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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Transforming our understanding of chloroplast-associated genes through comprehensive characterization of protein localizations and protein-protein interactions

Bioenergy crops are a renewable source of fuels and are a critical base for building a carbon-neutral economy. Rational engineering of bioenergy crops has the potential to enhance the yields. However, our ability to engineer plants is limited because the functions of most genes remain unknown. Systematic characterization of gene function in plants thus has the potential to greatly accelerate bioenergy research. Here, we focus on the chloroplast, an underexplored energy-producing organelle that is a hallmark of plants. The chloroplast is one of the promising targets of biofuel crop engineering efforts because of its central role in photosynthesis, metabolism, and intracellular signaling. However, the protein composition of the chloroplast and the functions of most of its proteins remain poorly characterized. At the core of this project, we sought to comprehensively determine the localization of chloroplast-associated proteins and generate a spatially defined protein-protein interaction network for chloroplast. For this purpose, we used the leading model alga Chlamydomonas reinhardtii, which greatly increased experimental speed and throughput. We illustrated the value of our findings to land plants by determining the localization of Arabidopsis thaliana land plant homologs of the Chlamydomonas proteins. Altogether, we were successful in determining the localization of 1,034 chloroplast-associated proteins in Chlamydomonas. The localizations provide numerous insights into the spatial organization of chloroplasts and how they function to support photosynthesis. The localization patterns of distinct proteins revealed new chloroplast structures and revealed new spatial organization inside the chloroplast. We also identified new components of known chloroplast structures, such as the chloroplast envelope, nucleoid, plastoglobuli, and pyrenoid. We identified these new components by investigating the interacting partners of known proteins. Many proteins localized in both the chloroplast and other cellular structures, thereby hinting at new functions and communication between cellular structures. We also applied machine learning on the atlas to generate predictions for the location of all of the proteins in Chlamydomonas. This enabled us to assign putative functions to many uncharacterized proteins based on their cellular location. Altogether, this research establishes a rich resource that opens new avenues of investigation and guides future work in deciphering and manipulating chloroplast function. Next, we developed an extensive protein-protein interaction network for the chloroplast by performing affinity purification-mass spectrometry on ~1,150 tagged chloroplast-associated proteins, the first such large-scale study in any photosynthetic organism. This dataset reveals 4,694 high-confidence protein-protein interactions, offering insights into the functions of thousands of conserved poorly-characterized chloroplast proteins. This systematic identification of protein-protein interactions in the chloroplast also provides multiple exciting new research directions and a detailed blueprint of the chloroplast's operation. This research lays the groundwork to decipher the inner workings of the chloroplast, the cell structure at the heart of photosynthesis. The spatial atlas and protein-protein interactions reveal chloroplast organizational features that would not have been accessible with traditional approaches. The localization mapping, insights into the function, and research materials generated further provide a rich resource for the research community to advance the understanding of how the chloroplast is organized to enable engineering of enhanced photosynthetic organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Structure-toxicity relationship of cefoperazone and its impurities to developing zebrafish by transcriptome and Raman analysis

Cefoperazone (CFP) is a potent antibacterial agent that is widely used for the treatment of bacterial infections. Previously, we found that both the C-7 and C-3 substituents of CFP are toxic functional groups, and two groups could affect gene expression in zebrafish embryos, thereby resulting in variable abnormal phenotypes. (6R, 7S)-cefoperazone (7S-CFP) is the 7-epimer of CFP and 1-methyl-1H-tetrazole-5-thiol (MTT) is the C-3 substituent of CFP. Both molecules are impurities isolated from CFP that can induce adverse effects. Transcriptome analysis was performed in the present study to identify differentially expressed genes (DEGs), coupled with Raman mapping of individual organ regions to detect changes in the biochemical composition of zebrafish embryos, which reflect the differences in distribution of the compounds. CFP, 7S–CFP, and MTT exposure altered the expression of 254, 368, and 1153 genes, respectively. Gene ontology analysis revealed that various processes related to development, growth, and morphology of tissues were significantly enriched with DEGs. We integrated seven co-DEGs with protein-protein interaction networks and identified various developmental processes that were regulated by the three compounds, including vasodilation, eye, brain, melanogenesis, and heart looping. Our findings suggested that Calca and Ptger4a may be potentially utilized as novel biomarkers for CFP, which causes bleeding. Raman analysis indicated that CFP, 7S-CFP, and MTT exhibited abnormal maps in tissues, which coincided with changes in their expression and morphological features. This study may provide bioinformatics and spectral information that may be used in further investigations on the relationship between structure and toxicity of drugs and impurities. - Highlights: • We studied the relationship between the structure and toxicity of cefoperazone. • Transcriptome strategy was performed to identify differentially expressed genes. • We integrated seven co-DEGs with protein-protein interaction networks. • Our results founded the novel biomarkers for cefoperazone, which causes bleeding. • Raman analysis indicated that three compounds exhibited abnormal maps in tissues.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Comparative proteomic analysis of outer membrane vesicles from Shigella flexneri under different culture conditions

