Comparisons of NMR Spectral Quality and Success in Crystallization Demonstrate that NMR and X-ray Crystallography Are Complementary Methods for Small Protein Structure Determination

2005 ◽  
Vol 127 (47) ◽  
pp. 16505-16511 ◽  
Author(s):  
David A. Snyder ◽  
Yang Chen ◽  
Natalia G. Denissova ◽  
Thomas Acton ◽  
James M. Aramini ◽  
...  
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
Protein Structure Determination ◽  
Spectral Quality ◽  
Small Protein ◽  
X Ray ◽  
Complementary Methods ◽  
X Ray Crystallography

scholarly journals PROBABILISTIC ENSEMBLES FOR IMPROVED INFERENCE IN PROTEIN-STRUCTURE DETERMINATION

2012 ◽  
Vol 10 (01) ◽  
pp. 1240009 ◽  
Author(s):  
AMEET SONI ◽  
JUDE SHAVLIK
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
Protein Structures ◽  
Three Dimensional ◽  
Protein Structure Determination ◽  
Complex Problem ◽  
Approximate Inference ◽  
X Ray ◽  
X Ray Crystallography ◽  
Inference Methods

Protein X-ray crystallography — the most popular method for determining protein structures — remains a laborious process requiring a great deal of manual crystallographer effort to interpret low-quality protein images. Automating this process is critical in creating a high-throughput protein-structure determination pipeline. Previously, our group developed ACMI, a probabilistic framework for producing protein-structure models from electron-density maps produced via X-ray crystallography. ACMI uses a Markov Random Field to model the three-dimensional (3D) location of each non-hydrogen atom in a protein. Calculating the best structure in this model is intractable, so ACMI uses approximate inference methods to estimate the optimal structure. While previous results have shown ACMI to be the state-of-the-art method on this task, its approximate inference algorithm remains computationally expensive and susceptible to errors. In this work, we develop Probabilistic Ensembles in ACMI (PEA), a framework for leveraging multiple, independent runs of approximate inference to produce estimates of protein structures. Our results show statistically significant improvements in the accuracy of inference resulting in more complete and accurate protein structures. In addition, PEA provides a general framework for advanced approximate inference methods in complex problem domains.

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 Protein structure determination using chemical shifts

2014 ◽  
Author(s):  
Anders S Christensen
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
Energy Function ◽  
Chemical Shifts ◽  
Protein A ◽  
Protein Structures ◽  
Protein Structure Determination ◽  
Coarse Grained ◽  
X Ray ◽  
Small Proteins

In this thesis, a protein structure determination using chemical shifts is presented. The method is implemented in the open source PHAISTOS protein simulation framework. The method combines sampling from a generative model with a coarse-grained force field and an energy function that includes chemical shifts. The method is benchmarked on folding simulations of five small proteins. In four cases the resulting structures are in excellent agreement with experimental data, the fifth case fail likely due to inaccuracies in the energy function. For the Chymotrypsin Inhibitor protein, a structure is determined using only chemical shifts recorded and assigned through automated processes. The CA-RMSD to the experimental X-ray for this structure is 1.1 Å. Additionally, the method is combined with very sparse NOE-restraints and evolutionary distance restraints and tested on several protein structures >100 residues. For Rhodopsin (225 residues) a structure is found at 2.5 Å CA-RMSD from the experimental X-ray structure, and a structure is determined for the Savinase protein (269 residues) with 2.9 Å CA-RMSD from the experimental X-ray structure.

scholarly journals Challenges in solving structures from radiation-damaged tomograms of protein nanocrystals assessed by simulation

2021 ◽  
Vol 77 (5) ◽  
pp. 572-586
Author(s):  
Ariana Peck ◽  
Qing Yao ◽  
Aaron S. Brewster ◽  
Petrus H. Zwart ◽  
John M. Heumann ◽  
...  
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
Single Particle ◽  
Protein Function ◽  
Processing Scheme ◽  
X Ray ◽  
Single Particle Reconstruction ◽  
X Ray Crystallography ◽  
Determination Methods ◽  
Particle Reconstruction

Structure-determination methods are needed to resolve the atomic details that underlie protein function. X-ray crystallography has provided most of our knowledge of protein structure, but is constrained by the need for large, well ordered crystals and the loss of phase information. The rapidly developing methods of serial femtosecond crystallography, micro-electron diffraction and single-particle reconstruction circumvent the first of these limitations by enabling data collection from nanocrystals or purified proteins. However, the first two methods also suffer from the phase problem, while many proteins fall below the molecular-weight threshold required for single-particle reconstruction. Cryo-electron tomography of protein nanocrystals has the potential to overcome these obstacles of mainstream structure-determination methods. Here, a data-processing scheme is presented that combines routines from X-ray crystallography and new algorithms that have been developed to solve structures from tomograms of nanocrystals. This pipeline handles image-processing challenges specific to tomographic sampling of periodic specimens and is validated using simulated crystals. The tolerance of this workflow to the effects of radiation damage is also assessed. The simulations indicate a trade-off between a wider tilt range to facilitate merging data from multiple tomograms and a smaller tilt increment to improve phase accuracy. Since phase errors, but not merging errors, can be overcome with additional data sets, these results recommend distributing the dose over a wide angular range rather than using a finer sampling interval to solve the protein structure.

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