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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 T g (by 30 °C), yield stress (by 1.5 MPa), ductility (by 3×), and lower depolymerization temperature (by 60 °C) than conventional nylon 6., (© 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

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 (BiL OH ). This HBPE exhibits full chemical recyclability despite its architectural complexity, showing quantitative selectivity for regeneration of BiL OH , via a unique cascade depolymerization mechanism. Moreover, distinct differences in materials properties and performance arising from topological variations between HBPE, hb -PBiL OH , and its linear analogue, l -PBiL OH , 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 (1 S , 4 S , 5 S )- hb -PBiL OH . More intriguingly, depolymerization of l -PBiL OH proceeds through an unexpected, initial topological transformation to the HBPE polymer, followed by the faster cascade depolymerization pathway adopted by hb -PBiL OH . 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.

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., Competing Interests: Declaration of Competing Interest Nothing declared., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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 × 10 6 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., Competing Interests: The authors declare the following competing financial interest(s): The National Renewable Laboratory has filed a provisional patent application no. 63/350973. Authors associated with the application include ANW, PSJ, MFC, MRN, CBH, NAR, and GTB. NAR and GTB also possess multiple patents and patent application on other bioderived polymers. The authors declare no other competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

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., (© 2023. The Author(s).)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

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., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

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., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)

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.

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., (Copyright © 2021 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

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.

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., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

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., Competing Interests: The authors have declared that no competing interests exist.

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., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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.

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., (Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

In several important classes of inverting carbohydrate-active enzymes, the identity of the catalytic base remains elusive, including in family 6 Glycoside Hydrolase (GH6) enzymes, which are key components of cellulase cocktails for cellulose depolymerization. Despite many structural and kinetic studies with both wild-type and mutant enzymes, especially on the Trichoderma reesei ( Hypocrea jecorina ) GH6 cellulase ( Tr Cel6A), the catalytic base in the single displacement inverting mechanism has not been definitively identified in the GH6 family. Here, we employ transition path sampling to gain insight into the catalytic mechanism, which provides unbiased atomic-level understanding of key order parameters involved in cleaving the strong glycosidic bond. Our hybrid quantum mechanics and molecular mechanics (QM/MM) simulations reveal a network of hydrogen bonding that aligns two active site water molecules that play key roles in hydrolysis: one water molecule drives the reaction by nucleophilic attack on the substrate and a second shuttles a proton to the putative base (D175) via a short water wire. We also investigated the case where the putative base is mutated to an alanine, an enzyme that is experimentally still partially active. The simulations predict that proton hopping along a water wire via a Grotthuss mechanism provides a mechanism of catalytic rescue. Further simulations reveal that substrate processive motion is 'driven' by strong electrostatic interactions with the protein at the product sites and that the -1 sugar adopts a 2 S O ring configuration as it reaches its binding site. This work thus elucidates previously elusive steps in the processive catalytic mechanism of this important class of enzymes.

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.

Chemicals with aldehyde moieties are useful in the synthesis of polymerization reagents, pharmaceuticals, pesticides, flavors, and fragrances because of their high reactivity. However, chemical synthesis of aldehydes from carboxylic acids has unfavorable thermodynamics and limited specificity. Enzymatically catalyzed reductive bioaldehyde synthesis is an attractive route that overcomes unfavorable thermodynamics by ATP hydrolysis in ambient, aqueous conditions. Carboxylic acid reductases (Cars) are particularly attractive, as only one enzyme is required. We sought to increase the knowledge base of permitted substrates for four Cars. Additionally, the Lys2 enzyme family was found to be mechanistically the same as Cars and two isozymes were also tested. Our results show that Cars prefer molecules where the carboxylic acid is the only polar/charged group. Using this data and other published data, we develop a support vector classifier (SVC) for predicting Car reactivity and make predictions on all carboxylic acid metabolites in iAF1260 and Model SEED., (© 2015 Wiley Periodicals, Inc.)

