The objective of this project is to produce a novel computational prototype to synthesize and visualize the structural models of higher-order protein complexes derived from the existing empirical data and the relative protein abundance of these complex assemblies from many organisms. Abundance will be estimated from the analysis of tandem mass spectrometry (MS/MS) spectra and used to calculate the Topological Scores (TopS) (developed by Sardiu) to determine the strength of interactions between proteins within complexes. The development of TopS with topological data analysis (TDA) has proven to be effective in revealing the modular architecture of the protein complexes within a protein interaction network whose composition and abundance rapidly changes over time in a proteome. However, the analysis, systematic comparisons, and interpretation of modular protein complexes pose an enormous challenge due to our inability to visualize the spatial arrangement and topological hierarchy of the protein assemblies which are essential for a complete understanding of cell biology and to unravel disease mechanisms. There is an urgent need to develop tools that translate the connectivity of proteins presented in a modular network of protein interactions into structural models to reveal the hierarchical order and the relative spatial arrangement of its constituents. Our central hypothesis is that the development of a novel prototype of iTopS (integrative topological scores with functional molecular modeling) will advance technical knowledge required to interpret the fundamentals of protein complexes from targeted -omics data. To achieve this, we will complete the following aims: (1) Expand TopS capabilities for data analysis, visualization, and access (at the PI’s lab). (2) Create an end-to-end workflow from targeted modular proteomes to structural complexes (between PI’s lab and PNNL) (3) Testing and Implementing Topological Scoring for the Analysis of ChIP-Seq ENCODE Datasets (at the PI’s lab).