Study of Protein Interfaces with Clustering

Sammanfattning: Protein-protein interactions occur in nature and have different functions. The interacting surface between two interacting proteins contains the respective protein's interface residues. In this thesis, a series of Python scripts are presented which can perform interface-interface comparisons with the method InterComp, to obtain a distance matrix of different protein interfaces. The distance matrix can be studied with the use of clustering algorithms such as DBSCAN. The result from clustering using DBSCAN shows that for the 77,017 protein interfaces studied, a majority of the protein interfaces are part of a single cluster while most of the remaining interfaces are noise for the tested parameters Eps and MinPts. The conclusion of this thesis is the effect on the number of clusters for the tested parameters Eps and MinPts when performing DBSCAN.