1 research outputs found
Unraveling the Role of Hydrogen Bonds in Thrombin via Two Machine Learning Methods
Hydrogen bonds play a critical role in the folding and
stability
of proteins, such as proteins and nucleic acids, by providing strong
and directional interactions. They help to maintain the secondary
and 3D structure of proteins, and structural changes in these molecules
often result from the formation or breaking of hydrogen bonds. To
gain insights into these hydrogen bonding networks, we applied two
machine learning models - a logistic regression model and a decision
tree model - to study four variants of thrombin: wild-type, ΔK9,
E8K, and R4A. Our results showed that both models have their unique
advantages. The logistic regression model highlighted potential key
residues (GLU295) in thrombin’s allosteric pathways, while
the decision tree model identified important hydrogen bonding motifs.
This information can aid in understanding the mechanisms of folding
in proteins and has potential applications in drug design and other
therapies. The use of these two models highlights their usefulness
in studying hydrogen bonding networks in proteins