Automation and assessment of de novo modeling with Pathwalking in near atomic resolution cryoEM density maps

2018 ◽  
Vol 204 (3) ◽  
pp. 555-563 ◽  
Author(s):  
Muyuan Chen ◽  
Matthew L. Baker
Keyword(s):  
De Novo ◽  
Atomic Resolution ◽  
Density Maps

scholarly journals Automatic building of protein atomic models from cryo-EM density maps using residue co-evolution

2020 ◽  
Author(s):  
Guillaume Bouvier ◽  
Benjamin Bardiaux ◽  
Riccardo Pellarin ◽  
Chiara Rapisarda ◽  
Michael Nilges
Keyword(s):  
Conformational Changes ◽  
Minimum Spanning Tree ◽  
De Novo ◽  
Three Dimensional ◽  
Weighted Graph ◽  
Atomic Resolution ◽  
Side Chain ◽  
Evolutionary Information ◽  
Atomic Models ◽  
Density Maps

AbstractElectron cryo-microscopy (cryo-EM) has emerged as a powerful method to obtain three-dimensional (3D) structures of macromolecular complexes at atomic or near-atomic resolution. However, de novo building of atomic models from near-atomic resolution (3-5 Å) cryo-EM density maps is a challenging task, in particular since poorly resolved side-chain densities hamper sequence assignment by automatic procedures at a lower resolution. Furthermore, segmentation of EM density maps into individual subunits remains a difficult problem when no three-dimensional structures of these subunits exist, or when significant conformational changes occur between the isolated and complexed form of the subunits. To tackle these issues, we have developed a graph-based method to thread most of the C-α trace of the protein backbone into the EM density map. The EM density is described as a weighted graph such that the resulting minimum spanning tree encompasses the high-density regions of the map. A pruning algorithm cleans the tree and finds the most probable positions of the C-α atoms, using side-chain density when available, as a collection of C-α trace fragments. By complementing experimental EM maps with contact predictions from sequence co-evolutionary information, we demonstrate that our approach can correctly segment EM maps into individual subunits and assign amino acids sequence to backbone traces to generate full-atom models.

Protein phasing at non-atomic resolution by combining Patterson andVLDtechniques

2014 ◽  
Vol 70 (7) ◽  
pp. 1994-2006 ◽  
Author(s):  
Rocco Caliandro ◽  
Benedetta Carrozzini ◽  
Giovanni Luca Cascarano ◽  
Giuliana Comunale ◽  
Carmelo Giacovazzo ◽  
...  
Keyword(s):  
Model Building ◽  
Protein Structures ◽  
Experimental Information ◽  
Atomic Resolution ◽  
Data Sets ◽  
Density Maps ◽  
Combined Use ◽  
Final Electron ◽  
Automatic Model Building

Phasing proteins at non-atomic resolution is still a challenge for anyab initiomethod. A variety of algorithms [Patterson deconvolution, superposition techniques, a cross-correlation function (Cmap), theVLD(vive la difference) approach, the FF function, a nonlinear iterative peak-clipping algorithm (SNIP) for defining the background of a map and thefree lunchextrapolation method] have been combined to overcome the lack of experimental information at non-atomic resolution. The method has been applied to a large number of protein diffraction data sets with resolutions varying from atomic to 2.1 Å, with the condition that S or heavier atoms are present in the protein structure. The applications include the use ofARP/wARPto check the quality of the final electron-density maps in an objective way. The results show that resolution is still the maximum obstacle to protein phasing, but also suggest that the solution of protein structures at 2.1 Å resolution is a feasible, even if still an exceptional, task for the combined set of algorithms implemented in the phasing program. The approach described here is more efficient than the previously described procedures:e.g.the combined use of the algorithms mentioned above is frequently able to provide phases of sufficiently high quality to allow automatic model building. The method is implemented in the current version ofSIR2014.

scholarly journals Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

Molecules ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 82 ◽  
Author(s):  
Eman Alnabati ◽  
Daisuke Kihara
Keyword(s):  
Electron Microscopy ◽  
De Novo ◽  
Molecular Structures ◽  
Structure Modeling ◽  
Rapid Progress ◽  
Macromolecular Complexes ◽  
Modeling Methods ◽  
Cryo Electron Microscopy ◽  
Density Maps

