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The homogeneous partial oxidation of 1,3-propanediol using oxygen at relatively mild conditions (430 K) has been observed to result in high selectivity to acrolein at low to moderate conversion with minor formation of formaldehyde and acetaldehyde (Diaz E. ChemSusChem 2010 3 1063-1070, DOI: 10.1002/cssc.201000142). In this work, various competing mechanistic postulates for the selective conversion of 1,3-propanediol to acrolein were tested quantitatively using mechanistic modeling. Specifically, mechanisms based on free-radical chemistry and concerted pathways, both of which have been postulated in the literature, were evaluated. Automated mechanism generation was used to comprehensively create detailed reaction mechanisms based on reaction families relevant to free-radical or concerted chemistries. A mechanism based on free-radical reactions alone was unable to account for the rate of conversion and high selectivity to acrolein that were observed experimentally. Concerted pathways based on both unimolecular and bimolecular dehydrogenation and dehydration routes successfully rationalized the experimental data, but the activation energies for dehydrogenation reactions required to match the data were significantly lower than those reported theoretically. A model involving both free-radical and concerted chemistry provided the most plausible quantitative description of the experimental data, capturing 1,3-propanediol conversion, product yields, and the dependence of rates on oxygen concentration well. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Glycoside hydrolases (GHs) distort carbohydrate ring geometry along particular "catalytic itineraries" during the cleavage of glycosidic bonds, illustrating the relationship between substrate conformation and reactivity. Previous theoretical studies of thermodynamics of isolated monosaccharides offer insights into the catalytic itineraries of particular sugars. However, kinetic accessibility of carbohydrate puckering conformations and the role of exocyclic groups have not yet been thoroughly addressed. Here we present the first complete library of low-energy local minima and puckering interconversion transition states for five biologically relevant pyranose sugars: β-xylose, β-mannose, α-glucose, β-glucose, and β-N-acetylglucosamine. These were obtained by a thorough theoretical investigation each of the 38 TUPAC designated puckering geometries and all possible conformations of the exocyclic groups. These calculations demonstrate that exocyclic groups must be explicitly considered when examining these interconversion pathways. Furthermore, these data enable evaluation of previous hypotheses of why enzymes perturb ring geometries from the low-energy equatorial chair (4C1) conformation. They show that the relative thermodynamics alone do not universally correlate with GH catalytic itineraries. For some sugars, particular puckers offer both catalytically favorable electronic structure properties, such as anomeric carbon partial charge, and low kinetic barriers to achieve a given puckering conformation. However, different factors correlate with catalytic itineraries for other sugars; for β-N-acetylglucosamine, the key N-acetyl arm confounds the puckering landscape and appears to be the crucial factor. Overall, this study reveals a more comprehensive understanding of why particular puckering geometries are favored in carbohydrate catalysis concomitant with the complexity of glycobiology. [ABSTRACT FROM AUTHOR]

1,5-Hydrogen transfer reactions in methyl acrylate and butyl acrylate free-radical polymerization are studied using quantum chemistry and transition state theory to estimate the kinetic parameters ( ktr, Ea, and A) with tetrameric radicals, requiring a number of atoms that ranks among the largest polymeric mimics to date. A two-step transformation accounted for the overall reaction: rotation from an extended conformation to a coiled conformation and abstraction of the fifth hydrogen atom by the end-chain radical. UB3LYP/6-31G(d) was used for geometry optimization, validation of the transition states, and calculation of frequencies that were used to obtain thermodynamic properties. The more computationally demanding level of theory, MPWB1K/6-31G(d,p), was used for calculation of the electronic energy. [ABSTRACT FROM AUTHOR]

Abstract: Molecular simulations were used to screen a diverse collection of eight homochiral metal–organic frameworks (MOFs) for their ability to separate 19 chiral compounds by enantioselective adsorption. The simulation model was validated by comparison with available experimental data. It was found that high enantioselectivity is strongly correlated with a close match between the size of the pore and the size of the chiral sorbate molecule. However, there is also a possibility of no enantioselectivity even when the size of the pore matches with the size of the chiral sorbate molecule. A four-point model was used to explain this observation, and a solution to promote high enantioselectivity has been proposed. [Copyright &y& Elsevier]

This study aimed at investigating the performance of a series of basis sets, density functional theory (DFT) functionals, and the IEF-PCM solvation model in the accurate calculation of ¹H and 13C NMR chemical shifts in toluene-d8. We demonstrated that, on a test set of 37 organic species with various functional moieties, linear scaling significantly improved the calculated shifts and was necessary to obtain more accurate results. Inclusion of a solvation model produced larger deviations from the experimental data as compared to the gas-phase calculations. Moreover, we did not find any evidence that very large basis sets were necessary to reproduce the experimental NMR data. Ultimately, we recommend the use of the BMK functional. For the 1H shifts the use of the 6-311G(d) basis set gave linearly scaled mean unsigned (MU) and root-mean-square (rms) errors of 0.15 ppm and 0.21 ppm, respectively. For the calculation of the 13C chemical shifts the 6-31G(d) basis set produced MUE of 1.82 ppm and RMSE of 3.29 ppm. [ABSTRACT FROM AUTHOR]