Highlights: • We utilized mTRAQ-based quantification to study protein changes in Congo red-induced OMVs. • A total of 148 proteins were identified in S. flexneri-derived OMVs. • Twenty-eight and five proteins are significantly up- and down-regulated in the CR-induced OMV, respectively. • The result implied that a special sorting mechanism of particular proteins into OMVs may exist. • Key node proteins in the protein interaction network might be important for pathogenicity. - Abstract: The production of outer membrane vesicles (OMVs) is a common and regulated process of gram-negative bacteria. Nonetheless, the processes of Shigella flexneri OMV production still remain unclear. S. flexneri is the causative agent of endemic shigellosis in developing countries. The Congo red binding of strains is associated with increased infectivity of S. flexneri. Therefore, understanding the modulation pattern of OMV protein expression induced by Congo red will help to elucidate the bacterial pathogenesis. In the present study, we investigated the proteomic composition of OMVs and the change in OMV protein expression induced by Congo red using mTRAQ-based quantitative comparative proteomics. mTRAQ labelling increased the confidence in protein identification, and 148 total proteins were identified in S. flexneri-derived OMVs. These include a variety of important virulence factors, including Ipa proteins, TolC family, murein hydrolases, and members of the serine protease autotransporters of Enterobacteriaceae (SPATEs) family. Among the identified proteins, 28 and five proteins are significantly up- and down-regulated in the Congo red-induced OMV, respectively. Additionally, by comprehensive comparison with previous studies focused on DH5a-derived OMV, we identified some key node proteins in the protein–protein interaction network that may be involved in OMV biogenesis and are common to all gram-negative bacteria.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Direct Observation of Xe and Kr Adsorption in a Xe-Selective Microporous Metal–Organic Framework

The cryogenic separation of noble gases is energy-intensive and expensive, especially when low concentrations are involved. Metal organic frameworks containing polarizing groups within their pore spaces are predicted to be efficient Xe/Kr solid state adsorbents but no experimental insights on the nature of the Xe-network interaction is available to date. Here we report a new microporous metal organic framework (designated SBU2) that is selective toward Xe over Kr at ambient conditions, with Xe/Kr selectivity of about 10 and Xe capacity of 37.1 wt% at 298 K. Results of single crystal diffraction show Xe selectivity may be attributed to the specific geometry of the pores, forming cages built with phenyl rings and enriched with polar -OH groups, both serving as strong adsorption sites for polarizable Xe gas. The Xe/Kr separation in SBU2 was investigated with experimental and computational breakthrough methods. These experiments showed that Xe broke through the column after 3 min, followed by Kr at 4.5 min after injection of gases mixture, which confirmed that SBU2 has a real practical potential for separating Xe from Kr. Computational calculations show that SBU2 capacity and adsorption selectivity is comparable to the best-performing unmodified MOFs such as Ni-MOF-74 or Co Formate.

