Systematic characterization of human disease associated protein complexes

Protein complexes carry out nearly all biological functions. When protein complexes are disrupted, they give rise to many diseases not limited to cancer, cardiovascular disease, and developmental disorders which collectively, affect 148 million Americans and kill nearly 1 million Americans each year. A grand challenge in molecular biology is the identification and characterization of protein complexes. While tremendous progress has been made in accurately identifying protein complexes, we still lack a basic understanding of many complexes. Specifically, we currently do not know the components of all human protein complexes including subtly altered variants which differ in function. We lack understanding of the degree to which complexes differ between cell types. Lastly, we lack high quality structural models for all protein complexes to aid interpretation of disease mutations. Here, we propose to 1) develop machine learning approaches to identify biologically relevant protein complex variants from >32,000 mass spectrometry experiments; 2) build computational workflows to construct cell-type specific protein complex maps focusing on early embryonic development; and 3) create a direct contact network of physically associated proteins which will prioritize structural modeling (e.g. AlphaFold) efforts to interpret disease mutations. This work aims to provide a more accurate and comprehensive understanding of protein complexes which will ultimately lead to novel therapeutic targets and strategies. Project Number: 1R35GM164016-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of General Medical Sciences (NIGMS) | Principal Investigator: Kevin Drew | Institution: UNIVERSITY OF ILLINOIS AT CHICAGO, Chicago, IL | Award Amount: $380,350 | Activity Code: R35 | Study Section: Special Emphasis Panel[ZRG1 MBBC-A (55)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11331386