DeepSSETracer 2.0: Improved Deep Learning Model Performance for Protein Secondary Structure Segmentation from Cryo-EM...

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.