WikiSysop: Created page with "== Citation == Sazzed, Salim. Determining Protein Secondary Structures in Heterogeneous Medium-Resolution Cryo-EM Images Using CryoSSESeg. 2024. ACS Omega == Abstract == While the acquisition of cryo-electron microscopy (cryo-EM) at near-atomic resolution is becoming more prevalent, a considerable number of density maps are still resolved only at intermediate resolutions (5−10 Å). Due to the large variation in quality among these medium-resolution density maps, ext..."

2024-08-12T06:56:31Z

Created page with "== Citation == Sazzed, Salim. Determining Protein Secondary Structures in Heterogeneous Medium-Resolution Cryo-EM Images Using CryoSSESeg. 2024. ACS Omega == Abstract == While the acquisition of cryo-electron microscopy (cryo-EM) at near-atomic resolution is becoming more prevalent, a considerable number of density maps are still resolved only at intermediate resolutions (5−10 Å). Due to the large variation in quality among these medium-resolution density maps, ext..."

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== Citation ==

Sazzed, Salim. Determining Protein Secondary Structures in Heterogeneous Medium-Resolution Cryo-EM Images Using CryoSSESeg. 2024. ACS Omega

== Abstract ==

While the acquisition of cryo-electron microscopy
(cryo-EM) at near-atomic resolution is becoming more prevalent, a
considerable number of density maps are still resolved only at
intermediate resolutions (5−10 Å). Due to the large variation in
quality among these medium-resolution density maps, extracting
structural information from them remains a challenging task. This
study introduces a convolutional neural network (CNN)-based
framework, cryoSSESeg, to determine the organization of protein
secondary structure elements in medium-resolution cryo-EM images.
CryoSSESeg is trained on approximately 1300 protein chains derived
from around 500 experimental cryo-EM density maps of varied
quality. It demonstrates strong performance with residue-level F1
scores of 0.76 for helix detection and 0.60 for β-sheet detection on
average across a set of testing chains. In comparison to traditional image processing tools like SSETracer, which demand significant
manual intervention and preprocessing steps, cryoSSESeg demonstrates comparable or superior performance. Additionally, it
demonstrates competitive performance alongside another deep learning-based model, Emap2sec. Furthermore, this study
underscores the importance of secondary structure quality, particularly adherence to expected shapes, in detection performance,
emphasizing the necessity for careful evaluation of the data quality.

== Keywords ==

== Links ==

https://pubs.acs.org/doi/full/10.1021/acsomega.4c02608

== Related software ==

== Related methods ==

== Comments ==

2024Sazzed CryoSSESeg - Revision history