Spatial Structures of Fibrillar Proteins

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Protein Structure ◽  
Translational Symmetry ◽  
Spatial Structures ◽  
Linear Structures ◽  
Plane Model ◽  
Protein Molecules ◽  
Folded Structures

The protein molecules are considerate in the space of the highest dimension with a change in dimension with demand at the conformation of the molecules. It was shown that the widespread quasi-plane model of the Pouling protein structure do not reflect and even contradict the spatial structures of the protein in various conformations. It was found that the linear structures and folded structures of the protein in space of the highest dimension have translational symmetry. The elementary elements of protein translational symmetry were determined, their dimensions were calculated (9 for the linear structures and 23 for folded structures).

On the Possible Spatial Structures of the β – Amyloid.

2022 ◽  
Vol 7 (1) ◽  
pp. 0-0
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Spatial Structure ◽  
Spatial Models ◽  
Globular Proteins ◽  
Spatial Structures ◽  
Higher Dimensional ◽  
Protein Molecules ◽  
Β Amyloid ◽  
First Time

Spatial models of the β - structures of protein molecules, forming layers of amino acids, in principle, of unlimited length for both antiparallel and parallel conformation have been constructed. It is shown that the simplified flat Pauling models do not reflect the spatial structure of these layers. Using the recently developed theory of higher-dimensional polytopic prismahedrons, models of the volumetric filling of space with amino acid molecules are constructed. The constructed models for the first time mathematically describe the native structures of globular proteins.

scholarly journals Abstract P-5: Neural Network Approaches to Classify 3D Protein Structures from the Data of X-ray Laser Radiation Diffraction from Single Particles

2021 ◽  
Vol 11 (Suppl_1) ◽  
pp. S13-S13
Author(s):  
Valery Novoseletsky ◽  
Mikhail Lozhnikov ◽  
Grigoriy Armeev ◽  
Aleksandr Kudriavtsev ◽  
Alexey Shaytan ◽  
...  
Keyword(s):  
Neural Network ◽  
Protein Structure ◽  
Image Classification ◽  
Structure Determination ◽  
Protein Structures ◽  
Protein Structure Determination ◽  
Family Type ◽  
X Ray ◽  
Protein Molecules ◽  
Diffraction Patterns

Background: Protein structure determination using X-ray free-electron laser (XFEL) includes analysis and merging a large number of snapshot diffraction patterns. Convolutional neural networks are widely used to solve numerous computer vision problems, e.g. image classification, and can be used for diffraction pattern analysis. But the task of protein structure determination with the use of CNNs only is not yet solved. Methods: We simulated the diffraction patterns using the Condor software library and obtained more than 1000 diffraction patterns for each structure with simulation parameters resembling real ones. To classify diffraction patterns, we tried two approaches, which are widely known in the area of image classification: a classic VGG network and residual networks. Results: 1. Recognition of a protein class (GPCRs vs globins). Globins and GPCR-like proteins are typical α-helical proteins. Each of these protein families has a large number of representatives (including those with known structure) but we used only 8 structures from every family. 12,000 of diffraction patterns were used for training and 4,000 patterns for testing. Results indicate that all considered networks are able to recognize the protein family type with high accuracy. 2. Recognition of the number of protein molecules in the liposome. We considered the usage of lyposomes as carriers of membrane or globular proteins for sample delivery in XFEL experiments in order to improve the X-ray beam hit rate. Three sets of diffractograms for liposomes of various radius were calculated, including diffractograms for empty liposomes, liposomes loaded with 5 bacteriorhodopsin molecules, and liposomes loaded with 10 bacteriorhodopsin molecules. The training set consisted of 23625 diffraction patterns, and test set of 7875 patterns. We found that all networks used in our study were able to identify the number of protein molecules in liposomes independent of the liposome radius. Our findings make this approach rather promising for the usage of liposomes as protein carriers in XFEL experiments. Conclusion: Thus, the performed numerical experiments show that the use of neural network algorithms for the recognition of diffraction images from single macromolecular particles makes it possible to determine changes in the structure at the angstrom scale.

scholarly journals Oxidant-Mediated Protein Amino Acid Conversion

Antioxidants ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 50 ◽  
Author(s):  
Yuichiro Suzuki
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Biological System ◽  
Redox Regulation ◽  
Amino Acid Sequences ◽  
Biological Oxidation ◽  
Oxidation Reactions ◽  
Protein Amino Acid ◽  
Peroxiredoxin 6 ◽  
Protein Molecules

Biological oxidation plays important roles in the pathogenesis of various diseases and aging. Carbonylation is one mode of protein oxidation. It has been reported that amino acids that are susceptible to carbonylation are arginine (Arg), proline (Pro), lysine, and threonine residues. The carbonylation product of both Arg and Pro residues is glutamyl semialdehyde. While chemically the oxidation reactions of neither Pro to glutamyl semialdehyde nor Arg to glutamyl semialdehyde are reversible, experimental results from our laboratory suggest that the biological system may drive the reduction of glutamyl semialdehyde to Pro in the protein structure. Further, glutamyl semialdehyde can be oxidized to become glutamic acid (Glu). Therefore, I hypothesize that biological oxidation post-translationally converts Arg to Pro, Arg to Glu, and Pro to Glu within the protein structure. Our mass spectrometry experiments provided evidence that, in human cells, 5–10% of peroxiredoxin 6 protein molecules have Pro-45 replaced by Glu. This concept of protein amino acid conversion challenges the dogma that amino acid sequences are strictly defined by nucleic acid sequences. I propose that, in the biological system, amino acid replacements can occur post-translationally through redox regulation, and protein molecules with non-DNA coding sequences confer functions.

