\[table:Rosetta-denovo\] demonstrates that Rosetta-denovo is very time-consuming. We ran Rosetta-denovo in a cluster with 200 computational nodes for 2 rounds. The CPU time was calculated by summing up all the tasks. It took Rosetta-denovo hundreds of hours to finish one round of computation, whereas our approach took only a few minutes and outperformed Rosetta-denovo by a huge margin.

![An example of threading results by MCTS+PBNet.[]{data-label="fig:exp-threading"}](1_4_exp-threading_small.pdf){width="0.9\linewidth"}

  -------------- ---------- ------------ ---------- -----------
                                                    
                  Coverage      Time      Coverage  
  Rosetta (R1)      0.20       133 h        0.39       90 h
  Rosetta (R2)      0.24       260 h        0.62       261 h
  Ours (MCTS)     **0.88**   **11.3 m**   **0.91**   **6.8 m**
  -------------- ---------- ------------ ---------- -----------

  : Threading accuracy and efficiency compared with Rosetta-denovo. R1 and R2 denotes round 1 and 2. []{data-label="table:Rosetta-denovo"}

### Comparison with other 3D Detection Methods

We adapted MV3D and Frustum-PointNet to the amino acid detection task. For MV3D, we cropped and projected the volumes into the bird’s eye view and the front view (FV) and then trained MV3D. For Frustum-PointNet, we selected the voxels with density value is higher than the mean of the volumes as the points set. In the training step, we projected the 3D ground-truth boxes into their FV as the input. In the testing step, 