Xenon, Krypton↗
DOE OSTI2013

Evolutionary analyses of non-family genes in plants

There are a large number of non-family (NF) genes that do not cluster into families with three or more members per genome. While gene families have been extensively studied, a systematic analysis of NF genes has not been reported. We performed comparative studies on NF genes in 14 plant species. Based on the clustering of protein sequences, we identified ~94,000 NF genes across these species that were divided into five evolutionary groups: Viridiplantae-wide, angiosperm-specific, monocot-specific, dicot-specific, and those that were species-specific. Our analysis revealed that the NF genes resulted largely from less frequent gene duplications and/or a higher rate of gene loss after segmental duplication relative to genes in both low-copy-number families (LF; 3 10 copies per genome) and high-copy-number families (HF; >10 copies). Furthermore, we identified functions enriched in the NF gene set as compared with the HF genes. We found that NF genes were involved in essential biological processes shared by all plant lineages (e.g., photosynthesis and translation), as well as gene regulation and stress responses associated with phylogenetic diversification. In particular, our analysis of an Arabidopsis protein-protein interaction network revealed that hub proteins with the top 10% most connections were over-represented in the NF set relative to the HF set. This research highlights the roles that NF genes may play in evolutionary and functional genomics research.

Ye, Chuyu↗
DOE OSTI2020

The functional roles of species in metacommunities, as revealed by metanetwork analyses of bird–plant frugivory networks

Abstract Understanding how biodiversity and interaction networks change across environmental gradients is a major challenge in ecology. We integrated metacommunity and metanetwork perspectives to test species’ functional roles in bird–plant frugivory interactions in a fragmented forest landscape in Southwest China, with consequences for seed dispersal. Availability of fruit resources both on and under trees created vertical feeding stratification for frugivorous birds. Bird–plant interactions involving birds feeding only on‐the‐tree or both on and under‐the‐tree (shared) had a higher centrality and contributed more to metanetwork organisation than interactions involving birds feeding only under‐the‐tree. Moreover, bird–plant interactions associated with large‐seeded plants disproportionately contributed to metanetwork organisation and centrality. Consequently, on‐the‐tree and shared birds contributed more to metanetwork organisation whereas under‐the‐tree birds were more involved in local processes. We would expect that species’ roles in the metanetwork will translate into different conservation values for maintaining functioning of seed‐dispersal networks.

Li, Hai‐Dong↗
DOE OSTI2017

Collaborative Research: Quantifying Climate Feedbacks of the Terrestrial Biosphere under Thawing Permafrost Conditions in the Arctic

Our overall goal in this research was to quantify the potential for threshold changes in natural emission rates of trace gases, particularly methane and carbon dioxide, from pan-arctic terrestrial systems under the spectrum of anthropogenically-forced climate warming, and the conditions under which these emissions provide a strong feedback mechanism to global climate warming. This goal was motivated under the premise that polar amplification of global climate warming will induce widespread thaw and degradation of the permafrost, and would thus cause substantial changes to the landscape of wetlands and lakes, especially thermokarst (thaw) lakes, across the Arctic. Through a suite of numerical experiments that encapsulate the fundamental processes governing methane emissions and carbon exchanges – as well as their coupling to the global climate system - we tested the following hypothesis in the proposed research: There exists a climate warming threshold beyond which permafrost degradation becomes widespread and stimulates large increases in methane emissions (via thermokarst lakes and poorly-drained wetland areas upon thawing permafrost along with microbial metabolic responses to higher temperatures) and increases in carbon dioxide emissions from well-drained areas. Besides changes in biogeochemistry, this threshold will also influence global energy dynamics through effects on surface albedo, evapotranspiration and water vapor. These changes would outweigh any increased uptake of carbon (e.g. from peatlands and higher plant photosynthesis) and would result in a strong, positive feedback to global climate warming. In collaboration with our Purdue and MIT colleagues, we have attempted to quantify global climate warming effects on land-atmosphere interactions, land-river network interactions, permafrost degradation, vegetation shifts, and land use influence water, carbon, and nitrogen fluxes to and from terrestrial ecosystems in the pan-arctic along with their uncertainties. Based on our study results along with a review of observed and projected climate changes in Northern Eurasia by others, we have also outlined a more integrated modelling approach that may be developed and applied in future studies to better capture the influence of earth system feedbacks and human activities on the evolution of climate change effects over time. Specifically, we have examined: 1) how evapotranspiration and water availability have been changing in Northern Eurasia and may change in the future including the impact of forcing uncertainties (Liu et al., 2013, 2014, 2015); 2) how soil consumption of atmospheric methane across the globe have been influenced and may be influenced by climate change and nitrogen deposition during the 20th and 21st centuries (Zhuang et al., 2013); 3) how wetland inundation extent influences net CO2 and CH4 fluxes from northern high latitudes (Zhuang et al., 2015); 4) the relative effects of various environmental factors (including permafrost degradation) on terrestrial dissolved organic carbon (DOC) loading of river networks across the pan-Arctic and how they have changed over the 20th century (Kicklighter et al., 2013); 5) the impacts of recent and future permafrost thaw on land-atmosphere greenhouse gas exchange across the pan-Arctic (Gao et al., 2012, 2013; Hayes et al., 2014; Kicklighter et al. 2015a, 2018); 6) how climate-induced vegetation shifts may affect carbon fluxes and future land use in Northern Eurasia (Jiang et al., 2012, 2016; Kicklighter et al., 2014a) and the globe (Zhuang et al. 2015b); 7) the relative importance of legacies from past land use, future land-use change and climate change on projections of terrestrial carbon fluxes (Monier et al., 2015; Kicklighter et al., 2016); and 8) how the effects of earth system feedbacks and human activities can be better incorporated in assessments of climate change impacts (Monier et al., 2017; Groisman et al., 2018).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Identification and characterization of circular RNAs in rapid atrial pacing dog atrial tissue

