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


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 resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use 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, suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

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
Bioinformatics ; 2021 May 18.
Artigo em Inglês | MEDLINE | ID: mdl-34003971


MOTIVATION: Finding biosynthetic pathways is essential for metabolic engineering of organisms to produce chemicals, biodegradation prediction of pollutants and drugs, and for the elucidation of bioproduction pathways of secondary metabolites. A key step in biosynthetic pathway design is the extraction of novel metabolic pathways from big networks that integrate known biological, as well as novel, predicted biotransformations. However, the efficient analysis and the navigation of big biochemical networks remain a challenge. RESULTS: Here, we propose the construction of searchable graph representations of metabolic networks. Each reaction is decomposed into pairs of reactants and products, and each pair is assigned a weight, which is calculated from the number of conserved atoms between the reactant and the product molecule. We test our method on a biochemical network that spans 6,546 known enzymatic reactions to show how our approach elegantly extracts biologically relevant metabolic pathways from biochemical networks, and how the proposed network structure enables the application of efficient graph search algorithms that improve navigation and pathway identification in big metabolic networks. The weighted reactant-product pairs of an example network and the corresponding graph search algorithm are available online. The proposed method extracts metabolic pathways fast and reliably from big biochemical networks, which is inherently important for all applications involving the engineering of metabolic networks. AVAILABILITY: SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nat Commun ; 12(1): 1760, 2021 03 19.
Artigo em Inglês | MEDLINE | ID: mdl-33741955


Plant natural products (PNPs) and their derivatives are important but underexplored sources of pharmaceutical molecules. To access this untapped potential, the reconstitution of heterologous PNP biosynthesis pathways in engineered microbes provides a valuable starting point to explore and produce novel PNP derivatives. Here, we introduce a computational workflow to systematically screen the biochemical vicinity of a biosynthetic pathway for pharmaceutical compounds that could be produced by derivatizing pathway intermediates. We apply our workflow to the biosynthetic pathway of noscapine, a benzylisoquinoline alkaloid (BIA) with a long history of medicinal use. Our workflow identifies pathways and enzyme candidates for the production of (S)-tetrahydropalmatine, a known analgesic and anxiolytic, and three additional derivatives. We then construct pathways for these compounds in yeast, resulting in platforms for de novo biosynthesis of BIA derivatives and demonstrating the value of cheminformatic tools to predict reactions, pathways, and enzymes in synthetic biology and metabolic engineering.

Produtos Biológicos/metabolismo , Vias Biossintéticas/genética , Biologia Computacional/métodos , Engenharia Metabólica/métodos , Noscapina/metabolismo , Saccharomyces cerevisiae/metabolismo , Alcaloides/biossíntese , Benzilisoquinolinas/metabolismo , Noscapina/química , Plantas/genética , Plantas/metabolismo , Saccharomyces cerevisiae/genética , Software
ACS Synth Biol ; 9(6): 1479-1482, 2020 06 19.
Artigo em Inglês | MEDLINE | ID: mdl-32421310


The ATLAS of Biochemistry is a repository of both known and novel predicted biochemical reactions between biological compounds listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG). ATLAS was originally compiled based on KEGG 2015, though the number of KEGG reactions has increased by almost 20 percent since then. Here, we present an updated version of ATLAS created from KEGG 2018 using an increased set of generalized reaction rules. Furthermore, we improved the accuracy of the enzymes that are predicted for catalyzing novel reactions. ATLAS now contains ∼150 000 reactions, out of which 96% are novel. In this report, we present detailed statistics on the updated ATLAS and highlight the improvements with regard to the previous version. Most importantly, 107 reactions predicted in the original ATLAS are now known to KEGG, which validates the predictive power of our approach. The updated ATLAS is available at

Bases de Dados Factuais , Enzimas/metabolismo , Redes e Vias Metabólicas
Biotechnol J ; 12(1)2017 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-27897385


Reaction atom mappings track the positional changes of all of the atoms between the substrates and the products as they undergo the biochemical transformation. However, information on atom transitions in the context of metabolic pathways is not widely available in the literature. The understanding of metabolic pathways at the atomic level is of great importance as it can deconvolute the overlapping catabolic/anabolic pathways resulting in the observed metabolic phenotype. The automated identification of atom transitions within a metabolic network is a very challenging task since the degree of complexity of metabolic networks dramatically increases when we transit from metabolite-level studies to atom-level studies. Despite being studied extensively in various approaches, the field of atom mapping of metabolic networks is lacking an automated approach, which (i) accounts for the information of reaction mechanism for atom mapping and (ii) is extendable from individual atom-mapped reactions to atom-mapped reaction networks. Hereby, we introduce a computational framework, iAM.NICE (in silico Atom Mapped Network Integrated Computational Explorer), for the systematic atom-level reconstruction of metabolic networks from in silico labelled substrates. iAM.NICE is to our knowledge the first automated atom-mapping algorithm that is based on the underlying enzymatic biotransformation mechanisms, and its application goes beyond individual reactions and it can be used for the reconstruction of atom-mapped metabolic networks. We illustrate the applicability of our method through the reconstruction of atom-mapped reactions of the KEGG database and we provide an example of an atom-level representation of the core metabolic network of E. coli.

Algoritmos , Biologia Computacional/métodos , Escherichia coli/metabolismo , Redes e Vias Metabólicas , Carbono/metabolismo , Simulação por Computador , Bases de Dados Factuais , Enzimas/química , Enzimas/metabolismo , Glicólise , Fluxo de Trabalho
ACS Synth Biol ; 5(10): 1155-1166, 2016 10 21.
Artigo em Inglês | MEDLINE | ID: mdl-27404214


Because the complexity of metabolism cannot be intuitively understood or analyzed, computational methods are indispensable for studying biochemistry and deepening our understanding of cellular metabolism to promote new discoveries. We used the computational framework along with cheminformatic tools to assemble the whole theoretical reactome from the known metabolome through expansion of the known biochemistry presented in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We constructed the ATLAS of Biochemistry, a database of all theoretical biochemical reactions based on known biochemical principles and compounds. ATLAS includes more than 130 000 hypothetical enzymatic reactions that connect two or more KEGG metabolites through novel enzymatic reactions that have never been reported to occur in living organisms. Moreover, ATLAS reactions integrate 42% of KEGG metabolites that are not currently present in any KEGG reaction into one or more novel enzymatic reactions. The generated repository of information is organized in a Web-based database ( ) that allows the user to search for all possible routes from any substrate compound to any product. The resulting pathways involve known and novel enzymatic steps that may indicate unidentified enzymatic activities and provide potential targets for protein engineering. Our approach of introducing novel biochemistry into pathway design and associated databases will be important for synthetic biology and metabolic engineering.

Fenômenos Bioquímicos , Bases de Dados Genéticas , Engenharia Metabólica , Biologia Sintética , Fenômenos Fisiológicos Celulares , Internet , Redes e Vias Metabólicas , Metaboloma , Reprodutibilidade dos Testes