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., (© 2016 American Institute of Chemical Engineers.)

There have been many achievements in applying biochemical synthetic routes to the synthesis of commodity chemicals. However, most of these endeavors have focused on optimizing and increasing the yields of naturally existing pathways. We sought to evaluate the potential for biosynthesis beyond the limits of known biochemistry towards the production of small molecule drugs that do not exist in nature. Because of the potential for improved yields compared to total synthesis, and therefore lower manufacturing costs, we focused on drugs for diseases endemic to many resource poor regions, like tuberculosis and HIV. Using generalized biochemical reaction rules, we were able to design biochemical pathways for the production of eight small molecule drugs or drug precursors and identify potential enzyme-substrate pairs for nearly every predicted reaction. All pathways begin from native metabolites, abrogating the need for specialized precursors. The simulated pathways showed several trends with the sequential ordering of reactions as well as the types of chemistries used. For some compounds, the main obstacles to finding feasible biochemical pathways were the lack of appropriate, natural starting compounds and a low diversity of biochemical coupling reactions necessary to synthesize molecules with larger molecular size., (Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Background: In spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography-mass spectrometry (LC-MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases., Description: Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC-MS accurate mass data enabled the identity of an unknown peak to be confidently predicted., Conclusions: MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures. Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.

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 10(5)-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., Availability and Implementation: Python files will be available for download at http://tyolab.northwestern.edu/tools/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

In this work, we carried out a comprehensive density functional theory (DFT) study on the basis of a trimer-to-tetramer radical reaction model to assess a cost-effective approach to perform the calculation of kinetic and thermodynamic properties of methyl methacrylate (MMA) free-radical homopolymerization. By comparing results from several different functionals (PBE, M06-2X, wB97XD, KMLYP, and MPW1B95), in conjunction with a series of basis sets (6-31G(d,p), 6-31+G(d,p), 6-31G(2df,p), 6-311G(d,p), 6-311+G(d,p), 6-311+G(2df,p), 6-311+G(2df,2p)), we show that calculations using M06-2X/6-311+G(2df,p)//B3LYP/6-31G(2df,p) provide an activation energy of 5.25 kcal mol(-1) for the homopropagation step, which is within 1 kcal mol(-1) of the experimental value. However, this method predicts a heat of polymerization of 17.37 kcal mol(-1) that is larger than the experimental value by 3.5 kcal mol(-1). MPW1B95/6-311+G(2df,p) on the B3LYP/6-31G(2df,p) geometries produces a heat of polymerization value within 1 kcal mol(-1) of experimental data, yet overestimates the activation energy by 3 kcal mol(-1). In addition, we evaluated the performance of ONIOM MO:MO calculations on the geometry optimization of species comprising our MMA polymerization model and found that ONIOM(B3LYP/6-31G(2df,p):B3LYP/6-31G(d)) is capable of producing geometries in very good agreement with the full B3LYP/6-31G(2df,p) calculations. Subsequent calculations of energies using M06-2X/6-311+G(2df,p) based on the ONIOM geometries provided an activation energy value comparable to that based on the full B3LYP/6-31G(2df,p) geometries.

Tissue development and disease progression are multi-stage processes controlled by an evolving set of key regulatory factors, and identifying these factors necessitates a dynamic analysis spanning relevant time scales. Current omics approaches depend on incomplete biological databases to identify critical cellular processes. Herein, we present TRACER (TRanscriptional Activity CEll aRrays), which was employed to quantify the dynamic activity of numerous transcription factor (TFs) simultaneously in 3D and networks for TRACER (NTRACER), a computational algorithm that allows for cellular rewiring to establish dynamic regulatory networks based on activity of TF reporter constructs. We identified major hubs at various stages of culture associated with normal and abnormal tissue growth (i.e., ELK-1 and E2F1, respectively) and the mechanism of action for a targeted therapeutic, lapatinib, through GATA-1, which were confirmed in human ErbB2 positive breast cancer patients and human ErbB2 positive breast cancer cell lines that were either sensitive or resistant to lapatinib.