The thermochemical properties for selected hydrogenated silicon clusters (SixHy, x = 3-13, y = 0-18) were calculated using quantum chemical calculations and statistical thermodynamics. Standard enthalpy of formation at 298 K and standard entropy and constant pressure heat capacity at various temperatures, i.e., 298-6000 K, were calculated for 162 hydrogenated silicon clusters using G3//B3LYP. The hydrogenated silicon clusters contained ten to twenty fused Si-Si bonds, i.e., bonds participating in more than one three- to six-membered ring. The hydrogenated silicon clusters in this study involved different degrees of hydrogenation, i.e., the ratio of hydrogen to silicon atoms varied widely depending on the size of the cluster and/or degree of multifunctionality. A group additivity database composed of atom-centered groups and ring corrections, as well as bond-centered groups, was created to predict thermochemical properties most accurately. For the training set molecules, the average absolute deviation (AAD) comparing the G3//B3LYP values to the values obtained from the revised group additivity database for standard enthalpy of formation and entropy at 298 K and constant pressure heat capacity at 500, 1000, and 1500 K were 3.2%, 1.9%, 0.40%, 0.43%, and 0.53%, respectively. Sensitivity analysis of the revised group additivity parameter database revealed that the group parameters were able to predict the thermochemical properties of molecules that were not used in the training set within an AAD of 3.8% for standard enthalpy of formation at 298 K. [ABSTRACT FROM AUTHOR]

A variety of quantum mechanical (QM) methods, basis sets and solute cavity descriptions using the conductor-like polarisable continuum model for the modelling of reaction free energies in solution have been evaluated and compared. In order to test the performance of each QM level of theory and cavity model, five common types of organic reactions in different solvents have been considered. The study shows that the hybrid metageneralised-gradient approximation M05-2X functional, in conjunction with the MG3S basis set and UA0 or universal force field cavities, produces results in the best overall agreement with experimental data. In addition, within the UA0 formalism, explicit cavities should be built on the hydrogen atoms that undergo transformation during chemical reaction. [ABSTRACT FROM AUTHOR]

Stochastic mechanistic models for gradient copolymerisation systems were developed based on a kinetic Monte Carlo algorithm. Due to the discrete nature of the simulations, chains were tracked as binary strings allowing for the storage of the complete sequence of each polymer chain, allowing for an unprecedented level of detail. Models were developed that simulated styrene/4-acetoxystyrene semi-batch gradient copolymer syntheses, and explicit sequence information was determined using simulation results. The chemical composition distribution was mapped for the copolymers, which is capable of providing a visual description of both the size and overall composition distributions of the copolymer and a qualitative description of the chain architecture. This methodology was expanded to track the explicit sequences of each chain that was used to determine the number and weight fraction sequence length distributions. Simulation results show that tail end compositional tapering was never fully achieved. Case studies were conducted to determine the major factors affecting tail end tapering, including increasing the initial batch fraction and varying the 4-acetoxystyrene flow rate. While an increase in initial batch fraction increases 4-acetoxystyrene significantly, head end tapering is lost, while a large increase in flow rate is not capable of fully tapering the tail end. [ABSTRACT FROM AUTHOR]

In this study, the formation of oxazolidinone intermediates along the reaction coordinates of two l-proline-catalyzed reactions was investigated using high-level quantum mechanical calculations. Our results suggest that the final product is produced via routes other than the currently-adopted catalytic cycle for l-proline, including routes where oxazolidinones are directly involved. [ABSTRACT FROM AUTHOR]

Background: Bioremediation offers a promising pollution treatment method in the reduction and elimination of man-made compounds in the environment. Computational tools to predict novel biodegradation pathways for pollutants allow one to explore the capabilities of microorganisms in cleaning up the environment. However, given the wealth of novel pathways obtained using these prediction methods, it is necessary to evaluate their relative feasibility, particularly within the context of the cellular environment. Results: We have utilized a computational framework called BNICE to generate novel biodegradation routes for 1,2,4-trichlorobenzene (1,2,4-TCB) and incorporated the pathways into a metabolic model for Pseudomonas putida. We studied the cellular feasibility of the pathways by applying metabolic flux analysis (MFA) and thermodynamic constraints. We found that the novel pathways generated by BNICE enabled the cell to produce more biomass than the known pathway. Evaluation of the flux distribution profiles revealed that several properties influenced biomass production: 1) reducing power required, 2) reactions required to generate biomass precursors, 3) oxygen utilization, and 4) thermodynamic topology of the pathway. Based on pathway analysis, MFA, and thermodynamic properties, we identified several promising pathways that can be engineered into a host organism to accomplish bioremediation. Conclusions: This work was aimed at understanding how novel biodegradation pathways influence the existing metabolism of a host organism. We have identified attractive targets for metabolic engineers interested in constructing a microorganism that can be used for bioremediation. Through this work, computational tools are shown to be useful in the design and evaluation of novel xenobiotic biodegradation pathways, identifying cellularly feasible degradation routes. [ABSTRACT FROM AUTHOR]

