Research & Projects
Our research focuses on structural characterization of membrane proteins and de novo protein design in order to understand biological processes relevant to human disease and develop novel therapeutics.
We approach many diverse biological issues:
- Protein design: We use de novo protein design to test whether our knowledge has advanced sufficiently to allow generation of structure and function from first principles. Our lab also uses this method to incorporate desired features into biological systems.
- Integrin inhibitors: We study the mechanisms of signal transduction and conformational change in integrins, and have also designed novel small molecule inhibitors of specific integrins to use as therapeutics for tissue fibrosis and other diseases.
- Influenza: We study the matrix 2 protein (M2) of influenza A, which is a drug target found in the viral envelope of the flu.
- Alzheimer’s disease: We are interested in structurally characterizing and disrupting the accumulations of Aβ that are associated with Alzheimer’s disease.
- Bacterial sensing: We are interested in the mechanisms by which bacteria sense their environment and adapt to different environmental stresses.
- Computational tools: We have taken a nature-inspired approach to protein design.
Proteins catalyze countless vital physicochemical reactions. Proteins do this by coordinating the substrates in specific, three-dimensional orientations, so understanding the structure is essential to understanding the function, disease etiology and drug design. Our lab uses a de novo design approach to explore the principals that govern folding, protein-cofactor, and protein-protein interaction, as well as subsequent functions, for both water- and lipid-soluble proteins. We then use various biophysical methods to define the success of our designs. Please, check out our de novo metalloprotein, DF2, featured as the Molecule of the Month in the PDB.
Integrins are heterodimeric transmembrane proteins that play a pivotal role in the signaling pathways that regulate processes as diverse as cell proliferation, differentiation, apoptosis, and cell migration. In collaboration with Joel Bennett (UPenn), we study the mechanism of signal transduction of integrins such as αIIbβ3 and αvβ3, with a focus on the role of the proteins' membrane-spanning regions. In collaboration with Dean Sheppard (UCSF), we have also developed small molecule inhibitors of integrin α2β1 in the platelet collagen receptor and now identifying novel compounds to target the αvβ1 and α5β1 integrins to combat organ fibrosis and respiratory disease, respectively. Thus far, we have designed molecules targeting αvβ1 that effectively reduce fibrotic markers in mouse models of kidney and liver fibrosis, and have used these molecules to demonstrate that this integrin is responsible for activation of TGFβ on pulmonary fibroblast. Likewise, we have developed small molecules that potently and selectively inhibit the integrin α5β1, demonstrating that modulation of this integrin could be a novel approach to treat asthma. A current focus of the DeGrado focus is using medicinal chemistry approaches to further develop these drug candidates for pre-clinical testing.
We are interested in the M2 protein of influenza because it is both a drug target and a model system for proton transport. M2 is a proton channel that is the target of a class of drugs called the adamantanes, though viral mutations have caused adamantane-resistant strains of influenza to become prevalent. M2 is also one of the smallest proton channels found in nature, since it is a homotetramer, with a minimally functional monomer length of only 25 amino acids. We perform structural studies on this system using NMR and crystallography, and we also create new M2 inhibitors targeting M2 then test their effectiveness.
A wide variety of neurodegenerative diseases are characterized by protein misfolding. Tauopathies, for example, are a group of neurodegenerative diseases with the deposition of misfolded tau protein, including Alzheimer’s disease, chronic traumatic encephalopathy, progressive supranuclear palsy, corticobasal degeneration, etc. Although sharing the same pathogenic agent, these diseases show a broad spectrum of clinicopathological patterns. Similarly, conformational heterogeneity of the neuronal protein alpha-synuclein has been linked to diseases including Parkinson’s disease, dementia with Lewy bodies, and multiple system atrophy. We seek to gain a molecular and cellular understanding of pathogenic protein misfolding using a combination of biochemical/biophysical and cell biology methods.
The two-component system is an essential stimulus-response mechanism in bacteria. It consists of a transmembrane histidine kinase that senses the environment and a response regulator that mediates the cellular response. We study the protein structures and conformational dynamics associated with signal transduction via two-component systems.
Our methodology includes in-cell studies on bacteria, biochemical assays on purified proteins, structural and biophysical studies by x-ray crystallography, H/D exchange mass-spectrometry, NMR and EPR.
This project is synergistic with new antimicrobial development as many bacteria use two-component systems to develop antibiotic resistance.
We use several computational approaches to help us design proteins with desired properties. By efficiently searching the Protein Data Bank for backbone or amino acid motifs of interest, we can identify interactions frequently occurring in nature which can then be used as foundations for our designs. Often we combine this nature-inspired approach with molecular modeling tools such as Rosetta and molecular dynamics simulations. Some applications we pursue with this approach are the design of peptides that can modulate protein-protein interactions, the design of transmembrane proteins with desired ion-transport properties, and the design of metal or small molecule binding sites.
For amino acid motifs, we use a tool called Suns developed in our lab which has a built-in PyMol interface enabling the interactive building of motifs with the search results. For backbone motifs, we use MadCat, which efficiently compares the distance-map of a query motif to a database of pre-computed distance-maps for thousands of proteins from the PDB. Additionally, we have developed SuperCodons, a tool that allows the construction of a randomized DNA library that closely matches a chosen amino acid distribution. We have also developed the knowledge-based E(z)-3D Transmembrane Protein Orientation Potential, which predicts the most favorable orientation within a membrane for transmembrane proteins.