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Tam JYC, Lorsbach T, Schmidt S, Wicker JS. Holistic evaluation of biodegradation pathway prediction: assessing multi-step reactions and intermediate products. J Cheminform 2021; 13:63. [PMID: 34479624 PMCID: PMC8414759 DOI: 10.1186/s13321-021-00543-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 08/21/2021] [Indexed: 11/10/2022] Open
Abstract
The prediction of metabolism and biotransformation pathways of xenobiotics is a highly desired tool in environmental sciences, drug discovery, and (eco)toxicology. Several systems predict single transformation steps or complete pathways as series of parallel and subsequent steps. Their performance is commonly evaluated on the level of a single transformation step. Such an approach cannot account for some specific challenges that are caused by specific properties of biotransformation experiments. That is, missing transformation products in the reference data that occur only in low concentrations, e.g. transient intermediates or higher-generation metabolites. Furthermore, some rule-based prediction systems evaluate the performance only based on the defined set of transformation rules. Therefore, the performance of these models cannot be directly compared. In this paper, we introduce a new evaluation framework that extends the evaluation of biotransformation prediction from single transformations to whole pathways, taking into account multiple generations of metabolites. We introduce a procedure to address transient intermediates and propose a weighted scoring system that acknowledges the uncertainty of higher-generation metabolites. We implemented this framework in enviPath and demonstrate its strict performance metrics on predictions of in vitro biotransformation and degradation of xenobiotics in soil. Our approach is model-agnostic and can be transferred to other prediction systems. It is also capable of revealing knowledge gaps in terms of incompletely defined sets of transformation rules.
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Affiliation(s)
- Jason Y C Tam
- School of Computer Science, University of Auckland, Private Bag 92019, 1142, Auckland, New Zealand. .,enviPath UG & Co. KG, Postfach 230062, 55051, Mainz, Germany.
| | - Tim Lorsbach
- enviPath UG & Co. KG, Postfach 230062, 55051, Mainz, Germany
| | - Sebastian Schmidt
- Bayer AG, Crop Science Division, Environmental Safety, Alfred-Nobel-Straöe 50, 40789, Monheim am Rhein , Germany
| | - Jörg S Wicker
- School of Computer Science, University of Auckland, Private Bag 92019, 1142, Auckland, New Zealand.,enviPath UG & Co. KG, Postfach 230062, 55051, Mainz, Germany
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2
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MohammadiPeyhani H, Chiappino-Pepe A, Haddadi K, Hafner J, Hadadi N, Hatzimanikatis V. NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism. eLife 2021; 10:e65543. [PMID: 34340747 PMCID: PMC8331181 DOI: 10.7554/elife.65543] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 07/07/2021] [Indexed: 12/30/2022] Open
Abstract
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.
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Affiliation(s)
- Homa MohammadiPeyhani
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Anush Chiappino-Pepe
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Kiandokht Haddadi
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Jasmin Hafner
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Noushin Hadadi
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
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4
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Singh AK, Bilal M, Iqbal HMN, Raj A. Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 770:144561. [PMID: 33736422 DOI: 10.1016/j.scitotenv.2020.144561] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 12/13/2020] [Accepted: 12/13/2020] [Indexed: 02/05/2023]
Abstract
The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.
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Affiliation(s)
- Anil Kumar Singh
- Environmental Microbiology Laboratory, Environmental Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Muhammad Bilal
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian 223003, China
| | - Hafiz M N Iqbal
- Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey 64849, Mexico.
| | - Abhay Raj
- Environmental Microbiology Laboratory, Environmental Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
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Hafner J, MohammadiPeyhani H, Sveshnikova A, Scheidegger A, Hatzimanikatis V. Updated ATLAS of Biochemistry with New Metabolites and Improved Enzyme Prediction Power. ACS Synth Biol 2020; 9:1479-1482. [PMID: 32421310 PMCID: PMC7309321 DOI: 10.1021/acssynbio.0c00052] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
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 https://lcsb-databases.epfl.ch/atlas.
