Diseases are commonly the result of dysregulated complex interactions involving large sets of genes and proteins as products of these genes, and their cooperation with other cellular components. Interpreting protein-protein interactions at both network and molecular interaction levels with mutation knowledge requires a comprehensive research process that is fed from different sources. In this project, we developed a web-based tool, Gene2Phen, by integrating large-scale protein-protein interaction network, 3D protein structure information and interface mutation knowledge to aid researchers in exploring and comparing the molecular mechanism of different phenotypes. Gene2Phen works as an automatized pipeline tool to build, visualize and compare phenotype specific subnetworks, to examine protein- protein interactions associated with their structure and mutation data. Gene2Phen web tool prioritizes the human protein-protein network based on seed genes specific to a phenotype. From the prioritized-PPI network, users can generate a phenotype specific subnetwork. The phenotype-specific subnetworks can be visualized and compared interactively. Genome annotations and topological properties of each protein are shown in this interactive network representation. A unique feature of Gene2Phen is its ability to display 3D structural models of protein-protein interactions and their predicted protein-protein interfaces. Users can see the list of mutations which are mapped on predicted protein-protein interfaces. This allows users to study mutations altering protein-protein interfaces and their role in the phenotype-specific subnetworks. Gene2Phen, by automating the integration of protein-protein networks, protein structure, and disease – related mutations at large scale, will not only boost the productivity and efficiency, but it may be the leveraging step to the novel solutions/studies. 

M.Sc. Koç University-Biomedical Science and Engineering

B.Sc. Bilkent University-Computer Engineering