Future Faculty
Currently training with David Baker, these talented researchers are seeking their first faculty positions. Meet tomorrow’s scientific leaders today!
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| Name (Alphabetical by Last) | Research Interest |
|---|---|
| Mohamad Abedi, PhD | Cell signaling and cell therapies |
| Linna An, PhD | Small molecule binders and sensors; enzyme design |
| Nathan Ennist, PhD | Protein design for enhanced photosynthesis |
| Indrek Kalvet, PhD | New chemistry, enabled by protein design |
| Sam Pellock, PhD | Enzyme design with deep learning |
| Arvind Pillai, PhD | Designing conformationally dynamic proteins and molecular machines |
| Hao Shen, PhD | Controlled functional self-assembling protein filaments |
| Shunzhi Wang, PhD | Programmable protein self-assemblies by design |
| Xinru Wang, PhD | Structure-guided therapeutics development |
| Jason Zhang, PhD | Computationally designed biosensors for signaling |
Mohamad Abedi, PhD
PhD Institution: Mikhail Shapiro Lab, Caltech
Elucidating the laws of cell signaling and innovating next-generation cell therapies
My academic goal is to develop molecular and cellular technologies to better understand biology and improve cell therapies. During my PhD in bioengineering at Caltech with Mikhail Shapiro, I created ultrasound-controlled thermally responsive protein switches that enabled remote activation of tumor-killing microbes in vivo and extended this technology to control T cell function, merging molecularly targeted cell therapy with spatially targeted interventional radiology. As an HHMI/JCC fellow, I combine de novo protein design skills from the Baker lab with my cellular engineering experience to create synthetic biology tools with therapeutic applications, including orthogonal cellular communication networks, protein-based receptor degradation integrated with designed protein logic circuits, and engineered viruses for targeted in vivo genetic delivery. However, despite advancements in engineering new orthogonal signaling channels, current tools are inadequate for studying how cells orchestrate their complex signaling networks. Analogous to deciphering language through its words, understanding cell signaling hinges on deciphering the roles of individual signaling proteins. However, traditional approaches, which probe a few proteins in isolated cell types, restrict our exploration. To simultaneously probe thousands of proteins or even create novel signaling protein “words,” we require a paradigm shift—a bottom-up engineering approach with a toolkit of plug-and-play components capable of reconstructing cellular signaling pathways. By constructing cell signaling proteins from scratch through a hybrid high-throughput experimental and computational pipeline, I have developed and tested over a thousand natural and novel agonists, expanding our understanding of cell signaling beyond natural limits. Leveraging computational protein design, these de novo designed agonists can incorporate logical operations tailored to specific cells and environments, paving the way for smart agonists that enhance therapeutic outcomes in diseases like cancer and autoimmunity. This approach aims to expand our understanding of cellular communication, generate extensive datasets on the signalome, uncover fundamental rules of receptor-based cell signaling, and provide a blueprint for next-generation therapeutics.
Email: mabedi@uw.edu
Linna An, PhD
PhD Institution: Wilfred A. van der Donk Lab, University of Illinois at Urbana-Champaign
Small molecule binders and sensors; enzyme specificity design; enzyme assembly design
I received heavy training in natural product biosynthesis (PhD), enzymology (PhD), and machine learning-based protein design for protein-ligand complex engineering (Postdoc). In particular, I constructed a deep learning-based pipeline and provided general solutions for designing de novo small molecule binders towards diverse ligands, including flexible and polar ones, and conveniently turned them into sensors. I envision first principle-based protein-ligand complex engineering will bring technology breakthroughs in functional molecule generation, especially sensor design and enzyme engineering. Parallelly, I plan to build a cycle between “computational model”, “biochemical problems”, and “data”. Using current “computational models”, I will provide solutions to the sensing and catalyzing “biochemical problems”, which will generate molecule-function datasets, which in return feedback to further model training.
Email: linnaan@uw.edu
Nathan Ennist, PhD
PhD Institution: P. Leslie Dutton Lab, University of Pennsylvania
Protein design for enhanced photosynthesis
Photosynthesis employs chains of light-harvesting and electron-transfer proteins to convert solar energy into the chemical energy that sustains our biosphere. Studies of natural photosystems (including some of my own work) indicate that it is possible to improve the overall efficiency of the light reactions of photosynthesis to enhance production of food crops or renewable solar fuels. I have designed and characterized light-responsive proteins including photochemical reaction centers, chlorophyll special pair proteins, and light-harvesting chlorophyll antenna proteins. In ongoing work, I aim to design proteins that assemble multi-nuclear metal clusters for catalysis and tune the redox potentials of infrared-active bacteriochlorophyll molecules to drive high-energy reactions such as water oxidation. New developments in the field of computational protein design have made it easier than ever to design multi-cofactor protein complexes. In my future lab, I aim to design proteins that assemble electron transport chains for enhanced photosynthesis by applying the latest computational protein design methods in combination with the “Moser-Dutton ruler” to predict electron transfer rates. I will build and test photochemical reaction centers with optimized inter-cofactor distances and redox potentials with the goal of light-activated charge separation that has high quantum yield and thermodynamic efficiency. Successful water splitting using NIR light with wavelengths up to 1000 nm would nearly double the solar photon flux useful for oxygenic photosynthesis, paving the way toward high-efficiency production of food and biofuels.