Recent advances in computational approaches for creating pathways for novel biochemical reactions has motivated the development of approaches for identifying enzyme-substrate pairs that are attractive candidates for effecting catalysis. We present an improved structural-based strategy to probe and study enzyme-substrate binding based on binding geometry, energy, and molecule characteristics, which allows for in silico screening of structural features that imbue higher catalytic potential with specific substrates. The strategy is demonstrated using 2-keto acid decarboxylation with various pairs of 2-keto acids and enzymes. We show that this approach fitted experimental values for a wide range of 2-keto acid decarboxylases for different 2-keto acid substrates. In addition, we show that the structure-based methods can be used to select specific enzymes that may be promising candidates to catalyze decarboxylation of certain 2-keto acids. The key features and principles of the candidate enzymes evaluated by the strategy can be used to design novel biosynthesis pathways, to guide enzymatic mutation or to guide biomimetic catalyst design.

In the last several decades, significant efforts have been conducted to understand the fundamental reactivity of glucose derived from plant biomass in various chemical environments for conversion to renewable fuels and chemicals. For reactions of glucose in water, it is known that inorganic salts naturally present in biomass alter the product distribution in various deconstruction processes. However, the molecular-level interactions of alkali metal ions and glucose are unknown. These interactions are of physiological interest as well, for example, as they relate to cation-glucose cotransport. Here, we employ quantum mechanics (QM) to understand the interaction of a prevalent alkali metal, sodium, with glucose from a structural and thermodynamic perspective. The effect on β-glucose is subtle: a sodium ion perturbs bond lengths and atomic partial charges less than rotating a hydroxymethyl group. In contrast, the presence of a sodium ion significantly perturbs the partial charges of α-glucose anomeric and ring oxygens. Molecular dynamics (MD) simulations provide dynamic sampling in explicit water, and both the QM and the MD results show that sodium ions associate at many positions with respect to glucose with reasonably equivalent propensity. This promiscuous binding nature of Na(+) suggests that computational studies of glucose reactions in the presence of inorganic salts need to ensure thorough sampling of the cation positions, in addition to sampling glucose rotamers. The effect of NaCl on the relative populations of the anomers is experimentally quantified with light polarimetry. These results support the computational findings that Na(+) interacts similarly with α- and β-glucose.

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 IUPAC 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 ((4)C1) 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.

In vitro follicle growth has emerged as a technology that can provide new information about folliculogenesis and serve as part of a suite of methods currently under development to assist women whose fertility is threatened by cancer treatments. Though it has been shown that in vitro-grown follicles secrete peptide and steroid hormones, much of the follicular transcriptome remains unknown. Thus, microarray analysis was performed to characterize the transcriptome and secretome of in vitro-grown follicles. One prominently regulated gene product was cartilage oligomeric matrix protein (Comp): its mRNA was upregulated during the final 4 days of culture (P < 0.05) and COMP protein could be detected in medium from individual follicles. COMP expression localized to mural granulosa cells of large antral follicles both in vitro and in vivo, with maximal expression immediately preceding ovulation in cycling and chorionic gonadotropin-primed female mice. COMP was co-expressed with two known markers of follicle maturation, inhibin β(A) and gremlin, and was expressed only in TUNEL-negative follicles. In addition to other gene products identified in the microarray, COMP has potential utility as a marker of follicle maturation., (Copyright © 2012 Wiley Periodicals, Inc.)

