# A Machine Learning Approach for the Prediction of Thermostable \(\beta\)-Glucosidases
## 4 Results and Discussion
### Limitations and Perspectives  
Despite the great potential of using the models presented here, some limitations and biases of this study need to be clarified. First, the definition of the group of thermophilic enzymes was rather simplistic (only the organism's name was used). Applying the model to a more curated database would obtain more reliable results. However, few structures with experimental data on thermostability are publicly available, which would limit the experiments proposed here.  
Furthermore, the phylogenetic bias in model construction should be considered since most thermophilic organisms used to extract \(\beta\)-glucosidase are bacteria. However, the main objective of model construction is to identify the structural patterns of the enzymes of this group and verify whether they can be found in other enzymes.  
Additionally, the experiments were performed with 3D protein models. Despite recent evolutions in the computational modeling of proteins by new algorithms, such as AlphaFold [24], it is important to emphasize that the models are subject to prediction errors.  
In this work, a graph-based structural signature algorithm was used. Graph-based signatures model each atom as a node and pairs of atoms of different types that meet the pre-established cutoff distance as edges. One of the challenges when using this algorithm is to obtain explainability in the constructed models. Aiming to address this challenge, the Information Gain Ratio (IGR) method from Orange Data Mining was applied to obtain the most important features. Thus, the five most important attributes are the edges composed of pairs of atoms: acceptor x aromatic (distance up to 2.5 A), sulfide x sulfide  
Figure 7: CABSflex result. (**A**) RMSF for each residue of A0A0B2A6F6 (non-thermophilic) at the initial temperature (blue line) and a higher temperature (red line). (**B**) RMSF for each residue of A0A0B3 BP14 (thermophilic) at the initial temperature (blue line) and a higher temperature (red line). (**C**) Variation in RMSF (ARMSF) between A0A0B2A6F6 (salmon—50% transparent) and A0A0B3 BP14 (blue). The higher the peak, the more mobility the region will have at higher temperatures. Dark red regions indicate overlap. As the proteins have different sizes, the sequences were manually aligned (five gap regions are displayed).  
(distance up to 3.3 A), aromatic x sulfide (distance up to 3 A), neutral x sulfide (distance up to 3 A), and aromatic x neutral (distance up to 2.4 A). Details can be found in the Supplementary Material (Table S5).  
In summary, this indicates that pairs of acceptor and aromatic atoms at a distance of up to 2.5 A are the most important for the classification into thermostable or non-thermostable according to the IGR. Aromatic atoms are found in the aromatic rings of tryptophan, tyrosine, and phenylalanine. On the other hand, hydrogen acceptor atoms can be found in the main chains of all amino acids. This demonstrates the difficulty in explaining the results of classifications made by structural signatures. Therefore, it can represent a limitation of this work.  
In the future, it is intended to perform more robust molecular dynamics experiments to evaluate flexibility at high temperatures. Furthermore, experimental validations can be performed to evaluate enzymes originally defined as non-thermophilic (Supplementary Table S2), which were predicted by the model to be thermophilic.