Doctor of Philosophy, Johns Hopkins University (2011)
Chris Garcia, Postdoctoral Faculty Sponsor
Chemokines are small proteins that function as immune modulators through activation of chemokine G protein-coupled receptors (GPCRs). Several viruses also encode chemokines and chemokine receptors to subvert the host immune response. How protein ligands activate GPCRs remains unknown. We report the crystal structure at 2.9 angstrom resolution of the human cytomegalovirus GPCR US28 in complex with the chemokine domain of human CX3CL1 (fractalkine). The globular body of CX3CL1 is perched on top of the US28 extracellular vestibule, whereas its amino terminus projects into the central core of US28. The transmembrane helices of US28 adopt an active-state-like conformation. Atomic-level simulations suggest that the agonist-independent activity of US28 may be due to an amino acid network evolved in the viral GPCR to destabilize the receptor's inactive state.
View details for DOI 10.1126/science.aaa5026
View details for Web of Science ID 000350354200046
A method for non-invasive visualization of genetically labeled cells in animal disease models with micrometer-level resolution would greatly facilitate development of cell-based therapies. Imaging of fluorescent proteins (FPs) using red excitation light in the 'optical window' above 600 nm is one potential method for visualizing implanted cells. However, previous efforts to engineer FPs with peak excitation beyond 600 nm have resulted in undesirable reductions in brightness. Here we report three new red-excitable monomeric FPs obtained by structure-guided mutagenesis of mNeptune. Two of these, mNeptune2 and mNeptune2.5, demonstrate improved maturation and brighter fluorescence than mNeptune, whereas the third, mCardinal, has a red-shifted excitation spectrum without reduction in brightness. We show that mCardinal can be used to non-invasively and longitudinally visualize the differentiation of myoblasts into myocytes in living mice with high anatomical detail.
View details for DOI 10.1038/NMETH.2888
View details for Web of Science ID 000335873400026
View details for PubMedID 24633408
When part of a biological system cannot be investigated directly by experimentation, we face the problem of structure identification: how can we construct a model for an unknown part of a mostly known system using measurements gathered from its input and output? This problem is especially difficult to solve when the measurements available are noisy and sparse, i.e. widely and unevenly spaced in time, as is common when measuring biological quantities at the cellular level. Here we present a procedure to identify a static nonlinearity embedded between two dynamical systems using noisy, sparse measurements. To reduce the level of error caused by measurement noise, we introduce the concept of weighted-sum predictability. If we make the input and output subsystems weighted-sum predictable and normalize the measurements to their weighted sum, we achieve better noise reduction than through normalizing to a loading control. We then interpolate the normalized measurements to obtain continuous input and output signals, with which we solve directly for the input-output characteristics of the unknown static nonlinearity. We demonstrate the effectiveness of this structure identification procedure by applying it to identify a model for ergosterol sensing by the proteins Sre1 and Scp1 in fission yeast. Simulations with this model produced outputs consistent with experimental observations. The techniques introduced here will provide researchers with a new tool by which biological systems can be identified and characterized.
View details for DOI 10.1016/j.jtbi.2012.01.037
View details for Web of Science ID 000302113100024
View details for PubMedID 22310068