scholarly journals On the time variability of the HH jet ejection process

2010 ◽  
Vol 6 (S275) ◽  
pp. 392-395
Author(s):  
Fabio De Colle
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Velocity Variation ◽  
Time Variability ◽  
Ejection Process ◽  
Regularization Techniques ◽  
History Of ◽  
Density Map ◽  
Electron Density Map

AbstractTwo-dimensional emission line images of the HH30 jet were recently used (De Colle et al. 2010) to recover the three-dimensional structure of the jet by applying standard tomographic technique (“Tikhonov regularization techniques”). In this paper I show that it is possible to determine the ejection history of the HH30 jet by directly comparing the outcome of numerical simulations with the results of the tomographic inversion. In particular, it is shown that the HH30 jet electron density map is best reproduced by assuming a velocity variation at the base of the jet with a large scale periodicity (with a period of ~3 yrs) added to small scales velocity variation (with periods ≲months).

The low-resolution structure of human muscle aldolase

1981 ◽  
Vol 293 (1063) ◽  
pp. 209-214 ◽  
Keyword(s):  
Domain Structure ◽  
Electron Density ◽  
Heavy Atom ◽  
Three Dimensional ◽  
Human Muscle ◽  
Dimensional Structure ◽  
Density Map ◽  
Electron Density Map ◽  
Spherical Molecule ◽  
Resolution Structure

The three-dimensional structure of human muscle aldolase has been solved at 5 A resolution with the use of two isomorphous heavy atom derivatives. The enzyme’s four subunits are arranged about three mutually perpendicular intersecting twofold axes to form a compact spherical molecule. The subunit boundaries are clearly defined but a possible domain structure is not apparent in this preliminary electron density map.

Effect of Fibrinolytic Compound GDG on Crystal Structure of Lysozyme

2012 ◽  
Vol 554-556 ◽  
pp. 1768-1775
Author(s):  
Yong Xian Wang ◽  
Wen Hui Wu ◽  
Bin Bao ◽  
Rui Rui Song ◽  
Li Chun Sun
Keyword(s):  
Crystal Size ◽  
Main Chain ◽  
Protein Crystallization ◽  
Three Dimensional ◽  
Intrinsic Activity ◽  
Dimensional Structure ◽  
Sodium Acetate Buffer ◽  
Synchrotron Radiation Diffraction ◽  
Density Map ◽  
Electron Density Map

Lysozyme was extensively studied as a model of protein crystallization, we identified that 15 mg/ml lysozyme, 0.1 M sodium acetate buffer containing 5% (w/v) sodium chloride and 0.02% (w/v) sodium azide, pH 5.2, crystal nucleus were less and crystals could reach a certain size. Basic on GDG effected conformation of enzyme which enhancing the reciprocal activation of plasminogen and prourokinase via the elevation of intrinsic activity of prourokinase, we obtained the crystal of lysozyme added α-D-glucopyranoslydiacylglycerol (GDG), GDG: HEWL (c/c) = 2:1, 1:1, 1:2, performed the synchrotron radiation diffraction and collected the three dimensional structure data. It is showing that increased in number of crystal amount and decreased in volume of crystal size in crystallization of lysozyme accompanied by the concentration of GDG. It is not displayed effect on the main chain and most side chains of lysozyme in the presence of GDG in the electron density map.

The iron content of iron superoxide dismutase: determination by anomalous scattering

1983 ◽  
Vol 218 (1210) ◽  
pp. 119-126 ◽  
Keyword(s):  
Superoxide Dismutase ◽  
Iron Content ◽  
Three Dimensional ◽  
Anomalous Scattering ◽  
Total Iron Content ◽  
Iron Site ◽  
Iron Superoxide Dismutase ◽  
Density Map ◽  
The Difference ◽  
Electron Density Map

