Distance geometry and molecular conformation pdf
File Name: distance geometry and molecular conformation .zip
- Solving the molecular distance geometry problem with inaccurate distance data
- Molecular geometry
- Molecular Geometry Prediction using a Deep Generative Graph Neural Network
- Fast, efficient fragment-based coordinate generation for Open Babel
Solving the molecular distance geometry problem with inaccurate distance data
Metrics details. Rapidly predicting an accurate three dimensional geometry of a molecule is a crucial task for cheminformatics and across a wide range of molecular modeling. Consequently, developing a fast, accurate, and open implementation of structure prediction is necessary for reproducible cheminformatics research. We introduce a fragment-based coordinate generation implementation for Open Babel, a widely-used open source toolkit for cheminformatics. The new implementation improves speed and stereochemical accuracy, while retaining or improving accuracy of bond lengths, bond angles, and dihedral torsions.
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Molecular geometry is the three-dimensional arrangement of the atoms that constitute a molecule. It includes the general shape of the molecule as well as bond lengths , bond angles, torsional angles and any other geometrical parameters that determine the position of each atom. Molecular geometry influences several properties of a substance including its reactivity , polarity , phase of matter , color , magnetism and biological activity. The molecular geometry can be determined by various spectroscopic methods and diffraction methods. IR , microwave and Raman spectroscopy can give information about the molecule geometry from the details of the vibrational and rotational absorbance detected by these techniques. X-ray crystallography , neutron diffraction and electron diffraction can give molecular structure for crystalline solids based on the distance between nuclei and concentration of electron density. Gas electron diffraction can be used for small molecules in the gas phase.
Axel T. Marius Clore, Angela M. A series of three-dimensional structures of the 1—29 fragment of human growth hormone releasing factor in trifluoroethanol have been determined by molecular dynamics and distance geometry methods. The resulting structures satisfy information from nuclear Overhauser effect NOE distance data and an empirical potential energy function. Although the polypeptide was found to have an ordered structure in all simulations, the NOE data were not sufficient for global convergence to a unique three-dimensional geometry.
Molecular Geometry Prediction using a Deep Generative Graph Neural Network
Metrics details. The determination of protein structures satisfying distance constraints is an important problem in structural biology. Whereas the most common method currently employed is simulated annealing, there have been other methods previously proposed in the literature.
Fast, efficient fragment-based coordinate generation for Open Babel
We present a new iterative algorithm for the molecular distance geometry problem with inaccurate and sparse data, which is based on the solution of linear systems, maximum cliques, and a minimization of nonlinear least-squares function. Computational results with real protein structures are presented in order to validate our approach. The knowledge of the protein structure is very important to understand its function and to analyze possible interactions with other proteins. Different methods can be applied to acquire protein structural information.
Several recent advances in both the theory and algorithms for the application of Distance Geometry are reported. These advances were developed with the application to Protein Folding and Drug Design in mind. However, several results are of general interest and are applicable in statistics and the social sciences where the corresponding problems are known as multidimensional scaling. The spectral gradient algorithm is proposed as an alternate method to the majorization algorithm for solving the metric stress problem. The ALS Algorithm that is used to determine molecular conformations is similar to the alternating least squares approach used in nonmetric multidimensional scaling. Several theorems relating geometric structure and the properties of distance matrices are explored. These include a geometrical characterization of the structures which comprise a face of the cone of Euclidean distance matrices.
PDF | In order to better understand the many different distance geometry numerical algorithms, it is necessary to relate them to real-world.
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