Abstract: Reaction progress kinetic analysis of the proline-mediated intermolecular aldol reaction shows that the rate depends on the concentrations of both the donor ketone1 and the electrophilic aldehyde 2, implying that enamine formation cannot be rate-determining. The observed kinetics and deuterium isotope effects are consistent with formation of the product iminium species as the rate-determining step. [Copyright &y& Elsevier]

Abstract: A detailed, mechanistic model for high-density polyethylene pyrolysis was created based on the modeling framework developed in our previous work and was used to study the time evolution of low molecular weight products formed. Specifically, the role that unzipping, backbiting, and random scission reaction pathways play in the evolution of low molecular weight species was probed. The model tracked 151 species and included over 11,000 reactions. Rate parameters were adapted from our previous work, literature values, and regression against experimental data. The model results were found to be in excellent agreement with experimental data for the evolution of condensable low molecular weight products. The time evolution curves of specific low molecular weight products indicated that the random scission pathway was important for all species, while the backbiting pathway played a complementary role. Net rate analysis was used to further elucidate the competition between the pathways. Net rate analysis of end-chain radicals showed that the unzipping pathway was not competitive with the other pathways, as expected based on experimental yields of ethylene. The random scission pathway was found to be controlling, with the backbiting pathway playing a more minor role for product formation. By comparing the net rates for formation of specific mid-chain radicals via intramolecular hydrogen shift reactions, the contribution of the backbiting pathway was shown to vary, with radicals formed via the most facile x,x +4-intramolecular hydrogen transfer reactions being favored. [Copyright &y& Elsevier]

The potential application of a homochiral metal-organic framework (MOF) made from cadmium corners and BINOL-type linkers for enantioselective separations of chiral hydrocarbons was investigated using grand canonical Monte Carlo simulations. Adsorption of racemic mixtures of (R,S)-1,3-dimethyl-1,2-propadiene, (R,S)-1,2-dimethylcyclobutane and (R,S)-1,2-dimethylcyclopropane was simulated. The results show that an enantiomeric excess (ee) of over 50% can be achieved for 1,3-dimethyl-1,2-propadiene, while more moderate ee values were observed for the cyclic compounds. The adsorption sites, diastereomeric complexes and adsorption energies were analysed to understand the enantioselective adsorption. It is shown that the small zigzag pores of the homochiral MOF account primarily for the enantioselective adsorption, whereas the larger helical pores show almost no enantioselectivity. The results also demonstrate that both intrinsic enantioselectivity and accessibility of the adsorption sites are important for the overall enantioselective separation. [ABSTRACT FROM AUTHOR]

Abstract: Quantum chemical calculations were employed to develop a better understanding of the adsorption properties of BaNaY interacting with molecules relevant to deNO x catalysis. First, various basis sets and levels of theory were tested for barium-containing species and gas-phase reactions, and it was shown that the choice of the basis set for barium is critical. Density functional theory (DFT) with the B3LYP functional and SDD as the basis set was selected based on its combination of relative accuracy and speed. This level of theory was then used to calculate energies, geometries, and frequencies for acetaldehyde, acetic acid, nitromethane, and water adsorbed and in the gas phase. The predicted properties were compared to experiment where available and reasonable agreement was found. To study the effect of the zeolite framework on the adsorption properties, the size of the zeolite cluster was increased from 6T to 36T to 96T using the embedded ONIOM method. Inclusion of van der Waals interactions with increasing cluster size only changed the adsorption enthalpy by a small amount for all of the adsorbates studied. It was found that the interaction between the empty 6s orbital of Ba and the lone pair orbital of the oxygen atom in the adsorbed state, revealed by natural bond orbital analysis, correlates with the adsorption enthalpy and the gas-phase charge of the oxygen atom that interacts with Ba. [Copyright &y& Elsevier]

Abstract: A detailed mechanistic model for polystyrene pyrolysis was created that built on a modeling framework developed in our previous work and was used to probe three competing pathways to dimer formation: benzyl radical addition, 1,3-hydrogen shift, and 7,3-hydrogen shift, based on recent literature reports. To incorporate the chemistry involved in the 7,3-hydrogen shift pathway, the 1,7- and 7,3-hydrogen shift reaction families were added to the model. The updated version of the model tracks 75 species and over 3500 reactions. Rate parameters for all families were specified based on our previous work, more recent literature reports, and regression against limited experimental data. The model was able to accurately predict the experimental results for polystyrene pyrolysis for different reactor configurations for a temperature range of 100°C and two orders of magnitude of initial molecular weight for experimental data collected in our own lab and from Bouster and coworkers and Bockhorn and coworkers. The results from our model were studied using net rate analysis to gain insight into the competitiveness of the various reaction pathways to dimer formation. The net rate analysis demonstrated that 7,3-hydrogen shift is the dominant reaction pathway to dimer formation at the temperatures studied. Benzyl radical addition becomes a more competitive reaction pathway as the temperature increases, which is caused predominantly by an increase in the benzyl radical concentration with increasing temperature. Overall, it is quantitatively shown that both 7,3-hydrogen shift and benzyl radical addition are important pathways for dimer formation, with their relative competitiveness influenced by temperature. [Copyright &y& Elsevier]