Live-cell assays to measure cellular function performed within 3D cultures have the potential to elucidate the underlying processes behind disease progression and tissue formation. Cells cultured in 3D interact and remodel their microenvironment and can develop into complex structures. We have developed a transcription factor (TF) activity array that uses bioluminescence imaging (BLI) of lentiviral delivered luminescent reporter constructs that allows for the non-invasive imaging of TF activity in both 2D and 3D culture. Imaging can be applied repeatedly throughout culture to capture dynamic TF activity, though appropriate normalization is necessary. We investigated in-well normalization using Gaussia or Renilla luciferase, and external well normalization using firefly luciferase. Gaussia and Renilla luciferase were each unable to provide consistent normalization for long-term measurement of TF activity. However, external well normalization provided low variability and accounted for changes in cellular dynamics. Using external normalization, dynamic TF activities were quantified for five TFs. The array captured expected changes in TF activity to stimuli, however the array also provided dynamic profiles within 2D and 3D that have not been previously characterized. The development of the technology to dynamically track TF activity within cells cultured in both 2D and 3D can provide greater understanding of complex cellular processes., (Copyright © 2012 Wiley Periodicals, Inc.)

For over 90 years, researchers have postulated mechanisms for the cleavage of cellulose's glycosidic bonds and resulting formation of levoglucosan without reaching consensus. These reactions are key primary reactions in thermal processes for the production of carbon-neutral, renewable transportation fuels. Previous literature reports have proposed a variety of mainly heterolytic and homolytic mechanisms, but there has been insufficient evidence to settle the debate. Using density functional theory (DFT) methods and implicit solvent, we compared the likelihood of forming either radical or ionic intermediates. We discovered a concerted reaction mechanism that is more favorable than previously proposed mechanisms and is in better alignment with experimental findings. This new understanding of the mechanism of cellulose thermal decomposition opens the door to accurate process modeling and educated catalyst design, which are vital steps toward producing more cost-efficient renewable energy.

A series of metalloporphyrin dimers were modularly prepared and shown to catalyze the methanolysis of a phosphate triester, yielding rates that are large compared to the rate of the uncatalyzed reaction. Up to 1300-fold rate acceleration can be achieved via a combination of cavity-localized Lewis-acid activation and methoxide-induced methanolysis.

The microenviroment contributes to directing mammary epithelial cell (MEC) development and the progression of breast cancer. Three-dimensional culture models have been used to support formation of structures that display varying degrees of disorganization that parallel the degree of cancer. Synthetic hydrogels were employed to investigate the mechanisms by which specific adhesion signals in the microenvironment directed development. Polyethylene glycol-based hydrogels supported 3D growth of MECs and directed formation of a range of phenotypes that were functions of genotype, and identity and concentration of adhesion peptides RGD and YIGSR. Non-cancerous and cancerous MECs responded differentially to the same adhesion cues and produced variable structural organizations. An analysis of dynamic signaling pathways revealed differential activities of transcription factors within the MAPK and JAK/STAT pathways in response to genotype and adhesion. These results directly implicate adhesion in cancer development and demonstrate that AP1, CREB, STAT1, and STAT3 all contribute to the genotype dependence of cellular response to adhesion peptides. The tools presented in this work could be applied to other systems and connect extracellular cues with intracellular signaling to molecularly dissect tissue development and further biomaterials development., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Many natural and industrial processes involve a complex set of competing reactions that include several different species. Detailed kinetic modeling of such systems can shed light on the important pathways involved in various transformations and therefore can be used to optimize the process conditions for the desired product composition and properties. This review focuses on elucidating the various components involved in modeling the kinetics of pyrolysis and oxidation of polymers. The elementary free radical steps that constitute the chain reaction mechanism of gas-phase/nonpolar liquid-phase processes are outlined. Specification of the rate coefficients of the various reaction families, which is central to the theme of kinetics, is described. Construction of the reaction network on the basis of the types of end groups and reactive moieties in a polymer chain is discussed. Modeling frameworks based on the method of moments and kinetic Monte Carlo are evaluated using illustrations. Finally, the prospects and challenges in modeling biomass conversion are addressed.

