# A Deep Learning-Based Approach for Predicting Michaelis Constants from Enzymatic Reactions
## 4 Results
### Prediction Performance of DLERKm on Different Types of Enzymes  
To verify the prediction reliability of DLERKm for different types of enzymes, we divided the test set samples into wild-type and mutant-type based on the presence of mutation sites in the enzymatic reaction samples. Meanwhile, we divided the test samples into two datasets based on species (_E. coli_ and _H. sapiens_). The trained DLERKm was used to predict the samples in these four sets, and we compared the performances using the RMSE, MAE, R\({}^{2}\), and PCC metrics. As shown in Figure 9, the prediction results illustrate that DLERKm can provide better linear characteristics (Pearson correlation) on wild-type enzyme samples compared to mutant enzymes. As shown in Figure 10, the results demonstrate that DLERKm prediction performance on _H. sapiens_ outperforms the other three types of enzymes. Additionally, the violin plot indicates that the prediction results generally have a smoother distribution on the dataset. Specifically, as shown in Table 8, the model prediction results on wild-type enzymes show improvements of approximately 7.2%, 4.1%, 5.3%, and 2.6% in terms of RMSE, MAE, and R\({}^{2}\) compared with mutant enzymes, respectively. Moreover, for two species types in the test samples, DLERKm performs better on Homo sapiens samples. In the first two evaluation metrics (RMSE and MAE), the predictions for _H. sapiens_ show improvements of about 9.8% and 7.5% compared to _E. coli_, while the latter two metrics (R\({}^{2}\) and PCC) show an increase of 15.4% and 7.4%, respectively. Overall, DLERKm achieved good prediction performances across the four test subsets (wild-type, mutant, _E. coli_, and _H. sapiens_). For the wild-type enzymatic reaction and mutant-type enzymatic reaction test subsets, DLERKm performed better on the former than on the latter, which means that the model has a better feature extraction ability for the native enzyme sequences in organisms. Moreover, the differences in data distribution within the training samples or the feature variations among different species may have led to DLERKm achieving better prediction performances on _H. sapiens_ enzymatic samples compared to _E. coli_ enzymatic samples. Figure 8: Prediction metric values (PCC, R\({}^{2}\)) of DLERKm under different similarity (enzyme sequence) ranges. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **RMSE** & **MAE** & **R\({}^{2}\)** & **PCC** & **Sample Number** \\ \hline Wild-type & 0.7569 & 0.5400 & 0.5997 & 0.7744 & 1550 \\ Mutant & 0.8159 & 0.5633 & 0.5691 & 0.7544 & 475 \\ _E. coli_ & 0.8280 & 0.5984 & 0.5296 & 0.7277 & 593 \\ _H. sapiens_ & 0.7463 & 0.5235 & 0.6116 & 0.7820 & 1432 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: DLERKm evaluation metric values (RMSE, MAE, R\({}^{2}\), PCC) on enzymes (wild-type and mutant) and species (_H. sapiens_ and _E. coli_). Furthermore, we divided the corresponding test samples of _H. sapiens_ and _E. coli_ into wild-type enzymatic reactions and mutant enzymatic reactions. As shown in Figure 11, the DLERKm prediction results show considerable goodness of fit in predicting Km across all datasets. Moreover, Figure 12 shows that DLERKm prediction performance on _H. sapiens_ wild-type enzymatic reactions is better than on other enzymatic reactions. Additionally,  
Figure 10: DLERKm prediction results visualization for different types of enzymatic reactions. The bar chart on the left describes the four prediction metrics (RMSE, MAE, R\({}^{2}\), PCC) of DLERKm on different types of datasets. Specifically, for each metric, the bar charts from left to right represent the predicted values of DLERKm for wild-type enzymatic reactions, mutant enzymatic reactions, _E. coli_-type enzymatic reactions, and _H. sapiens_-type enzymatic reactions. The violin plot on the right shows the distribution of predicted values of DLERKm across four types of enzyme datasets. In each violin plot, the dashed line represents the range of the predicted values interquartile range.