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 contolled 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.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Friedman, Fred K
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Iowa State University
Other Basic Sciences
Organized Research Units
United States
Zip Code
Wang, Min; Zhou, Wen; Wu, Zhijun (2018) Equilibrium Distributions of Populations of Biological Species on Networks of Social Sites. J Biol Dyn :1-25
Faraggi, Eshel; Dunker, A Keith; Sussman, Joel L et al. (2018) Comparing NMR and X-ray protein structure: Lindemann-like parameters and NMR disorder. J Biomol Struct Dyn 36:2331-2341
Faraggi, Eshel; Kloczkowski, Andrzej (2017) Accurate Prediction of One-Dimensional Protein Structure Features Using SPINE-X. Methods Mol Biol 1484:45-53
Kouza, M; Banerji, A; Kolinski, A et al. (2017) Oligomerization of FVFLM peptides and their ability to inhibit beta amyloid peptides aggregation: consideration as a possible model. Phys Chem Chem Phys 19:2990-2999
Liu, Jie; Sankar, Kannan; Wang, Yuan et al. (2017) Directional Force Originating from ATP Hydrolysis Drives the GroEL Conformational Change. Biophys J 112:1561-1570
Faraggi, Eshel; Kouza, Maksim; Zhou, Yaoqi et al. (2017) Fast and Accurate Accessible Surface Area Prediction Without a Sequence Profile. Methods Mol Biol 1484:127-136
Sankar, Kannan; Jia, Kejue; Jernigan, Robert L (2017) Knowledge-based entropies improve the identification of native protein structures. Proc Natl Acad Sci U S A 114:2928-2933
Zimmermann, Michael T; Jia, Kejue; Jernigan, Robert L (2016) Ribosome Mechanics Informs about Mechanism. J Mol Biol 428:802-810
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
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

Showing the most recent 10 out of 82 publications