Segmentation and Quality Assessment  of Protein Secondary Structure

Segmentation of protein secondary structures

Summary: 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.

Relevant Publication:

[J2]  Sazzed, S., Determining Protein Secondary Structures in Heterogeneous Medium-Resolution Cryo-EM Images Using CryoSSESeg, In ACS Omega, 2024

Impact Factor: 4.1

[J1] Mu, Y.; Sazzed, S.; Alshammari, M.; Sun, J., & He, J., A Tool for Segmentation of Secondary Structures in 3D Cryo-EM Density Map Components Using Deep Convolutional Neural Networks, In Frontiers in Bioinformatics, 2021.

Impact Factor: 2.8

[C1] Deng, Y.; Mu, Y.; Sazzed, S.; Sun, J.; and He, J., Using Curriculum Learning in Pattern Recognition of 3-dimensional Cryo-electron Microscopy Density Maps, In ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (ACM BCB), 2020.

Quality assessment of alpha-helix in medium resolution image

Summary: Cryo-EM density maps at medium resolution (5–10 Å) show secondary structural features like α-helices and β-sheets, lacking detailed side chain information crucial for direct structure determination. Over 800 entries in the Electron Microscopy Data Bank (EMDB) link medium-resolution density maps with atomic models, displaying diverse similarities. This study introduces and assesses the F1 score, a local similarity criterion, to validate atomic models and classify structural features. The F1 score, ranging from zero to one, evaluates cylindrical agreement between helical density and atomic models. Analysis of 30,994 helices across 3,247 protein chains reveals F1 scores ranging from 0.171 to 0.848, indicating effective stratification of data. Higher F1 scores correlate with regions exhibiting high and spatially homogeneous local resolution (5–7.5 Å) in helical density. The proposed F1 scores serve as a discriminative classifier for validation studies and a ranking criterion for cryo-EM density features in databases.