Ovorubin, a chromoprotein from the eggs of the gastropod mollusc Pomacea canaliculata

1958 ◽  
Vol 149 (937) ◽  
pp. 571-587 ◽  
Keyword(s):  
Molecular Weight ◽  
Protein Structure ◽  
High Stability ◽  
Carotenoid Content ◽  
Pomacea Canaliculata ◽  
Carbohydrate Component ◽  
Prosthetic Group ◽  
Gastropod Mollusc ◽  
Protein Molecules ◽  
Prosobranch Snail

The eggs of Pomacea canaliculata australis (d’Orbigny), an amphibious freshwater prosobranch snail, have, as the most important nitrogenous constituent of the jelly surrounding the ovum, a red glycoprotein with a carotenoid prosthetic group. This protein, to which the name ovorubin has been given, has a high stability to denaturation by heat and by adsorption at interfaces. It is partially utilized during development of the ovum, although about two-thirds of the original ovorubin content of the egg is found in the visceral hump of the newly hatched animal. The carotenoid component is probably an ester or ether of astaxanthin, highly labile to alkali in the cold. The minimum molecular weight, calculated from the carotenoid content, lies in the region of 330 000. The carbohydrate component represents about 20% of the molecule. The carotenoid and the glycoprotein may readily be separated and recombined. Experiments on the apo-glycoprotein and the reconstituted caroteno-protein indicate that the carotenoid stabilizes the native configuration of the protein structure. It is suggested that stabilization of the configurations of protein molecules may be one of the roles of carotenoids in nature.

Analysis and Application of the Dynamic Stability of Overlying Multilayer Spatial Structures in Deep Coal Mine

2013 ◽  
Vol 353-356 ◽  
pp. 2433-2436
Author(s):  
Hong Shi ◽  
Yan Hui Ge ◽  
Jun Li
Keyword(s):  
Dynamic Stability ◽  
Coal Mine ◽  
Field Experiments ◽  
Stress Condition ◽  
Dynamic Condition ◽  
Recovery Ratio ◽  
Spatial Structures ◽  
Plane Model ◽  
Deep Coal Mine ◽  
Overlying Strata

With the deepening of mining, dynamic disasters appear in a large number in the coal mine, and the stress distribution and stress concentration caused by mining act as the important factors that induce the dynamic disasters. The range of overlying strata movement related to the appearing of mine press go beyond the range of traditional plane model, and influence the mining stress field as overlying strata spatial structures. The movement and distribution of overlying strata spatial structures are the theory bases of studying stress condition and dynamic condition of dynamic disaster. According to the characteristics of sediment rock and the movement of strata of overlying multilayer spatial structures in deep coal mine. By using the dynamic stability judgment rules controlled by the top coals recovery ratio, the author worked out the judgment curves of dynamic stability of overlying multilayer spatial structures and explained their application. This paper also analyzes the factors influencing the dynamic stability of overlying multilayer spatial structures. The suggested judgment rule and curve of dynamic stability have been tested by field experiments which achieved safe caving in isolated caving face by adjusting the top coals recovery ratio. The theoretical basis of designing the safe top coals recovery ratio can be provided according to different overlying strata, and the safe overlying strata controlling can be achieved in deep coal mining.

Rigidity Analysis of Protein Molecules

2015 ◽  
Vol 15 (3) ◽  
Author(s):  
Zahra Shahbazi ◽  
Ahmet Demirtas
Keyword(s):  
Protein Structure ◽  
Degrees Of Freedom ◽  
Kinematic Model ◽  
3D Structure ◽  
Closed Loops ◽  
Matlab Program ◽  
Acid Versus ◽  
Rigidity Analysis ◽  
Protein Molecules ◽  
Pebble Game

Intrinsic flexibility of protein molecules enables them to change their 3D structure and perform their specific task. Therefore, identifying rigid regions and consequently flexible regions of proteins has a significant role in studying protein molecules' function. In this study, we developed a kinematic model of protein molecules considering all covalent and hydrogen bonds in protein structure. Then, we used this model and developed two independent rigidity analysis methods to calculate degrees of freedom (DOF) and identify flexible and rigid regions of the proteins. The first method searches for closed loops inside the protein structure and uses Grübler–Kutzbach (GK) criterion. The second method is based on a modified 3D pebble game. Both methods are implemented in a matlab program and the step by step algorithms for both are discussed. We applied both methods on simple 3D structures to verify the methods. Also, we applied them on several protein molecules. The results show that both methods are calculating the same DOF and rigid and flexible regions. The main difference between two methods is the run time. It's shown that the first method (GK approach) is slower than the second method. The second method takes 0.29 s per amino acid versus 0.83 s for the first method to perform this rigidity analysis.

