The Pennsylvania State University The Graduate School The Huck Institutes of the Life Sciences ELUCIDATION AND SYNTHETIC DESIGN OF BIOCHEMICAL PATHWAYS USING NOVOSTOIC A Dissertation in Integrative Biosciences by Akhil Kumar 2017 Akhil Kumar Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May 2017 ii The dissertation of Akhil Kumar was reviewed and approved* by the following: Costas D. Maranas Donald B. Broughton Professor of Chemical Engineering Dissertation Advisor Chair of Committee Ross Cameron Hardison T. Ming Chu Professor of Biochemistry and Molecular Biology Reka Z Albert Distinguished Professor of Physics and Biology Andrew David Patterson Associate Professor of Molecular Toxicology Peter J. Hudson Director of Huck Institutes of Life Sciences *Signatures are on file in the Graduate School iii Abstract Next generation pathway design algorithms and tools facilitate with ease and speed the design of novel sophisticated biosynthetic routes. The development of computational designs for the biosynthesis of xenobiotics being the goal of this dissertation, we discuss and demonstrate solutions to two key challenges. The first challenge we identify is in the pace of extraction of metabolic knowledge i.e. directly using the data in the way it was published. Difficulties in directly using data from genome-scale metabolic models (GSMs) as well as semi-curated databases such as BRENDA1, KEGG2, and MetaCyc3, EcoCyc4, BioCyc3. The difficulties arise from the incompatibilities of representation, duplications, and errors i.e. with a single metabolite annotated with multiple names across different data sources. Also, in many cases, the same metabolite is annotated with multiple structures. This ambiguity gravely slows down the pooling of information across data sources. As a consequence, duplications in reaction information would not reveal otherwise (synthetic) lethal gene deletions. Such ambiguity affects the quality of predictions related to overall metabolic potential of an organism. In addition, non-standard metabolite names and ids prevent the direct comparisons needed to identify reactions that overlap multiple data sources. This would also lead to fragmented/disconnected datasets that would provide smaller reaction domains for pathway traversal algorithms. The second challenge we identify is in the capacity of various algorithms and computer-aided design (CAD) tools to conceive novel biosynthetic pathway designs while syncretizing various engineering challenges. To systemize the engineering calculations needed for designing biosynthesis of high-value iv chemicals, existing CAD tools explore the complex biochemical reaction space and enumerate metabolic engineering strategies for the heterologous production of target chemicals from substrates with native or engineered enzymes. Existing CAD tools are however limited and approximate in their design elements i.e. they do not consider all the metabolic engineering paradigms in an integrated fashion5. The design elements such as reaction rules, network size, non-linear pathway topology, mass-conservation, cofactor balance, thermodynamic feasibility, chassis selection, toxicity, yield, and cost have never been unified into a single scheme in current CAD tools, until this work. In the first chapter, we present the novel reaction rule based pathway design (CAD) tool and demonstrate with results i.e. biosynthetic designs to three pharmaceuticals namely phenylephrine, epinine and naproxen. The second chapter presents a novel atom mapping algorithm, which heavily uses the concept of prime factorization. In the third chapter, we demonstrate the algorithms we developed for the purposes of curating biochemical data i.e. development of MetRxn. Finally, in chapter 4) we present an example of the MetRxn data being leveraged within a metabolic model. v Table of Contents List of Figures ........................................................................................................ viii List of Tables .......................................................................................................... xi Acknowledgements .................................................................................................. xii Chapter 1 Pathway synthesis using de novo steps through uncharted biochemical spaces .............................................................................................................................. 1 1. Introduction ............................................................................................................... 2 2. Methods...................................................................................................................... 6 Description of the data and parameters required by rePrime and novoStoic: ....................................................................................................... 6 Developing a database of reaction rules using rePrime: ................................. 7 novoStoic ................................................................................................................ 15 3. Results ........................................................................................................................ 24 Phenylephrine synthesis: ..................................................................................... 24 Naproxen synthesis: ............................................................................................. 28 Epinine synthesis: ................................................................................................. 32 Oxidative degradation of Benzo[a]pyrene to catechol: ................................... 36 Discussion ...................................................................................................................... 41 Acknowledgment ......................................................................................................... 42 Figure and Tables ......................................................................................................... 43 Chapter 2 Maximum common molecular substructure queries within the MetRxn database .......................................................................................................................... 61 1. Introduction ............................................................................................................... 62 2. Methods...................................................................................................................... 66 Reduction of a* search space. .............................................................................. 72 Reaction atom mapping. ...................................................................................... 77 Common subgraphs between two reactions. .................................................... 82 Common subgraphs between two metabolic pathways. ................................ 83 3. Results and discussion ............................................................................................. 84 Application to e. Coli iaf1260 metabolic model. ............................................... 85 Alternate solutions in e. Coli iaf1260 metabolic model due to equivalent groups. ............................................................................................................ 86 Comparison with existing efforts: ...................................................................... 87 4. Summary .................................................................................................................... 92 Acknowledgments ........................................................................................................ 93 Abbreviations ................................................................................................................ 93 Figures and tables. ........................................................................................................ 94 vi Chapter 3 MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases ................................................................................ 115 Background .................................................................................................................... 116 Construction and Content ........................................................................................... 119 MetRxn construction ............................................................................................ 119 Step 1 Source data acquisition ............................................................................. 119 Step 2 Source data parsing ................................................................................... 120 Step 3 Metabolite charge and structural analysis ............................................. 120 Step 4 Metabolite synonyms and initial reaction reconciliation .................... 121 Step 5 Reaction charge and elemental balancing ............................................. 122 Step 6 Iterative reaction reconciliation ............................................................... 123 Data export and display ....................................................................................... 124 Source comparisons and visualization .............................................................. 124 MetRxn Scope ................................................................................................................ 125 Utility and Discussion .................................................................................................