# A Machine Learning Approach for the Prediction of Thermostable \(\beta\)-Glucosidases
## 4 Results and Discussion
### Thermostability Patterns in Enzymes Obtained from Non-Thermostable Organisms
#### 4.1.4 \(\beta\)-Glucosidases from Thermophilic Organisms Are Less Unstable at High Temperatures  
Several studies have described that thermophilic enzymes tend to present reduced flexibility and better compaction of the central regions when compared to the mesophilic ones. This allows for them to maintain their function at higher temperatures [52; 53; 54]. Therefore, one way to assess whether the enzyme has greater resistance to high temperatures is to check its mobility and compaction at low and high temperatures.  
The literature has described that several parameters can be used to quantify thermostability, such as evaluating the free energy of unfolding (\(\Delta\)G\({}_{\text{u}}\)), the thermal inactivation temperature (T\({}_{\text{50}}\)), and the melting temperature (T\({}_{\text{M}}\)), among others [52]. Considering in silico experiments, the thermostability evaluation can be made through molecular dynamics simulations, although they can have a high computing cost. Additionally, artificial intelligence techniques and heuristic-based algorithms have shown promise for thermostability prediction.  
Thus, I performed fast protein structure flexibility simulations using the CABSflex 2.0 software. Two experiments were performed for A0A0B3 BP14 (thermophilic) and A0A0B2A6F6 (non-thermophilic), respectively. First, I ran CABSflex with default parameters. Then, I set the temperature parameter to 3.0 (the default value is 1.4) and ran a second experiment. Importantly, the temperature parameter used by CABSflex cannot be directly linked to the actual temperature. This parameter controls the total energy of the modeled system. Thus, higher temperature values increase atomic mobility, leading to greater fluctuations [49]. CABSflex returns an RMSF (root mean square fluctuation in Angstroms) plot that indicates the mobility of the residue during the simulation.  
Figure 7 summarizes the results. For both proteins, greater flexibility at high temperatures is noticeable (red lines above the blue lines). However, A0A0B2A6F6 (non-thermophilic) presents greater flexibility at higher temperatures than the other one (Figure 7A), which may indicate greater instability. A peak higher than 16 A is noticeable between residues 305 and 330. This region is a loop at the entrance to the catalytic pocket. For A0A0B3 BP14 (thermophilic), the maximum peak was approximately 8 A, also in loop regions at the entrance to the active site (Figure 7B). The flexibility of certain regions at the highest temperature was close to the initial temperature, which may be in line with what is expected of a thermophilic enzyme. Strangely, in certain regions, the mobility at low temperatures was higher than at high temperatures, such as in the region between residues 176 and 183. This may have occurred due to a software mistake or characteristics of the enzyme itself. Complementary studies need to be carried out to better evaluate this.  
In summary, when we compare the variation in the final RMSF minus the initial one (Figure 7C), it is clear that the non-thermophilic one has more flexibility at high temperatures (note the peaks in the salmon color). It is noteworthy that its sequence presents gaps when compared to the thermophilic one, which has a smaller sequence size. These gaps generally occur in the loops at the entrance to the active site. This may have some relation to the thermostability characteristic. That is, the loss of residues in the loops at the entrance to the active site may reduce the instability caused by the higher temperature environment. Further studies need to be carried out to attest to this.