Computational tools and resources for designing new pathways to small molecules

The metabolic engineering community relies on computational methods for pathway design to produce important small molecules in microbial hosts. Metabolic network databases are continuously curated and updated with known and novel reactions that expand the known biochemistry based on different sets of enzymatic reaction rules. To address the complexity of the metabolic networks, elaborate methods were developed to transform them into computable graphs, navigate them, and construct the best possible pathways. However, the recent experimental research points to the new challenges and opportunities for the computational pathway design. Here, we review the most recent advances, especially in the last two years, in computational discovery of new pathways and their prospects for expanding metabolic capabilities. We draw attention to the potential ways of improvement for pathway design algorithms, including the expansion of Design-Build-Test-Learn cycle to novel compounds and reactions and the standardization for the reaction rules and metabolic reaction databases.

Optimization of the production of methyl ethyl ketone in recombinant Pseudomonas putida using large-scale kinetic models

Meriç Ataman, Vassily Hatzimanikatis, Ljubisa Miskovic, Milenko Tokic

The production of second generation of biofuels by conversion of inexpensive feedstocks is a basis for future bio-sustainable economy. Among many proposed molecules, methyl ethyl ketone (MEK) emerged as one of the most prominent fuel candidates due to its high-energy density, low emissions, and good transport properties. There is no known natural producer of MEK, but there were some recent studies to express its production pathway in different organisms. In all of these attempts, reported yields were very low. Among industrial workhorses such as E. coli and S. cerevisiae, P. putida has emerged as one of the most promising biofuel production hosts due to its tolerance to high toxicity compounds. For instance, it is reported that P. putida can grow in the presence of high concentrations of butanol1. This highly adaptive bacterium has been found to survive and grow on a broad range of substrates from pure caffeine to toxic industrial waste. In this study, we used the ORACLE framework to build, for the first time, a population of large-scale kinetic models of recombinant P. putida producing MEK. ORACLE2,3 (Optimization and Risk Analysis of Complex Living Entities) is a suite of computational tools capable of identifying enzymes with the highest impact on the production of target molecules. We started by embedding the MEK production pathway in the genome-scale model iJN7464 of P. putida, and then we derived a consistently reduced stoichiometric model. The obtained core model consists of 302 reactions and 210 mass balances distributed over cytosol, periplasm and extracellular environment. We then performed a series of tests to verify the consistency of the core model with its genome-scale counterpart. We next integrated fluxomics and metabolomics data from experiments and literature and performed Thermodynamic-Based Flux Analysis (TFA) to compute the thermodynamically feasible flux profiles that are consistent with the observed data. We further integrated the available information about the kinetic properties of enzymes involved in the modeled metabolic network. For reactions with missing or incomplete information about kinetic parameters, we performed a Monte Carlo sampling to generate missing information. We then constructed a population of 250’000 models, and we used these models to simultaneously optimize specific productivity of MEK and yield of MEK from glucose as a sole carbon source while taking into consideration the metabolic parameters such as catabolic and anabolic reduction charges. This way, we were able to identify enzymes that are potential candidates for metabolic engineering strategies towards obtaining a P. putida strain with improved specific productivity of MEK and its yield from glucose. This work demonstrates the potential and usefulness of ORACLE for optimizing production of biofuels and biochemicals in metabolic engineering and synthetic biology applications. 1. Rühl, J., Schmid, A. & Blank, L. M. Selected Pseudomonas putida strains able to grow in the presence of high butanol concentrations. Appl. Environ. Microbiol. 75, 4653–6 (2009). 2. Miskovic, L. & Hatzimanikatis, V. Production of biofuels and biochemicals: in need of an ORACLE. Trends in biotechnology 28, 391–7 (2010). 3. Chakrabarti, A., Miskovic, L., Soh, K. C. & Hatzimanikatis, V. Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints. Biotechnol. J. 8, 1043–57 (2013). 4. Nogales, J., Palsson, B. Ø. & Thiele, I. A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory. BMC Syst. Biol. 2, 79 (2008).

2017

Discovery and Evaluation of Novel Pathways for Production of the Second Generation of Biofuels

Meriç Ataman, Noushin Hadadi, Vassily Hatzimanikatis, Ljubisa Miskovic, Milenko Tokic

