Topology-Accelerated and Selective Cascade Depolymerization of Architecturally Complex Polyesters.

Despite considerable recent advances already made in developing chemically circular polymers (CPs), the current framework predominantly focuses on CPs with linear-chain structures of different monomer types. As polymer properties are determined by not only composition but also topology, manipulating the topology of the single-monomer-based CP systems from linear-chain structures to architecturally complex polymers could potentially modulate the resulting polymer properties without changing the chemical composition, thereby advancing the concept of monomaterial product design. To that end, here, we introduce a chemically circular hyperbranched polyester (HBPE), synthesized by a mixed chain-growth and step-growth polymerization of a rationally designed bicyclic lactone with a pendent hydroxyl group (BiLOH). This HBPE exhibits full chemical recyclability despite its architectural complexity, showing quantitative selectivity for regeneration of BiLOH, via a unique cascade depolymerization mechanism. Moreover, distinct differences in materials properties and performance arising from topological variations between HBPE, hb-PBiLOH, and its linear analogue, l-PBiLOH, have been revealed where generally the branched structure led to more favorable interchain interactions, and topology-amplified optical activity has also been observed for chiral (1S, 4S, 5S)-hb-PBiLOH. More intriguingly, depolymerization of l-PBiLOH proceeds through an unexpected, initial topological transformation to the HBPE polymer, followed by the faster cascade depolymerization pathway adopted by hb-PBiLOH. Overall, these results demonstrate that CP design can go beyond typical linear polymers, and rationally redesigned, architecturally complex polymers for their unique properties may synergistically impart advantages in topology-augmented depolymerization acceleration and selectivity for exclusive monomer regeneration.

Redesigned Nylon 6 Variants with Enhanced Recyclability, Ductility, and Transparency.

Geminal (gem-) disubstitution in heterocyclic monomers is an effective strategy to enhance polymer chemical recyclability by lowering their ceiling temperatures. However, the effects of specific substitution patterns on the monomer's reactivity and the resulting polymer's properties are largely unexplored. Here we show that, by systematically installing gem-dimethyl groups onto ϵ-caprolactam (monomer of nylon 6) from the α to ϵ positions, both the redesigned lactam monomer's reactivity and the resulting gem-nylon 6's properties are highly sensitive to the substitution position, with the monomers ranging from non-polymerizable to polymerizable and the gem-nylon properties ranging from inferior to far superior to the parent nylon 6. Remarkably, the nylon 6 with the gem-dimethyls substituted at the γ position is amorphous and optically transparent, with a higher Tg (by 30 °C), yield stress (by 1.5 MPa), ductility (by 3×), and lower depolymerization temperature (by 60 °C) than conventional nylon 6.

PolyID: Artificial Intelligence for Discovering Performance-Advantaged and Sustainable Polymers.

A necessary transformation for a sustainable economy is the transition from fossil-derived plastics to polymers derived from biomass and waste resources. While renewable feedstocks can enhance material performance through unique chemical moieties, probing the vast material design space by experiment alone is not practically feasible. Here, we develop a machine-learning-based tool, PolyID, to reduce the design space of renewable feedstocks to enable efficient discovery of performance-advantaged, biobased polymers. PolyID is a multioutput, graph neural network specifically designed to increase accuracy and to enable quantitative structure-property relationship (QSPR) analysis for polymers. It includes a novel domain-of-validity method that was developed and applied to demonstrate how gaps in training data can be filled to improve accuracy. The model was benchmarked with both a 20% held-out subset of the original training data and 22 experimentally synthesized polymers. A mean absolute error for the glass transition temperatures of 19.8 and 26.4 °C was achieved for the test and experimental data sets, respectively. Predictions were made on polymers composed of monomers from four databases that contain biologically accessible small molecules: MetaCyc, MINEs, KEGG, and BiGG. From 1.4 × 106 accessible biobased polymers, we identified five poly(ethylene terephthalate) (PET) analogues with predicted improvements to thermal and transport performance. Experimental validation for one of the PET analogues demonstrated a glass transition temperature between 85 and 112 °C, which is higher than PET and within the predicted range of the PolyID tool. In addition to accurate predictions, we show how the model's predictions are explainable through analysis of individual bond importance for a biobased nylon. Overall, PolyID can aid the biobased polymer practitioner to navigate the vast number of renewable polymers to discover sustainable materials with enhanced performance.