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Affiliation(s)
- Jasmin Hafner
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, CH-1015 Lausanne, Switzerland
| | - Homa MohammadiPeyhani
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, CH-1015 Lausanne, Switzerland
| | - Anastasia Sveshnikova
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, CH-1015 Lausanne, Switzerland
| | - Alan Scheidegger
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, CH-1015 Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, CH-1015 Lausanne, Switzerland
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Lin GM, Warden-Rothman R, Voigt CA. Retrosynthetic design of metabolic pathways to chemicals not found in nature. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.coisb.2019.04.004] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites. Proc Natl Acad Sci U S A 2019; 116:7298-7307. [PMID: 30910961 PMCID: PMC6462048 DOI: 10.1073/pnas.1818877116] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Recent advances in synthetic biochemistry have resulted in a wealth of novel hypothetical enzymatic reactions that are not matched to protein-encoding genes, deeming them “orphan.” A large number of known metabolic enzymes are also orphan, leaving important gaps in metabolic network maps. Proposing genes for the catalysis of orphan reactions is critical for applications ranging from biotechnology to medicine. In this work, the computational method BridgIT identified potential enzymes of orphan reactions and nearly all theoretically possible biochemical transformations, providing candidate genes to catalyze these reactions to the research community. The BridgIT online tool will allow researchers to fill the knowledge gaps in metabolic networks and will act as a starting point for designing novel enzymes to catalyze nonnatural transformations. Thousands of biochemical reactions with characterized activities are “orphan,” meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose the method BridgIT to identify candidate genes and catalyzing proteins for these reactions. This method introduces information about the enzyme binding pocket into reaction-similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest enzymes that catalyze the most-similar nonorphan reactions as candidates for also catalyzing the orphan ones. We performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2011 (a comprehensive enzymatic-reaction database) that became nonorphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 of 379 novel reactions in 2014 that were later cataloged in KEGG 2018, BridgIT predicted the exact or highly similar enzymes. BridgIT requires knowledge about only four connecting bonds around the atoms of the reactive sites to correctly annotate proteins for 93% of analyzed enzymatic reactions. Increasing to seven connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.
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Tokic M, Hadadi N, Ataman M, Neves D, Ebert BE, Blank LM, Miskovic L, Hatzimanikatis V. Discovery and Evaluation of Biosynthetic Pathways for the Production of Five Methyl Ethyl Ketone Precursors. ACS Synth Biol 2018; 7:1858-1873. [PMID: 30021444 DOI: 10.1021/acssynbio.8b00049] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The limited supply of fossil fuels and the establishment of new environmental policies shifted research in industry and academia toward sustainable production of the second generation of biofuels, with methyl ethyl ketone (MEK) being one promising fuel candidate. MEK is a commercially valuable petrochemical with an extensive application as a solvent. However, as of today, a sustainable and economically viable production of MEK has not yet been achieved despite several attempts of introducing biosynthetic pathways in industrial microorganisms. We used BNICE.ch as a retrobiosynthesis tool to discover all novel pathways around MEK. Out of 1325 identified compounds connecting to MEK with one reaction step, we selected 3-oxopentanoate, but-3-en-2-one, but-1-en-2-olate, butylamine, and 2-hydroxy-2-methylbutanenitrile for further study. We reconstructed 3 679 610 novel biosynthetic pathways toward these 5 compounds. We then embedded these pathways into the genome-scale model of E. coli, and a set of 18 622 were found to be the most biologically feasible ones on the basis of thermodynamics and their yields. For each novel reaction in the viable pathways, we proposed the most similar KEGG reactions, with their gene and protein sequences, as candidates for either a direct experimental implementation or as a basis for enzyme engineering. Through pathway similarity analysis we classified the pathways and identified the enzymes and precursors that were indispensable for the production of the target molecules. These retrobiosynthesis studies demonstrate the potential of BNICE.ch for discovery, systematic evaluation, and analysis of novel pathways in synthetic biology and metabolic engineering studies.
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Affiliation(s)
- Milenko Tokic
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland
| | - Noushin Hadadi
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland
| | - Meric Ataman
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland
| | - Dário Neves
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, D-52056 Aachen, Germany
| | - Birgitta E. Ebert
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, D-52056 Aachen, Germany
| | - Lars M. Blank
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, D-52056 Aachen, Germany
| | - Ljubisa Miskovic
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland
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Qiu Y, Jiang H, Ching WK, Cheng X. Discovery of Boolean metabolic networks: integer linear programming based approach. BMC SYSTEMS BIOLOGY 2018; 12:7. [PMID: 29671395 PMCID: PMC5907190 DOI: 10.1186/s12918-018-0528-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Background Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. Results In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Conclusions Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at “http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip”.
