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Student Faculty Collaborate
Data Science Institute

Zimmermann Laboratory

Location
HRC5 - Mellowes Center

Zimmermann lab joining with Dr.s Urrutia, Lomberk, and team, for ASHG 2025

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

Computational Structural Genomics

Computational Structural Genomics: A Transformational Approach for Biology and Medicine

Deep Variant Phenotyping (DVP) resolves variants by their underlying mechanism. In this generalized diagram, we depict the CIMG process. Investigators nominate cohorts to VEB, primarily VUS and a few pathogenic variants. We identify functional context to guide molecular modeling. In a recent example, our team evaluated the two functional states of the JAK3 homodimer by structure and dynamics. Our output includes a report where we mechanistically interpret the cohort, facilitating computer-enhanced hypothesis-driven research among the clinical and investigative collaborators.

ABCC6 molecular mechansims diagram

Quantifying Disparate Molecular Mechanisms Underlying the Effects of Numerous Variants of Uncertain Significance

We applied DVP to ABCC6 and resolved molecular mechanisms for 379 missense variants, 90% of which are clinical VUS. ABCC6 only has 45 ≥ 1* pathogenic variants. Thus, the transformative nature of DVP is apparent. Importantly, concepts like structural stability, individually, only resolve a subset of variants, showing how a comprehensive and integrative approach is needed. A) Domain organization of ABCC6. B) 3D hotspots that we identified. C) Six discrete mechanisms outlined.

MT supercomples pilot

Scaling Genomic Data Interpretation to Complexes, Super-Complexes, and Beyond

The MT super-complex reveals that VUS are distributed throughout its quaternary structure. A) We mark the location if each selected variant with a red sphere. B) MT-ND4:E222 and C) MT-ND5:E145 are within internal pockets lined with positive and negative charges, likely facilitating the flexible conformational changes that are required for electron transport. D) Selected mutations are nearby in space and typically at the ends of alpha helices where they also likely modulate flexibility. E) Structure-based stability calculations reveal the disruption to the complex.

Fold types variation

Quantifying the Domain-Type-Level Flexibilities that Need to be Accounted for Computational Structural Genomics

Quantifying Fold-Dependent Flexibility: We studied the entire human proteome as predicted by AlphaFold2 and quantified agreement between experiment and prediction, where possible. Across 628 CATH topologies that have at least 10 experimental structures we observed a spectrum of flexibility.

Piloting protein surface scores

Piloting protein surface-based scores for interpreting the effects of genetic variants at distant functional sites

Mutations that change solubility induce significant local electrostatic changes. A,C) Crystal structures of Granzyme H and IGF1R show local charge distribution for point mutations, circled in orange. B,D) The distribution of electrostatic potential differs between wild type and variant surfaces.

 

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.

Our Current Members

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Michael T. Zimmermann, PhD

Director, Computational Structural Genomics Unit and Associate Director, Computational Biology, Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine; Associate Professor, Data Science Institute

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Xiaowei Dong

PhD Student

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Jane Gresser, MS

Bioinformatics Analyst I

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Neshatul Haque, PhD

Postdoctoral Researcher

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Jessica Wagenknecht, PhD

Bioinformatics Analyst II

Image Gallery

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Mellowes Center timeline 2025

Mellowes Center timeline 2025

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MTZ at KLF Society 2024 Greece

MTZ at KLF Society 2024 Greece

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MTZ at UDNI 2025 Brasil

MTZ at UDNI 2025 Brasil

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Zimm lab at ASHG 2023

Zimm lab at ASHG 2023

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Zimm lab at ASHG 2024

Zimm lab at ASHG 2024

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Recent Publications

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

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