Design of novel enzyme-catalyzed reactions linked to protein sequences for finding enzyme engineering targets

A key challenge in metabolic engineering is to find and to improve biosynthetic pathways that lead to the cellular production of a given industrial, pharmaceutical or specialty chemical compound. In many cases, the enzymatic reactions required for bio-production have not been observed in nature and need to be designed from scratch. Computational approaches are essential to predict possible novel biotransformation and to find enzymes that can potentially catalyze the proposed reactions. In this work, we present two computational tools, BNICE.ch and BridgIT, and we demonstrate their concerted action to (i) predict hypothetical biotransformations and (ii) to link these novel reactions with well characterized enzymatic reactions and their associated genes. BNICE.ch reconstructs known reactions and generates novel reactions by applying its integrated, expert curated, generalized enzyme reaction rules on known metabolites. In order to find enzymes that potentially catalyze the biotransformation of these novel reactions, we assume that molecules with a similar reactive site and a similar atomic structure around the reactive site may be recognized and transformed by the same enzyme. Hence, BridgIT compares every predicted novel reaction to all known enzymatic reactions for which a protein sequence is available. Novel and known reactions are compared based on the reactive site of the substrates, the atoms surrounding the reactive site, and the breakage and formation of atomic bonds during the conversion of the substrate to the product. As a result, BridgIT reports a similarity score for each comparison of known reactions to novel reactions, thus giving an estimate of how possible it is that a given enzyme can catalyze a novel reaction. The results are organized in a database of known and hypothetical reactions called the “ATLAS of Biochemistry”1, where every hypothetical reaction is associated with its structurally most similar known enzymatic reactions, thus suggesting a plausible Gene-Protein-Reaction (GPR) association that can be used as a starting point for enzyme engineering. Our database currently spans more than 130’000 biochemically possible reactions between known metabolites from the Kyoto Encyclopedia of Genes and Genomes (KEGG). The ATLAS database and the BridgIT online tool are available on the web (http://lcsb-databases.epfl.ch/) and they can be used to extract potential reactions and pathways and to identify enzyme targets for metabolic and enzymatic engineering purposes. 1Hadadi, N., Hafner, J., Shajkofci, A., Zisaki, A., & Hatzimanikatis, V. (2016). ATLAS of Biochemistry: A repository of all possible biochemical reactions for synthetic biology and metabolic engineering studies. ACS Synthetic Biology, 2016.

Evaluating the similarity of biochemical reactions and its uses for mapping novel reactions to protein sequences

Noushin Hadadi, Vassily Hatzimanikatis, Homa Mohammadi Peyhani

A key challenge in metabolic engineering is to design novel or to improve existing biosynthetic pathways that lead to the cellular production of a given industrial or pharmaceutical compound. In many cases, the required enzymatic reactions for the biosynthesis of the target molecule need to be designed from scratch. BNICE.ch is a method that enables the design of de novo synthetic pathways through the postulation of novel biotransformations. However, finding enzymes that can potentially catalyze the proposed reactions remains a challenge. In this work, we propose a novel method, named BridgIT, to link novel reactions with well characterized enzymatic reactions and their associated genes. BridgIT compares every predicted novel reaction to all known enzymatic reactions for which a protein sequence is available. Novel and known reactions are compared based on the similarity of the reactive site of the substrates and the breakage and formation of atomic bonds during the conversion of the substrate to the product. As a result, BridgIT reports a similarity score for each comparison of known reactions to novel reactions, thus giving an estimate of the likelihood that a given enzyme can catalyze a novel reaction. The candidate proposed enzymes for de novo reactions by BridgIT, are either capable of catalyzing these reactions or they can serve as good initial sequences for the enzyme engineering. BridgIT online tool is freely available on the web (http://lcsb-databases.epfl.ch/) for academia upon subscription

2017

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

ATLAS of Biochemistry: A Repository of All Possible Biochemical Reactions for Synthetic Biology and Metabolic Engineering Studies

Noushin Hadadi, Jasmin Maria Hafner, Vassily Hatzimanikatis, Adrian Shajkofci, Aikaterini Zisaki

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 KEGG 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.

Amer Chemical Soc2016

Modeling, predicting and mining metabolism at atom-level resolution

Jasmin Maria Hafner

EPFL2020