Email: ennist@uw.edu
Indrek Kalvet, PhD
PhD Institution: Franziska Schoenebeck Lab, RWTH Aachen University
New chemistry, enabled by protein design
My future research program will exist at the interface of organic, computational, and biochemistry, enabling new chemistry through designed biocatalysis. The overarching goal will be to develop new chemical reactions and to expand the biocatalytic toolbox using de novo protein design. I believe that the synergy of protein design and synthetic catalyst development constitutes a powerful way of achieving control over chemical reactivities and selectivities, as well as gaining a broader understanding of the fundamentals of chemical reactivity and enzyme catalysis. I intend to leverage my doctoral training in synthetic and computational chemistry and my postdoctoral training in de novo protein design. Through this, I have gained computational expertise ranging from quantum chemistry to machine learning applications, and experimental expertise spanning from organometallic chemistry to molecular biology. I have developed new selective cross-coupling methods, uncovered the principles of the reactivity of Pd(I) and Ni(I) catalysts, and designed new-to-nature catalytic cofactor-binding proteins for various synthetic applications.
Email: ikalvet@uw.edu
Sam Pellock, PhD
PhD Institution: Matthew Redinbo Lab, UNC-Chapel Hill
Enzyme design with deep learning
Enzymes are exquisite molecular machines that dramatically improve reaction rates in mild, aqueous conditions. From the efficient synthesis of valuable medicines to the degradation of plastic waste, enzymes can address pressing issues in the modern world. Despite the clear value and intense study of enzymes, they have eluded rational design. Recent advances in protein design driven by deep learning have resulted in unprecedented improvements in the speed and accuracy of molecular modeling that have brought the difficult task of enzyme design within reach. With chemistry and protein structure as guiding principles, I aim to use protein design to engineer natural enzymes and design new ones to understand how we can tune active site structure to yield a target function and apply this understanding to develop novel catalysts for applications in sustainability and medicine.
Email: spellock@uw.edu
Arvind Pillai, PhD
PhD Institution: Joseph Thornton Lab at the University of Chicago
Designing conformationally dynamic proteins and molecular machines
My research interests lie at the intersection of protein design and evolution. They are twofold: (1) to explore how complex protein features like folding, catalysis, and allostery emerged “from scratch” during natural evolution, addressing a fundamental gap in our understanding of molecular evolution and biochemistry (Pillai et al., Protein Science 2020); and (2) to develop effective computational methods for engineering complex functions— particularly those that rely on allostery and conformational dynamics—into synthetic proteins, with the ultimate aim of building novel biosensors, molecular machines, and switchable nanomaterials. During my PhD, I combined phylogenetic methods with experimental characterization of putative ancestral proteins to provide the first biophysically detailed description of how Hemoglobin, a model protein for structure-function studies, evolved its complex multimeric architecture and allosteric function from simpler ancestors (Pillai et al., Nature 2020). In my postdoctoral research at the Baker Lab, I applied insights from probing hemoglobin’s origins to design allosteric assemblies made up of de novo proteins (Pillai et al., Nature 2024). I engineered a range of switchable nanostructures, including dimers, rings, and cages, decisively demonstrating that modern protein design methods can accurately specify energy landscapes that include multiple distinct oligomeric states whose occupancy can be tuned allosterically. In future work, I aim to extend these computational tools to explore a range of protein functions connected to conformational dynamics. This includes designing metamorphic proteins that toggle between folds with different functions, engineering new kinds of metabolic control over natural enzymes, and coupling enzyme function to therapeutically relevant biomarkers and metabolites. On the evolutionary front, I plan to use ancestral reconstruction and phylogenetic approaches to probe how novel catalytic functions and conformational dynamics arose within protein families during historical evolution. Additionally, I will leverage high-throughput folding assays to investigate whether evolution can generate completely novel protein structures from random genomic sequences through pathways of exonization, recombination, and mutation, or from pre-existing folded proteins. By combining these evolutionary and engineering approaches, I hope to not only deepen our understanding of molecular evolution but also pave the way for allosteric devices and molecular machines that are useful in biotechnology and medicine.