Coupling chemistry and biology for the synthesis of advanced bioproducts.

Chemical and biological syntheses can both lead to a myriad of compounds. Biology enables us to harness the metabolism of microbial cell factories to produce key target molecules from renewable biomass-derived substrates. Although bio-based feedstocks are sustainably sourced and more benign than the rapidly depleting fossil fuels that chemical processes have historically relied on, limiting pathways solely to biological reactions may not equate to a greener process overall. In fact, bioreactors rely on substantial quantities of water and can be inefficient since organisms typically operate around ambient conditions and are sensitive to perturbations in their environment. Hybridizing biosynthetic pathways with green chemistry can instead be a more potent strategy to reduce our net manufacturing footprint. Emerging chemistries have demonstrated considerable success in performing complex transformations on biological feedstocks without significant solvent use. Many of these transformations would be too slow to perform enzymatically or infeasible altogether. Here, we put forth the concept that by carefully considering the merits and drawbacks of synthetic biology and chemistry as well as one's own use case, there exist many opportunities for coupling the two. Merging these syntheses can unlock a wider suite of functional group transformations, thereby enabling future manufacturing processes to sustainably access a larger space of valuable, platform chemicals.

Pickaxe: a Python library for the prediction of novel metabolic reactions.

BACKGROUND: Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user's application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. RESULTS: Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. CONCLUSION: Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.

A dynamic kinetic model captures cell-free metabolism for improved butanol production.

Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 h. We present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.

New Perspectives into Cellulose Fast Pyrolysis Kinetics Using a Py-GC × GC-FID/MS System.

Cellulose pyrolysis is reportedly influenced by factors such as sample size, crystallinity, or different morphologies. However, there seems to be a lack of understanding of the mechanistic details that explain the observed differences in the pyrolysis yields. This study aims to investigate the influence of particle size and crystallinity of cellulose by performing pyrolysis reactions at temperatures of 673-873 K using a micropyrolyzer apparatus coupled to a GC × GC-FID/TOF-MS and a customized GC-TCD. Over 60 product species have been identified and quantified for the first time, including water. Crystalline cellulose with an average particle size of 30-50 × 10-6 m produced 50-60 wt % levoglucosan. Predominantly amorphous cellulose with an average particle size of 10-20 × 10-6 m resulted in remarkably low yields (10-15 wt %) of levoglucosan complemented by higher yields of water and glycolaldehyde. A detailed kinetic model for cellulose pyrolysis was used to obtain mechanistic insights into the different pyrolysis product compositions. The kinetics of the mid-chain dehydration and fragmentation reactions strongly influence the total yields of low-molecular weight products (LMWPs) and are affected by cellulose chain arrangement. Levoglucosan yields are very sensitive to the activation of parallel cellulose decomposition reactions. This can be attributed to the mid-chain reactions forming smaller chains with the levoglucosan ends, which remain in the solid phase and react further to form LMWPs. Direct quantification of water helped to improve the description of the dehydration, giving further indications of the dominant role of mid-chain reaction pathways in amorphous cellulose pyrolysis.

Catalytic Conversion of Alkenes on Acidic Zeolites: Automated Generation of Reaction Mechanisms and Lumping Technique.

