## 8 Case Study and Feedback
### Case Study
#### 8.1.1 Pocket Correlation Calculation Results of P2V-DTW  
In this experiment, the Fpocket [28] tool is used to extract dynamic molecular pockets from the above two groups of data, and a variety of dynamic features of pockets are extracted. After generating the vector representation of pockets, the P2V-DTW method is used to calculate the correlation between pockets. We verify the effectiveness of the P2V-DTW method proposed in this paper, calculate the correlation of molecular pockets, and verify whether using the shape combined with topological representation of protein pockets can improve the correlation calculation to a certain extent. The evaluation result of correlation is mainly based on whether more accurate allosteric pockets can be selected according to the correlation between pockets, and the ranking of real allosteric pockets calculated according to the correlation determines the advantages and disadvantages of the method.  
Compare the six cases using P2V-DTW and not using P2V-DTW when processing volume, topology, and volume and topology data respectively. In this paper, the above six cases are tested and compared. The calculated results are shown in Table 2. This is worth mentioning because the use of P2V-DTW method requires the acquisition of topological relations, and it is impossible to directly process volume data. When volume data is processed without a P2V-DTW method, it is the correlation calculation method proposed in D3 Pockets [2].  
Table 2 shows the real allosteric pocket correlation ranking obtained by using P2V-DTW and not using P2V-DTW methods to calculate the correlation between orthosteric and allosteric pockets. The higher the allosteric pocket ranking is, the higher the accuracy of the algorithm is. For example, in the GPX4 dataset, the P2V-DTW method and volume plus topology data combination can attain the similarity ranking between pockets and orthosteric pockets, and allosteric pockets are ranked as 2 in all pockets. Comparing the pocket correlation calculated by the two sets of protein trajectory datasets, it is found that using the P2V-DTW method and volume plus topology data in the GPX4 dataset has the greatest effect on the correlation calculation, which is significantly improved compared with D3 Pockets method. The calculation effect of using the P2V-DTW method in the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Volume**} & **GPX** & \multirow{2}{*}{**Vol + Topo**} & \multirow{2}{*}{**Volume**} & **ACE** & **Topology** & **Vol + Topo** \\  & & & & & & \\ \hline P2V-DTW & - & 5 & 2 & - & 3 & 3 \\ No & 4 & 4 & 4 & 8 & 4 \\ P2V-DTW & (D3 Pockets) & & & & (D3 Pockets) & 4 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of pocket similarity rank of calculation methods.  
ACE2 dataset is better than that without the P2V-DTW method. When the P2V-DTW method is not used, the calculation effect of correlation can also be effectively improved by using topological data, especially compared with the D3 Pockets method, and the effect is significantly improved.  
To further illustrate the effectiveness of our method, we compare our method with D3 Pockets. The results of the similarity calculations are shown in Table 3. The smaller value for the P2V-DTW method means the two pockets are more relevant than the two pockets are, with pocket 4 being the second in the similarity ranking. The value of the D3 Pockets method closer to 1 means the two pockets are more relevant, with pocket 4 being the fourth in the similarity ranking. Therefore, using protein morphological data plus topological data combined with the P2V-DTW algorithm to calculate pocket correlation makes the overall performance is better.