scholarly journals NMR Protein Structure Calculation and Sphere Intersections

2020 ◽  
Vol 8 (1) ◽  
pp. 89-101
Author(s):  
Carlile Lavor ◽  
Rafael Alves ◽  
Michael Souza ◽  
Luis Aragón José

AbstractNuclear Magnetic Resonance (NMR) experiments can be used to calculate 3D protein structures and geometric properties of protein molecules allow us to solve the problem iteratively using a combinatorial method, called Branch-and-Prune (BP). The main step of BP algorithm is to intersect three spheres centered at the positions for atoms i − 3, i − 2, i − 1, with radii given by the atomic distances di−3,i, di−2,i, di−1,i, respectively, to obtain the position for atom i. Because of uncertainty in NMR data, some of the distances di−3,i should be represented as interval distances [{\underline{d}_{i - 3,i}},{\bar d_{i - 3,i}}], where {\underline{d}_{i - 3,i}} \le {d_{i - 3,i}} \le {\bar d_{i - 3,i}}. In the literature, an extension of the BP algorithm was proposed to deal with interval distances, where the idea is to sample values from [{\underline{d}_{i - 3,i}},{\bar d_{i - 3,i}}]. We present a new method, based on conformal geometric algebra, to reduce the size of [{\underline{d}_{i - 3,i}},{\bar d_{i - 3,i}}], before the sampling process. We also compare it with another approach proposed in the literature.

2009 ◽  
Vol 43 (3) ◽  
pp. 131-143 ◽  
Author(s):  
Mike P. Williamson ◽  
C. Jeremy Craven

2003 ◽  
pp. 157-194 ◽  
Author(s):  
Peter Güntert
Keyword(s):  

Author(s):  
Hai-Jun Su ◽  
Jesse Parker ◽  
Kazem Kazerounian ◽  
Horea Ilies

This paper presents an initial comparison of two approaches to energy minimization of protein molecules, namely the Molecular Dynamic Simulation and the Kineto-Static Compliance Method. Both methods are well established and are promising contenders to the seemingly insurmountable task of global optimization in the protein molecules potential energy terrain. The Molecular Dynamic Simulation takes the form of Constrained Multibody Dynamics of interconnected rigid bodies, as implemented at the Virtual Reality Application Center from Iowa State University. The Kineto-Static Compliance Method is implemented in the Protofold Computer package developed in the Mechanical Engineering Department at the University of Connecticut. The simulation results of both methods are compared through the trajectory of potential energy, the Root Mean Square Deviation (RMSD) of the alpha carbons, as well as based on visual observations. The preliminary results indicate that both techniques are very effective in converging the protein structure to a state with significantly less potential energy. At present, the converged solutions for the two methods are, however, different from each other and are very likely different from the global minimum potential energy state.


2000 ◽  
Vol 48 (7) ◽  
pp. 2775-2779 ◽  
Author(s):  
Makoto Shimoyamada ◽  
Kayoko Tômatsu ◽  
Satomi Oku ◽  
Kenji Watanabe

2005 ◽  
Vol 127 (47) ◽  
pp. 16621-16628 ◽  
Author(s):  
Alexander Grishaev ◽  
Justin Wu ◽  
Jill Trewhella ◽  
Ad Bax

2021 ◽  
Vol 11 (Suppl_1) ◽  
pp. S13-S13
Author(s):  
Valery Novoseletsky ◽  
Mikhail Lozhnikov ◽  
Grigoriy Armeev ◽  
Aleksandr Kudriavtsev ◽  
Alexey Shaytan ◽  
...  

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.


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