In an effort of overcoming the limited availability of fossil energy resources the focus of the research and development in the area of biofuels has moved towards developing the second generation of fuels that should be produced via microbial fermentation. The idea is to use as a feedstock inexpensive and abundant waste materials such as lignocellulosic biomass. The second generation biofuels should satisfy several criteria such as lower emission, higher energy density and should be less corrosive to engines. However, currently used industrial workhorses such as E. coli and S. cerevisiae do not produce many of these molecules naturally. Furthermore, for majority of these molecules there are no known biochemical pathways. The need for discovery of novel biosynthetic pathways towards desired molecules sparked the development of computational tools that are capable of reconstructing feasible reaction steps between a given set of starting compounds (precursors) and a molecule of interest (a target compound). In this study, we performed the retrobiosynthesis analysis for all the candidates applying Biochemical Network Integrated Computational Explorer (BNICE.ch) and we reconstructed the metabolic network for 69 fuel compounds. We analyzed compounds (biological or chemical) and reactions one-step away from these target molecules and the appearance of the potential substrates in their reconstructed metabolic network. Based on the results of this analysis, coupled with other criteria such as the Gibes free energy of formation, combustion potential and expert opinion, we chose the 5 highest ranked fuel candidates for further pathway reconstruction studies. For these 5 fuel candidates we have created 1.8 million de novo pathways with different number of reaction steps for further evaluation. We have then embedded the reconstructed pathways in the genome-°©‐scale models of the two chassis organisms E. coli and P. putida and we performed the pathway evaluation with respect to their maximum theoretical yield using two methods (i) Flux Balance Analysis (FBA), and (ii) Thermodynamic-based Flux Analysis (TFA). All the pathways that did not satisfy the thermodynamic constraints were rejected. Our results indicated that in the evaluation and ranking of the pathways the thermodynamics is a necessary consideration. Specifically, we showed that the majority of the pathways that were determined as feasible based on FBA, they were not feasible based on TFA. Moreover, we also showed that some pathways that were determined as feasible in both organisms based on FBA, they were not feasible in both of them based on TFA. Also, the yield of the feasible pathways can differ depending on the organism. These results demonstrate how TFA can also guide the selection of a suitable chassis organism. For all discovered reactions in the feasible pathways we have also identified known enzymes from the KEGG database with the closest reaction similarity that could be engineered for the implementation of novel reactions. This study shows the full potential of computational tools for discovery and design of novel synthetic pathways and their relevance for future developments in the area of metabolic engineering and synthetic biology.

2016

Modeling, predicting and mining metabolism at atom-level resolution

Jasmin Maria Hafner

Living organisms can catalyze many thousands of biochemical reactions that they use to convert energy and matter, which provides them with the essentials for life. The sum of these chemical reactions happening in an organism is called metabo-lism. Understanding metabolism is crucial to elucidate the fundamental principles of biology, and further to enable us to redesign it for the sustainable, biosynthetic pro-duction of bio-based fuels, commodity chemicals and medicines. Mathematical mod-els are essential to organize and understand the complexity of metabolism. They usu-ally represent metabolism as a network of reactions, but they tend to neglect the exact molecular structure of the metabolites. To redesign metabolic reactions, how-ever, a mechanistic understanding of metabolic reactions and their catalysts, proteins called enzymes, is essential. In this work, a mathematical description of enzymatic reaction mechanism, called generalized reaction rules, is applied to computationally simulate and predict meta-bolic processes at the level of atoms. Each reaction rule describes the catalytic activi-ty of an enzyme, or a group of enzymes, at the mechanistic level by encoding the re-arrangement of atoms in the reaction. The reaction rules are called âgeneralizedâ, because they mimic the ability of a single enzyme to catalyze multiple reactions by acting on a range of substrates. Using these reaction rules, we first developed a computational representation of me-tabolism that allowed tracking single atoms throughout complex metabolic reaction networks. The principle of atom-tracking was then used to develop a graph-theory based method to represent and analyze metabolic networks, and to reliably identify of metabolic pathways for the biosynthesis of chemicals. Next, we applied the gener-alized reaction rules to predict all possible novel, hypothetical reactions from known biological compounds, and we stored the five million generated novel reactions them in a database called ATLAS. Finally, the developed tools and resources were applied to specific engineering and research problems, such as the biosynthetic pathway design for the biofuel bisabolene and the plastic precursor 1,4-butanediol. We further pre-dicted a biosynthesis route for the pharmaceutical tetrahydropalmatine and engi-neered a yeast strain to produce it. Finally, we show that our tools can be used to mine available genome sequences to find organisms that can degrade xenobiotics. Our findings suggest that the atom-level representation of metabolism can greatly contribute to its understanding, exploration and prediction. Given the complexity of atom-level modeling of metabolic processes, we propose metrics that can approxi-mate the atom-level information to conserve the information at the level of big, hy-pothetical metabolic networks like ATLAS. This database plus the developed pathway search techniques form a valuable resource for scientist to help characterizing un-known biosynthesis pathways towards secondary metabolites, and for metabolic engi-neers for designing novel bioproduction pathways for chemicals. Hopefully, these considerations will contribute to a better understanding of metabolism, advance the exploration of the bioproduction of drugs and other valuable molecules, and acceler-ate metabolic engineering efforts to realize the switch from a petroleum-based chem-ical industry towards a more sustainable, bio-based production of societyâs chemical needs.

EPFL2020