# A Deep Learning-Based Approach for Predicting Michaelis Constants from Enzymatic Reactions
## 4 Results
### The Impact of Enzyme Sequence Similarity on DLERKm Prediction Performance  
To validate the generalization ability of DLERKm in predicting other enzymes, we constructed different training subsets by removing training samples that were most similar to the top-K (K = 5, 10, 15, 20) entries in the test set. As shown in Table 6, increasing the number of removed samples from the training set can decrease the similarity between the training and test samples.  
Moreover, we trained DLERKm on four different training subsets and tested it on the test set. As shown in Table 7, the results illustrate that the prediction performances of DLERKm decreases as the difference between the training and test set samples increases. This trend is similar to the results of other prediction models [22; 23; 25]. However, it is worth noting that the PCC metric values remain stable above 0.5, which means that even with a large difference in sequence similarity, DLERKm is still able to provide valuable insights for enzyme screening.  
Furthermore, Figure 8 shows the prediction results of the trained DLERKm models according to four training subsets on three sequence similarity ranges. For a given training subset, the model evaluation metric values generally increase as the similarity between enzyme sequences increases. By observing the changes in DLERKm performance metric values across different training subsets, we find that as more samples are removed, the model prediction performance deteriorates. However, in the training subsets corresponding to the Top-5 and Top-10, DLERKm achieves better performances despite having fewer training samples when the training similarity range is 0.80-0.95. We suppose that some redundant samples are removed, which instead helps the model learn the characteristics of enzymatic reactions more effectively.  
Overall, although changes in sequence similarity generally affect the model results of DLERKm, it still provides relatively reliable predictions even in extreme cases (Top-20). Additionally, through multiple comparisons of DLERKm model prediction performance at different sequence similarities, we find that as sequence similarity increases, the model prediction performance gradually improves. Interestingly, in the test samples under Top-5 and Top-10 with a similarity range of 0.80-0.95, the latter shows better performance. This  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Top-K** & **0.40–0.80** & **0.80–0.95** & **0.95–1.00** \\ \hline Top-5 & 98 & 100 & 1627 \\ Top-10 & 214 & 431 & 1380 \\ Top-15 & 317 & 570 & 1138 \\ Top-20 & 411 & 682 & 932 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The distribution of test set samples after removing the top-K most similar enzyme sequences from the training set, where top-K is determined by selecting the K most similar training samples for each test sequence. The column names in the table (e.g., “0.40–0.80”) represent the similarity range between test samples and training samples, while the values in the table indicate the number of test samples falling within the corresponding similarity range.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Top-K** & **RMSE** & **MAE** & **R\({}^{2}\)** & **PCC** \\ \hline Top-5 & 0.8994 & 0.6511 & 0.4374 & 0.6734 \\ Top-10 & 0.9517 & 0.7111 & 0.3701 & 0.6176 \\ Top-15 & 1.0275 & 0.7649 & 0.2657 & 0.5640 \\ Top-20 & 1.0595 & 0.8009 & 0.2194 & 0.5229 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation metric values (RMSE, MAE, R\({}^{2}\), PCC) of DLERKm on the test set trained with different training subsets.  
suggests that constructing a high-quality enzyme catalysis reaction dataset is one of the key directions for improving the model prediction performance.