Protein-protein etkileşimlerinin çoklu ölçekte analizi ve tahmini

Proteins act coherently in the cells and their roles span functions as diverse as being molecular machines and signaling. The mechanism behind this excellent synchronization is still uncovered. However, considerable effort has been centered on identifying of binding partners and binding regions, because the vast majority of the chores in the living cell involve protein?protein interactions. Proteins interact through their interfaces which contain hot spots, the residues contributing more to the binding energy. Hot spots are important for drug targeting and interaction specificity. In addition, structural modeling of protein interactions and incorporating them into the protein interaction networks are prerequisites for understanding cell function. Hence, the focus of this dissertation is directed to the question ?how do the proteins interact?? rather than the question ?which proteins interact?? at the top level. Towards this aim, firstly, this dissertation focuses on the prediction of hot spots in protein interfaces and their organization. Here, an efficient hot spot prediction model is developed and implemented that reaches an accuracy of 70% on the experimental data. A web server, namely HotPoint, is constructed based on this model. In another aspect, a novel graph-based method based on minimum cut trees developed to determine the organization of hot spots which reveal the cooperative relation between them. Nature presents a limited number of distinct binding site motifs and structurally different protein pairs can use the same binding architectures. Based on this origin, secondly, a multi-scale combinatorial strategy is illustrated to model protein complexes at proteome-level. This work shows how available structural information can help in modeling a pathway by using structural similarity. Here, the sample pathway is the tumor suppressor protein p53 pathway. Finally, the multi-partner proteins dataset is extracted from Protein Databank. Integration of time notion into protein interaction networks is demonstrated on two hub proteins, p53 and Mdm2 using both predictions and available structural data.