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Dive into the research topics where Oleg V. Sobolev is active.

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Featured researches published by Oleg V. Sobolev.


Acta Crystallographica Section D-biological Crystallography | 2015

FEM: Feature-enhanced map

Pavel V. Afonine; Nigel W. Moriarty; Marat Mustyakimov; Oleg V. Sobolev; Thomas C. Terwilliger; Dušan Turk; Alexandre Urzhumtsev; Paul D. Adams

The non-iterative feature-enhancing approach improves crystallographic maps’ interpretability by reducing model bias and noise and strengthening the existing signal.


Acta Crystallographica Section D Structural Biology | 2017

Polder maps: improving OMIT maps by excluding bulk solvent

Dorothee Liebschner; Pavel V. Afonine; Nigel W. Moriarty; Billy K. Poon; Oleg V. Sobolev; Thomas C. Terwilliger; Paul D. Adams

Residual OMIT maps can be improved by the selective exclusion of bulk solvent from the OMIT region.


Acta Crystallographica Section D-biological Crystallography | 2018

Real-space refinement in PHENIX for cryo-EM and crystallography

Pavel V. Afonine; Billy K. Poon; Randy J. Read; Oleg V. Sobolev; Thomas C. Terwilliger; Alexandre Urzhumtsev; Paul D. Adams

A description is provided of the implementation of real-space refinement in the phenix.real_space_refine program from the PHENIX suite and its application to the re-refinement of cryo-EM-derived models.


Acta Crystallographica Section D-biological Crystallography | 2018

Automated map sharpening by maximization of detail and connectivity

Thomas C. Terwilliger; Oleg V. Sobolev; Pavel V. Afonine; Paul D. Adams

A procedure for optimizing the sharpening of a map based on maximizing the level of detail and connectivity of the map has been developed and applied to 361 pairs of deposited cryo-EM maps and associated models.


Acta Crystallographica Section D-biological Crystallography | 2012

Detection of alternative conformations by unrestrained refinement.

Oleg V. Sobolev; Vladimir Y. Lunin

Unrestrained refinement is stable for the vast majority of atoms when working at atomic resolution. Nevertheless, geometrical restraints should be retained in refinement for residues that are present in several (alternative) conformations in the crystal used for the X-ray experiment; otherwise, such residues deteriorate significantly. The authors believe that a large distortion of a residue in unrestrained refinement may hint at the presence of alternative conformations of this residue. To obtain these hints in a routine way, two methods of analyzing the shifts of atomic centres resulting from several cycles of unrestrained refinement are described. A simple diagram plotting the values of the atomic shifts against the residue number may give an idea of the crystallographic order of different parts of the structure at a qualitative level. To put the analysis on a more quantitative basis, several decision-making procedures were developed and tested which compose a list of residues that are likely to be present in alternative conformations or to be disordered and so should be checked thoroughly using Fourier syntheses and included in the model with alternative conformations when necessary. The parameters and performance of the suggested procedures were estimated by the use of 203 PDB structures refined at resolutions better than 1.2 Å. Decision-making procedures based on analysis of atomic shifts were found to be more reliable than similar procedures based on atomic displacement parameters or density values calculated at atomic centres.


Journal of Applied Crystallography | 2015

Programming new geometry restraints: parallelity of atomic groups

Oleg V. Sobolev; Pavel V. Afonine; Paul D. Adams; Alexandre Urzhumtsev

Details are described of the calculation of new parallelity restraints recently introduced in cctbx and PHENIX.


bioRxiv | 2018

A fully automatic method yielding initial models from high-resolution electron cryo-microscopy maps

Thomas C. Terwilliger; Paul D. Adams; Pavel V. Afonine; Oleg V. Sobolev

A fully automated procedure for optimization and interpretation of reconstructions from cryo-EM is developed and applied to 476 datasets with resolution of 4.5 Å or better, including reconstructions of 47 ribosomes and 32 other protein-RNA complexes. The median fraction of residues in the deposited structures reproduced automatically was 71% for reconstructions determined at resolutions of 3 Å or better and 47% for those at lower resolution.


Journal of Structural Biology | 2018

Map segmentation, automated model-building and their application to the Cryo-EM Model Challenge

Thomas C. Terwilliger; Paul D. Adams; Pavel V. Afonine; Oleg V. Sobolev

A recently-developed method for identifying a compact, contiguous region representing the unique part of a density map was applied to 218 Cryo-EM maps with resolutions of 4.5 Å or better. The key elements of the segmentation procedure are (1) identification of all regions of density above a threshold and (2) choice of a unique set of these regions, taking symmetry into consideration, that maximize connectivity and compactness. This segmentation approach was then combined with tools for automated map sharpening and model-building to generate models for the 12 maps in the 2016 Cryo-EM Model Challenge in a fully automated manner. The resulting models have completeness from 24% to 82% and RMS distances from reference interpretations of 0.6 Å-2.1 Å.


Nature Methods | 2018

A fully automatic method yielding initial models from high-resolution cryo-electron microscopy maps

Thomas C. Terwilliger; Paul D. Adams; Pavel V. Afonine; Oleg V. Sobolev

We report a fully automated procedure for the optimization and interpretation of reconstructions from cryo-electron microscopy (cryo-EM) data, available in Phenix as phenix.map_to_model. We applied our approach to 476 datasets with resolution of 4.5 Å or better, including reconstructions of 47 ribosomes and 32 other protein–RNA complexes. The median fraction of residues in the deposited structures reproduced automatically was 71% for reconstructions determined at resolutions of 3 Å or better and 47% for those at resolutions worse than 3 Å.A fully automated method for modeling protein and protein–RNA complex structure from cryo-EM data, requiring minimal user intervention, is described.


Acta Crystallographica Section A | 2017

Video tutorials for the Phenix software suite

Dorothee Liebschner; Pavel V. Afonine; Nigel W. Moriarty; Billy K. Poon; Oleg V. Sobolev; Paul D. Adams

The World Wide Web is abundant of video tutorials covering many different topics. For example, a search for “tutorial” yields 178 million results on the video sharing website Youtube. The success of the tutorials is without doubt aided by easy-to-use software for creating the videos and the broad coverage of high-speed internet connections enabling convenient upload and access. This also reflects the desire of the audience to have an audio-visual support for procedures, which could be given in written format instead. In 2017, the Phenix Tutorials Youtube channel [1] was launched. Phenix [2] is a program for the automated determination of molecular structures using X-ray crystallography and other methods. The extensive online manual [3] covers about 180 separate html pages (more than 600 letter size printed pages), including tutorials, FAQs and descriptions of many Phenix tools. To expand the documentation resources, video tutorials for commonly used tools were created and made accessible on YouTube. The videos give a short introduction about a Phenix tool, summarize the required input files and parameters, explain how to run the program and – when appropriate – discuss the results. It is planned to keep adding tutorials addressing new tools or commonly asked questions from users. Here, we present our experience with the Phenix video tutorials, such as feedback from the community, viewing statistics and how to create them.

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Paul D. Adams

Lawrence Berkeley National Laboratory

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Pavel V. Afonine

Lawrence Berkeley National Laboratory

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Thomas C. Terwilliger

Los Alamos National Laboratory

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Billy K. Poon

Lawrence Berkeley National Laboratory

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Nigel W. Moriarty

Lawrence Berkeley National Laboratory

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Vladimir Y. Lunin

Russian Academy of Sciences

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