Many aspects of protein motion can be comprehended with coarse-grained models. Our hypothesis is that atomic detail is not required to explain many aspects of protein behavior, and this simplification can facilitate a deeper understanding. The overall goal is to develop an understanding of how protein motions and function are controlled by structure, why protein sequences fold to a limited set of structures, and to establish the roles of tight packing and the shapes of proteins on their motions. In this project we will investigate the relationships among motions, shapes, structures, interactions and levels of cooperativity.
Aim I : Modeling protein dynamics with Elastic Networks. We will use elastic network models to study how proteins restrict their motions to the motions most essential for function. Normal mode analyses will be performed to discern these important functional motions with high computational efficiency to develop molecular mechanisms. We will investigate the atomic motions in active sites of enzymes to see how the large domain motions control the atom movements. We will use elastic networks to interpret single molecule pulling experiments and predict the order in which proteins unravel. Preliminary results show that elastic network models are applicable not only to fluctuations around native conformations, but also to transient states arising when an external force is applied to deform a protein and break its native contacts. These results suggest that structure controls the global motions of proteins, even for transient states. To further verify this hypothesis we will perform more single molecule pulling simulations, and structural analyses of transient protein conformations along folding pathways. The major successes achieved with the elastic models rely upon having good representations of the packing density and protein shape, which we will investigate in Aim II.
Aim II : Modeling Protein Packing and Cooperativity of Interactions. Dense packing of residues in proteins is one of their most important characteristic features. We plan to continue our studies of internal packing. The emphasis for new potentials will be on the relative orientations of amino acids in proteins. We will develop many-body contact potentials for identifying native structures among decoys in threading, and also study orientational distributions within clusters of nearby residues in proteins, using regular polyhedra such as icosahedra, or Catalan solids such as tetrakis hexahedra. Our rationale is to use various polyhedral models to comprehend protein packing and amino acid interactions for developing improved many-body potentials. A better understanding of the cooperativity of interactions within proteins is extremely important because this directly influences the ways in which proteins move and respond to forces.
Both Aims are highly interconnected and will significantly advance our knowledge of protein structure, dynamics and function.

Public Health Relevance

Success in this project will affect many fields of molecular science - from the selection of protein targets for drug design to a general comprehension of how cells function. Progress on this project is critical for developing ways to meaningfully simulate cellular components and to utilize the rapidly growing cell imaging data. Improving the abilities to model protein motions can impact public health in important ways by enhancing our basic understanding of protein behavior and by facilitating better, more effective selection of protein targets for drug design.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM072014-07
Application #
7997224
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Hagan, Ann A
Project Start
2004-07-01
Project End
2012-12-31
Budget Start
2011-01-01
Budget End
2011-12-31
Support Year
7
Fiscal Year
2011
Total Cost
$308,307
Indirect Cost
Name
Iowa State University
Department
Other Basic Sciences
Type
Organized Research Units
DUNS #
005309844
City
Ames
State
IA
Country
United States
Zip Code
50011
Rashin, Alexander A; Jernigan, Robert L (2016) Clusters of Structurally Similar MHC I HLA-A2 Molecules, Found with a New Method, Suggest Mechanisms of T-Cell Receptor Avidity. Biochemistry 55:167-85
Zimmermann, Michael T; Jia, Kejue; Jernigan, Robert L (2016) Ribosome Mechanics Informs about Mechanism. J Mol Biol 428:802-10
Leelananda, Sumudu P; Kloczkowski, Andrzej; Jernigan, Robert L (2016) Fold-specific sequence scoring improves protein sequence matching. BMC Bioinformatics 17:328
Chopra, Nikita; Wales, Thomas E; Joseph, Raji E et al. (2016) Dynamic Allostery Mediated by a Conserved Tryptophan in the Tec Family Kinases. PLoS Comput Biol 12:e1004826
Na, Hyuntae; Jernigan, Robert L; Song, Guang (2015) Bridging between NMA and Elastic Network Models: Preserving All-Atom Accuracy in Coarse-Grained Models. PLoS Comput Biol 11:e1004542
Katebi, Ataur R; Jernigan, Robert L (2015) Aldolases Utilize Different Oligomeric States To Preserve Their Functional Dynamics. Biochemistry 54:3543-54
Katebi, Ataur R; Sankar, Kannan; Jia, Kejue et al. (2015) The use of experimental structures to model protein dynamics. Methods Mol Biol 1215:213-36
Faraggi, Eshel; Kloczkowski, Andrzej (2015) GENN: a GEneral Neural Network for learning tabulated data with examples from protein structure prediction. Methods Mol Biol 1260:165-78
Sankar, Kannan; Liu, Jie; Wang, Yuan et al. (2015) Distributions of experimental protein structures on coarse-grained free energy landscapes. J Chem Phys 143:243153
Sachnev, Vasily; Saraswathi, Saras; Niaz, Rashid et al. (2015) Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer. BMC Bioinformatics 16:166

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