proposals for selection, we may face an exploration-exploitation dilemma, where the algorithm may fall into a local optimum. To balance this dilemma, we follow [@AIIDE1614003] to use the $Upper$ $Confidence$ $Bound$ $for$ $Trees$ (UCT) [@mcts] method to optimize the action selection.

The UCT method is designed for better action selection strategy. At step $t$, the next action $a_{t+1}$ is: $$\begin{aligned}
  a_{t+1}  & = \arg \max_{a} ( \frac{V_a}{n_a} + C \sqrt{\frac{2 \ln N_a}{n_a}}),\end{aligned}$$ where $V_{a}$ is the reward at action $a$. $N_a$ is the number of simulations that the node has been visited, and $n_a$ is the number of simulations that the node has been followed.

### Tree Pruning by Peptide Bond Recognition Network

Since protein structure is highly twisted, a pair of amino acids that are close to each other in 3D space might be far apart in primary in the sequence. Experienced biologists distinguish whether two amino acids are connected or not, by examining whether there is a peptide bond between them. We also designed a Peptide Bond Recognition Network (PBNet) to predict whether there is a peptide bond between two proposals. With PBNet, we can efficiently remove 50% edges in the KNN graph on average, which largely improves 