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Affiliation(s)
- Yushan Qiu
- College of Mathematics and Statistics, Shenzhen University, Nanhai Avenue 3688, Shenzhen, 518060, China
| | - Hao Jiang
- Department of Mathematics, School of Information, Renmin University of China, No.59 Zhong Guan Cun Avenue, Hai Dian District, Beijing, 100872, China.
| | - Wai-Ki Ching
- Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong, Hong Kong
| | - Xiaoqing Cheng
- School of Mathematics and Statistics, Xi'An Jiaotong University, No.28 West Xianning Road, Xi'An, 710049, China
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10
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Strategies for microbial synthesis of high-value phytochemicals. Nat Chem 2018; 10:395-404. [DOI: 10.1038/s41557-018-0013-z] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 01/25/2018] [Indexed: 01/28/2023]
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Abstract
The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types) of the molecular networks, for example, genome-scale metabolic network (GMN), transcriptional regulatory network (TRN), and signal transduction network (STN). It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling) of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.
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Affiliation(s)
- Tong Hao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Dan Wu
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Lingxuan Zhao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Qian Wang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Edwin Wang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China.,Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Jinsheng Sun
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China.,Tianjin Bohai Fisheries Research Institute, Tianjin, China
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Pandit AV, Srinivasan S, Mahadevan R. Redesigning metabolism based on orthogonality principles. Nat Commun 2017; 8:15188. [PMID: 28555623 PMCID: PMC5459945 DOI: 10.1038/ncomms15188] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 03/08/2017] [Indexed: 01/25/2023] Open
Abstract
Modifications made during metabolic engineering for overproduction of chemicals have network-wide effects on cellular function due to ubiquitous metabolic interactions. These interactions, that make metabolic network structures robust and optimized for cell growth, act to constrain the capability of the cell factory. To overcome these challenges, we explore the idea of an orthogonal network structure that is designed to operate with minimal interaction between chemical production pathways and the components of the network that produce biomass. We show that this orthogonal pathway design approach has significant advantages over contemporary growth-coupled approaches using a case study on succinate production. We find that natural pathways, fundamentally linked to biomass synthesis, are less orthogonal in comparison to synthetic pathways. We suggest that the use of such orthogonal pathways can be highly amenable for dynamic control of metabolism and have other implications for metabolic engineering.
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Affiliation(s)
- Aditya Vikram Pandit
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ontario, Canada M5S 3E5
| | - Shyam Srinivasan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ontario, Canada M5S 3E5
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ontario, Canada M5S 3E5
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College Street, Toronto, Ontario, CanadaM5S 3G9
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Hadadi N, Hafner J, Soh KC, Hatzimanikatis V. Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites. Biotechnol J 2017; 12. [DOI: 10.1002/biot.201600464] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 12/13/2022]
Affiliation(s)
- Noushin Hadadi
- Laboratory of Computational Systems Biotechnology (LCSB); Swiss Federal Institute of Technology (EPFL); Lausanne Switzerland
| | - Jasmin Hafner
- Laboratory of Computational Systems Biotechnology (LCSB); Swiss Federal Institute of Technology (EPFL); Lausanne Switzerland
| | - Keng Cher Soh
- Laboratory of Computational Systems Biotechnology (LCSB); Swiss Federal Institute of Technology (EPFL); Lausanne Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology (LCSB); Swiss Federal Institute of Technology (EPFL); Lausanne Switzerland
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14
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Hadadi N, Hafner J, Shajkofci A, Zisaki A, Hatzimanikatis V. ATLAS of Biochemistry: A Repository of All Possible Biochemical Reactions for Synthetic Biology and Metabolic Engineering Studies. ACS Synth Biol 2016; 5:1155-1166. [PMID: 27404214 DOI: 10.1021/acssynbio.6b00054] [Citation(s) in RCA: 96] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
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 BNICE.ch 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 ( http://lcsb-databases.epfl.ch/atlas/ ) 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.
