Tritium planigraphy: A tool for studying the spatial structure of proteins and their complexes

2000 ◽  
Vol 34 (5) ◽  
pp. 712-723 ◽  
Author(s):  
E. N. Bogacheva ◽  
A. B. Shishkov
Keyword(s):  
Spatial Structure ◽  
Tritium Planigraphy ◽  
Structure Of Proteins

scholarly journals Towards some regularities in organization of spatial structure of proteins

1993 ◽  
Vol 9 (1) ◽  
pp. 3-9
Author(s):  
S. Z. Maltchenko ◽  
N. A. Chashchin
Keyword(s):  
Spatial Structure ◽  
Structure Of Proteins

scholarly journals Progress in Determination of Protein Spatial Structure Based on Machine Learning

2021 ◽  
pp. 54-60
Author(s):  
B. Biletskyy
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Protein Structure ◽  
Spatial Structure ◽  
Protein Structures ◽  
Significant Progress ◽  
Homologous Proteins ◽  
Artificial Neural ◽  
Structure Of Proteins

Introduction. The task of determining the spatial structure of proteins is one of the most important unsolved problems of mankind. Life on the planet Earth is called protein, because protein molecules are the drivers of life processes in living organisms. Proteins make up about 80% of the dry mass of the cell and coordinate the processes of metabolism. The functions of proteins are defined by its spatial structure. The results of recent competitions in methods for determining protein structures have shown significant progress in this important area. One of the research groups presented the AlphaFold 2 method, the accuracy of which reached the accuracy of experimental methods. Purpose of the article. The aim of the work is to consider and analyze the basic principles of the AlphaFold software package for determining the spatial structure of proteins. Results. We consider the main stages in the process of recognizing the structure of a protein using the AlphaFold program complex. The stages and corresponding methods include: search for homologous proteins based on multiple alignment methods, construction of protein-specific differentiated potential using artificial neural networks and protein structure energy optimization using gradient descent and limited sampling. We discuss how combination of various bioinformatics techniques powered by data from open data sources can lead to significant improvements in accuracy of protein structure prediction. Special attention is paid to the use of artificial neural networks for building the smooth protein-specific potential and following energy minimization based on constructed potential. Conclusions. The combination of a number of methods and the use of information from protein and genetic data banks allows us to make significant progress in solving the extremely important task of determining the structure of a protein. Keywords: protein spatial structure, Machine Learning, AlphaFold.

scholarly journals Tritium planigraphy: From the accessible surface to the spatial structure of a protein

1998 ◽  
Vol 95 (6) ◽  
pp. 2790-2794 ◽  
Author(s):  
E. N. Bogacheva ◽  
V. I. Gol'danskii ◽  
A. V. Shishkov ◽  
A. V. Galkin ◽  
L. A. Baratova
Keyword(s):  
Spatial Structure ◽  
Tritium Planigraphy ◽  
Accessible Surface

Modeling of protein spatial structure using tritium planigraphy

BIOPHYSICS ◽  
2011 ◽  
Vol 56 (6) ◽  
pp. 1011-1020 ◽  
Author(s):  
E. N. Bogacheva ◽  
A. N. Bogachev ◽  
I. B. Dmitriev ◽  
A. A. Dolgov ◽  
A. L. Chulichkov ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Tritium Planigraphy

Observations of the spatial structure of interstellar hydrogen

1967 ◽  
Vol 31 ◽  
pp. 45-46
Author(s):  
Carl Heiles
Keyword(s):  
High Resolution ◽  
Spatial Structure ◽  
Velocity Dispersion ◽  
Temperature Variations

High-resolution 21-cm line observations in a region aroundlII= 120°,b11= +15°, have revealed four types of structure in the interstellar hydrogen: a smooth background, large sheets of density 2 atoms cm-3, clouds occurring mostly in groups, and ‘Cloudlets’ of a few solar masses and a few parsecs in size; the velocity dispersion in the Cloudlets is only 1 km/sec. Strong temperature variations in the gas are in evidence.

Results of Spatial Structure of Atmosphere Radiation in a Spectral Range (1.5–2) µm Research

2018 ◽  
pp. 7-13
Author(s):  
Anton M. Mishchenko ◽  
Sergei S. Rachkovsky ◽  
Vladimir A. Smolin ◽  
Igor V . Yakimenko
Keyword(s):  
Spatial Structure ◽  
Unmanned Aerial Vehicle ◽  
Wavelength Range ◽  
Spectral Range ◽  
Near Ir ◽  
Optoelectronic Systems ◽  
Aerial Vehicle

Results of experimental studying radiation spatial structure of atmosphere background nonuniformities and of an unmanned aerial vehicle being the detection object are presented. The question on a possibility of its detection using optoelectronic systems against the background of a cloudy field in the near IR wavelength range is also considered.

Relaxin and the Fine Structure of Proteins

1998 ◽  
Author(s):  
Christian Schwabe ◽  
Erika E. Büllesbach
Keyword(s):  
Fine Structure ◽  
Structure Of Proteins
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