Document Type

Conference Paper

Publication Date

2025

DOI

10.1145/3768322.3769026

Publication Title

BCB Companion '25: Companion Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Pages

6 pp.

Conference Name

16th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, October 11-15, 2025, Philadelphia, PA

Abstract

DeepSSETracer is a method for segmenting protein secondary structure from medium-resolution (5-10Å) cryogenic electron microscopy (cryo-EM) density maps. We conducted experiments and ablation studies to examine the effects of normalization methods, max-pooling, activation functions, and loss calculation region on DeepSSETracer. By combining multiple technical improvements, the performance of the new version, DeepSSETracer 2.0, was significantly enhanced compared to DeepSSETracer 1.1. On a set of 77 test cases, the weighted average per-voxel F1 score increased from 62.1% to 70.3% for helix detection, and from 47.8% to 62.5% for β-sheet detection. While each of the five modifications in the network enhanced the detection of both helices and β-sheets, the improvement on β-sheets was even more pronounced. The ablation studies show that the most enhanced accuracy comes from the replacement of batch normalization with instance normalization, which accounts for increased F1 scores by 3% (helix) and 6.3% (β-sheet). These results show that relatively modest network tuning can significantly improve segmentation, suggesting that further incremental gains remain possible within the U-Net deep learning architecture.

Rights

© 2025 Copyright is held by the owner/authors.

This work is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Original Publication Citation

Hawickhorst, B., Nguyen, T., Wriggers, W., Sun, J., & He, J. (2025). DeepSSETracer 2.0: Improved deep learning model performance for protein secondary structure segmentation from cryo-EM maps. In M. X. Shi & X. Qian (Eds.), BCB Companion '25: Companion Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (Article 12). Association for Computing Machinery. https://doi.org/10.1145/3768322.3769026

ORCID

0000-0001-5326-3152 (Wriggers), 0009-0000-8905-7553 (Sun)

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