Knudsen dynamics simulations were performed in cylindrical pores with multiple sections of different diameters. The number and location of hits between particles and the pore walls can be related to the known distributions in the uniform diameter cases. Both sweep-gas and pass-through modes of operation were examined. The contact of the particles traveling inside the pores with the pore walls can be regulated by adjusting the aspect ratio (section length/section diameter) of the pore sections and the ratio of their cross-sectional areas. The results were found to be independent of the transition angle between sections except for very smooth transitions. Analytical expressions were developed to relate the transmission probability in pores of multiple sections to the transmission probabilities of the constituent sections. [Copyright &y& Elsevier]

Quantum chemistry and transition state theory were used to study 10 different radical reactions of peroxy radicals abstracting the phenolic hydrogen of several antioxidants. The B3LYP functional with a 6-31G(d) basis set was used to characterize transition states with as many as 20 heavy atoms. The impact of quantum tunneling was investigated for a representative reaction, and it was shown that the Wigner correction provides a good approximation to more advanced semi-classical transmission coefficients. A kinetic correlation was developed that is suitable for use in kinetic modeling, allowing antioxidant chemistry to be included in large mechanistic models of hydrocarbon oxidation. [Copyright &y& Elsevier]

The emergence of antimicrobial resistance has led to an increase in research directed toward the engineering of novel polyketides. To date, less than 10 000 polyketide structures have been discovered experimentally; however, the theoretical analysis of polyketide biosynthesis performed suggests that over a billion possible structures can be synthesized. Polyketide synthesis, which involves the formation of a linear chain and its subsequent cyclization, is catalyzed by an enzyme complex called polyketide synthase (PKS). There are a number of variables in the linear chain synthesis controlled by the PKS: the number, identity, stereochemistry and sequence of the monomer units used in the elongation steps, and the degree of reduction that occurs after each of the condensation reactions. The theoretical analysis performed demonstrates that changes in these variables lead to the formation of different polyketide linear chains and, consequently, a high diversity of polyketide structures. The complexity in the number of possible structures led to the implementation of this system in BNICE, a computational framework that generates all possible biochemical pathways using a given set of enzyme reaction rules. This formulation allowed the analysis of the evolution of diversity in the synthesis mechanism and the construction of the pathway architecture of polyketide biosynthesis. It is expected that, after future implementation of the cyclization reactions, this framework can be used to identify all possible polyketides and their corresponding synthesis pathways. Consequently, this formulation would prove useful in guiding experimental approaches to engineer novel polyketides, a number of which will likely have medicinal properties. [ABSTRACT FROM AUTHOR]

Photolysis is an important loss process of carbonyl, peroxy, and nitrate species in the atmosphere. In general, photolysis rate coefficients depend on the intensity of radiation, wavelength, temperature, and the specific molecule reacting. One key source of differences in rate coefficients between different molecules is the absorption cross-section, σ, which is specific to the reactive molecule under consideration. In order to calculate rate coefficients for photolytic processes, a value of σ must be specified or estimated. To this end, a group additivity approach was developed that can be used to estimate the values of σ of species that photolyze primarily in the 290–370 nm wavelength region. We demonstrate that this approach can be used to predict accurately unknown values of σ of species for which experimental data are not available. [Copyright &y& Elsevier]

Quantum chemical calculations have been performed to evaluate the adsorption of NO on cobalt in several extraframework environments within ferrierite zeolites. All possible arrangements of the two nearby framework aluminum atoms have been considered for the B and G ferrierite cobalt sites. The cobalt–nitrosyl orientation, i.e., bent versus linear, depends on the local zeolite environment. The enthalpies and free energies of adsorption are also functions of the local zeolite environment. NO adsorption is favored by about 50 kJ/mol on cobalt in the B site compared to the G site. This large difference is attributable to two factors: differences in NO-induced strain on the cobalt–zeolite environments as well as an intrinsic difference based on the coordinative unsaturation of cobalt. A simulation of the temperature-programmed desorption profile reveals three distinct cobalt environments for adsorbed NO, showing the dependence of a macroscopic property that is related to catalytic activity on the local zeolite environment near a metal cation. This observation suggests that the increase in the turnover of NO to N2 per cobalt atom with increasing cobalt loading reported in the literature may be related to the same factors. A detailed analysis of the cobalt–nitrosyl bond was also performed. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Grand canonical Monte Carlo (GCMC) simulations demonstrate that catenation can be beneficial for improving hydrogen storage in metal–organic frameworks at cryogenic temperatures and low pressures but not necessarily at room temperature. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Genetic engineering of microbes is a promising strategy to enable more efficient and environmentally friendly production routes for chemicals and materials traditionally produced from petroleum. While the tools to both edit microbial genomes and measure the resulting changes to cellular physiology are growing increasingly efficient, our ability to predict which genetic interventions will have the greatest likelihood of achieving the desired phenotype is still lacking. In particular, computational methods are needed that are able to efficiently ingest the increasing amount of data generated from large biological datasets to guide subsequent rounds of experiments. This study presents an efficient algorithm coupled to modern computational tools that permits a closer integration of modeling in the design, build, test, and learn cycle of genetic engineering. [ABSTRACT FROM AUTHOR]