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Affiliation(s)
- Noushin Hadadi
- Laboratory of Computational
Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland
| | - Jasmin Hafner
- Laboratory of Computational
Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland
| | - Adrian Shajkofci
- Laboratory of Computational
Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland
| | - Aikaterini Zisaki
- Laboratory of Computational
Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational
Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland
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Khater S, Anand S, Mohanty D. In silico methods for linking genes and secondary metabolites: The way forward. Synth Syst Biotechnol 2016; 1:80-88. [PMID: 29062931 PMCID: PMC5640692 DOI: 10.1016/j.synbio.2016.03.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Revised: 02/18/2016] [Accepted: 03/01/2016] [Indexed: 11/26/2022] Open
Abstract
In silico methods for linking genomic space to chemical space have played a crucial role in genomics driven discovery of new natural products as well as biosynthesis of altered natural products by engineering of biosynthetic pathways. Here we give an overview of available computational tools and then briefly describe a novel computational framework, namely retro-biosynthetic enumeration of biosynthetic reactions, which can add to the repertoire of computational tools available for connecting natural products to their biosynthetic gene clusters. Most of the currently available bioinformatics tools for analysis of secondary metabolite biosynthetic gene clusters utilize the “Genes to Metabolites” approach. In contrast to the “Genes to Metabolites” approach, the “Metabolites to Genes” or retro-biosynthetic approach would involve enumerating the various biochemical transformations or enzymatic reactions which would generate the given chemical moiety starting from a set of precursor molecules and identifying enzymatic domains which can potentially catalyze the enumerated biochemical transformations. In this article, we first give a brief overview of the presently available in silico tools and approaches for analysis of secondary metabolite biosynthetic pathways. We also discuss our preliminary work on development of algorithms for retro-biosynthetic enumeration of biochemical transformations to formulate a novel computational method for identifying genes associated with biosynthesis of a given polyketide or nonribosomal peptide.
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Affiliation(s)
- Shradha Khater
- Bioinformatics Center, National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Swadha Anand
- Bioinformatics Center, National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Debasisa Mohanty
- Bioinformatics Center, National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110067, India
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16
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Andreozzi S, Chakrabarti A, Soh KC, Burgard A, Yang TH, Van Dien S, Miskovic L, Hatzimanikatis V. Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models. Metab Eng 2016; 35:148-159. [PMID: 26855240 DOI: 10.1016/j.ymben.2016.01.009] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2015] [Revised: 12/16/2015] [Accepted: 01/29/2016] [Indexed: 11/24/2022]
Abstract
Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant Escherichia coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation. Independent earlier experiments on this strain confirmed that the computationally obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and effectiveness of ORACLE for the accelerated design of microbial cell factories.
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Affiliation(s)
- Stefano Andreozzi
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Anirikh Chakrabarti
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Keng Cher Soh
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | | | | | | | - Ljubisa Miskovic
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland.
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17
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Ganter M, Kaltenbach HM, Stelling J. Predicting network functions with nested patterns. Nat Commun 2015; 5:3006. [PMID: 24398547 DOI: 10.1038/ncomms4006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Accepted: 11/24/2013] [Indexed: 12/20/2022] Open
Abstract
Identifying suitable patterns in complex biological interaction networks helps understanding network functions and allows for predictions at the pattern level: by recognizing a known pattern, one can assign its previously established function. However, current approaches fail for previously unseen patterns, when patterns overlap and when they are embedded into a new network context. Here we show how to conceptually extend pattern-based approaches. We define metabolite patterns in metabolic networks that formalize co-occurrences of metabolites. Our probabilistic framework decodes the implicit information in the networks' metabolite patterns to predict metabolic functions. We demonstrate the predictive power by identifying 'indicator patterns', for instance, for enzyme classification, by predicting directions of novel reactions and of known reactions in new network contexts, and by ranking candidate network extensions for gap filling. Beyond their use in improving genome annotations and metabolic network models, we expect that the concepts transfer to other network types.
