , Modelos Biológicos , Mutação/genética , Parasitos/genética , Parasitos/crescimento & desenvolvimento , Fenótipo , Plasmodium berghei/metabolismo , Ploidias , Reprodução
9.
PLoS Comput Biol ; 13(3): e1005397, 2017 03.
Artigo em Inglês | MEDLINE | ID: mdl-28333921

RESUMO

Novel antimalarial therapies are urgently needed for the fight against drug-resistant parasites. The metabolism of malaria parasites in infected cells is an attractive source of drug targets but is rather complex. Computational methods can handle this complexity and allow integrative analyses of cell metabolism. In this study, we present a genome-scale metabolic model (iPfa) of the deadliest malaria parasite, Plasmodium falciparum, and its thermodynamics-based flux analysis (TFA). Using previous absolute concentration data of the intraerythrocytic parasite, we applied TFA to iPfa and predicted up to 63 essential genes and 26 essential pairs of genes. Of the 63 genes, 35 have been experimentally validated and reported in the literature, and 28 have not been experimentally tested and include previously hypothesized or novel predictions of essential metabolic capabilities. Without metabolomics data, four of the genes would have been incorrectly predicted to be non-essential. TFA also indicated that substrate channeling should exist in two metabolic pathways to ensure the thermodynamic feasibility of the flux. Finally, analysis of the metabolic capabilities of P. falciparum led to the identification of both the minimal nutritional requirements and the genes that can become indispensable upon substrate inaccessibility. This model provides novel insight into the metabolic needs and capabilities of the malaria parasite and highlights metabolites and pathways that should be measured and characterized to identify potential thermodynamic bottlenecks and substrate channeling. The hypotheses presented seek to guide experimental studies to facilitate a better understanding of the parasite metabolism and the identification of targets for more efficient intervention.


Assuntos
Metabolismo Energético/fisiologia , Genes Essenciais/fisiologia , Modelos Biológicos , Necessidades Nutricionais/fisiologia , Plasmodium falciparum/fisiologia , Proteínas de Protozoários/metabolismo , Simulação por Computador , Análise do Fluxo Metabólico/métodos , Metaboloma/fisiologia , Termodinâmica
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