Quantum chemical calculations are emerging as an effective way to screen catalysts for particular applications. In this contribution, we demonstrate the power of density functional theory to study CO2 hydrolysisby six carbonic anhydrase mimics, evaluating thermodynamic and kinetic parameters at the mechanistic level. A microkinetic model was then built based on the kinetics and thermodynamics calculated from first principles. The intrinsic reaction rate constant was calculated from the results of the microkinetic model and compared with experimental data. Overall, the rate constants were in good agreement with experimental values, except for zinc-tri and complex b, which were overestimated. This was ascribed to their ineffective complexation with Zn2+. How the reaction rate constants vary with time was also investigated. From 0 to 12 ms, the rate constants of complexes a and d decreased to 50 and 67% of their initial values, respectively; the rate constants of complexes b and f2 were almost invariant with time; the rate constant of complex f1 showed an unusual double sigmoidal shape. The pKa values of these six carbonic anhydrase mimics as well as three additional mimics were calculated. A correlation between pKa values and the binding free energy of OH-was obtained by fitting data from five zinc(II) aza-macrocyclic complexes. The reaction rate constants were found to increase linearly with the pKa value, indicating CO2 adsorption is the rate-limiting step. [ABSTRACT FROM AUTHOR]

The use of kinetic models is key for analyzing, designing, controlling, and optimizing the manufacturing of high value chemicals. In particular, microkinetic modeling relies on large-scale networks of elementary reaction steps that map the conversion of feed materials to final products by considering a large number of pathways and intermediate species for a given reactor configuration. Intuitively, several of the (often automatically generated) reaction steps might be redundant or insignificant, and thus determining the true governing reaction network is critical to understanding and modeling the underlying chemistry. This work introduces a nonlinear dynamic optimization framework for discovering governing reaction networks from data, whereby both the model structure (the elementary reaction steps) and the model parameters (reaction rate constants, pre-exponential factors, and activation energies) are simultaneously learned from composition time series data. The proposed framework can also achieve dimensionality reduction to produce accurate reduced-order microkinetic models that are computationally parsimonious and thus better suited for applications involving simulation and optimization. Two numerical examples of different dimensions are presented to illustrate the key properties of our approach. • We present a data-driven optimization framework for discovering microkinetic models. • Dimensionality reduction is performed to identify reduced-order microkinetic models. • A sequential elimination heuristic is proposed to improve computational scalability. • Extensive numerical experiments are developed to validate the proposed framework. [ABSTRACT FROM AUTHOR]

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. • A kinetic model captures timecourse data, including butanol production, in two experimental conditions in cell-free extracts. • Model hypothesizes metabolic mechanisms to explain experimentally observed behaviors between conditions. • Strategies to increase butanol production are predicted, the most promising of which is experimentally validated. • Analyses reveal how model parameters are constrained between many possible parameter sets within an ensemble. [ABSTRACT FROM AUTHOR]

Motivation: The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods—SimIndex (SI) and SimZyme—which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers–Waterman type pathway search algorithm for further paring down pertinent networks. Results: Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 105-fold. Subsequent Byers–Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions [ABSTRACT FROM AUTHOR]

Density functional theory (DFT) calculations were used to study the mechanism of CO 2 hydrolysis by Co-(1,4,7,10-tetrazacyclododecane), also referred to as cobalt-cyclen, and evaluate the associated thermodynamic and kinetic parameters. A microkinetic model was then built based on the kinetics and thermodynamics derived from first principles. The intrinsic reaction rate constant was calculated to be 10572 M −1 s −1 , three times larger than that of zinc-cyclen. The high activity was ascribed to a very large p K a value of 13.3. The monodentate structure of a key intermediate along the reaction coordinate, [Cyclen-Co-HCO 3 ] + , was more stable than the bidentate isomer, due to a hydrogen bond formed between bicarbonate and the ligand. The reaction rate constant decreased significantly over 0–12 ms, which was attributed to the fast decrease of the concentration of the catalytic form, Co OH − . The conversion of CO 2 at 1000 ms as a function of pH was calculated to compare the relative activity of zinc-cyclen and cobalt-cyclen, and zinc-cyclen was found to be a much better catalyst. Through calculating the ratio of the net rate to the forward rate of each elementary reaction, the rate-limiting step of the catalytic cycle was identified as the adsorption of CO 2 , which was the same as that for zinc-cyclen, though the product-releasing step was regarded as more difficult in cobalt-cyclen, compared to zinc-cyclen. [ABSTRACT FROM AUTHOR]