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Affiliation(s)
- Mathias Ganter
- 1] Department of Biosystems Science & Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Mattenstr. 26, 4058 Basel, Switzerland [2]
| | - Hans-Michael Kaltenbach
- 1] Department of Biosystems Science & Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Mattenstr. 26, 4058 Basel, Switzerland [2]
| | - Jörg Stelling
- Department of Biosystems Science & Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Mattenstr. 26, 4058 Basel, Switzerland
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18
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Shih SCC, Goyal G, Kim PW, Koutsoubelis N, Keasling JD, Adams PD, Hillson NJ, Singh AK. A Versatile Microfluidic Device for Automating Synthetic Biology. ACS Synth Biol 2015; 4:1151-64. [PMID: 26075958 DOI: 10.1021/acssynbio.5b00062] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
New microbes are being engineered that contain the genetic circuitry, metabolic pathways, and other cellular functions required for a wide range of applications such as producing biofuels, biobased chemicals, and pharmaceuticals. Although currently available tools are useful in improving the synthetic biology process, further improvements in physical automation would help to lower the barrier of entry into this field. We present an innovative microfluidic platform for assembling DNA fragments with 10× lower volumes (compared to that of current microfluidic platforms) and with integrated region-specific temperature control and on-chip transformation. Integration of these steps minimizes the loss of reagents and products compared to that with conventional methods, which require multiple pipetting steps. For assembling DNA fragments, we implemented three commonly used DNA assembly protocols on our microfluidic device: Golden Gate assembly, Gibson assembly, and yeast assembly (i.e., TAR cloning, DNA Assembler). We demonstrate the utility of these methods by assembling two combinatorial libraries of 16 plasmids each. Each DNA plasmid is transformed into Escherichia coli or Saccharomyces cerevisiae using on-chip electroporation and further sequenced to verify the assembly. We anticipate that this platform will enable new research that can integrate this automated microfluidic platform to generate large combinatorial libraries of plasmids and will help to expedite the overall synthetic biology process.
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Affiliation(s)
- Steve C. C. Shih
- Sandia National Laboratories, 7011 East Avenue, Livermore, California 94550, United States
| | - Garima Goyal
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road, Berkeley, California 94720, United States
| | - Peter W. Kim
- Sandia National Laboratories, 7011 East Avenue, Livermore, California 94550, United States
| | - Nicolas Koutsoubelis
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road, Berkeley, California 94720, United States
| | - Jay D. Keasling
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road, Berkeley, California 94720, United States
- Department of Chemical & Biomolecular Engineering, Department of Bioengineering, University of California, Berkeley, California 94720, United States
| | - Paul D. Adams
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road, Berkeley, California 94720, United States
| | - Nathan J. Hillson
- Physical
Bioscience Division, Lawrence Berkeley National Laboratory, 1 Cyclotron
Road, Berkeley, California 94720, United States
| | - Anup K. Singh
- Sandia National Laboratories, 7011 East Avenue, Livermore, California 94550, United States
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19
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Hadadi N, Hatzimanikatis V. Design of computational retrobiosynthesis tools for the design of de novo synthetic pathways. Curr Opin Chem Biol 2015; 28:99-104. [DOI: 10.1016/j.cbpa.2015.06.025] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Revised: 06/16/2015] [Accepted: 06/21/2015] [Indexed: 12/28/2022]
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20
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Identifying a gene knockout strategy using a hybrid of the bat algorithm and flux balance analysis to enhance the production of succinate and lactate in Escherichia coli. BIOTECHNOL BIOPROC E 2015. [DOI: 10.1007/s12257-014-0466-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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21
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Currin A, Swainston N, Day PJ, Kell DB. Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently. Chem Soc Rev 2015; 44:1172-239. [PMID: 25503938 PMCID: PMC4349129 DOI: 10.1039/c4cs00351a] [Citation(s) in RCA: 251] [Impact Index Per Article: 27.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Indexed: 12/21/2022]
Abstract
The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.
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Affiliation(s)
- Andrew Currin
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- School of Chemistry , The University of Manchester , Manchester M13 9PL , UK
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
| | - Neil Swainston
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