Email: apillai1@uw.edu
Hao Shen, PhD
PhD Institution: David Baker Lab, University of Washington
Controlled functional self-assembling protein filaments
I’ve been dedicated to the de novo design of self-assembling protein filaments for a decade. I started with single-component fiber with defined structures, tunable diameter, and dynamic assembly. By constantly exploring new technologies and transforming our methodologies, I am now able to design multi-component, ligand-induced, environmentally responsive, and nucleation-controlled protein filaments with up to 10 micrometers in length and high rigidity strength. In parallel to method improvements, I have designed functional protein fibers for applications such as conductive nanowires, fibers with energy-transferable ligands, and fibers that bind to carbon nanotubes or exhibit designed fluorescence. With the ability to design any functional protein into a helical filament assembly, I am deeply interested in designing protein fiber-based nanomachines, such as nanoscale fiber walkers and self-assembling smart materials. The controlled assembly can serve as a signal amplification platform for diagnostic applications. The defined filament structures can be used for scaffolding protein of interest for cryoEM structure determination. These protein filaments can also interact with inorganic materials to create complex functional materials. In addition to the profound biotechnological applications, precisely designed helical protein filaments provide significant opportunities to study protein and molecular self-assembly mechanisms and their interactions with cell biology through in vivo assembly.
Email: shenh2@uw.edu
Shunzhi Wang, PhD
PhD Institution: Chad Mirkin lab, Northwestern
Programmable protein self-assemblies by design
As a result of evolutionary selection, the subunits of naturally occurring protein complexes often fit together with substantial shape complementarity to generate architectures optimal for function; however, emulating such a level of programmability through de novo design remains a significant challenge. The ability to create designer protein assemblies such as bioprobes, vaccines, and delivery vehicles shall catalyze transformative advances and discoveries in human health and sustainable development. My current research is centered on the computational design of symmetric and quasi-symmetric protein assemblies with highly tailorable architectures and properties. By harnessing the capabilities of reinforcement learning algorithms, I pioneered a top-down approach to design geometrically constrained protein architectures as potent vaccines and modular nanopores (Science 2023). In addition, I have co-developed the first general strategy for the computational design of 3D protein crystals (Nat. Mater. 2023), and am exploring them as genetically encodable nanomaterials for intracellular applications. During my PhD, I discovered particle analogs of electrons in colloidal crystals engineered with DNA (Nat. Mater. 2022; Science 2019). Through my doctoral work and postdoctoral studies, I have gained valuable expertise in computational protein design, materials sciences, and DNA chemistry. Recently, I was honored with a Burroughs Wellcome Career Award at the Scientific Interface (2024) to support my transition to a junior faculty position. In my future lab, employing an iterative “understanding-by-design” approach, I plan to explore the design of programmable supramolecular assemblies with emergent biological properties in living systems, such as de novo virus-like particles for delivery and custom microcompartments as intracellular nanoreactors.
Email: swang523@uw.edu
Xinru Wang, PhD
PhD Institution: Rebecca Page Lab, Brown University
Structure-guided therapeutics development
My research focuses on using interdisciplinary approaches in structural biology, cell biology, and machine learning-based protein design to develop protein-based therapeutics for diverse applications. I thrive in a research environment that encompasses a wide range of disciplines, allowing me to address clinically relevant questions using both computational and experimental methods. Throughout my academic journey, I have acquired a versatile skill set, including expertise in computational protein design, high-throughput protein interactions screening, Macromolecular X-ray crystallography, solution Nuclear Magnetic Resonance, etc. By leveraging the latest structural understanding in membrane receptor proteins and applying the cutting-edge computational protein design methods, my postdoctoral works developed protein based agonists that can specifically regulate cell signaling pathways by regulating receptor conformation and ligand binding kinetics. The outcomes of my work have significant translational potential while remaining rooted in fundamental basic science research. In my independent research group, I aim to addressing challenges in regulating cell signaling from both outside and inside of the cells through structure-guided, machine learning-based protein design.
Email: xinruw7@uw.edu
Jason Zhang, PhD
PhD Institution: Jin Zhang Lab, UCSD
Computationally designed biosensors for signaling
Thousands of biochemical reactions occur simultaneously in each cell. These events are more than just a random collection of molecular interactions and reactions but need to be organized into compartments to ensure specificity and efficiency. My central hypothesis is that spatiotemporal regulation of these signaling activities enables their functional specificity. Furthermore, this intricate organization is necessary for normal cell function as disruption of it is hijacked in various diseases such as cancer (Zhang J et al. Cell 2020). In investigating this hypothesis, I develop genetically encodable biosensors that can track these signaling activities in space and time (Zhang J et al. Science Adv 2021). However, there are many physiologically relevant targets where there are no biosensors for due to the paucity of native protein domains. Thus, my unique approach is to bridge this gap in making biosensors for physiologically important targets by expanding the protein universe with AI-based protein design (Zhang J et al. Nat Biotech 2022, Zhang J et al. Nat Biotech 2024). With these biosensors, I address important biological questions with a unique perspective such as uncovering the signaling mechanisms, independent of DNA and RNA changes, underlying drug resistance (Zhang J et a., Nat Chem Bio accepted). In the future, I aim to expand protein design for other more challenging targets such as phosphorylation that can enable the creation of a biosensor for any arbitrary target, essentially to detect the previously undetectable.
Email: jzz0428@uw.edu