Noushin Hadadi

Design of computational retrobiosynthesis tools for the design of de novo synthetic pathways.

Designing putative metabolic pathways is of great interest in synthetic biology. Retrobiosynthesis is a discipline that involves the design, evaluation, and optimization of de novo biosynthetic pathways for the production of high-value compounds and drugs from renewable resources and natural or engineered enzymes. The best candidate pathways are then engineered within a metabolic network of microorganisms that serve as synthetic platforms for synthetic biology. The complexity of biological chemistry and metabolism requires computational approaches to explore the full possibilities of engineering synthetic pathways towards target compounds. Herein, we discuss recent developments in the design of computational tools for retrosynthetic biochemistry and outline the workflow and design elements for such tools.

A computational framework for integration of lipidomics data into metabolic pathways

Lipids are important compounds for human physiology and as renewable resources for fuels and chemicals. In lipid research, there is a big gap between the currently available pathway-level representations of lipids and lipid structure databases in which the number of compounds is expanding rapidly with high-throughput mass spectrometry methods. In this work, we introduce a computational approach to bridge this gap by making associations between metabolic pathways and the lipid structures discovered increasingly thorough lipidomics studies. Our approach, called NICELips (Network Integrated Computational Explorer for Lipidomics), is based on the formulation of generalized enzymatic reaction rules for lipid metabolism, and it employs the generalized rules to postulate novel pathways of lipid metabolism. It further integrates all discovered lipids in biological networks of enzymatic reactions that consist their biosynthesis and biodegradation pathways. We illustrate the utility of our approach through a case study of bis(monoacylglycero)phosphate (BMP), a biologically important glycerophospholipid with immature synthesis and catabolic route(s). Using NICELips, we were able to propose various synthesis and degradation pathways for this compound and several other lipids with unknown metabolism like BMP, and in addition several alternative novel biosynthesis and biodegradation pathways for lipids with known metabolism. NICELips has potential applications in designing therapeutic interventions for lipid-associated disorders and in the metabolic engineering of model organisms for improving the biobased production of lipid-derived fuels and chemicals.

Molecular thermodynamics of metabolism: hydration quantities and the equation-of-state approach

The present work is part of a series of papers aiming at a thorough understanding of the thermodynamics of metabolism over a broad range of external conditions. The focus here is on the systematic study of solvation/hydration of a variety of fluids via an equation-of-state approach. This approach permits the study not only of the overall free energy, enthalpy or entropy of hydration but also their key components from cavitation, charging, and solute conformations/solvent restructuring contributions. These latter components shed light into the mechanism of hydration and contribute to our understanding of solvation phenomena at remote conditions of temperature and pressure. Hydrogen bonding is of central importance in this respect and is handled via the partial solvation parameter (PSP) approach. The developed solvation model is used for the estimation of the hydration quantities of key metabolites. The challenges and perspectives of this equation-of-state approach are critically discussed.

Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites

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

Exploring biochemical pathways for mono-ethylene glycol (MEG) synthesis from synthesis gas

Mono-ethylene glycol (MEG) is an important petrochemical with widespread use in numerous consumer products. The current industrial MEG-production process relies on non-renewable fossil fuel-based feedstocks, such as petroleum, natural gas, and naphtha; hence, it is useful to explore alternative routes of MEG-synthesis from gases as they might provide a greener and more sustainable alternative to the current production methods. Technologies of synthetic biology and metabolic engineering of microorganisms can be deployed for the expression of new biochemical pathways for MEG-synthesis from gases, provided that such promising alternative routes are first identified. We used the BNICE.ch algorithm to develop novel and previously unknown biological pathways to MEG from synthesis gas by leveraging the Wood-Ljungdahl pathway of carbon fixation of acetogenic bacteria. We developed a set of useful pathway pruning and analysis criteria to systematically assess thousands of pathways generated by BNICE.ch. Published genome-scale models of Moorella thermoacetica and Clostridium ljungdahlii were used to perform the pathway yield calculations and in-depth analyses of seven (7) newly developed biological MEG-producing pathways from gases, including CO2, CO, and H2. These analyses helped identify not only better candidate pathways, but also superior chassis organisms that can be used for metabolic engineering of the candidate pathways. The pathway generation, pruning, and detailed analysis procedures described in this study can also be used to develop biochemical pathways for other commodity chemicals from gaseous substrates.

Knowledge of the Neighborhood of the Reactive Site up to Three Atoms Can Predict Biochemistry and Protein Sequences

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 a new method, BridgIT, to identify candidate genes and protein sequences for these reactions, and this method introduces, for the first time, information about the enzyme binding pocket into reaction similarity comparisons. We performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from KEGG 2011 that became non-orphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 out of 379 novel reactions in 2014 that were later catalogued in KEGG 2018, BridgIT predicted the exact or highly similar enzyme sequences. BridgIT requires knowledge about only three connecting bonds around the atoms of the reactive sites to correctly identify protein sequences for 93% of analyzed enzymatic reactions.Thousands of biochemical reactions with characterized biochemical activities are still orphan. Novel reactions predicted by pathway generation tools also lack associated protein sequences and genes. Mapping orphan and novel reactions back to the known biochemistry and proposing genes for their catalytic functions is a daunting problem. We propose a new method, BridgIT, to identify candidate genes and protein sequences for orphan and novel enzymatic reactions. BridgIT introduces, for the first time, the information of the enzyme binding pocket into reaction similarity comparisons. It ascertains the similarity of two reactions by comparing the reactive sites of their substrates and their surrounding structures, along with the structures of the generated products. BridgIT compares orphan and novel reactions to enzymatic reactions with known protein sequences, and then, it proposes protein sequences and genes of the most similar non-orphan reactions as candidates for catalyzing the novel or orphan reactions. We performed BridgIT analysis of orphan reactions from KEGG 2011 (Kyoto Encyclopedia of Genes and Genomes, published in 2011) that became non-orphan in KEGG 2016, and BridgIT correctly predicted enzymes with identical third- and fourth-level EC numbers for 91% and 56% of these reactions, respectively. BridgIT results revealed that it is sufficient to know information about six atoms together with their connecting bonds around the reactive sites of the substrates to match a protein sequence to the catalytic activity of enzymatic reactions with maximal accuracy. Moreover, the same information about only three atoms around the reactive site allowed us to correctly match 87% of the analyzed enzymatic reactions. Finally, we used BridgIT to provide candidate protein sequences for 137000 novel enzymatic reactions from the recently introduced ATLAS of Biochemistry. A web-tool of BridgIT can be consulted at http://lcsb-databases.epfl.ch/BridgIT/. AUTHORS SUMMARY The recent advances in pathway generation tools have resulted in a wealth of de novo hypothetical enzymatic reactions, which lack knowledge of the protein-encoding genes associated with their functionality. Moreover, nearly half of known metabolic enzymes are orphan, i.e., they also lack an associated gene or protein sequence. Proposing genes for catalytic functions of de novo and orphan reactions is critical for their utility in various applications ranging from biotechnology to medicine. In this work, we propose a novel computational method that will bridge the knowledge gap and provide candidate genes for both de novo and orphan reactions. We demonstrate that information about a small chemical structure around the reactive sites of substrates is sufficient to correctly assign genes to the functionality of enzymatic reactions.