Computational Analysis of Protein Interfaces

Protein-protein interactions are important for the understanding of biological pathways. The physical properties of protein-protein interfaces are one of the factors that determine its interaction behavior. Studying protein-protein interfaces, specifically “hot spot ” residues that account for the majority of free energy in binding may reveal more information about their properties associated with binding. It is important to predict hot spots because they can help us design novel protein interfaces through a better understanding of their interaction behavior. We used machine learning to generate knowledge-based-decision-tree models that explore protein interfaces relative to flexibility and determined if this feature would improve the prediction of hot spots. The machine learning algorithm was unable to use the flexibility feature to improve the prediction of hot spots. However, important trends in the data were observed regarding the flexibility of hot spots based on whether they were hydrophobic or polar