Barrels are found in the outer membrane of gram-negative bacteria, acid-fast gram-positive bacteria, eukaryotic mitochondria, chloroplasts (e.g., in E. coli, N. meningitidis, N. gonorrheae, and mycobacteria such as M. tuberculosis). Many pore-forming exotoxins from gram-positive bacteria are also -barrel membrane proteins (e.g., ?-hemolysin of S. aureus and protective antigen of B. anthracis). ?-barrel membrane proteins are important for many fundamental biological processes. They control the ex- change and transport of ions and organic molecules across the bacterial and mitochondrial outer membranes. They are essential for protein translocation in all domains of life, except archaea. They regulate metabolism and apoptosis. They are also important for immune surveillance, in providing resistance to antibiotics, and are key determinants of bacterial virulence. As a result, ?-barrel membrane proteins are important therapeutic targets for developing drugs and vaccines against infectious diseases. They are also the focus of significant engineering efforts in developing biological nanopores for high-throughput DNA sequencing, as well as nano-devices for targeted cancer drug delivery. Although much has been learned through experimental and computational studies, current knowledge of ?-barrel membrane proteins is incomplete Only a small number of structures are known, and there is a lack of understanding of the general organizing principles of ?-barrel membrane proteins. The long term goal of the proposed research is to gain fundamental understanding and mechanistic insight into the structures, interactions, and functions of ?-barrel membrane proteins, and to develop enabling technology for design of ?-barrel membrane proteins with enhanced biophysical properties.
The specific aims are to: 1) Develop computational models of physical principles governing the assembly of ?-barrel membrane proteins. Coarse-grained models will be developed to account for key determinants of structural stability and protein-protein interactions (PPIs) of ?-barrel membrane proteins. This will enable quantitative assessment of protein stability through computation. 2) Predict structures, oligomerization state, and protein-protein interfaces of ?-barrel membrane proteins. The focus will be on the challenging tasks of predicting structures of novel architecture or structures with no known templates. In addition, methods will be developed to predict protein oligomerization states and to identify protein-protein interaction sites. Structures with known templates will also be predicted through detection of remote homologs using newly developed technique of evolutionary analysis. 3) Develop engineering principles for designing ?-barrel porins with desirable stability, oligomerization state, and pore geometry. Design strategies for ?-barrel membrane porins with altered oligomerization states and altered stability will be developed. Proteins with enhanced as well as weakened stability, for both monomeric and oligomeric proteins will be designed. In addition, porins with complex pore geometry using naturally occurring building blocks will also be designed. 4) Experimental validation of computational prediction and design. Computational predictions will be verified by experimental studies. Extensive mutant studies will be carried out to test whether designed ?-barrel membrane proteins have the intended changes in stability, in oligomerization state, as well as in geometry.

Public Health Relevance

Barrel membrane proteins are an important class of membrane proteins that are related to many infectious diseases and are important for cell homeostasis. The proposed work will generate computational tools for prediction of structures and protein-protein interactions of ?-barrel membrane proteins. These tools will be useful for developing anti-infectious and anti-cancer drugs, as well as vaccines, and will also enable design of effective bio-nanopores useful for low-cost DNA sequencing and for developing anti-cancer drugs.

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
Chin, Jean
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Illinois at Chicago
Biomedical Engineering
Biomed Engr/Col Engr/Engr Sta
United States
Zip Code
Cao, Youfang; Terebus, Anna; Liang, Jie (2016) State Space Truncation with Quantified Errors for Accurate Solutions to Discrete Chemical Master Equation. Bull Math Biol 78:617-61
Gürsoy, Gamze; Liang, Jie (2016) Three-dimensional chromosome structures from energy landscape. Proc Natl Acad Sci U S A 113:11991-11993
Montefiori, Lindsey; Wuerffel, Robert; Roqueiro, Damian et al. (2016) Extremely Long-Range Chromatin Loops Link Topological Domains to Facilitate a Diverse Antibody Repertoire. Cell Rep 14:896-906
Cao, Youfang; Terebus, Anna; Liang, Jie (2016) ACCURATE CHEMICAL MASTER EQUATION SOLUTION USING MULTI-FINITE BUFFERS. Multiscale Model Simul 14:923-963
Lin, Meishan; Gessmann, Dennis; Naveed, Hammad et al. (2016) Outer Membrane Protein Folding and Topology from a Computational Transfer Free Energy Scale. J Am Chem Soc 138:2592-601
Ismael, Amber; Tian, Wei; Waszczak, Nicholas et al. (2016) Gβ promotes pheromone receptor polarization and yeast chemotropism by inhibiting receptor phosphorylation. Sci Signal 9:ra38
Im, Wonpil; Liang, Jie; Olson, Arthur et al. (2016) Challenges in structural approaches to cell modeling. J Mol Biol 428:2943-64
Estrada Mallarino, Luisa; Fan, Enguo; Odermatt, Meike et al. (2015) TtOmp85, a β-barrel assembly protein, functions by barrel augmentation. Biochemistry 54:844-52
Liang, Jie; Cao, Youfang; Gursoy, Gamze et al. (2015) Multiscale Modeling of Cellular Epigenetic States: Stochasticity in Molecular Networks, Chromatin Folding in Cell Nuclei, and Tissue Pattern Formation of Cells. Crit Rev Biomed Eng 43:323-46
Tang, Ke; Wong, Samuel W K; Liu, Jun S et al. (2015) Conformational sampling and structure prediction of multiple interacting loops in soluble and β-barrel membrane proteins using multi-loop distance-guided chain-growth Monte Carlo method. Bioinformatics 31:2646-52

Showing the most recent 10 out of 62 publications