ABSTRACT Density functional theory (DFT) calculations were used to study the mechanism of CO2 hydrolysis by Zn-(1,5,9-triazacyclododecane) and Zn-cyclam and evaluate the associated thermodynamic and kinetic parameters. Microkinetic models were then built based on the kinetics and thermodynamics derived from first principles. Both catalysts showed very similar behavior to Zn-cyclen, which we have reported previously, but with multiple distinctions. The intrinsic reaction rate constants for Zn-(1,5,9-triazacyclododecane) and Zn-cyclam were calculated to be 2693 and 4623 M−1 s−1, respectively, which is in reasonable agreement with experimental values reported or estimated. The CO2 adsorption step was found to be a rate-limiting step for all three catalysts. Zn-cyclam has the lowest barrier for this step due to the highest p Ka or nucleophilicity of the Zn-OH− form, and, therefore, the highest intrinsic activity. However, the observed reaction rate constant also depends on the availability of the catalyst. The decrease in the observed reaction rate constant over 0-12 ms was ascribed to the decrease in the concentration of the catalytic form, Zn-OH−, which was primarily converted to Zn-HCO3−. The reaction rate constant of Zn-cyclam dropped much faster than those of Zn-cyclen and Zn-(1,5,9-triazacyclododecane) due to lower energy of the Zn-HCO3− form. The conversion of CO2 at 1000 ms as a function of pH was calculated to compare the relative activity of these catalysts, and Zn-cyclen was found to be the best catalyst. [ABSTRACT FROM AUTHOR]

Density functional theory (DFT) calculations were used to study the mechanism of CO2 hydrolysis by Zn-(1,4,7,10-tetraazacyclododecane), also referred to as zinc-cyclen, and evaluate the associated thermodynamic and kinetic parameters. A microkinetic model was then built based on the kinetics and thermodynamics derived from first principles. The model describes the sigmoidal dependence of the observed reaction rate constant on pH, which is similar to behavior observed for CO2 hydrolysis catalysts in nature, i.e., human carbonic anhydrase. The inflection point is identical to the pKa value of the zinc-cyclen complex, in good agreement with previous results. The overall reaction rate constant was calculated to be 3055 M-1 s-1, which is in excellent agreement with the range of experimental values reported (2691.5-3300 M-1 s-1). Importantly, though, the microkinetic model quantifies how the reaction rate and the associated overall rate constant varies as a function of time, underscoring that observed overall rate constants are a function of the species' concentrations used to extract them, which may not be the initial conditions. The decrease in initial reaction rate over 0-12 ms was ascribed to the decrease in the concentration of the catalytic form, Zn-OH-, which was primarily converted to Zn-HCO3-. Through calculating the ratio of the net rate to the forward rate of each elementary reaction, the rate-limiting step of the catalytic cycle was identified as the adsorption of CO2. To aid in the interpretation of the complex microkinetic model, an analytical rate expression was derived based on a simplified potential energy surface comprised of three major regions: the adsorption of CO2, the release of HCO3- via displacement by water, and the deprotonation of Zn-H2O. Analysis showed quantitatively how the observed reaction rate constant depends not only on how fast a CO2 molecule is captured but also on how deep the Zn-HCO3- sits. [ABSTRACT FROM AUTHOR]

A kinetic Monte Carlo model of polyurethane polymerization which explicitly tracks the polymer sequences is developed and shared. This model is benchmarked against theoretical and experimental polyurethane data and used to investigate the effect on oligomer distributions of unequal reactivity of the first and second isocyanate to react. The reverse reactions using thermodynamic consistency are then added to the framework, and analogous to the addition polymerization concept of ceiling temperature, equilibrium chain length distributions at various temperatures are calculated. For a mixture of three monomers AA, BB, and CC, where BB and CC do not react with one another, are present in stoichiometric proportions, and have different enthalpies of reaction with AA, an odd‐even effect emerges. Odd length chains are more likely than even length chains for temperatures at which BB and CC have significantly different equilibrium conversions. The concept of ceiling temperature that is typically cited for addition polymers is extended here to provide a measure of conditions under which depolymerization for recycling is favored. [ABSTRACT FROM AUTHOR]

Abstract: The three-point model and molecular simulation are used to understand the enantioselective adsorption of a homologous series of chiral compounds on a common chiral selector that is incorporated on a solid surface. We demonstrate how the enantioselectivity of homologous chiral compounds may go down, go up, remain the same, or even reverse. Conditions that lead to consistent enantioselectivity for homologous chiral compounds are also proposed. [Copyright &y& Elsevier]

