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Small molecule drugs target many small molecule metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism that enable easy navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism. Furthermore, this work challenges a fundamental dogma in modern molecular biology - the presumption that individual protein structural and chemical requirements are the dominant constraints in small molecule metabolic enzyme evolution. We directly test that assumption by weighing the absolute and relative constraints imposed by structural homology, metabolic pathway context, and transcriptional coregulation. We believe this work is the first to explicitly argue - from the molecular level perspective of genomic data - that selection constrains enzyme evolution as much at the level of metabolic pathway organization as it does at the level of individual protein structure.

Full electrochemical models are well-established as the standards for accurately predicting battery behavior. These are especially useful for cell design since performance can be evaluated based on various model parameters. Conventional models can be coupled with additional physics such as diffusion-induced stress, crack propagation, and battery degradation mechanisms to test different physical phenomena. However, these models are too computationally intensive to implement in battery management systems (BMSs), which utilize control and estimation algorithms to monitor performance and ensure safety. Single particle models (SPMs), which are computationally efficient approximations of pseudo-two-dimensional (P2D) models, are often used instead. However, most SPMs are only valid for low C-rates. In this paper, a new ODE-based SPM is proposed that captures high C-rate effects by including electrolyte physics and stress-diffusion coupling. Models describing these physics are validated by comparing simulation results to the P2D model at several C-rates. A single lumped parameter is used to quantify the intensity of the stress-diffusion coupling in an electrode. Simulation results are presented to show how variation of this parameter affects charge-discharge cycle times at different C-rates. Next, the improved SPM is linearized and presented as both state space and transfer function models. The frequency responses of the linearized SPM and P2D model are compared with and without stress-diffusion coupling. Further, the effects of varying the stress-diffusion coupling intensity on the frequency response are discussed. The proposed linearized model is more computationally efficient than full electrochemical and P2D models, and includes key physics that are missing in most low-order battery models. As a result, the proposed model is well-suited for a wide variety of BMS applications and offers improvements at high C-rates that are necessary for fast charging applications.

Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the “effect space” comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism., Author Summary All humans, plants, and animals use enzymes to metabolize food for energy, build and maintain the body, and get rid of toxins. Drugs used to clear infections or cure cancer often target enzymes in bacteria or cancer cells, but the drugs can interfere with the proper function of human enzymes as well. Recent studies have mapped drugs to enzymes and many other targets in humans and other organisms, but have not focused on metabolism. In this study, we present a new method to predict what enzymes drugs might affect based on the chemical similarity between classes of drugs and the natural chemicals used by enzymes. We have applied the method to 246 known drug classes and a collection of 385 organisms (including 65 National Institutes of Health Priority Pathogens) to create maps of potential drug action in metabolism. We also show how the predicted connections can be used to find new ways to kill pathogens and to avoid unintentionally interfering with human enzymes.

Library history is an important part of the library profession. Despite this, it seems to receive little attention in modern library school programs. This is unfortunate, because an understanding of library history can greatly enhance our understanding of why modern libraries have developed into what they are today. An analysis of one library is the easiest way to accomplish this as a single library can be seen as a microcosm of the larger library community. Based largely on archival research, this paper briefly reviews the history of the Washington and Lee University Library from 1938 to 2003 and evaluates it in relation to the development of academic libraries in general during that same period.

Incubation of peripheral blood leukocytes with arachidonic acid (and ionophore A23187) led to the formation of leukotriene B4, delta 6-trans-leukotriene B4, delta 6-trans-12-epi-leukotriene B4, 5-hydroxy-icosatetraenoic acid, 12-hydroxy-icosatetraenoic acid and of 5S,12S-dihydroxy-6,8,10,14-(E,Z,E,Z)-icosatetraenoic acid (5S,12S-DiHETE). Incubation of leukocytes with leukotriene A4 resulted in the formation of leukotriene B4 and of its two delta 6-trans-isomers but not of the 5S, 12S-DiHETE. 18O2 labeling experiments have shown that the hydroxyl groups at C5 and C12 in the 5S,12S-DiHETE are derived from molecular oxygen. The tetraacetylenic analog of arachidonic acid was found to be potent inhibitor of the formation of the 5S,12S-DiHETE whereas it potentiated the synthesis of the 5-hydroxy acid and of leukotriene B4. Addition of the 12-hydroxy-icosatetraenoic acid to leukocytes, or of the 5-hydroxy-icosatetraenoic acid to a suspension of platelets caused the formation of the 5S,12S-DiHETE. It is concluded that the 5S,12S-DiHETE is not derived from leukotriene A4 but is a product of the successive reactions of arachidonic acid with two lipoxygenases of different positional specificities.