Unraveling the role of Hydrogen bonds via two machine learning methods

Hydrogen bonds are essential for the creation and stability of protein structures because of their strong directional nature, short distance ranges, and abundance in folded proteins. H-bonds between atoms can maintain the protein's secondary structure and overall 3D structure. Protein structural changes are associated with the creation and destruction of hydrogen bonds. So, studying the hydrogen bonding network can help us better understand the allosteric pathway of the protein. In this research, we used two machine learning models, the logistic regression model, and the decision tree model, to study hydrogen bonding networks. We used these two models to study the H-bonds of four thrombin variants, WT, $Delta$K9, E8K, and R4A. We discovered that each model has unique benefits. The logistic regression model assesses the overall significance of each hydrogen bond, whereas the decision tree is better at detecting the hydrogen bonding motifs for each system.