scholarly journals A Timeless Tale: G4 structure recognition by the fork protection complex triggers unwinding by DDX 11 helicase

2020 ◽  
Vol 39 (18) ◽  
Author(s):  
Catherine H Freudenreich
Keyword(s):  
Structure Recognition ◽  
Fork Protection Complex

scholarly journals Nanopore Technology for the Application of Protein Detection

Nanomaterials ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1942
Author(s):  
Xiaoqing Zeng ◽  
Yang Xiang ◽  
Qianshan Liu ◽  
Liang Wang ◽  
Qianyun Ma ◽  
...  
Keyword(s):  
Single Molecule ◽  
Protein Detection ◽  
High Sensitivity ◽  
Single Molecule Detection ◽  
Sequence Detection ◽  
Fast Detection ◽  
Material Basis ◽  
Sensing Technology ◽  
Detection Technology ◽  
Structure Recognition

Protein is an important component of all the cells and tissues of the human body and is the material basis of life. Its content, sequence, and spatial structure have a great impact on proteomics and human biology. It can reflect the important information of normal or pathophysiological processes and promote the development of new diagnoses and treatment methods. However, the current techniques of proteomics for protein analysis are limited by chemical modifications, large sample sizes, or cumbersome operations. Solving this problem requires overcoming huge challenges. Nanopore single molecule detection technology overcomes this shortcoming. As a new sensing technology, it has the advantages of no labeling, high sensitivity, fast detection speed, real-time monitoring, and simple operation. It is widely used in gene sequencing, detection of peptides and proteins, markers and microorganisms, and other biomolecules and metal ions. Therefore, based on the advantages of novel nanopore single-molecule detection technology, its application to protein sequence detection and structure recognition has also been proposed and developed. In this paper, the application of nanopore single-molecule detection technology in protein detection in recent years is reviewed, and its development prospect is investigated.

scholarly journals Coordinated Degradation of Replisome Components Ensures Genome Stability upon Replication Stress in the Absence of the Replication Fork Protection Complex

PLoS Genetics ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. e1003213 ◽  
Author(s):  
Laura C. Roseaulin ◽  
Chiaki Noguchi ◽  
Esteban Martinez ◽  
Melissa A. Ziegler ◽  
Takashi Toda ◽  
...  
Keyword(s):  
Replication Fork ◽  
Genome Stability ◽  
Replication Stress ◽  
Fork Protection Complex

scholarly journals Using haptic cues to aid nonvisual structure recognition

2008 ◽  
Vol 5 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Caroline Jay ◽  
Robert Stevens ◽  
Roger Hubbold ◽  
Mashhuda Glencross
Keyword(s):  
Structure Recognition ◽  
Haptic Cues

scholarly journals Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yue Li ◽  
Xuyang Zhou ◽  
Timoteo Colnaghi ◽  
Ye Wei ◽  
Andreas Marek ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Chemical Species ◽  
Atom Probe ◽  
Local Order ◽  
Face Centered Cubic ◽  
Ordered Structure ◽  
Ordered Structures ◽  
Distribution Maps ◽  
Structure Recognition ◽  
Fcc Alloys

AbstractNanoscale L12-type ordered structures are widely used in face-centered cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyze the complete point cloud (>10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type δ′–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future.

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