HotRegion v2.0: Protein-protein etkileşim arayüzlerindeki sıcak bölgeleri tahmin etmek için yeni bir yöntem

Proteins interact with each other through their interface to fulfil essential functions in the cell. The study of protein interactions will have a profound effect on understanding various biological pathways. Binding free energies are not uniformly distributed among the residues found in protein-protein interfaces (PPI). Hot regions are tightly packed residue clusters in PPIs that account for the majority of the binding free energy of proteins, and hence, are crucial for the stability of complexes. Providing specificity to binding sites, these regions are of great importance for drug discovery in pharmaceutical research. Experimental discovery of hot regions is time-consuming and requires high effort. Hence, there is a need for computational methods. The existing hot region prediction algorithms perform clustering on computationally predicted hot spots and ignore the potential existence of non-hot spot residues in hot regions. However, experimental studies have demonstrated that non-hot spot residues can also be found in hot regions. In this thesis, using unsupervised learning approaches, we propose a novel method to predict hot regions which may contain both hot spot and non-hot spot residues. We combine affinity propagation (AP) and density-based spatial clustering of applications with noise (DBSCAN) to cluster interface residues and develop a web-based tool to predict and visualize hot regions. Furthermore, we have developed a database that contains hot region information for more than 600.000 protein complexes. In our web server, users can query data from our database or submit a new run to obtain hot regions of a complex that is not found in the database. Our tool demonstrates a 3D structural visualization of the complex with colored hot regions and highlighted hot spot residues. Structural features of interface residues, i.e., accessible surface area values and knowledge-based pair potentials, are also indicated. In order to evaluate our method, we have compared our results with the experimental studies. The precision of our algorithm is 0.68 and the accuracy is 0.62. Additionally, we have conducted a case study to test the significance of our predicted regions for clinical studies. We have investigated the complex of human programmed death-1 (PD-1) and its ligand PD-L1. Our algorithm correctly identified the residues which are known to be significant for PD-L1-antibodies and small-inhibitors. Lastly, we have compared our algorithm with our previous hot region prediction method. The results have shown that our tool outperforms the previous version of HotRegion and may be a leveraging step to novel pharmaceutical studies.