Facilitated By

San Antonio Medical Foundation

Struct. Invest. of Hsp70 and Tgfb by Solution Studies and High-Perform. Comput.

UT Health San Antonio

The UT Health San Antonio, with missions of teaching, research and healing, is one of the country’s leading health sciences universities.

Principal Investigator(s)
Brookes, Emre H.
Funded by
NIH
Research Start Date
Status
Active

This grant proposes the development of a novel methodology, based on computational approaches and experiments performed under physiological solution conditions, to complement high-resolution methods for the structure determination of biological macromolecules and their assemblies, and the application of this methodology to two relevant cases, Hsp70 and TGFø. Starting with high-resolution structures, the developed software will generate alternative conformations using molecular dynamics. The conformations will be grouped into equivalence classes (ECs). Small angle X-ray and/or neutron scattering profiles, radius of gyration and hydrodynamic molecular parameters, such as the sedimentation coefficient, translational and rotational diffusion coefficients, and intrinsic viscosity, will be computed for each EC. The ECs will be globally fitted to experimental data, resulting in a distribution of contributing ECs. This information will provide important structural and dynamic detail obtained under physiological conditions and help to validate corresponding data from other high-resolution techniques. The proposed analysis strategy is based on well-established first principles and has universal applicability to any biological systems asking similar questions. We will test our software on two systems where relevant open questions remain. Hsp70 chaperones are 2-domain proteins that mediate a large number of protein processing and folding reactions. Upon binding ATP or ADP, Hsp70s undergo conformational changes that result in release or binding of their protein substrates. These processes are important in neurodegenerative disease and cancer. No crystal structure of an Hsp70 in an ATP state has ever been determined. A structure of the related protein Hsp110 bound to ATP has been determined and has been proposed to model for the Hsp70:ATP conformation, but this has not been tested directly. Controversy exists regarding the interpretation of the crystal structures of Hsp70s. The conformational state of Hsp70 in its nucleotide-free, ADP, and ATP states, and with and without bound peptide substrates will be analyzed under solution conditions where conformations will not be influenced by crystal packing forces or solvent components that are not physiological. TGFøs are proteins which regulate cell proliferation, cell differentiation, and expression of extracellular matrix proteins. Three isoforms have arisen in mammals which play prominent roles in human disease, especially cancer. TGFø1 and -ø3 can have opposing roles in tumor growth and metastasis. Understanding the origins of these differences in activity requires a detailed understanding of their structural dynamics. Dimeric TGFø binds with receptors TRII and TRI to signal across cell membranes, and can exist in an open or closed state. The open and closed ternary states TGFø:TRII:TRI form differing signaling complexes. The proposed analysis method will quantify the extent which TGFø1 and -ø3 exist in their ternary complexes with their receptors in an open state. For both cases, our method will provide additional detail by quantifying multiple conformations existing in solution rather than insisting on a single state. PUBLIC HEALTH RELEVANCE: Hsp70 and TGF? proteins play prominent roles in neurodegenerative disease and cancer. The novel application proposed here combines advanced computational methods with a global analysis of data from several solution-based experimental techniques to help answer unsolved structural and functional questions. This multi-faceted approach will provide greater resolution and detail than current techniques, will further advance our knowledge, and can assist the development of therapeutics.

Disease Modeling
Clinical Care
Cancer
Neuroscience