Abstract: The steric effects of a metal-organic framework (MOF) on the enantioselectivity of a (salen)Mn were studied using classical atomistic modeling. Rotational energy profiles for the approach of 2,2-dimethyl-2H-chromene to the active site of (salen)Mn were mapped for the homogeneous catalyst and the catalyst immobilized as a linker in a MOF. The model corroborated that the Re enantioface is favored by the homogeneous catalyst. It was shown that the predicted enantioselectivity when chromene approaches the more accessible (salen)Mn in the MOF is highly sensitive to the distance between the (salen)Mn linkers of the interpenetrated frameworks. Calculated rotational energy profiles for a hypothetical non-interpenetrated MOF revealed that this MOF catalyst should exhibit enantioselectivity intermediate to the interpenetrated MOF and the homogeneous catalyst. Finally, the continuous chirality measure of the catalyst was found to correlate well with the energy of the homogeneous catalyst–reactant complex, suggesting that high chirality content is related to high enantioselectivity. This correlation, however, did not apply for some of the MOF catalysts. For heterogeneous catalysts, the mechanism of asymmetric induction depends on the steric environment in addition to the chirality of the catalyst. [Copyright &y& Elsevier]

Hybrid quantum mechanics/molecular mechanics (QM/MM) methods were employed to study electronic effects of 5,5′-substituents on the enantioselectivity of (salen)Mn catalysts. These methods were shown to effectively capture electronic effects of substituents if the substituents were treated with a simple electrostatic coupling using point charges. Correlations between calculated properties of (salen)MnO indicative of its reactivity and the modified Hammett constant σpara쇪 applied to study electronic framework effects on enantioselectivity for (salen)Mn heterogenized in a metal−organic framework (MOF). The results suggest that these effects are too small to account for all of the decrease in enantioselectivity observed experimentally and that steric constraints of the framework likely play a role. The reactivity correlations also enabled fast ligand screening for the prediction of highly enantioselective (salen)Mn catalysts that could be used in MOFs. A modified version of the DREIDING force field was also developed to allow QM/MM calculations of (salen)Mn species. [ABSTRACT FROM AUTHOR]

Abstract: Density functional theory (DFT) and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations were used to study stable intermediates for alkene epoxidation using Mn-porphyrin catalysts. For the reaction intermediate involving complexation of the alkene with the oxidized Mn-porphyrin, four intermediates have been proposed in the literature. A concerted mechanism with no intermediate has also been proposed, and these five mechanisms could all involve the formation of a product complex. Our calculations show that the product complex has the lowest energy, followed by the radical intermediate. The metallaoxetane intermediate is much higher in energy, and the calculations do not support carbocation or pi-radical cation intermediates. A polarizable continuum model was used to account for solvent effects, and the calculated energies of solvation are comparable for all minima along the reaction path. [Copyright &y& Elsevier]

We offer an overview of the popular one- dimensional (1-D) hindered rotor model that is often used for quantum mechanical treatment of internal rotation. This model is put in context with other methods used for treating anharmonic motions. The 1-D hindered rotor scheme is general for tops of any symmetry and has been used to provide accurate treatment of hindered rotors in a wide range of systems. One obstacle preventing wider use of the model is its lack of incorporation into common electronic structure codes. We have developed an algorithm for consistently treating all tops in a molecule, and we present simple codes which interface with electronic structure codes to provide thermochemical properties (S, C p , H) of individual species and reactions that have been corrected for internal rotations. Finally, we use this approach to give sensible advice about how the model can be used best. We show that dramatic changes in the reduced moment of inertia do not necessarily cause comparable changes in the properties of individual hindered rotors. We demonstrate that the rotational hindrance potential can be accurately determined using relatively coarse step sizes. Finally, we show that internal rotation in transition states can be treated using a “frozen transition state” approximation at a significant computational savings. We also discuss the relationship between calculated properties of hindered rotors and the choice of method and basis set used. [ABSTRACT FROM AUTHOR]

Owing to the improvement of properties including conductivity, toughness and permeability, polymer nanocomposites are slated for applications ranging from membranes to fuel cells. The enhancement of polymer properties by the addition of inorganic nanoparticles is a complex function of interfacial interactions, interfacial area and the distribution of inter-nanofiller distances. The latter two factors depend on nanofiller dispersion, making it difficult to develop a fundamental understanding of their effects on nanocomposite properties. Here, we design model poly(methyl methacrylate)–silica and poly(2-vinyl pyridine)–silica nanocomposites consisting of polymer films confined between silica slides. We compare the dependence of the glass-transition temperature (Tg) and physical ageing on the interlayer distance in model nanocomposites with the dependence of silica nanoparticle content in real nanocomposites. We show that model nanocomposites provide a simple way to gain insight into the effect of interparticle spacing on Tg and to predict the approximate ageing response of real nanocomposites. [ABSTRACT FROM AUTHOR]

