Fitting Low-Resolution Protein Structures into Cryo-EM Density Maps by Multiobjective Optimization of Global and Local Correlations

2021 ◽  
Author(s):  
Biao Zhang ◽  
Wenyi Zhang ◽  
Robin Pearce ◽  
Yang Zhang ◽  
Hong-Bin Shen
Keyword(s):  
Multiobjective Optimization ◽  
Protein Structures ◽  
Low Resolution ◽  
Density Maps ◽  
Local Correlations ◽  
Global And Local

scholarly journals Refinement of Protein Structures into Low-Resolution Density Maps Using Rosetta

2009 ◽  
Vol 392 (1) ◽  
pp. 181-190 ◽  
Author(s):  
Frank DiMaio ◽  
Michael D. Tyka ◽  
Matthew L. Baker ◽  
Wah Chiu ◽  
David Baker
Keyword(s):  
Protein Structures ◽  
Low Resolution ◽  
Density Maps

scholarly journals Machine learning to estimate the local quality of protein crystal structures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ikuko Miyaguchi ◽  
Miwa Sato ◽  
Akiko Kashima ◽  
Hiroyuki Nakagawa ◽  
Yuichi Kokabu ◽  
...  
Keyword(s):  
Electron Density ◽  
Protein Structures ◽  
Three Dimensional ◽  
Protein Crystal ◽  
Low Resolution ◽  
X Ray Crystallography ◽  
Density Maps ◽  
Local Quality ◽  
Density Map ◽  
Electron Density Map

AbstractLow-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, called quality assessment based on an electron density map (QAEmap), which evaluates local protein structures determined by X-ray crystallography and could be applied to correct structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and putative high-resolution experimental electron density map. This correlation could be used as a metric to modify the structure. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution.

scholarly journals QAEmap: A Novel Local Quality Assessment Method for Protein Crystal Structures Using Machine Learning

2021 ◽  
Author(s):  
Ikuko Miyaguchi ◽  
Miwa Sato ◽  
Akiko Kashima ◽  
Hiroyuki Nakagawa ◽  
Yuichi Kokabu ◽  
...  
Keyword(s):  
High Resolution ◽  
Quality Assessment ◽  
Electron Density ◽  
Protein Structures ◽  
Assessment Method ◽  
Protein Crystal ◽  
Low Resolution ◽  
Density Maps ◽  
Density Map ◽  
Electron Density Map

Abstract Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, quality assessment based on an electron density map (QAEmap), that evaluates local protein structures determined by X-ray crystallography and corrects structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and the putative high-resolution experimental electron density map. This estimates how well the structure fits the high-resolution map. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution.

SIR2004: an improved tool for crystal structure determination and refinement

2005 ◽  
Vol 38 (2) ◽  
pp. 381-388 ◽  
Author(s):  
Maria C. Burla ◽  
Rocco Caliandro ◽  
Mercedes Camalli ◽  
Benedetta Carrozzini ◽  
Giovanni L. Cascarano ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Ab Initio ◽  
Structure Determination ◽  
Protein Structures ◽  
Medium Size ◽  
Graphical Interface ◽  
Asymmetric Unit ◽  
Density Maps ◽  
Data Resolution ◽  
User Friendly

SIR2004is the evolution of theSIR2002program [Burla, Camalli, Carrozzini, Cascarano, Giacovazzo, Polidori & Spagna (2003).J. Appl. Cryst.36, 1103]. It is devoted to the solution of crystal structures by direct and Patterson methods. Several new features implemented inSIR2004make this program efficient: it is able to solveab initioboth small/medium-size structures as well as macromolecules (up to 2000 atoms in the asymmetric unit). In favourable circumstances, the program is also able to solve protein structures with data resolution up to 1.4–1.5 Å, and to provide interpretable electron density maps. A powerful user-friendly graphical interface is provided.

On `globbicity' of low-resolution protein structures

1999 ◽  
Vol 55 (1) ◽  
pp. 230-237 ◽  
Author(s):  
D. Y. Guo ◽  
Robert H. Blessing ◽  
David A. Langs ◽  
G. David Smith
Keyword(s):  
Protein Structures ◽  
Low Resolution

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.

Sign in / Sign up

Export Citation Format

Share Document