with APRoI layer, which further improved mAP to 0.711. This indicates the importance of preserving the aspect ratio for detection in this task. We also found that the output size of APRoI layer is important, as mAP improved when we changed the target size from $8^3$ to $16^3$, the mAP has a large improvement. Finally, we used the sequence-guided neighbor loss training strategy and further improved the mAP to 0.891. Fig. \[fig:exp-detection\] shows a qualitative result of detection. Although the gain of mAP from neighbor loss was only marginal, the sequence coverage percentage of the threading result improved substantially.

### Main Chain Threading

We mainly compared the proposed MCTS algorithm with a Depth First Search (DFS) method. DFS$_o$ and DFS$_d$ represent using IoU and distance as the selection criterion, respectively. The proposed MCTS algorithm outperformed the DFS based methods. The PBNet can be applied to both DFS and MCTS algorithms, as it is used to prune the trees by examining the existence of peptide bonds. It can be seen in Table. \[table:threading\] shows that both threading algorithms are largely improved with PBNet. PBNet achieved 89.8% accuracy for peptide bond recognition. In Fig. \[fig:exp-threading\] shows some qualitative results of threading.

Table. 