The concept of generalized enzyme reactions suggests that a wide variety of substrates can undergo enzymatic transformations, including those whose biotransformation has not yet been realized. The use of quantum chemistry to evaluate kinetic feasibility is an attractive approach to identify enzymes for the proposed transformation. However, the sheer number of novel transformations that can be generated makes this impractical as a screening approach. Therefore, it is essential to develop structure/activity relationships based on quantities that are more efficient to calculate. In this work, we propose a structure/activity relationship based on the free energy of binding or reaction of non-native substrates to evaluate the catalysis relative to that of native substrates. While Brønsted-Evans-Polanyi (BEP) relationships such as that proposed here have found broad application in heterogeneous catalysis, their extension to enzymatic catalysis is limited. We report here on density functional theory (DFT) studies for C-C bond formation and C-C bond cleavage associated with the decarboxylation of six 2-keto acids by a thiamine-containing enzyme (EC 1.2.7.1) and demonstrate a linear relationship between the free energy of reaction and the activation barrier. We then applied this relationship to predict the activation barriers of 17 chemically similar novel reactions. These calculations reveal that there is a clear correlation between the free energy of formation of the transition state and the free energy of the reaction, suggesting that this method can be further extended to predict the kinetics of novel reactions through our computational framework for discovery of novel biochemical transformations.

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 (1)H and (13)C NMR chemical shifts in toluene-d(8). 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 (1)H 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 (13)C chemical shifts the 6-31G(d) basis set produced MUE of 1.82 ppm and RMSE of 3.29 ppm.

The thermochemical properties for selected hydrogenated silicon clusters (Si(x)H(y), 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., (© 2011 American Chemical Society)

We present a new computational strategy for the design and evaluation of novel enzymatic pathways for the biosynthesis of fuels and chemicals. The approach combines the use of the Biochemical Network Integrated Computational Explorer (BNICE) framework and a structure-based screening method for rapid generation and evaluation of novel enzymatic reactions and pathways. The strategy is applied to a case study of 1-butanol production from pyruvate, which yielded nine novel biosynthetic pathways. Using screening criteria based on pathway length, thermodynamic feasibility, and metabolic flux analysis, all nine novel pathways were deemed to be attractive candidates. To further assess their feasibility of implementation, we introduced a new screening criterion based on structural complementarity using molecular docking methods. We show that this approach correctly reproduces the native binding poses for a wide range of enzymes in key classes related to 1-butanol production and provides qualitative agreement with experimental measures of catalytic activity for different substrates. In addition, we show that the structure-based methods can be used to select specific proteins that may be promising candidates to catalyze novel reactions.

Kinetic parameters for the dominant pathways during the addition of the four Si(2)H(2) isomers, i.e., trans-HSiSiH, SiSiH(2), Si(H)SiH, and Si(H(2))Si, to monosilane, SiH(4), and disilane, Si(2)H(6), have been calculated using G3//B3LYP, statistical thermodynamics, conventional and variational transition state theory, and internal rotation corrections. The direct addition products of the multifunctional Si(2)H(2) isomers were monofunctional substituted silylenes, hydrogen-bridged species, and silenes. During addition to monosilane and disilane, the SiSiH(2) isomer was found to be most reactive over the temperature range of 800 to 1200 K. Revised parameters for the Evans-Polanyi correlation and a representative pre-exponential factor for multifunctional silicon hydride addition and elimination reaction families under pyrolysis conditions were regressed from the reactions in this study. This revised kinetic correlation was found to capture the activation energies and rate coefficients better than the current literature methods.

A molecular modeling strategy to screen the capacity of known enzymes to catalyze the reactions of non-native substrates is presented. The binding of pyruvic acid and non-native ketoacids in the active site of pyruvate ferredoxin oxidoreductase was examined using docking analysis, and our results suggest that enzyme-non-native ketoacid-bound species are feasible. Quantum mechanics/molecular mechanics methods were then used to study the geometry of the covalent intermediate formed from the enzyme and the various ketoacids. Finally, quantum mechanical methods were used to study the decarboxylation reaction of 2-keto acids at the mechanistic level. This hierarchical screening ranked the substrates from those that cannot be accommodated by the enzyme (phenyl pyruvate) to those whose conversion rate would most closely approach that of the native substrate (2-ketobutanoic acid and 2-ketovaleric acid). Most notably, our investigation suggests that novel pathways generated using generalized enzyme actions may be screened using the hierarchical approach employed here.