scholarly journals Comparison of Spatial Structures and Packaging of Phosphorybosil Pyrophosphate Synthetase 2 from Thermus thermophilus HB27 in Rhombohedral and Tetragonal Crystals

Crystals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1128
Author(s):  
Yulia Abramchik ◽  
Evgeniy Zayats ◽  
Maria Kostromina ◽  
Dmitry Lykoshin ◽  
Ilya Fateev ◽  
...  
Keyword(s):  
Thermus Thermophilus ◽  
Thermophilic Bacterium ◽  
Structural Basis ◽  
Lithium Sulfate ◽  
Spatial Structures ◽  
Phosphoribosyl Pyrophosphate ◽  
Space Group ◽  
Tetragonal Crystals ◽  
Protein Molecules ◽  
Enzyme Molecules

We report the spatial structure of phosphoribosyl pyrophosphate synthetase 2 from the thermophilic bacterium Thermus thermophilus HB27 (TthPRPPS2) obtained at a 1.85 Å resolution using a diffraction set collected from rhombohedral crystals (space group R32-h), grown with lithium sulfate as a precipitant. This crystal structure was compared with the structure of TthPRPPS2, previously obtained at a 2.2 Å resolution using diffraction sets from the tetragonal crystals (space group P41212), grown with ammonium sulfate as a precipitant. The comparison of these structures allows the study of the differences between protein molecules in both crystalline structures, as well as the packaging of enzyme molecules in crystals of both spatial groups. Our results may contribute to the research of the structural basis of catalytic activity and substrate specificity of this enzyme.

Positioning a protein molecule in the asymmetric unit using very low order reflections and packing restraints

1999 ◽  
Vol 32 (3) ◽  
pp. 530-535
Author(s):  
Klas M. Andersson
Keyword(s):  
Protein Structure ◽  
Spherical Model ◽  
Protein Molecule ◽  
Low Resolution ◽  
Structure Factors ◽  
Unknown Protein ◽  
Phase Extension ◽  
Protein Molecules ◽  
True Protein ◽  
Low Order

A method for predicting the position of protein molecules in the unit cell is presented. This prediction is based on the structure-factor amplitudes of the very low order reflections and packing considerations. With very low resolution data, the calculated electron density is very blurred, such that a protein molecule may well be approximated as a sphere. A sphere with the same volume as the unknown protein was translated in small (2–3 Å) steps in the corresponding Cheshire cell until maximum overlap between the amplitudes calculated from the sphere and the true protein structure was found. A molecular packing can be calculated to restrain the allowable regions. This makes the positioning of the protein molecule even more reliable. Structure factors of the ten or so lowest resolution reflections were calculated with a sphere at the best position. These structure factors agreed closely with those of the true protein structure. The translation algorithm has been successfully tested for 16 proteins. For 12 out of 16 proteins tested, the position of the centre of the molecule was correctly predicted to within 5 Å. A qualitative deduction of deviations from the spherical model can be gained by comparing structure factors from the spherical model and the true protein. The very low resolution phasing obtained by this method may be used as powerful starting set for phase-extension methods such as maximum entropy.

scholarly journals Evaluating Autoencoder-Based Featurization and Supervised Learning for Protein Decoy Selection

Molecules ◽  
2020 ◽  
Vol 25 (5) ◽  
pp. 1146 ◽  
Author(s):  
Fardina Fathmiul Alam ◽  
Taseef Rahman ◽  
Amarda Shehu
Keyword(s):  
Neural Network ◽  
Protein Structure ◽  
Supervised Learning ◽  
Structure Prediction ◽  
Tertiary Structures ◽  
Classic Problem ◽  
Model Search ◽  
Protein Molecules ◽  
Nonlinear Features ◽  
Additional Reason

Rapid growth in molecular structure data is renewing interest in featurizing structure. Featurizations that retain information on biological activity are particularly sought for protein molecules, where decades of research have shown that indeed structure encodes function. Research on featurization of protein structure is active, but here we assess the promise of autoencoders. Motivated by rapid progress in neural network research, we investigate and evaluate autoencoders on yielding linear and nonlinear featurizations of protein tertiary structures. An additional reason we focus on autoencoders as the engine to obtain featurizations is the versatility of their architectures and the ease with which changes to architecture yield linear versus nonlinear features. While open-source neural network libraries, such as Keras, which we employ here, greatly facilitate constructing, training, and evaluating autoencoder architectures and conducting model search, autoencoders have not yet gained popularity in the structure biology community. Here we demonstrate their utility in a practical context. Employing autoencoder-based featurizations, we address the classic problem of decoy selection in protein structure prediction. Utilizing off-the-shelf supervised learning methods, we demonstrate that the featurizations are indeed meaningful and allow detecting active tertiary structures, thus opening the way for further avenues of research.

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