The number of iron atoms in the dimeric iron-containing superoxide dismutase from Pseudomonas ovalis and their atomic positions have been determined directly from anomalous scattering measurements on crystals of the native enzyme. To resolve the long-standing question of the total amount of iron per molecule for this class of dismutase, the occupancy of each site was refined against the measured Bijvoet differences. The enzyme is a symmetrical dimer with one iron site in each subunit. The iron position is 9 ņ from the intersubunit interface. The total iron content of the dimer is 1.2±0.2 moles per mole of protein. This is divided between the subunits in the ratio 0.65:0.55; the difference between them is probably not significant. Since each subunit contains, on average, slightly more than half an iron atom we conclude that the normal state of this enzyme is two iron atoms per dimer but that some of the metal is lost during purification of the protein. Although the crystals are obviously a mixture of holo- and apo-enzymes, the 2.9 Å electron density map is uniformly clean, even at the iron site. We conclude that the three-dimensional structures of the iron-bound enzyme and the apoenzyme are identical.

scholarly journals Biophysical and biochemical investigations of RNA catalysis in the hammerhead ribozyme

1999 ◽  
Vol 32 (3) ◽  
pp. 241-284 ◽  
Author(s):  
William G. Scott
Keyword(s):  
Metal Ions ◽  
Conformational Change ◽  
Large Scale ◽  
Enzyme Inhibitor ◽  
Hammerhead Ribozyme ◽  
Three Dimensional ◽  
Chemical Mechanism ◽  
Dimensional Structure ◽  
Three Dimensional Structure ◽  
Hammerhead Rna

1. How do ribozymes work? 2412. The hammerhead RNA as a prototype ribozyme 2422.1 RNA enzymes 2422.2 Satellite self-cleaving RNAs 2422.3 Hammerhead RNAs and hammerhead ribozymes 2443. The chemical mechanism of hammerhead RNA self-cleavage 2463.1 Phosphodiester isomerization via an SN2(P) reaction 2473.2 The canonical role of divalent metal ions in the hammerhead ribozyme reaction 2513.3 The hammerhead ribozyme does not actually require metal ions for catalysis 2543.4 Hammerhead RNA enzyme kinetics 2574. Sequence requirements for hammerhead RNA self-cleavage 2604.1 The conserved core, mutagenesis and functional group modifications 2604.2 Ground-state vs. transition-state effects 2615. The three-dimensional structure of the hammerhead ribozyme 2625.1 Enzyme–inhibitor complexes 2625.2 Enzyme–substrate complex in the initial state 2645.3 Hammerhead ribozyme self-cleavage in the crystal 2645.4 The requirement for a conformational change 2655.5 Capture of conformational intermediates using crystallographic freeze-trapping 2665.6 The structure of a hammerhead ribozyme ‘early’ conformational intermediate 2675.7 The structure of a hammerhead ribozyme ‘later’ conformational intermediate 2685.8 Is the conformational change pH dependent? 2695.9 Isolating the structure of the cleavage product 2715.10 Evidence for and against additional large-scale conformation changes 2745.11 NMR spectroscopic studies of the hammerhead ribozyme 2786. Concluding remarks 2807. Acknowledgements 2818. References 2811. How do ribozymes work? 241The discovery that RNA can be an enzyme (Guerrier-Takada et al. 1983; Zaug & Cech, 1986) has created the fundamental question of how RNA enzymes work. Before this discovery, it was generally assumed that proteins were the only biopolymers that had sufficient complexity and chemical heterogeneity to catalyze biochemical reactions. Clearly, RNA can adopt sufficiently complex tertiary structures that make catalysis possible. How does the three- dimensional structure of an RNA endow it with catalytic activity? What structural and functional principles are unique to RNA enzymes (or ribozymes), and what principles are so fundamental that they are shared with protein enzymes?

scholarly journals Crystal structure of a tankyrase 1–telomere repeat factor 1 complex

2016 ◽  
Vol 72 (4) ◽  
pp. 320-327 ◽  
Author(s):  
Bo Li ◽  
Ruihong Qiao ◽  
Zhizhi Wang ◽  
Weihong Zhou ◽  
Xin Li ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Large Scale ◽  
Sparse Matrix ◽  
Critical Role ◽  
Three Dimensional ◽  
Mitotic Cell ◽  
Ankyrin Repeat ◽  
Dimensional Structure ◽  
Telomere Repeat ◽  
Tankyrase 1

