Future Faculty

Currently training with David Baker, these talented researchers are seeking their first faculty positions. Meet tomorrow’s scientific leaders today!

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

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