Due to their inherently complex nature, the architecture and motions of large macromolecular assemblies composed of rigid constituents are typically dissected using multiple techniques. While often combined on a case-by-case basis, the lack of theoretical tools to optimally integrate information from different sources is a major barrier to generating a more complete/accurate understanding of important assemblies. Herein are proposed new information fusion algorithms for these assemblies, and their associated functional motions. This extends classical information science to the case of data on the Lie group of rigid-body motions. Utilizing data from electron microscopy (EM) and small-angle X-ray scattering (SAXS) measurements, these fusion algorithms will be applied to two large biomolecular assemblies: (1) the ionotropic glutamate receptor (iGluR), and (2) the Chd1-nucleosome complex.
The specific aims are as follows: SA1: To develop new information-theoretic methods based on Euclidean-group calculus and probability theory to improve fitting of macromolecular structures into EM densities and SAXS envelopes, and to perform information fusion of compatible biophysical information from different modalities to produce greater understanding than when methods are taken individually. SA2: To apply mathematically optimized models of iGluR quaternary structure to uncover physiologically relevant conformational changes inaccessible to individual experimental methods. SA3: To develop and apply new mathematical models of flexibility and ensemble dynamics of the nucleosome alone and in complex with the Chd1 chromatin remodeler using EM and SAXS, leading to a better understanding of the structure-motion-function relationship. The results will validate novel algorithms for fusing information from different experimental approaches to determine conformational changes in macromolecular complexes. If successful, these algorithms will provide new mechanistic insights into the iGluR family of ligand-gated ion channels, implicated in stroke and Alzheimer's disease, and the Chd1 remodeler, which has been linked to several types of cancer.

Public Health Relevance

Maintaining normal cellular signaling and growth requires complex actions of large macromolecular assemblies. Understanding both the structure and dynamics of important biological assemblies is essential for identifying how cells are transformed into diseased states.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM113240-03
Application #
9059739
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Wehrle, Janna P
Project Start
2014-07-10
Project End
2019-04-30
Budget Start
2016-05-01
Budget End
2017-04-30
Support Year
3
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Engineering (All Types)
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205
Kim, Jin Seob; Chirikjian, Gregory S (2018) Symmetrical Parameterization of Rigid Body Transformations for Biomolecular Structures. J Comput Biol 25:72-88
Nodelman, Ilana M; Bleichert, Franziska; Patel, Ashok et al. (2017) Interdomain Communication of the Chd1 Chromatin Remodeler across the DNA Gyres of the Nucleosome. Mol Cell 65:447-459.e6
Kim, Jin Seob; Afsari, Bijan; Chirikjian, Gregory S (2017) Cross-Validation of Data Compatibility Between Small Angle X-ray Scattering and Cryo-Electron Microscopy. J Comput Biol 24:13-30
Clausen, Rasmus P; Mohr, Andreas Ø; Riise, Erik et al. (2016) A parallel panning scheme used for selection of a GluA4-specific Fab targeting the ligand-binding domain. Int J Biol Macromol 92:779-787
Nodelman, Ilana M; Horvath, Kyle C; Levendosky, Robert F et al. (2016) The Chd1 chromatin remodeler can sense both entry and exit sides of the nucleosome. Nucleic Acids Res 44:7580-91
Chirikjian, G S (2015) Conformational Modeling of Continuum Structures in Robotics and Structural Biology: A Review. Adv Robot 29:817-829
Dong, Hui; Kim, Jin Seob; Chirikjian, Gregory S (2015) Computational Analysis of SAXS Data Acquisition. J Comput Biol 22:787-805