CORE 3 : Modeling Core

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MODELING CORE SUMMARY To improve our understanding of infectious diseases, the Host Pathogen Map Initiative (HPMI) has launched systematic efforts to analyze the networks of molecular interactions that interconnect pathogens and hosts. While parts of this work is experimental, this Modeling Core presents the central computational framework. A first computational theme concerns methods to determine human subcellular substructures and components as well as how they are altered by interactions with pathogenic factors. Aim 1 focuses on creating 3D models of host-pathogen protein complexes. It will apply established methods of integrative structural biology to data from the HPMI projects, including cryo-electron microscopy (cryo-EM), proteomics (PPI, PTM, and APEX) and CRISPR/Cas9-based genetic studies. Initial efforts will focus on the Nsp2-Rap1Gds1, ORF8-IL17RA, and ORF9B-MARK2 complexes involving SARS-CoV-2 and human proteins, as well as Pks13 from Mycobacterium tuberculosis, all identified in previous work by the HPMI. Aim 2 focuses on mapping host cell components at scales at and above the protein complex, extending to larger compartments and organelles. It will expand on our recent compelling proof-of-concept for creating an unbiased hierarchical map of subcellular components in human cells. These whole-cell maps will be analyzed to reveal specific subcellular components targeted by pathogens and/or that are under mutational selection in either pathogen or host. A second computational theme concerns methods to integrate host-pathogen cell maps with functional analysis and predictive medicine. Aim 3 uses the maps to build interpretable deep learning systems for prediction of infectious disease outcomes including disease severity. This aim draws from our previous work to establish “visible” learning models (DCell and DrugCell), which are not black boxes but have internal organization determined by prior knowledge of biological structure. We will construct such models from HPMI cell maps, incorporating key improvements over our first-generation pilots. Finally, Aim 4 will use visible deep learning systems alongside other machine learning models to design and evaluate combinatorial biomarkers that govern M. tuberculosis and SARS-CoV2 infection in clinical settings. Through these aims, we advance our basic knowledge of the macromolecular structures and functions most crucial for infection, while embedding this knowledge within intelligent systems for precision medicine.