Identifying molecular mechanisms or therapeutic targets is typically based on large-scale cellular analysis that measures the abundance of mRNA or protein; however, abundance does not necessarily correlate with activity. We report a method for direct large-scale quantification of active pathways that employs a cellular array with parallel gene delivery of constructs that report pathway activity. The reporter constructs encode luciferase, whose expression is influenced by binding of transcription factors (TFs), which are the downstream targets of signaling pathways. Luciferase levels are quantified by bioluminescence imaging (BLI), which allows for rapid, non-invasive measurements. Activity profiles by BLI of 32 TFs were robust, consistent, and reproducible, and correlated with standard cell lysis techniques. The array identified five TFs with differential activity during phorbol-12-myristate-13-acetate (PMA)-induced differentiation of breast cancer cells. A system for rapid, large-scale, BLI quantification of pathway activity provides an enabling technology for mechanistic studies of cellular responses and processes., (2010 Wiley Periodicals, Inc.)

Background: Extracellular activation of signal transduction pathways and their downstream target transcription factors (TFs) are critical regulators of cellular processes and tissue development. The intracellular signaling network is complex, and techniques that quantify the activities of numerous pathways and connect their activities to the resulting phenotype would identify the signals and mechanisms regulating tissue development. The ability to investigate tissue development should capture the dynamic pathway activity and requires an environment that supports cellular organization into structures that mimic in vivo phenotypes. Taken together, our objective was to develop cellular arrays for dynamic, large-scale quantification of TF activity as cells organized into spherical structures within 3D culture., Methodology/principal Findings: TF-specific and normalization reporter constructs were delivered in parallel to a cellular array containing a well-established breast cancer cell line cultured in Matrigel. Bioluminescence imaging provided a rapid, non-invasive, and sensitive method to quantify luciferase levels, and was applied repeatedly on each sample to monitor dynamic activity. Arrays measuring 28 TFs identified up to 19 active, with 13 factors changing significantly over time. Stimulation of cells with β-estradiol or activin A resulted in differential TF activity profiles evolving from initial stimulation of the ligand. Many TFs changed as expected based on previous reports, yet arrays were able to replicate these results in a single experiment. Additionally, arrays identified TFs that had not previously been linked with activin A., Conclusions/significance: This system provides a method for large-scale, non-invasive, and dynamic quantification of signaling pathway activity as cells organize into structures. The arrays may find utility for investigating mechanisms regulating normal and abnormal tissue growth, biomaterial design, or as a platform for screening therapeutics.

The mechanism of H(2) addition and elimination reactions in selected silicon hydrides (Si(x)H(y), x = 1-10, y = 4-20) was modeled using quantum chemical calculations, statistical thermodynamics, transition state theory and transition state group additivity. Rate coefficients for 25 H(2) addition reactions were calculated using G3//B3LYP. For nearly every reaction, the overall conversion exhibits two steps. In the addition direction, the reactants first meet to form an adduct which then converts into a saturated silicon hydride via homolytic H-H bond cleavage. Values for the single-event Arrhenius pre-exponential factor, Ã, and the activation energy, E(a), were calculated from the G3//B3LYP rate coefficients, and a group additivity scheme was developed to predict à and E(a). The values predicted by group additivity are more accurate than kinetic correlations currently used in the literature, which rely on representative à values and the Evans-Polanyi correlation. The factors that have the most pronounced effect on à and E(a) were investigated, and stabilization of the divalent silicon atom of the unsaturated silicon hydride with electron-donating substituents was found to influence kinetic parameters considerably. The rate coefficients for H(2) addition reactions were found to correlate reasonably well with the difference in energy between the highest occupied molecular orbital of H(2) (E(HOMO)) and the lowest unoccupied molecular orbital of the reactant silylene (E(LUMO)).

