Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe Dynamics and Infrared Spectrocopy of Monomeric and Dimeric Wild Type and Mutant Insulin
55
0
0.0
(
0
)
Added by
Seyedeh Maryam Salehi
Publication date
2020
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The infrared spectroscopy and dynamics of -CO labels in wild type and mutant insulin monomer and dimer are characterized from molecular dynamics simulations using validated force fields. It is found that the spectroscopy of monomeric and dimeric forms in the region of the amide-I vibration differs for residues B24-B26 and D24-D26, which are involved in dimerization of the hormone. Also, the spectroscopic signatures change for mutations at position B24 from phenylalanine - which is conserved in many organisms and known to play a central role in insulin aggregation - to alanine or glycine. Using three different methods to determine the frequency trajectories - solving the nuclear Schrodinger equation on an effective 1-dimensional potential energy curve, instantaneous normal modes, and using parametrized frequency maps - lead to the same overall conclusions. The spectroscopic response of monomeric WT and mutant insulin differs from that of their respective dimers and the spectroscopy of the two monomers in the dimer is also not identical. For the WT and F24A and F24G monomers spectroscopic shifts are found to be $sim 20$ cm$^{-1}$ for residues (B24 to B26) located at the dimerization interface. Although the crystal structure of the dimer is that of a symmetric homodimer, dynamically the two monomers are not equivalent on the nanosecond time scale. Together with earlier work on the thermodynamic stability of the WT and the same mutants it is concluded that combining computational and experimental infrared spectroscopy provides a potentially powerful way to characterize the aggregation state and dimerization energy of modified insulins.
rate research
Read More
We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics. We augment an existing biomedical model by introducing time-varying dynamics driven by a machine learning sequence model. Our model maintains a physiologically plausible inductive bias and clinically interpretable parameters -- e.g., insulin sensitivity -- while inheriting the flexibility of modern pattern recognition algorithms. Critical to modeling success are the flexible, but structured representations of subject variability with a sequence model. In contrast, less constrained models like the LSTM fail to provide reliable or physiologically plausible forecasts. We conduct an extensive empirical study. We show that allowing biomedical model dynamics to vary in time improves forecasting at long time horizons, up to six hours, and produces forecasts consistent with the physiological effects of insulin and carbohydrates.
We derive approximate equations of motion for excited state dynamics of a multilevel open quantum system weakly interacting with light to describe fluorescence detected single molecule spectra. Based on the Frenkel exciton theory, we construct a model for the chlorophyll part of the LHCII complex of higher plants and its interaction with previously proposed excitation quencher in the form of the lutein molecule Lut 1. The resulting description is valid over a broad range of timescales relevant for single molecule spectroscopy, i.e. from ps to minutes. Validity of these equations is demonstrated by comparing simulations of ensemble and single-molecule spectra of monomeric LHCII with experiments. Using a conformational change of the LHCII protein as a switching mechanism, the intensity and spectral time traces of individual LHCII complexes are simulated, and the experimental statistical distributions are reproduced. Based on our model, it is shown that with reasonable assumptions about its interaction with chlorophylls, Lut 1 can act as an efficient fluorescence quencher in LHCII.
Background: Distinction of IDH mutant and wildtype GBMs is challenging on MRI, since conventional imaging shows considerable overlap. While few studies employed deep-learning in a mixed low/high grade glioma population, a GBM-specific model is still lacking in the literature. Our objective was to develop a deep-learning model for IDH prediction in GBM by using Convoluted Neural Networks (CNN) on multiparametric MRI. Methods: We included 100 adult patients with pathologically proven GBM and IDH testing. MRI data included: morphologic sequences, rCBV and ADC maps. Tumor area was obtained by a bounding box function on the axial slice with widest tumor extension on T2 images and was projected on every sequence. Data was split into training and test (80:20) sets. A 4 block 2D - CNN architecture was implemented for IDH prediction on every MRI sequence. IDH mutation probability was calculated with softmax activation function from the last dense layer. Highest performance was calculated accounting for model accuracy and categorical cross-entropy loss (CCEL) in the test cohort. Results: Our model achieved the following performance: T1 (accuracy 77%, CCEL 1.4), T2 (accuracy 67%, CCEL 2.41), FLAIR (accuracy 77%, CCEL 1.98), MPRAGE (accuracy 66%, CCEL 2.55), rCBV (accuracy 83%, CCEL 0.64). ADC achieved lower performance. Conclusion: We built a GBM-tailored deep-learning model for IDH mutation prediction, achieving accuracy of 83% with rCBV maps. High predictivity of perfusion images may reflect the known correlation between IDH, hypoxia inducible factor (HIF) and neoangiogenesis. This model may set a path for non-invasive evaluation of IDH mutation in GBM.
Groups of beating flagella or cilia often synchronize so that neighboring filaments have identical frequencies and phases. A prime example is provided by the unicellular biflagellate Chlamydomonas reinhardtii, which typically displays synchronous in-phase beating in a low-Reynolds number version of breaststroke swimming. We report here the discovery that ptx1, a flagellar dominance mutant of C. reinhardtii, can exhibit synchronization in precise antiphase, as in the freestyle swimming stroke. Long-duration high-speed imaging shows that ptx1 flagella switch stochastically between in-phase and antiphase states, and that the latter has a distinct waveform and significantly higher frequency, both of which are strikingly similar to those found during phase slips that stochastically interrupt in-phase beating of the wildtype. Possible mechanisms underlying these observations are discussed.
We study by simulation the physics of two colloidal particles in a cholesteric liquid crystal with tangential order parameter alignment at the particle surface. The effective force between the pair is attractive at short range and favors assembly of colloid dimers at specific orientations relative to the local director field. When pulled through the fluid by a constant force along the helical axis, we find that such a dimer rotates, either continuously or stepwise with phase-slip events. These cases are separated by a sharp dynamical transition and lead, respectively, to a constant or an ever-increasing phase lag between the dimer orientation and the local nematic director.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
