Carbon clusters formed from shocked benzene.
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ABSTRACT:
SUBMITTER: Dattelbaum DM
PROVIDER: S-EPMC8410786 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Identifying Ca(2+) -binding sites in proteins is the first step toward understanding the molecular basis of diseases related to Ca(2+) -binding proteins. Currently, these sites are identified in structures either through X-ray crystallography or NMR analysis. However, Ca(2+) -binding sites are not always visible in X-ray structures due to flexibility in the binding region or low occupancy in a Ca(2+) -binding site. Similarly, both Ca(2+) and its ligand oxygens are not directly observed in NMR structures. To improve our ability to predict Ca(2+) -binding sites in both X-ray and NMR structures, we report a new graph theory algorithm (MUG(C) ) to predict Ca(2+) -binding sites. Using carbon atoms covalently bonded to the chelating oxygen atoms, and without explicit reference to side-chain oxygen ligand co-ordinates, MUG(C) is able to achieve 94% sensitivity with 76% selectivity on a dataset of X-ray structures composed of 43 Ca(2+) -binding proteins. Additionally, prediction of Ca(2+) -binding sites in NMR structures was obtained by MUG(C) using a different set of parameters, which were determined by the analysis of both Ca(2+) -constrained and unconstrained Ca(2+) -loaded structures derived from NMR data. MUG(C) identified 20 of 21 Ca(2+) -binding sites in NMR structures inferred without the use of Ca(2+) constraints. MUG(C) predictions are also highly selective for Ca(2+) -binding sites as analyses of binding sites for Mg(2+) , Zn(2+) , and Pb(2+) were not identified as Ca(2+) -binding sites. These results indicate that the geometric arrangement of the second-shell carbon cluster is sufficient not only for accurate identification of Ca(2+) -binding sites in NMR and X-ray structures but also for selective differentiation between Ca(2+) and other relevant divalent cations.
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Project description:Weak noncovalent intermolecular interactions play a pivotal role in many biological processes such as cell adhesion or immunology, where the overall binding strength is controlled through bond association and dissociation dynamics as well as the cooperative action of many parallel bonds. Among the various molecules participating in weak bonds, carbohydrate-carbohydrate interactions are probably the most ancient ones allowing individual cells to reversibly enter the multicellular state and to tell apart self and nonself cells. Here, we scrutinized the kinetics and thermodynamics of small homomeric Lewis X-Lewis X ensembles formed in the contact zone of a membrane-coated colloidal probe and a solid supported membrane ensuring minimal nonspecific background interactions. We used an atomic force microscope to measure force distance curves at Piconewton resolution, which allowed us to measure the force due to unbinding of the colloidal probe and the planar membrane as a function of contact time. Applying a contact model, we could estimate the free binding energy of the formed adhesion cluster as a function of dwell time and thereby determine the precise size of the contact zone, the number of participating bonds, and the intrinsic rates of association and dissociation in the presence of calcium ions. The unbinding energy per bond was found to be on the order of 1 kBT. Approximately 30 bonds were opened simultaneously at an off-rate of koff = 7 ± 0.2 s(-1).
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Project description:PurposeDuring gas station operation, unburned fuel can be released to the environment through distribution, delivery, and storage. Due to the toxicity of fuel compounds, setback distances have been implemented to protect the general population. However, these distances treat gasoline sales volume as a categorical variable and only account for the presence of a single gas station and not clusters, which frequently occur. This paper introduces a framework for recommending setback distances for gas station clusters based on estimated lifetime cancer risk from benzene exposure.MethodsUsing the air quality dispersion model AERMOD, we simulated levels of benzene released to the atmosphere from single and clusters of generic gas stations and the associated lifetime cancer risk under meteorological conditions representative of Albany, New York.ResultsCancer risk as a function of distance from gas station(s) and as a continuous function of total sales volume can be estimated from an equation we developed. We found that clusters of gas stations have increased cancer risk compared to a single station because of cumulative emissions from the individual gas stations. For instance, the cancer risk at 40 m for four gas stations each dispensing 1 million gal/year is 9.84 × 10-6 compared to 2.45 × 10-6 for one gas station.ConclusionThe framework we developed for estimating cancer risk from gas station(s) could be adopted by regulatory agencies to make setback distances a function of sales volume and the number of gas stations in a cluster, rather than on a sales volume category.Supplementary informationThe online version contains supplementary material available at 10.1007/s40201-020-00601-w.
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2013-08-15 | GSE49402 | GEO