Finding the energy minima of systems with constraints is a challenging problem. We develop a minimization method based on the projection operator technique to enforce distance and angle constraints in minimization and reaction-path dynamics. The application of the projection operator alone does not maintain the constraints, i.e., they are slightly violated. Therefore, we use the SHAKE-methodology to enforce the constraints after each minimization step. We have extended theta -SHAKE for bend angles and introduce phi-SHAKE and chi-SHAKE to constrain dihedral and out-of-plane angles, respectively. Two case studies are presented: (1) A mode analysis of united-atom n-butane with various internal degrees of freedom kept frozen and (2) the minimization of chromene at a fixed approach toward the catalytic site of a (salen)Mn. The obtained information on energetics can be used to explain why specific enantioselectivity is observed. Previous minimization methods work for the free molecular case, but fail when molecules are tightly confined.

The members of a homologous series of chiral compounds often show inconsistent enantioselectivities when separated on the same chiral stationary phase. The reasons behind this are subject to debate due to the lack of an efficient way to probe the molecular level separation mechanisms on conventional chiral stationary phases. Homochiral metal-organic frameworks (HMOFs) are a family of new porous, crystalline materials that can be used as chiral stationary phases. The regular structures of HMOFs make possible the detailed interrogation of chiral separation mechanisms through molecular modeling. In this report, molecular modeling techniques were used to study the enantioselectivities of a homologous series of chiral compounds in two different HMOFs. We demonstrate that a shift in the adsorption site is one reason for the lack of a correlation between the enantioselectivities of a homologous series of chiral compounds. In addition, we demonstrate that a smooth HMOF pore can help preserve the adsorption site and the separation mechanism, and hence achieve consistent enantioselectivity for a homologous series of chiral compounds.

Accurate rate coefficients for 40 bimolecular substituted silylene addition reactions for silicon hydrides containing up to nine silicon atoms are calculated using the G3//B3LYP method. The overall reactions exhibit two steps: the reactants first meet to form an adduct, which then converts into a saturated silicon hydride. Values for the single-event Arrhenius pre-exponential factor, A, and the activation energy, E(a), are calculated from the G3//B3LYP rate coefficients corrected for internal rotations, and a group additivity scheme is developed to predict A and E(a). The values predicted by group additivity are more accurate than structure-reactivity relationships currently used in the literature, which rely on representative A values and the Evans-Polanyi correlation. The structural factors that have the most pronounced effect on A and E(a) are considered, and the presence of rings is shown to influence these values strongly.

Sustainable microbial production of high-value organic compounds such as 3-hydroxypropanoate (3HP) is becoming an increasingly attractive alternative to organic syntheses that utilize petrochemical feedstocks. We applied the Biochemical Network Integrated Computational Explorer (BNICE) framework to the automated design and evaluation of novel biosynthetic routes for the production of 3HP from pyruvate. Among the pathways generated by the BNICE framework were all of the known pathways for the production of 3HP as well as numerous novel pathways. The pathways generated by BNICE were ranked based on four criteria: pathway length, thermodynamic feasibility, maximum achievable yield to 3HP from glucose, and maximum achievable activity at which 3HP can be produced. Four pathways emerged from this ranking as the most promising for the biosynthesis of 3HP, and three of these pathways, including the shortest pathways discovered, were novel. We also discovered novel routes for the biosynthesis of 28 commercially available compounds that are currently produced exclusively through organic synthesis. Examination of the optimal pathways for the biosynthesis of these 28 compounds in E. coli revealed pyruvate and succinate to be ideal intermediates for achieving high product yields from glucose., (2010 Wiley Periodicals, Inc.)