- School of Computer Science , The University of Manchester , Manchester M13 9PL , UK
| | - Philip J. Day
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
- Faculty of Medical and Human Sciences , The University of Manchester , Manchester M13 9PT , UK
| | - Douglas B. Kell
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- School of Chemistry , The University of Manchester , Manchester M13 9PL , UK
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
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22
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Tessaro D, Pollegioni L, Piubelli L, D’Arrigo P, Servi S. Systems Biocatalysis: An Artificial Metabolism for Interconversion of Functional Groups. ACS Catal 2015. [DOI: 10.1021/cs502064s] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- D. Tessaro
- Dipartimento
di Chimica, Materiali e Ingegneria Chimica “Giulio Natta”, Politecnico di Milano, p.za L. da Vinci 32, 20133 Milano, Italy
- The
Protein Factory, Centro Interuniversitario di Biotecnologie Proteiche, Politecnico di Milano and Università degli Studi dell’Insubria, via Mancinelli 7, 20131 Milano, Italy
| | - L. Pollegioni
- Dipartimento
di Biotecnologie e Scienze della Vita, Università degli Studi dell’Insubria, via J.H. Dunant 3, 21100 Varese, Italy
- The
Protein Factory, Centro Interuniversitario di Biotecnologie Proteiche, Politecnico di Milano and Università degli Studi dell’Insubria, via Mancinelli 7, 20131 Milano, Italy
| | - L. Piubelli
- Dipartimento
di Biotecnologie e Scienze della Vita, Università degli Studi dell’Insubria, via J.H. Dunant 3, 21100 Varese, Italy
- The
Protein Factory, Centro Interuniversitario di Biotecnologie Proteiche, Politecnico di Milano and Università degli Studi dell’Insubria, via Mancinelli 7, 20131 Milano, Italy
| | - P. D’Arrigo
- Dipartimento
di Chimica, Materiali e Ingegneria Chimica “Giulio Natta”, Politecnico di Milano, p.za L. da Vinci 32, 20133 Milano, Italy
- The
Protein Factory, Centro Interuniversitario di Biotecnologie Proteiche, Politecnico di Milano and Università degli Studi dell’Insubria, via Mancinelli 7, 20131 Milano, Italy
| | - S. Servi
- The
Protein Factory, Centro Interuniversitario di Biotecnologie Proteiche, Politecnico di Milano and Università degli Studi dell’Insubria, via Mancinelli 7, 20131 Milano, Italy
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23
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Araki M, Cox RS, Makiguchi H, Ogawa T, Taniguchi T, Miyaoku K, Nakatsui M, Hara KY, Kondo A. M-path: a compass for navigating potential metabolic pathways. Bioinformatics 2014; 31:905-11. [DOI: 10.1093/bioinformatics/btu750] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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24
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Hadadi N, Cher Soh K, Seijo M, Zisaki A, Guan X, Wenk MR, Hatzimanikatis V. A computational framework for integration of lipidomics data into metabolic pathways. Metab Eng 2014; 23:1-8. [DOI: 10.1016/j.ymben.2013.12.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2013] [Revised: 11/26/2013] [Accepted: 12/24/2013] [Indexed: 10/25/2022]
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25
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Arora PK, Bae H. Integration of bioinformatics to biodegradation. Biol Proced Online 2014; 16:8. [PMID: 24808763 PMCID: PMC4012781 DOI: 10.1186/1480-9222-16-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2014] [Accepted: 04/19/2014] [Indexed: 12/22/2022] Open
Abstract
Bioinformatics and biodegradation are two primary scientific fields in applied microbiology and biotechnology. The present review describes development of various bioinformatics tools that may be applied in the field of biodegradation. Several databases, including the University of Minnesota Biocatalysis/Biodegradation database (UM-BBD), a database of biodegradative oxygenases (OxDBase), Biodegradation Network-Molecular Biology Database (Bionemo) MetaCyc, and BioCyc have been developed to enable access to information related to biochemistry and genetics of microbial degradation. In addition, several bioinformatics tools for predicting toxicity and biodegradation of chemicals have been developed. Furthermore, the whole genomes of several potential degrading bacteria have been sequenced and annotated using bioinformatics tools.
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Affiliation(s)
- Pankaj Kumar Arora
- School of Biotechnology, Yeungnam University, Gyeongsan 712-749, Republic of Korea
| | - Hanhong Bae
- School of Biotechnology, Yeungnam University, Gyeongsan 712-749, Republic of Korea
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26
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Lu W, Tamura T, Song J, Akutsu T. Integer programming-based method for designing synthetic metabolic networks by Minimum Reaction Insertion in a Boolean model. PLoS One 2014; 9:e92637. [PMID: 24651476 PMCID: PMC3961429 DOI: 10.1371/journal.pone.0092637] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Accepted: 02/25/2014] [Indexed: 01/10/2023] Open
Abstract
In this paper, we consider the Minimum Reaction Insertion (MRI) problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA)-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at "http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html."
