The goal of the proposed project is to develop several new coarse-grained computational models to describe protein structures and protein dynamics. These innovations will let us approach much larger structures, as well as to comprehend important functional behaviors more readily. The realization of the proposed project might be extremely important for the development of computational cell biology and for practical applications in drug design. The project focuses on three specific aims:
Specific Aim I : Study protein dynamics using the Gaussian Network Model and Anisotropic Network Model. We will develop a mixed coarse-grained method where the 'interesting' or functional parts of proteins are modeled at a higher resolution than the remainder of the structure. By using this approach, normal mode analysis can be performed to discern the important functional motions with high computational efficiency for large biologically important molecules.
Specific Aim II : Develop an extremely efficient transfer matrix method for attrition-free generation of lattice proteins on the square lattice in 2-dimensions and for the cubic lattice in 3-dimensions. The proposed method is an extension of the transfer matrix method for generating and the enumerating compact self-avoiding walks on lattices previously developed by the project's investigators.
Specific Aim I ll: Conduct an off-lattice study of the dependencies between protein shapes and their conformations. The goal is to generate libraries of possible three-dimensional protein structures using a minimal set of assumptions. We constrict the shape of the protein within a three-dimensional ellipsoid of revolution and generate all possible compact protein conformations within the shape. We will also study in a systematic way the interdependence between the structures and the shapes of the proteins, as well as their dynamics with the Gaussian Network Models developed in the Specific Aim 1. These findings open the way for more abstract and mathematical representations of biological structure that can lead directly to a better and more complete comprehension of structure and function.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZGM1-CMB-0 (MB))
Program Officer
Whitmarsh, John
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Iowa State University
Schools of Arts and Sciences
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
Leelananda, Sumudu P; Kloczkowski, Andrzej; Jernigan, Robert L (2016) Fold-specific sequence scoring improves protein sequence matching. BMC Bioinformatics 17:328
Leelananda, Sumudu P; Jernigan, Robert L; Kloczkowski, Andrzej (2016) Predicting Designability of Small Proteins from Graph Features of Contact Maps. J Comput Biol 23:400-11
Zimmermann, Michael T; Jia, Kejue; Jernigan, Robert L (2016) Ribosome Mechanics Informs about Mechanism. J Mol Biol 428:802-810

Showing the most recent 10 out of 82 publications