# A Deep Learning-Based Approach for Predicting Michaelis Constants from Enzymatic Reactions
## 3 Materials and Methods  
### Benchmark Dataset  
Sabio-RK [26] and UniProt [27] are essential databases for metabolic model constructions, which provide critical data support for deep learning-based predictions modeling. Sabio-RK is a database for enzymatic reaction kinetics that integrates kinetic parameters determrined by experiments (such as Km, Kcat, and Ki), enzymatic reaction (SabioReactionID), and enzyme sequence IDs (UniProtKB AC). UniProt is a widely recognized protein \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Related Work** & **Architecture** & **Input** & **Other Inputs** \\ \hline [PERSON] et al. [31] & Linear regression model & enzyme-substrate & Organism \\ [PERSON] et al. [32] & Multilayer perceptron & enzyme-substrate & / \\ [PERSON] et al. [33] & Multilayer perceptron & enzyme-substrate & Physicochemical properties \\ MLAGO [34] & Random forest model & / & EC, Compound, Organism \\ MPEK [35] & ProtT5, Mole-BERT, Gating network & enzyme-substrate & pH, Temp, Organism \\ EITLEM [38] & ESM-1v, Fingerprints, ProMolAtt & enzyme-substrate & / \\ UniKP [39] & ProtT5, SMILES Transformer, Ensemble module & enzyme-substrate & pH, Temp \\ DLERKm (This study) & ESM-2, RXNFP, Fingerprints, Channel attention & enzyme-substrate-product & / \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of related works on Km predictions. database that offers comprehensive enzyme information, including protein sequences and functional annotations. As shown in Figure 1A, to construct a ternary dataset of substrates, products, and enzyme sequences for model training and Km prediction, we first collected Sabio-RK for enzymatic reaction entries that include Km values. Then, we collected enzymatic reaction samples from two species with the highest data available on the platform: Homo sapiens (_H. sapiens_) and Escherichia coli (_E. coli_). This resulted in a total of 16,322 samples, and more details can be found in Figures S1-S4 of the Supplementary Materials. Furthermore, we removed samples without Km values or corresponding UniProt IDs for enzyme sequences, resulting in 4146 enzyme reaction samples from _E. coli_ and 8672 enzyme reaction samples from _H. sapiens_. Using the SabioReactionID and UniProtKB AC from these samples, we retrieved substrate SMILES, product SMILES, and enzyme amino acid sequences from Sabio-RK and UniProt databases, where samples that could not be retrieved from the databases or lacks mutation site information were excluded. As a consequence, we obtained yielded a total of 10,122 enzymatic reaction samples. Figure 1: The framework of DLERKm. (**A**) The _E. coli_ and _H. sapiens_ data used in this study were obtained from the SABIO-RK database in which samples include the information of enzymatic reaction IDs, UniProtKB AC numbers, and Km values. After processing, we obtained 2962 enzyme reaction samples from _E. coli_ and 7160 enzyme reaction samples from _H. sapiens_, respectively, containing the information of substrates, products, enzyme sequences, and Km values. (**B**) The pre-trained model ESM-2 is employed to extract features from enzyme sequences. (**C**) The pre-trained model RXNFP is  
used to extract meaningful features from the enzymatic reaction SMILES strings. (**D**) Molecular set features are extracted using molecular fingerprints and logical operations. (**E**) The extracted features are refined using a channel attention mechanism and fed into a feedforward neural network to predict Km values. Furthermore, we split the datasets of two species into training and test subsets according to the ratio of 8:2. As shown in Figure 2, the benchmark dataset from _E. coli_ include 1932 samples of wild-type enzymes and 1030 samples of mutant enzymes. To maintain the ratio of wild-type and mutant enzymes, we randomly selected 386 wild-type and 207 mutant enzyme reaction samples to form the test set, and the remaining 2369 samples were assigned to the training set. Similarly, the benchmark dataset from _H.