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Student Faculty Collaborate
Data Science Institute

Zimmerman Laboratory

Location
HRC5 - Mellowes Center

Michael Zimmermann Laboratory

Interests

  • Human genetic diseases of the epigenome
  • Mechanistic interpretation of inter-individual genetic variation using protein structural models
  • Rare disease discovery and diagnosis via enhanced computational approaches
  • Scaling our approaches developed for heritable genetic diseases, to populations and their many one-of-a-kind variations
  • Integrative modeling to bridge across multiple experiments and derive highly dynamic models of protein complex’s function
  • Multi-omics data as functional readouts of chromatin regulating enzymes

 

Mechanisms of the BAF Chromatin Remodeling Complex

The BAF complex, named Brahma Associated Factor for its necessary role in cellular transcription, is a critical regulator of the genome. Genetic changes to the many genes that collectively encode BAF function cause a spectrum of rare diseases of the epigenome and frequently contribute to cancer. Our team is integrative and AI-empowered approaches to model the dynamic features of this complex in detail and in multiple states. In this way, we are bringing to light more context-aware and sensitive genomics. Additionally, we are pioneering ways to address the extensive unresolved and intrinsically disordered sections. This flythrough animation highlights the active site of the SMARCA4 ATPase, then zooms out to one model of the complex, and shows a representation of the long unresolved regions.

 

RAG Complex Modeling for Rare Diseases

The RAG complex is required for our immune system to adapt to pathogens. We work with the nation’s experts for Inborn Errors of Immunity, where children are born without a normally formed immune system. Our team models the protein complex in multiple steps of its functional cycle. In this way, we go beyond structural bioinformatics to better bridge the structure-function relationship to cell biology and the context-specific and mechanistic information that the field needs for next-generation genomics interpretation. Our approach increases diagnostic rates, points to testable hypotheses, and paves paths towards therapeutics.

Current Members

Neshatul Haque
nehaque@mcw.edu
Jessica Wagenknecht
jwagenknecht@mcw.edu
Xiaowei Dong
xdong@mcw.edu

Recent Publications

Publications of Michael T. Zimmermann PhD from the Faculty Collaboration Database

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