Acid-catalyzed hydrocarbon transformations are essential for industrial processes, including oligomerization, cracking, alkylation, and aromatization. However, these chemistries are extremely complex, and computational (automatic) reaction network generation is required to capture these intricacies. The approach relies on the concept that underlying mechanisms for the transformations can be described by a limited number of reaction families applied to various species, with both gaseous and protonated intermediate species tracked. Detailed reaction networks can then be tailored to each industrially relevant process for better understanding or for application in kinetic modeling, which is demonstrated here. However, we show that these networks can grow very large (thousands of species) when they are bound by typical carbon number and rank criteria, and lumping strategies are required to decrease computational expense. For acid-catalyzed hydrocarbon transformations, we propose lumping isomers based on carbon number, branch number, and ion position to reach high carbon limits while maintaining the high resolution of species. Two case studies on propene oligomerization verified the lumping technique in matching a fully detailed model as well as experimental data.

MINE 2.0: enhanced biochemical coverage for peak identification in untargeted metabolomics.

SUMMARY: Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource. AVAILABILITY AND IMPLEMENTATION: The MINE 2.0 website can be accessed at https://minedatabase.ci.northwestern.edu. The MINE 2.0 web API documentation can be accessed at https://mine-api.readthedocs.io/en/latest/. The data and code underlying this article are available in the MINE-2.0-Paper repository at https://github.com/tyo-nu/MINE-2.0-Paper. MINE 2.0 source code can be accessed at https://github.com/tyo-nu/MINE-Database (MINE construction), https://github.com/tyo-nu/MINE-Server (backend web API) and https://github.com/tyo-nu/MINE-app (web app). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantifying Effects of Active Site Proximity on Rates of Methanol Dehydration to Dimethyl Ether over Chabazite Zeolites through Microkinetic Modeling.

Control of the spatial proximity of Brønsted acid sites within the zeolite framework can result in materials with properties that are distinct from materials synthesized through conventional crystallization methods or available from commercial sources. Recent experimental evidence has shown that turnover rates of different acid-catalyzed reactions increase with the fraction of proximal sites in chabazite (CHA) zeolites. The catalytic conversion of oxygenates is an important research area, and the dehydration of methanol to dimethyl ether (DME) is a well-studied reaction as part of methanol-to-olefin chemistry catalyzed by solid acids. Published experimental data have shown that DME formation rates (per acid site) increase systematically with the fraction of proximal acid sites in the six-membered ring of CHA. Here, we probe the effect of acid site proximity in CHA on methanol dehydration rates using electronic structure calculations and microkinetic modeling to identify the primary causes of this chemistry and their relationship to the local structure of the catalyst at the nanoscale. We report a density functional theory-parametrized microkinetic model of methanol dehydration to DME, catalyzed by acidic CHA zeolite with direct comparison to experimental data. Effects of proximal acid sites on reaction rates were captured quantitatively for a range of operating conditions and catalyst compositions, with a focus on total paired acid site concentration and reactant clustering to form higher nuclearity complexes. Next-nearest neighbor paired acid sites were identified as promoting the formation of methanol trimer clusters rather than the inhibiting tetramer or pentamer clusters, resulting in large increases in the rate for DME production due to the lower energy barriers present in the concerted methanol trimer reaction pathway. The model framework developed in this study can be extended to other zeolite materials and reaction chemistries toward the goal of rational design and development of next-generation catalytic materials and chemical processes.

Probing Monomer and Dimer Adsorption Trends in the MFI Framework.

Porous aluminosilicates such as zeolites are ubiquitous catalysts for the production of high-value and industrially relevant commodity chemicals, including the conversion of hydrocarbons, amines, alcohols, and others. Bimolecular reactions are an important subclass of reactions that can occur on Brønsted acid sites of a zeolite catalyst. Kinetic modeling of these systems at the process scale requires the interaction energetics of reactants and the active sites to be described accurately. It is generally known that adsorption is a coverage-dependent phenomenon, with lower heats of adsorption observed for molecules at higher coverage. However, few studies have systematically investigated the coadsorption of molecules on a single active site, specifically focusing on the strength of interaction of the second adsorbate after the initial adsorption step. In this work, we quantify the unimolecular and bimolecular adsorption energies of varying adsorbates, including paraffins, olefins, alcohols, amines, and noncondensible gases in the acidic and siliceous ZSM-5 frameworks. As a special case, olefin adsorption was examined for physisorption and chemisorption regimes, characterized by π-complex, framework alkoxide and carbenium ion adsorption, respectively. The effects of functional groups and molecular size were quantified, and correlations that relate the adsorption of the second adsorbate identity to that of the first adsorbate are provided.