Cryo-electron microscopy (cryo-EM) has now become a widely used technique for structure determination of macromolecular complexes. For modeling molecular structures from density maps of different resolutions, many algorithms have been developed. These algorithms can be categorized into rigid fitting, flexible fitting, and de novo modeling methods. It is also observed that machine learning (ML) techniques have been increasingly applied following the rapid progress of the ML field. Here, we review these different categories of macromolecule structure modeling methods and discuss their advances over time.

scholarly journals EM-Fold: De Novo Atomic-Detail Protein Structure Determination from Medium-Resolution Density Maps

Structure ◽  
2012 ◽  
Vol 20 (3) ◽  
pp. 464-478 ◽  
Author(s):  
Steffen Lindert ◽  
Nathan Alexander ◽  
Nils Wötzel ◽  
Mert Karakaş ◽  
Phoebe L. Stewart ◽  
...  
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
De Novo ◽  
Protein Structure Determination ◽  
Density Maps ◽  
Medium Resolution

Use of Patterson-based methods automatically to determine the structures of heavy-atom-containing proteins with up to 6000 non-hydrogen atoms in the asymmetric unit

2006 ◽  
Vol 39 (5) ◽  
pp. 728-734 ◽  
Author(s):  
Maria Cristina Burla ◽  
Rocco Caliandro ◽  
Benedetta Carrozzini ◽  
Giovanni Luca Cascarano ◽  
Liberato De Caro ◽  
...  
Keyword(s):  
Crystal Structures ◽  
Protein Data Bank ◽  
Heavy Atom ◽  
Data Bank ◽  
Atomic Resolution ◽  
Asymmetric Unit ◽  
Hydrogen Atoms ◽  
Heavy Atoms ◽  
Density Maps ◽  
Resolution Data

The Patterson superposition methods described by Burlaet al.[J. Appl. Cryst.(2006),39, 527–535], based on the use of the `multiple implication functions', have been enriched by supplementary filtering techniques based on some general (resolution-dependent) features of both the Patterson and the electron density maps. The method has been implemented in a modified version of the programSIR2004and tested using a set of 20 crystal structures selected from the Protein Data Bank, having a number of non-hydrogen atoms in the asymmetric unit larger than 2000, atomic resolution data and some heavy atoms (equal to or heavier than Ca). The new phasing procedure is able to solve most of the test structures, among which there are two proteins with more than 6000 non-hydrogen atoms in the asymmetric unit, so extending by far the complexity today commonly considered as the limit for Patterson-based methods (i.e.about 2000 non-hydrogen atoms).

scholarly journals De novo protein structure determination from near-atomic-resolution cryo-EM maps

2015 ◽  
Vol 12 (4) ◽  
pp. 335-338 ◽  
Author(s):  
Ray Yu-Ruei Wang ◽  
Mikhail Kudryashev ◽  
Xueming Li ◽  
Edward H Egelman ◽  
Marek Basler ◽  
...  
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
De Novo ◽  
Protein Structure Determination ◽  
Atomic Resolution

scholarly journals Thresholding of cryo-EM density maps by false discovery rate control

2018 ◽  
Author(s):  
Maximilian Beckers ◽  
Arjen J. Jakobi ◽  
Carsten Sachse
Keyword(s):  
False Discovery Rate ◽  
Background Noise ◽  
Multiple Hypothesis Testing ◽  
Molecular Structures ◽  
Atomic Resolution ◽  
Detection Rates ◽  
False Discovery ◽  
Density Maps ◽  
Density Values ◽  
Subtomogram Averaging

AbstractCryo-EM now commonly generates close-to-atomic resolution as well as intermediate resolution maps from macromolecules observed in isolation and in situ. Interpreting these maps remains a challenging task due to poor signal in the highest resolution shells and the necessity to select a threshold for density analysis. In order to facilitate this process, we developed a statistical framework for the generation of confidence maps by multiple hypothesis testing and false discovery rate (FDR) control. In this way, 3D confidence maps contain separated signal from background noise in the form of local detection rates of EM density values. We demonstrate that confidence maps and FDR-based thresholding can be used for the interpretation of near-atomic resolution single-particle structures as well as lower resolution maps determined by subtomogram averaging. Confidence maps represent a conservative way of interpreting molecular structures due to minimized noise. At the same time they provide a detection error with respect to background noise, which is associated with the density and particularly beneficial for the interpretation of weaker cryo-EM densities in cases of conformational flexibility and lower occupancy of bound molecules and ions to the structure.

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