We work in a highly collaborative and interdisciplinary team to tackle some of the challenges of 21st century. We strive to be innovative, transparent, and inclusive. Our main interest is to integrate experimental data and computational methods to discover new solutions.
Current areas of focus in our lab are:
Peptides are small stretches of amino acids (<40 residues) that can be made out of non-canonical building blocks. Many naturally occurring peptides are involved in important functions such as self-defense and signaling.
In addition to their interesting biological features, peptides are attractive as therapeutics due to their small size, stability, and ease of synthesis.
1. Modeling peptide behavior in solution
We sample the conformational landscape of peptides and its contribution to peptide features such as its binding affinity using a combination of Rosetta-based backbone sampling, molecular dynamics simulations, and machine learning.
Combining these conformational sampling with large experimental data, we're also generating new insight into what are the contributing factors to peptides ability to cross the cell membrane and whether there are any differences in permeability between mammalian cells versus bacterial cells.
2. Designing peptide-based binders
Another area of research in our lab is to develop computational methods to design peptides that can bind selectively and strongly to targets of interest. Some of the current targets we're interested in are viral intra- and extracellular targets and GPCRs.
Novel functional proteins
Proteins are tiny superheroes of our cells. They perform most of the essential functions that sustain life on earth. These natural proteins have inspired scientists to create new proteins to address some of the challenges we face in our daily life.
Our lab uses state-of-the-art computational and experimental approaches to design novel functional molecules with applications ranging from biomedicine to bio-catalysis.
Figure art inspired by grocklin
1. New enzymes
Enzymes design is an attractive area of research for generation of green biocatalysts. However, enzyme design remains one of the most challenging areas in the de novo design of proteins. Our lab combines computational design methods (Rosetta, sequence-based learning methods, generative learning methods) with high throughput experimental data to shed light on features that correspond to the success of enzyme design methods. We will then use this knowledge to generate more efficient methods to design enzymes that catalyze novel reactions.
2. Selective biosensors
We use computationally designed proteins and peptides as sensing modules in biosensors. The flexibility and modularity of the design process allows for rapid generation of selective molecules. The stability of designed binders enables generation of sensing platforms that do not need cold-chain transport, resulting in higher accessibility of diagnostics. The current target of interest in our lab is mmp family of enzymes, potential biomarkers of pre-inflammatory gum disease.
3. Protein binders as delivery molecules
In collaboration with the Hettiaratchi lab at the Knight Campus, we are designing proteins that can bind to a number of protein targets of interest in a site-selective manner and with desired binding affinities. These binders will be used as delivery molecules to transfer the cargo to the site of injury. We're interested in expanding our target scope to small molecules as well.
Protein design for understanding biology
The function of our cells is regulated through a complex network of protein-protein interactions (PPIs). Some proteins in this network interact with multiple other proteins. These so-called hub proteins are often central to regulating biological processes and are often the point of attack in diseases such as cancer. Despite their importance, the use of similar regions in the hub proteins for binding to different partners makes understanding the role of each PPI very challenging.
Our lab uses computational protein design to selectively target these PPIs.
1. Targeting binding through disorder-to-order transition
Many hub proteins accomplish their promiscuous binding through engaging disordered region. In many cases, the disordered regions goes through a so-called disorder-to-order transition upon binding. Using protein design, we are selectively stabilizing the ordered binding motif, thus inhibiting binding to only one partner. We use this small genetically encodable designed binders as tools to study the cellular effects of inhibiting the interaction of interest.
Thanks to these organizations for funding our research
The Donald E. and Delia B. Baxter Foundation [2021-]
NIH (Director's New Innovator Award) [2021-2026]
Rosetta mini-grant [2022-]
University of Oregon
Knight Campus Center