Telomere repeat factor 1 (TRF1) is a subunit of shelterin (also known as the telosome) and plays a critical role in inhibiting telomere elongation by telomerase. Tankyrase 1 (TNKS1) is a poly(ADP-ribose) polymerase that regulates the activity of TRF1 through poly(ADP-ribosyl)ation (PARylation). PARylation of TRF1 by TNKS1 leads to the release of TRF1 from telomeres and allows telomerase to access telomeres. The interaction between TRF1 and TNKS1 is thus important for telomere stability and the mitotic cell cycle. Here, the crystal structure of a complex between the N-terminal acidic domain of TRF1 (residues 1–55) and a fragment of TNKS1 covering the second and third ankyrin-repeat clusters (ARC2-3) is presented at 2.2 Å resolution. The TNKS1–TRF1 complex crystals were optimized using an `oriented rescreening' strategy, in which the initial crystallization condition was used as a guide for a second round of large-scale sparse-matrix screening. This crystallographic and biochemical analysis provides a better understanding of the TRF1–TNKS1 interaction and the three-dimensional structure of the ankyrin-repeat domain of TNKS.

scholarly journals SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

2019 ◽  
Author(s):  
Sushant Kumar ◽  
Arif Harmanci ◽  
Jagath Vytheeswaran ◽  
Mark B. Gerstein
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Mutations ◽  
Dimensional Structure ◽  
Rapid Decline ◽  
Ras Signaling ◽  
Cancer Genes ◽  
Structural Variations ◽  
Pathogenic Variants

AbstractA rapid decline in sequencing cost has made large-scale genome sequencing studies feasible. One of the fundamental goals of these studies is to catalog all pathogenic variants. Numerous methods and tools have been developed to interpret point mutations and small insertions and deletions. However, there is a lack of approaches for identifying pathogenic genomic structural variations (SVs). That said, SVs are known to play a crucial role in many diseases by altering the sequence and three-dimensional structure of the genome. Previous studies have suggested a complex interplay of genomic and epigenomic features in the emergence and distribution of SVs. However, the exact mechanism of pathogenesis for SVs in different diseases is not straightforward to decipher. Thus, we built an agnostic machine-learning-based workflow, called SVFX, to assign a “pathogenicity score” to somatic and germline SVs in various diseases. In particular, we generated somatic and germline training models, which included genomic, epigenomic, and conservation-based features for SV call sets in diseased and healthy individuals. We then applied SVFX to SVs in six different cancer cohorts and a cardiovascular disease (CVD) cohort. Overall, SVFX achieved high accuracy in identifying pathogenic SVs. Moreover, we found that predicted pathogenic SVs in cancer cohorts were enriched among known cancer genes and many cancer-related pathways (including Wnt signaling, Ras signaling, DNA repair, and ubiquitin-mediated proteolysis). Finally, we note that SVFX is flexible and can be easily extended to identify pathogenic SVs in additional disease cohorts.

scholarly journals An Initiation Process of Tropical Depression-type Disturbances under the Influence of Upper-level Troughs

2021 ◽  
Author(s):  
Yuya Hamaguchi ◽  
Yukari N. Takayabu
Keyword(s):  
Large Scale ◽  
Convective Available Potential Energy ◽  
Three Dimensional ◽  
Lower Troposphere ◽  
Dimensional Structure ◽  
Convective Heating ◽  
Upper Troposphere ◽  
Tropical Depression ◽  
Upper Level ◽  
Extratropical Forcing

AbstractIn this study, the statistical relationship between tropical upper-tropospheric troughs (TUTTs) and the initiation of summertime tropical-depression type disturbances (TDDs) over the western and central North Pacific is investigated. By applying a spatiotemporal filter to the 34-year record of brightness temperature and using JRA-55 reanalysis products, TDD-event initiations are detected and classified as trough-related (TR) or non-trough-related (non-TR). The conventional understanding is that TDDs originate primarily in the lower-troposphere; our results refine this view by revealing that approximately 30% of TDDs in the 10°N-20°N latitude ranges are generated under the influence of TUTTs. Lead-lag composite analysis of both TR- and non-TR-TDDs clarifies that TR-TDDs occur under relatively dry and less convergent large-scale conditions in the lower-troposphere. This result suggests that TR-TDDs can form in a relatively unfavorable low-level environment. The three-dimensional structure of the wave activity flux reveals southward and downward propagation of wave energy in the upper troposphere that converges at the mid-troposphere around the region where TR-TDDs occur, suggesting the existence of extratropical forcing. Further, the role of dynamic forcing associated with the TUTT on the TR-TDD-initiation is analyzed using the quasi-geostrophic omega equation. The result reveals that moistening in the mid-to-upper troposphere takes place in association with the sustained dynamical ascent at the southeast side of the TUTT, which precedes the occurrence of deep convective heating. Along with a higher convective available potential energy due to the destabilizing effect of TUTTs, the moistening in the mid-to-upper troposphere also helps to prepare the environment favorable to TDDs initiation.