Abstract: Grand canonical Monte Carlo simulations have been carried out to investigate the adsorption of water from the vapor phase into the zeolite silicalite. For truly hydrophobic micropores, the simulations predict essentially no adsorption of water at low pressures, followed by rapid pore filling as pressure is increased. The effect of silanol defects in real silicalite samples was explored through simulations using “seeded” water molecules to represent hydrophilic defects. These defects promote adsorption of some water at low pressures, as molecules form hydrogen-bonded clusters around the defects. The defects also shift the pore filling to a lower pressure than in the completely hydrophobic material. [Copyright &y& Elsevier]

We analyzed the glassy-state structural relaxation of polymersnear surfaces and interfaces by monitoring fluorescence in multilayerfilms. Relative to that of bulk, the rate of structural relaxation ofpoly(methyl methacrylate) is reduced by a factor of 2 at a free surfaceand by a factor of 15 at a silica substrate interface; the latterexhibits a nearly complete arresting of relaxation. The distribution inrelaxation rates extends more than 100 nanometers into the filminterior, a distance greater than that over which surfaces andinterfaces affect the glass transition temperature. [ABSTRACT FROM AUTHOR]

The extraframework locations of many metal cations in pentasil zeolites, such as ferrierite, are not known, but new infrared bands in the 800–1000 cm−1 region are experimentally observed upon exchange. To investigate the origin of these bands and to better understand cation siting, density functional theory calculations were performed on ferrierite cluster models for seven divalent metals: magnesium, manganese, iron, cobalt, nickel, copper, and zinc. The calculated properties, such as the metal–zeolite binding energy, follow a U-shaped trend with respect to the number of metal d electrons. The calculated infrared bands are consistent with the occupation of the B and G ferrierite sites by the metal cations. However, a definitive assignment of the published experimental infrared bands to particular extraframework sites was not possible. Despite this limitation, all seven metals were shown to perturb the ferrierite framework similarly. All of the cations coordinate to four framework oxygen atoms and the framework T–O bonds associated with these oxygen atoms are elongated by the metal. This elongation is shown to be proportional to the wavenumber of the infrared bands. [Copyright &y& Elsevier]

A novel MCM-41-supported base catalyst, Ba-MCM-41, was synthesized with a modified impregnation method. Ba2SiO4 was synthesized on the MCM-41 pore wall as confirmed by characterization using X-ray diffraction, X-ray photoelectron spectroscopy, X-ray fluorescence, N2 adsorption, 29Si MAS NMR and transmission electron microscopy. It was also demonstrated that a highly ordered Ba-MCM-41 catalyst with high barium loading (4.1–25.2 wt.%) and dispersion was obtained using this method. The basicity of the catalyst, which was characterized according to the binding energy of O 1s, was increased with increasing amounts of barium in the catalyst. [Copyright &y& Elsevier]

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. • Curated a minimal yet comprehensive set of 1224 enzymatic reaction rules. • Can be applied in open-source platforms to design novel biosynthetic pathways. • Redundant and erroneous rules eliminated to enhance computational efficiency. • Outperforms existing rulesets by representing more enzymatic transformations. [ABSTRACT FROM AUTHOR]

• Linseed oil chemistry in the context of drying of paint is comprehensively reviewed. • The review discusses experimental data similar to outcomes of computational modeling. • Advances in computational modeling related to oils are discussed. This review summarizes computational modeling literature concerning linseed oil autoxidation in the context of oil paint. After discussing the chemical structure and composition of linseed oil, an overview is given of polymer properties of the material and the experimental techniques used to infer these properties. The review proceeds with the survey of existing computational modeling techniques used to investigate the drying process of linseed oil. [ABSTRACT FROM AUTHOR]

[Display omitted] • Oligomerization of alkenes upgrades shale gas feedstocks into liquid products. • Microkinetic modelling reveals mechanistic details of propene oligomerization. • The model predicts conversion and selectivity under a range of experimental conditions. • Microkinetic modelling supports catalyst and process optimization. The microkinetic modelling methodology that we developed previously to describe Brønsted acid-catalyzed propene oligomerization on medium-pore MFI zeolites has been extended successfully to large-pore Beta zeolites. The extension of the model was supported by the identification of the key descriptors that account for the different topologies and acid strengths of the zeolite frameworks (physisorption enthalpies, stabilization enthalpies, frequency factors). The model is validated with experimental conversion and selectivity data measured in a plug-flow reactor on a commercial Beta zeolite over a range of operating conditions. Analysis of net reaction rates allowed identifying the preferred pathways that increase oligomerization selectivity toward C 9 species with increasing propene pressure. The model was additionally used to investigate how the stabilization enthalpies of chemisorbed intermediates, an important catalyst descriptor, influenced the selectivity and surface coverage at iso -conversion. This analysis provides mechanistic insights into the propene oligomerization reaction network and its dependence on zeolite topology, and demonstrates how microkinetic models can describe catalyst behaviour and aid in catalyst and process optimization. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Biology can expand the scope of chemical manufacturing, but limitations persist. • Green chemistry offers a sustainable alternative to petrochemical synthesis. • Chemical transformations in biology and chemistry complement one another. • Computational synthesis planning expedites the design of hybrid synthesis routes. [ABSTRACT FROM AUTHOR]