Curating a comprehensive set of enzymatic reaction rules for efficient novel biosynthetic pathway design.

Enzyme substrate promiscuity has significant implications for metabolic engineering. The ability to predict the space of possible enzymatic side reactions is crucial for elucidating underground metabolic networks in microorganisms, as well as harnessing novel biosynthetic capabilities of enzymes to produce desired chemicals. Reaction rule-based cheminformatics platforms have been implemented to computationally enumerate possible promiscuous reactions, relying on existing knowledge of enzymatic transformations to inform novel reactions. However, past versions of curated reaction rules have been limited by a lack of comprehensiveness in representing all possible transformations, as well as the need to prune rules to enhance computational efficiency in pathway expansion. To this end, we curated a set of 1224 most generalized reaction rules, automatically abstracted from atom-mapped MetaCyc reactions and verified to uniquely cover all common enzymatic transformations. We developed a framework to systematically identify and correct redundancies and errors in the curation process, resulting in a minimal, yet comprehensive, rule set. These reaction rules were capable of reproducing more than 85% of all reactions in the KEGG and BRENDA databases, for which a large fraction of reactions is not present in MetaCyc. Our rules exceed all previously published rule sets for which reproduction was possible in this coverage analysis, which allows for the exploration of a larger space of known enzymatic transformations. By leveraging the entire knowledge of possible metabolic reactions through generalized enzymatic reaction rules, we are able to better utilize underground metabolic pathways and accelerate novel biosynthetic pathway design to enable bioproduction towards a wider range of new molecules.

Noncontact catalysis: Initiation of selective ethylbenzene oxidation by Au cluster-facilitated cyclooctene epoxidation.

Traditionally, a catalyst functions by direct interaction with reactants. In a new noncontact catalytic system (NCCS), an intermediate produced by one catalytic reaction serves as an intermediary to enable an independent reaction to proceed. An example is the selective oxidation of ethylbenzene, which could not occur in the presence of either solubilized Au nanoclusters or cyclooctene, but proceeded readily when both were present simultaneously. The Au-initiated selective epoxidation of cyclooctene generated cyclooctenyl peroxy and oxy radicals that served as intermediaries to initiate the ethylbenzene oxidation. This combined system effectively extended the catalytic effect of Au. The reaction mechanism was supported by reaction kinetics and spin trap experiments. NCCS enables parallel reactions to proceed without the constraints of stoichiometric relationships, offering new degrees of freedom in industrial hydrocarbon co-oxidation processes.

Ligands, Receptors, and Transcription Factors that Mediate Inter-Cellular and Intra-Cellular Communication during Ovarian Follicle Development.

Reliably producing a competent oocyte entails a deeper comprehension of ovarian follicle maturation, a very complex process that includes meiotic maturation of the female gamete, the oocyte, together with the mitotic divisions of the hormone-producing somatic cells. In this report, we investigate murine ovarian folliculogenesis in vivo using publicly available time-series microarrays from primordial to antral stage follicles. Manually curated protein interaction networks were employed to identify autocrine and paracrine signaling between the oocyte and the somatic cells (granulosa and theca cells) at multiple stages of follicle development. We established plausible protein-binding interactions between expressed genes that encode secreted factors and expressed genes that encode cellular receptors. Some computationally identified signaling interactions are well established, such as the paracrine signaling from the oocyte to the somatic cells through the oocyte-secreted growth factor Gdf9, while others are novel connections in term of ovarian folliculogenesis, such as the possible paracrine connection from somatic-secreted factor Ntn3 to the oocyte receptor Neo1. Additionally, we identified several of the likely transcription factors that might control the dynamic transcriptome during ovarian follicle development, noting that the YAP/TAZ signaling pathway is very active in vivo. This novel dynamic model of signaling and regulation can be employed to generate testable hypotheses regarding follicle development that could be validated experimentally, guiding the improvement of culture media to enhance in vitro ovarian follicle maturation and possibly novel therapeutic targets for reproductive diseases.