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Affiliation(s)
- Wei Lu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan
| | - Takeyuki Tamura
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan
| | - Jiangning Song
- Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan
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27
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Pathway and protein engineering approaches to produce novel and commodity small molecules. Curr Opin Biotechnol 2013; 24:1137-43. [DOI: 10.1016/j.copbio.2013.02.019] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2013] [Revised: 02/07/2013] [Accepted: 02/20/2013] [Indexed: 11/19/2022]
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28
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Jimenez-Lopez JC, Kotchoni SO, Hernandez-Soriano MC, Gachomo EW, Alché JD. Structural functionality, catalytic mechanism modeling and molecular allergenicity of phenylcoumaran benzylic ether reductase, an olive pollen (Ole e 12) allergen. J Comput Aided Mol Des 2013; 27:873-95. [PMID: 24154826 DOI: 10.1007/s10822-013-9686-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2013] [Accepted: 10/16/2013] [Indexed: 01/22/2023]
Abstract
Isoflavone reductase-like proteins (IRLs) are enzymes with key roles in the metabolism of diverse flavonoids. Last identified olive pollen allergen (Ole e 12) is an IRL relevant for allergy amelioration, since it exhibits high prevalence among atopic patients. The goals of this study are the characterization of (A) the structural-functionality of Ole e 12 with a focus in its catalytic mechanism, and (B) its molecular allergenicity by extensive analysis using different molecular computer-aided approaches covering (1) physicochemical properties and functional-regulatory motifs, (2) sequence analysis, 2-D and 3D structural homology modeling comparative study and molecular docking, (3) conservational and evolutionary analysis, (4) catalytic mechanism modeling, and (5) sequence, structure-docking based B-cell epitopes prediction, while T-cell epitopes were predicted by inhibitory concentration and binding score methods. Structural-based detailed features, phylogenetic and sequences analysis have identified Ole e 12 as phenylcoumaran benzylic ether reductase. A catalytic mechanism has been proposed for Ole e 12 which display Lys133 as one of the conserved residues of the IRLs catalytic tetrad (Asn-Ser-Tyr-Lys). Structure characterization revealed a conserved protein folding among plants IRLs. However, sequence polymorphism significantly affected residues involved in the catalytic pocket structure and environment (cofactor and substrate interaction-recognition). It might also be responsible for IRLs isoforms functionality and regulation, since micro-heterogeneities affected physicochemical and posttranslational motifs. This polymorphism might have large implications for molecular differences in B- and T-cells epitopes of Ole e 12, and its identification may help designing strategies to improve the component-resolving diagnosis and immunotherapy of pollen and food allergy through development of molecular tools.
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Affiliation(s)
- Jose C Jimenez-Lopez
- Department of Biochemistry, Cell and Molecular Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Profesor Albareda 1, 18008, Granada, Spain,
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29
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Zhuang K, Bakshi BR, Herrgård MJ. Multi-scale modeling for sustainable chemical production. Biotechnol J 2013; 8:973-84. [PMID: 23520143 DOI: 10.1002/biot.201200272] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2012] [Revised: 01/18/2013] [Accepted: 02/11/2013] [Indexed: 11/10/2022]
Abstract
With recent advances in metabolic engineering, it is now technically possible to produce a wide portfolio of existing petrochemical products from biomass feedstock. In recent years, a number of modeling approaches have been developed to support the engineering and decision-making processes associated with the development and implementation of a sustainable biochemical industry. The temporal and spatial scales of modeling approaches for sustainable chemical production vary greatly, ranging from metabolic models that aid the design of fermentative microbial strains to material and monetary flow models that explore the ecological impacts of all economic activities. Research efforts that attempt to connect the models at different scales have been limited. Here, we review a number of existing modeling approaches and their applications at the scales of metabolism, bioreactor, overall process, chemical industry, economy, and ecosystem. In addition, we propose a multi-scale approach for integrating the existing models into a cohesive framework. The major benefit of this proposed framework is that the design and decision-making at each scale can be informed, guided, and constrained by simulations and predictions at every other scale. In addition, the development of this multi-scale framework would promote cohesive collaborations across multiple traditionally disconnected modeling disciplines to achieve sustainable chemical production.
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Affiliation(s)
- Kai Zhuang
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark.