Highlights: • Revealed the expression characterization of circRNAs in dog atrial tissue. • Compared differentially expressed circRNAs between atrial fibrillation and control dog. • Found extensive interaction among circRNAs and AF related miRNAs and mRNAs. Circular RNAs (circRNAs) have emerged as novel molecules of interest in gene regulation as other noncoding RNAs, and participating in the process of many diseases. However, the expression and functions of circRNAs in Rapid atrial pacing (RAP) dog atrial tissue still unknown. 12 canines were randomly assigned to control and pacing group. RAP at 500 beats per minute was maintained 14 days in the pacing group. The expression characterization of circRNAs were revealed by high-throughput sequencing. We totally predicted 15,990 circRNAs in dog atrial tissues. Moreover, we found 146 differentially expressed circRNAs between control and RAP dogs. Five circRNAs were selected for subsequent RT-PCR validation, and four circRNAs confirmed with the high throughput sequencing analysis. GO analysis showed that the differentially expressed circRNAs might involve in the process of “structural constituent of cytoskeleton, ion channel activity”. We explored the circRNA-miRNA interaction network, and found extensive interaction among differentially expressed circRNAs and AF related miRNAs and mRNAs. Our work firstly identified the characterization of circRNAs in the dog atrial, and revealed the differentially expressed circRNAs in the RAP dog, this might lay a solid foundation on the function of circRNA in the mechanisms of AF.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks

Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein–protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL’s scalability to cluster large datasets still remains a bottleneck due to high running times and memory demands. In this paper, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ~70 million nodes with ~68 billion edges in ~2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. Finally, HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Indira: intent-based networking and rendering for networks (Indira) v1

Intelligent Network Deployment Intent Renderer Application an interactive network assistant that will help us configure a data path between two scientific end-point groups (EPGs) to optimize the transfer of elephant data flows. This tool is able to talk to scientists gather network requirements and then convert to network programming code and automate the end-to-end provisioning.

Pouyoul, Eric↗
DOE OSTI2020

In Silico Insights into Protein–Protein Interaction Disruptive Mutations in the PCSK9-LDLR Complex

Gain-of-function mutations in PCSK9 (proprotein convertase subtilisin/kexin type 9) lead to reduced uptake of LDL (low density lipoprotein) cholesterol and, therefore, increased plasma LDL levels. However, the mechanism by which these mutants reduce LDL reuptake is not fully understood. Here, we have used molecular dynamics simulations, MM/PBSA (Molecular Mechanics/Poisson–Boltzmann Surface Area) binding affinity calculations, and residue interaction networks, to investigate the protein–protein interaction (PPI) disruptive effects of two of PCSK9's gain-of-function mutations, Ser127Arg and Asp374Tyr on the PCSK9 and LDL receptor complex. In addition to these PPI disruptive mutants, a third, non-interface mutation (Arg496Trp) is included as a positive control. Our results indicate that Ser127Arg and Asp374Tyr confer significantly improved binding affinity, as well as different binding modes, when compared to the wild-type. These PPI disruptive mutations lie between the EGF(A) (epidermal growth factor precursor homology domain A) of the LDL receptor and the catalytic domain of PCSK9 (Asp374Tyr) and between the prodomain of PCSK9 and the β-propeller of the LDL receptor (Ser127Arg). The interactions involved in these two interfaces result in an LDL receptor that is sterically inhibited from entering its closed conformation. This could potentially implicate the prodomain as a target for small molecule inhibitors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Loads Bias Genetic and Signaling Switches in Synthetic and Natural Systems