Bayesian inference of metabolic kinetics from genome-scale multiomics data.

Modern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, learning from these data to predict how a cell will respond to genetic changes, a key need for engineering, remains challenging. A promising technique for leveraging omics measurements in metabolic modeling involves the construction of kinetic descriptions of the enzymatic reactions that occur within a cell. Parameterizing these models from biological data can be computationally difficult, since methods must also quantify the uncertainty in model parameters resulting from the observed data. While the field of Bayesian inference offers a wide range of methods for efficiently estimating distributions in parameter uncertainty, such techniques are poorly suited to traditional kinetic models due to their complex rate laws and resulting nonlinear dynamics. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and inference methods. We demonstrate that detailed information on the posterior distribution of parameters can be obtained efficiently at a variety of problem scales, including nearly genome-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological data and in developing new, efficient strain designs.

Dynamic genome-scale cell-specific metabolic models reveal novel inter-cellular and intra-cellular metabolic communications during ovarian follicle development.

BACKGROUND: The maturation of the female germ cell, the oocyte, requires the synthesis and storing of all the necessary metabolites to support multiple divisions after fertilization. Oocyte maturation is only possible in the presence of surrounding, diverse, and changing layers of somatic cells. Our understanding of metabolic interactions between the oocyte and somatic cells has been limited due to dynamic nature of ovarian follicle development, thus warranting a systems approach. RESULTS: Here, we developed a genome-scale metabolic model of the mouse ovarian follicle. This model was constructed using an updated mouse general metabolic model (Mouse Recon 2) and contains several key ovarian follicle development metabolic pathways. We used this model to characterize the changes in the metabolism of each follicular cell type (i.e., oocyte, granulosa cells, including cumulus and mural cells), during ovarian follicle development in vivo. Using this model, we predicted major metabolic pathways that are differentially active across multiple follicle stages. We identified a set of possible secreted and consumed metabolites that could potentially serve as biomarkers for monitoring follicle development, as well as metabolites for addition to in vitro culture media that support the growth and maturation of primordial follicles. CONCLUSIONS: Our systems approach to model follicle metabolism can guide future experimental studies to validate the model results and improve oocyte maturation approaches and support growth of primordial follicles in vitro.

A Robust Strategy for Sustainable Organic Chemicals Utilizing Bioprivileged Molecules.

Biobased chemicals will inevitably be an important part of a sustainable organic chemical industry. Current efforts in biobased chemicals are largely driven by opportunistic chemical product targets requiring complete technology development from feedstock to final product for a specific molecule. To enhance the development of biobased chemicals, it is important to create strategies that can be more systematic and can leverage advancements across multiple final products. Discussed here is the concept of bioprivileged molecules, which are chemical intermediates that have the potential to be efficiently converted into a range of product molecules that can both directly replace existing petrochemicals and are novel molecules that impart enhanced performance properties in end-use applications.

Acceleration Strategies to Enhance Metabolic Ensemble Modeling Performance.

