# A Machine Learning Approach for the Prediction of Thermostable \(\beta\)-Glucosidases
## 4 Results and Discussion
### Thermostability Patterns in Enzymes Obtained from Non-Thermostable Organisms
#### 4.1.2 Case Study 2--Exploring Some Results in Detail  
To further examine the performance of the proposed model and explore the results obtained, I present a case study involving four selected structures (Table 4). The first structure, a \(\beta\)-glucosidase from _Microbacterium manageri_ (UniProt ID: A0A0B2A6F6), was originally classified as non-thermostable (real class: negative) and correctly predicted by the model as negative (model hit). The second structure, from _Thermonanerobacter_ sp. _Y513_ (UniProt ID: A0A0B3 BP14), was defined as thermostable (real class: positive) and correctly predicted as positive (model hit). Conversely, the third structure, from _Candidatus Falkowbacteria bacterium_, was initially defined as negative but was incorrectly predicted as positive (it can be a model error or--in an optimistic scenario--the model detected a new thermostable enzyme). Similarly, the fourth structure, from _Bifidobacterium thermacidophilum_, was initially described as positive but incorrectly classified by the model as negative (probably a model error).  
When we analyze the three-dimensional structures, we can see they are pretty similar. When superimposing them using structural alignment, we obtain an RMSD lower than 0.81 in all cases (superimposition of the three structures against A0A0G0 PX59 using the PyMOL \"align\" command). The four structures belong to the GH1 family of glycoside hydrolases from CAZY (Carbohydrate-Active enZYmes Database) [51]. The GH1 family comprises highly conserved TIM Barrel-type structures, where eight alpha-helices intercalate with eight beta strands that fold into a barrel shape (Figure 5A). This suggests that factors related to thermostability should not be directly linked to the positioning of the backbone but to other factors.  
On the other hand, when we consider the sequence level, we can notice that the four structures have identities ranging from 31% to 50% (Figure 5B). The negative structure (non-thermophilic), which was incorrectly predicted as positive (thermophilic), has identities ranging from 31 to 36% compared to the others, being the most different. This demonstrates the difficulty of detecting thermostability patterns, considering only similarities between sequences. Structural signatures consider a higher level of detail, as they consider the atoms' geometric coordinates, the atomic types, and their pharmacophic  
\begin{table}
\begin{tabular}{c c c c} \hline
**Uniprot ID** & **Organism** & **Real Class** & **Prediction** \\ \hline A0A0B2A6F6 & _Microbacterium mangrovi_ & Negative & Negative \\ \hline A0A0B3 BP14 & _Thermanaerobacter_ sp. _Y513_ & Positive & Positive \\ \hline A0A0G0 PX59 & _Candidatus Falkowbacteria bacterium_ & Negative & Positive \\ \hline A0A08F2J5 & _Bifidobacterium thermacidophilum_ & Positive & Negative \\ \hline \end{tabular}
\end{table}
Table 4: Examples selected for case study 2.  
properties. This indicates they have a more significant potential to find less easily perceived structural details.  
However, how can we know that the enzymes labeled as non-thermostable are non-thermostable? It is important to remember that they were chosen randomly. The following subsections present some experiments to assess this.