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30
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Production of bulk chemicals via novel metabolic pathways in microorganisms. Biotechnol Adv 2012; 31:925-35. [PMID: 23280013 DOI: 10.1016/j.biotechadv.2012.12.008] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2012] [Revised: 12/09/2012] [Accepted: 12/23/2012] [Indexed: 02/05/2023]
Abstract
Metabolic engineering has been playing important roles in developing high performance microorganisms capable of producing various chemicals and materials from renewable biomass in a sustainable manner. Synthetic and systems biology are also contributing significantly to the creation of novel pathways and the whole cell-wide optimization of metabolic performance, respectively. In order to expand the spectrum of chemicals that can be produced biotechnologically, it is necessary to broaden the metabolic capacities of microorganisms. Expanding the metabolic pathways for biosynthesizing the target chemicals requires not only the enumeration of a series of known enzymes, but also the identification of biochemical gaps whose corresponding enzymes might not actually exist in nature; this issue is the focus of this paper. First, pathway prediction tools, effectively combining reactions that lead to the production of a target chemical, are analyzed in terms of logics representing chemical information, and designing and ranking the proposed metabolic pathways. Then, several approaches for potentially filling in the gaps of the novel metabolic pathway are suggested along with relevant examples, including the use of promiscuous enzymes that flexibly utilize different substrates, design of novel enzymes for non-natural reactions, and exploration of hypothetical proteins. Finally, strain optimization by systems metabolic engineering in the context of novel metabolic pathways constructed is briefly described. It is hoped that this review paper will provide logical ways of efficiently utilizing 'big' biological data to design and develop novel metabolic pathways for the production of various bulk chemicals that are currently produced from fossil resources.
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31
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Abstract
As the field of synthetic biology is developing, the prospects for de novo design of biosynthetic pathways are becoming more and more realistic. Hence, there is an increasing need for computational tools that can support these efforts. A range of algorithms has been developed that can be used to identify all possible metabolic pathways and their corresponding enzymatic parts. These can then be ranked according to various properties and modelled in an organism-specific context. Finally, design software can aid the biologist in the integration of a selected pathway into smartly regulated transcriptional units. Here, we review key existing tools and offer suggestions for how informatics can help to shape the future of synthetic microbiology.
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32
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Sweetlove LJ, Ratcliffe RG. Flux-balance modeling of plant metabolism. FRONTIERS IN PLANT SCIENCE 2011; 2:38. [PMID: 22645533 PMCID: PMC3355794 DOI: 10.3389/fpls.2011.00038] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2011] [Accepted: 07/28/2011] [Indexed: 05/17/2023]
Abstract
Flux-balance modeling of plant metabolic networks provides an important complement to (13)C-based metabolic flux analysis. Flux-balance modeling is a constraints-based approach in which steady-state fluxes in a metabolic network are predicted by using optimization algorithms within an experimentally bounded solution space. In the last 2 years several flux-balance models of plant metabolism have been published including genome-scale models of Arabidopsis metabolism. In this review we consider what has been learnt from these models. In addition, we consider the limitations of flux-balance modeling and identify the main challenges to generating improved and more detailed models of plant metabolism at tissue- and cell-specific scales. Finally we discuss the types of question that flux-balance modeling is well suited to address and its potential role in metabolic engineering and crop improvement.
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33
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Mu F, Unkefer CJ, Unkefer PJ, Hlavacek WS. Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds. Bioinformatics 2011; 27:1537-45. [PMID: 21478194 PMCID: PMC3102224 DOI: 10.1093/bioinformatics/btr177] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2010] [Revised: 02/23/2011] [Accepted: 03/25/2011] [Indexed: 12/30/2022] Open
Abstract
MOTIVATION Our knowledge of the metabolites in cells and their reactions is far from complete as revealed by metabolomic measurements that detect many more small molecules than are documented in metabolic databases. Here, we develop an approach for predicting the reactivity of small-molecule metabolites in enzyme-catalyzed reactions that combines expert knowledge, computational chemistry and machine learning. RESULTS We classified 4843 reactions documented in the KEGG database, from all six Enzyme Commission classes (EC 1-6), into 80 reaction classes, each of which is marked by a characteristic functional group transformation. Reaction centers and surrounding local structures in substrates and products of these reactions were represented using SMARTS. We found that each of the SMARTS-defined chemical substructures is widely distributed among metabolites, but only a fraction of the functional groups in these substructures are reactive. Using atomic properties of atoms in a putative reaction center and molecular properties as features, we trained support vector machine (SVM) classifiers to discriminate between functional groups that are reactive and non-reactive. Classifier accuracy was assessed by cross-validation analysis. A typical sensitivity [TP/(TP+FN)] or specificity [TN/(TN+FP)] is ≈0.8. Our results suggest that metabolic reactivity of small-molecule compounds can be predicted with reasonable accuracy based on the presence of a potentially reactive functional group and the chemical features of its local environment. AVAILABILITY The classifiers presented here can be used to predict reactions via a web site (http://cellsignaling.lanl.gov/Reactivity/). The web site is freely available.
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Affiliation(s)
- Fangping Mu
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
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