Developing reliable, predictive kinetic models of metabolism is a difficult, yet necessary, priority toward understanding and deliberately altering cellular behavior. Constraint-based modeling has enabled the fields of metabolic engineering and systems biology to make great strides in interrogating cellular metabolism but does not provide sufficient insight into regulation or kinetic limitations of metabolic pathways. Moreover, the growth-optimized assumptions that constraint-based models often rely on do not hold when studying stationary or persistor cell populations. However, developing kinetic models provides many unique challenges, as many of the kinetic parameters and rate laws governing individual enzymes are unknown. Ensemble modeling (EM) was developed to circumnavigate this challenge and effectively sample the large kinetic parameter solution space using consistent experimental datasets. Unfortunately, EM, in its base form, requires long solve times to complete and often leads to unstable kinetic model predictions. Furthermore, these limitations scale prohibitively with increasing model size. As larger metabolic models are developed with increasing genetic information and experimental validation, the demand to incorporate kinetic information increases. Therefore, in this work, we have begun to tackle the challenges of EM by introducing additional steps to the existing method framework specifically through reducing computation time and optimizing parameter sampling. We first reduce the structural complexity of the network by removing dependent species, and second, we sample locally stable parameter sets to reflect realistic biological states of cells. Lastly, we presort the screening data to eliminate the most incorrect predictions in the earliest screening stages, saving further calculations in later stages. Our complementary improvements to this EM framework are easily incorporated into concurrent EM efforts and broaden the application opportunities and accessibility of kinetic modeling across the field.

Dynamic transcription factor activity networks in response to independently altered mechanical and adhesive microenvironmental cues.

Multiple aspects of the local extracellular environment profoundly affect cell phenotype and function. Physical and chemical cues in the environment trigger intracellular signaling cascades that ultimately activate transcription factors (TFs) - powerful regulators of the cell phenotype. TRACER (TRanscriptional Activity CEll aRrays) was employed for large-scale, dynamic quantification of TF activity in human fibroblasts cultured on hydrogels with a controlled elastic modulus and integrin ligand density. We identified three groups of TFs: responders to alterations in ligand density alone, substrate stiffness or both. Dynamic networks of regulatory TFs were constructed computationally and revealed distinct TF activity levels, directionality (i.e., activation or inhibition), and dynamics for adhesive and mechanical cues. Moreover, TRACER networks predicted conserved hubs of TF activity across multiple cell types, which are significantly altered in clinical fibrotic tissues. Our approach captures the distinct and overlapping effects of adhesive and mechanical stimuli, identifying conserved signaling mechanisms in normal and disease states.

Exploring De Novo metabolic pathways from pyruvate to propionic acid.

Industrial biotechnology provides an efficient, sustainable solution for chemical production. However, designing biochemical pathways based solely on known reactions does not exploit its full potential. Enzymes are known to accept non-native substrates, which may allow novel, advantageous reactions. We have previously developed a computational program named Biological Network Integrated Computational Explorer (BNICE) to predict promiscuous enzyme activities and design synthetic pathways, using generalized reaction rules curated from biochemical reaction databases. Here, we use BNICE to design pathways synthesizing propionic acid from pyruvate. The currently known natural pathways produce undesirable by-products lactic acid and succinic acid, reducing their economic viability. BNICE predicted seven pathways containing four reaction steps or less, five of which avoid these by-products. Among the 16 biochemical reactions comprising these pathways, 44% were validated by literature references. More than 28% of these known reactions were not in the BNICE training dataset, showing that BNICE was able to predict novel enzyme substrates. Most of the pathways included the intermediate acrylic acid. As acrylic acid bioproduction has been well advanced, we focused on the critical step of reducing acrylic acid to propionic acid. We experimentally validated that Oye2p from Saccharomyces cerevisiae can catalyze this reaction at a slow turnover rate (10(-3) s(-1) ), which was unknown to occur with this enzyme, and is an important finding for further propionic acid metabolic engineering. These results validate BNICE as a pathway-searching tool that can predict previously unknown promiscuous enzyme activities and show that computational methods can elucidate novel biochemical pathways for industrial applications. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:303-311, 2016.