Biological protein interactions networks such as signal transduction or gene transcription networks are often treated as modular, allowing motifs to be analyzed in isolation from the rest of the network. Modularity is also a key assumption in synthetic biology, where it is similarly expected that when network motifs are combined together, they do not lose their essential characteristics. However, the interactions that a network module has with downstream elements change the dynamical equations describing the upstream module and thus may change the dynamic and static properties of the upstream circuit even without explicit feedback. In this work we analyze the behavior of a ubiquitous motif in gene transcription and signal transduction circuits: the switch. We show that adding an additional downstream component to the simple genetic toggle switch changes its dynamical properties by changing the underlying potential energy landscape, and skewing it in favor of the unloaded side, and in some situations adding loads to the genetic switch can also abrogate bistable behavior. We find that an additional positive feedback motif found in naturally occurring toggle switches could tune the potential energy landscape in a desirable manner. We also analyze autocatalytic signal transduction switches and show that a ubiquitous positive feedback switch can lose its switch-like properties when connected to a downstream load. Our analysis underscores the necessity of incorporating the effects of downstream components when understanding the physics of biochemical network motifs, and raises the question as to how these effects are managed in real biological systems. This analysis is particularly important when scaling synthetic networks to more complex organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Case Study of the Glycoside Hydrolase Enzyme Mechanism Using an Automated QM-Cluster Model Building Toolkit

Glycoside hydrolase enzymes are important for hydrolyzing the β-1,4 glycosidic bond in polysaccharides for deconstruction of carbohydrates. The two-step retaining reaction mechanism of Glycoside Hydrolase Family 7 (GH7) was explored with different sized QM-cluster models built by the Residue Interaction Network ResidUe Selector (RINRUS) software using both the wild-type protein and its E217Q mutant. The first step is the glycosylation, in which the acidic residue 217 donates a proton to the glycosidic oxygen leading to bond cleavage. In the subsequent deglycosylation step, one water molecule migrates into the active site and attacks the anomeric carbon. Residue interaction-based QM-cluster models lead to reliable structural and energetic results for proposed glycoside hydrolase mechanisms. The free energies of activation for glycosylation in the largest QM-cluster models were predicted to be 19.5 and 31.4 kcal mol −1 for the wild-type protein and its E217Q mutant, which agree with experimental trends that mutation of the acidic residue Glu217 to Gln will slow down the reaction; and are higher in free energy than the deglycosylation transition states (13.8 and 25.5 kcal mol −1 for the wild-type protein and its mutant, respectively). For the mutated protein, glycosylation led to a low-energy product. This thermodynamic sink may correspond to the intermediate state which was isolated in the X-ray crystal structure. Hence, the glycosylation is validated to be the rate-limiting step in both the wild-type and mutated enzyme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes

Motivation: Alignment of protein–protein interaction networks can be used for the unsupervised prediction of functional modules, such as protein complexes and signaling pathways, that are conserved across different species. To date, various algorithms have been proposed for biological network alignment, many of which attempt to incorporate topological similarity between the networks into the alignment process with the goal of constructing accurate and biologically meaningful alignments. Especially, random walk models have been shown to be effective for quantifying the global topological relatedness between nodes that belong to different networks by diffusing node-level similarity along the interaction edges. However, these schemes are not ideal for capturing the local topological similarity between nodes. Results: Here, we propose MONACO, a novel and versatile network alignment algorithm that finds highly accurate pairwise and multiple network alignments through the iterative optimal matching of ‘local’ neighborhoods around focal nodes. Extensive performance assessment based on real networks as well as synthetic networks, for which the ground truth is known, demonstrates that MONACO clearly and consistently outperforms all other state-of-the-art network alignment algorithms that we have tested, in terms of accuracy, coherence and topological quality of the aligned network regions. Furthermore, despite the sharply enhanced alignment accuracy, MONACO remains computationally efficient and it scales well with increasing size and number of networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network

