Michael S. Chapman, PhD

Professor and Chair

Biochemistry

Wurdack Chair of Biochemical Sciences

Contact Information

Email ChapmanMS@missouri.edu
Phone 573-882-4845
Address 117 Schweitzer Hall
Website Chapman Lab
CV Download PDF

Education

BS Cell & Molecular Biology University of London, Kings College London, England
MS Crystallography University of London, Birkbeck College London, England
PhD Biochemistry University of California Los Angeles, Calif.

Research Area

Structural Biology: Viral-Host Interactions and Enzyme Dynamics

Research Description

The Chapman group applies biophysical and other approaches to viral-host interactions, and to understanding enzyme mechanism and dynamics. We are Structural Biologists, applying X-ray crystallography, cryo-Electron Microscopy (cryo-EM), and sometimes NMR to an understanding of molecular structure and dynamics in function and mechanism that we probe through biochemical kinetics and molecular virology.

Structural Virology – Host Interactions:

In 2017, Luxturna™, a treatment for congenital blindness, became the first in vivo gene therapy approved by the US Food & Drug Administration. This, and treatments for hemophilia and other genetic diseases, are using Adeno-Associated Virus (AAV) to deliver DNA to afflicted cells. Our structure-function analyses continue to advance our fundamental understanding of the atomic interactions during cell entry & trafficking, and during immune neutralization, laying key foundations for the engineering of gene therapy vectors that are efficient and specific enough to treat a broader array of genetic diseases.

Our contributions started with the first atomic stucture (AAV-2; right), determined, in 2002, by X-ray crystallography, followed by other natural human variants, and the engineered vector, AAV-DJ (by cryo-EM). EM has also been needed to visualize complexes of AAV with the extracellular glycans and proteins used by AAV for cell attachment and entry, and to characterize the surface regions recognized by neutralizing antibodies (that should be evaded in gene therapy treatments). Learning that glycan interactions were less specific than anticipated, and failing to detect binding to proteins that had been implicated in entry, we initiated a genomic screen with Jan Carette (Stanford) for key host factors in viral transduction that yielded the identity of the hitherto uncharacterized dominant receptor, AAVR, on which AAV hitch-hikes on its way from the cell surface to the nucleus. Current research includes the biochemical and structural characterization of the receptor and its interactions with the virus.

Enzyme Dynamics

The term “Protein Structure” is widely used, but is a bit misleading, because the functional form is usually not the static form in crystal structure databases, but dynamic in ways that have remained uncharted. Our model system is arginine kinase, an enzyme that buffers cellular ATP levels.  It is larger than those previously studied, and more representative of the domain rotations and loop motions expected in proteins.

Our crystal structures show a large conformational change on substrate-binding.  NMR relaxation dispersion and residual dipolar coupling measurements have been used to characterize the equilibrium protein dynamics of the substrate-free enzyme and the transition state analog complex, mapping the locations and timescales of backbone motions. The enzyme exhibits intrinsic fluctuations along modes that correspond to the larger changes seen on substrate binding. Temperature-dependent studies of activation bariers shows that the protein conformational changes, not substrate chemistry, limits enzyme turnover rate. Contrary to conventional wisdom, dynamics increase as the reaction proceeds to the transition state, providing experimental support for new theories of reaction turnover proposed by Ruth Nussinov.

Current studies involve characterization of the protein dynamics at intermediate steps along the reaction path, and improved computational methods to integrate information from diverse experimental methods (see below).

Computational Methods of Refinement

Refinement improves the accuracy of structures ~3-fold by optimizing the agreement with experimental data and precepts of stereochemistry. We are developing methods particularly suitable for electron microscopy, and for models derived from diverse biophysical data when one technique is not enough. We are exploring algorithms to transform known (component) structures to their conformations in complexes, without over-fitting lower resolution data that might be available – by limiting minimalist transitions to those ocurring in nature. Ccomputer algorithms are developed for use by the biophysics community, but our studies of virus structures and dynamic proteins provide the first applications.

Collaborators

Much of our research is interdisciplinary and our group has a long history of collaboration and co-mentorship through which we learn new approaches.

Selected Publications

Full list of publications here.

Current Funding

List of current funding for Chapman.


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