The Perseus OB2 Superbubble and the Taurus-Auriga-California-Perseus Molecular Cloud Loop

2021 ◽  
Author(s):  
Xuepeng Chen ◽  
Weihua Guo ◽  
Jiangcheng Feng ◽  
Yang Su ◽  
Yan Sun ◽  
...  
Keyword(s):  
Atomic Hydrogen ◽  
Molecular Cloud ◽  
Three Dimensional ◽  
Solar Neighborhood ◽  
Dimensional Structure ◽  
Wide Field ◽  
Star Forming ◽  
Stellar Feedback ◽  
History Of ◽  
Cloud Complex

Abstract Located at a distance of about 300 pc, Perseus OB2 (or Per~OB2 for short) is one of the major OB associations in the solar vicinity\cite{Zeeuw99,Belikov2002}, which has blown a supershell with a diameter of about 15 degree seen in the atomic hydrogen line surveys\cite{Sancisi1974,Heiles1984,Hartmann1997}. It was long considered that stellar feedback from the Per~OB2 association had formed a superbubble that swept up the surrounding interstellar medium into the observed supershell\cite{Bally2008}. Here we report the three-dimensional structure of the Per~OB2 superbubble, based on wide-field atomic hydrogen and molecular gas (traced by CO) surveys. The measured diameter of the superbubble is roughly 330 pc. Multiple atomic hydrogen shells/loops with expansion velocities of about 10 km/s are revealed in the superbubble, suggesting a complicated evolution history of the superbubble. Furthermore, the inspections of the morphology, kinematics and timescale of the Taurus-Auriga, California, and Perseus molecular clouds shows that the cloud complex is a super molecular cloud loop circling around and co-expanding with the Per~OB2 superbubble. We conclude that the Taurus-Auriga-California-Perseus loop, the largest star-forming molecular cloud complex in the solar neighborhood, is formed from the feedback of the Per~OB2 superbubble.

Electron-density maps

2002 ◽  
Author(s):  
David Blow
Keyword(s):  
Fourier Transform ◽  
Electron Density ◽  
Three Dimensional ◽  
Two Dimensions ◽  
Unit Cell ◽  
Density Maps ◽  
Wave Cycle ◽  
Density Map ◽  
The Fourier Transform ◽  
Electron Density Map

When everything has been done to make the phases as good as possible, the time has come to examine the image of the structure in the form of an electron-density map. The electron-density map is the Fourier transform of the structure factors (with their phases). If the resolution and phases are good enough, the electron-density map may be interpreted in terms of atomic positions. In practice, it may be necessary to alternate between study of the electron-density map and the procedures mentioned in Chapter 10, which may allow improvements to be made to it. Electron-density maps contain a great deal of information, which is not easy to grasp. Considerable technical effort has gone into methods of presenting the electron density to the observer in the clearest possible way. The Fourier transform is calculated as a set of electron-density values at every point of a three-dimensional grid labelled with fractional coordinates x, y, z. These coordinates each go from 0 to 1 in order to cover the whole unit cell. To present the electron density as a smoothly varying function, values have to be calculated at intervals that are much smaller than the nominal resolution of the map. Say, for example, there is a protein unit cell 50 Å on a side, at a routine resolution of 2Å. This means that some of the waves included in the calculation of the electron density go through a complete wave cycle in 2 Å. As a rule of thumb, to represent this properly, the spacing of the points on the grid for calculation must be less than one-third of the resolution. In our example, this spacing might be 0.6 Å. To cover the whole of the 50 Å unit cell, about 80 values of x are needed; and the same number of values of y and z. The electron density therefore needs to be calculated on an array of 80×80×80 points, which is over half a million values. Although our world is three-dimensional, our retinas are two-dimensional, and we are good at looking at pictures and diagrams in two dimensions.

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