Spin glasses, generally defined as disordered systems with randomized competing interactions, are a widely investigated complex system. Theoretical models describing spin glasses are broadly used in other complex systems, such as those describing brain function, error-correcting codes5 or stock-market dynamics. This wide interest in spin glasses provides strong motivation to generate an artificial spin glass within the framework of artificial spin ice systems. Here we present the experimental realization of an artificial spin glass consisting of dipolar coupled single-domain Ising-type nanomagnets arranged onto an interaction network that replicates the aspects of a Hopfield neural network. Using cryogenic X-ray photoemission electron microscopy (XPEEM), we performed temperature-dependent imaging of thermally driven moment fluctuations within these networks and observed characteristic features of a two-dimensional Ising spin glass. Specifically, the temperature dependence of the spin glass correlation function follows a power-law trend predicted from theoretical models on two-dimensional spin glasses. As a result, we observe clear signatures of the hard-to-observe rugged spin glass free energy in the form of sub-aging, out-of-equilibrium autocorrelations and a transition from stable to unstable dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Stereoselective synthesis of a 4-α-glucoside of valienamine and its X-ray structure in complex with Streptomyces coelicolor GlgE1-V279S

Glycoside hydrolases (GH) are a large family of hydrolytic enzymes found in all domains of life. As such, they control a plethora of normal and pathogenic biological functions. Thus, understanding selective inhibition of GH enzymes at the atomic level can lead to the identification of new classes of therapeutics. In these studies, we identified a 4-α-glucoside of valienamine (8) as an inhibitor of Streptomyces coelicolor (Sco) GlgE1-V279S which belongs to the GH13 Carbohydrate Active EnZyme family. The results obtained from the dose–response experiments show that 8 at a concentration of 1000 µM reduced the enzyme activity of Sco GlgE1-V279S by 65%. The synthetic route to 8 and a closely related 4-α-glucoside of validamine (7) was achieved starting from readily available D-maltose. A key step in the synthesis was a chelation-controlled addition of vinylmagnesium bromide to a maltose-derived enone intermediate. X-ray structures of both 7 and 8 in complex with Sco GlgE1-V279S were solved to resolutions of 1.75 and 1.83 Å, respectively. Structural analysis revealed the valienamine derivative 8 binds the enzyme in an E 2 conformation for the cyclohexene fragment. Also, the cyclohexene fragment shows a new hydrogen-bonding contact from the pseudo-diaxial C(3)–OH to the catalytic nucleophile Asp 394 at the enzyme active site. Asp 394, in fact, forms a bidentate interaction with both the C(3)–OH and C(7)-OH of the inhibitor. In contrast, compound 7 disrupts the catalytic sidechain interaction network of Sco GlgE1-V279S via steric interactions resulting in a conformation change in Asp 394. These findings will have implications for the design other aminocarbasugar-based GH13-inhibitors and will be useful for identifying more potent and selective inhibitors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Radius measurement via super-resolution microscopy enables the development of a variable radii proximity labeling platform

The elucidation of protein interaction networks is critical to understanding fundamental biology as well as developing new therapeutics. Proximity labeling platforms (PLPs) are state-of-the-art technologies that enable the discovery and delineation of biomolecular networks through the identification of protein-protein interactions. These platforms work via catalytic generation of reactive probes at a biological region of interest; these probes then diffuse through solution and covalently “tag” proximal biomolecules. The physical distance that the probes diffuse determines the effective labeling radius of the PLP and is a critical parameter that influences the scale and resolution of interactome mapping. As such, by expanding the degrees of labeling resolution offered by PLPs, it is possible to better capture the various size scales of interactomes. At present, however, there is little quantitative understanding of the labeling radii of different PLPs. Here, we report the development of a superresolution microscopy-based assay for the direct quantification of PLP labeling radii. Using this assay, we provide direct extracellular measurements of the labeling radii of state-of-the-art antibody-targeted PLPs, including the peroxidase-based phenoxy radical platform (269 ± 41 nm) and the high-resolution iridium-catalyzed µMap technology (54 ± 12 nm). Last, we apply these insights to the development of a molecular diffusion-based approach to tuning PLP resolution and introduce a new aryl-azide-based µMap platform with an intermediate labeling radius (80 ± 28 nm).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