Personal Page for Linna An, Ph.D

The research career of Linna An

I did my undergrad at the University of Science and Technology of China (USTC) with Prof. Gaolin Liang (now moved to SEU) on molecular imaging.

Then, I was trained as a biochemist during Ph.D. at the Wilfred A. van der Donk lab at the University of Illinois Urbana-Champaign (UIUC). I worked on using bioinformatics to find new biosynthetic gene clusters, and characterize their synthesis product in lab. This is a new way to find new drugs from nature, including antibiotics. I successfully heterologously identified some new natrual products. Particularly, I also unveiled a unique substrate-assisted bond formation mechanism in a previously unknown family of enzymes (An, Nat. Chem. Biol. 2018).

Right before COVID, I joined the David Baker lab at University of Washtingon (UW) as a protein designer, developing deep learning-based method to design functional proteins.

I focused on small molecule-protein interaction problems because it is the unavoidable mountain to climb for solving any ligand-related engineering problem. It was difficult to deal with back in 2020, and some challenges remain till today: the large sampling space, the high accuracy requirements, and small datasets. I came up with a probably obvious idea: if we can generate proteins with high shape complementary interfaces to ligands, we should be able to bind them. Even though it was an seemingly obvious and easy idea, it took us 3.5 years to actually make it happen in reality, during which almost all steps are optimized or equipped with new tools.

I first teamed up with Dr. Derrick R . Hicks and Dr. Dmitri Zorine (now developer at ZipBio, Inc), and we developed an AF2-based hallucination method, which can efficiently sample diverse pocket-containing proteins. These proteins are high quality, and ready to bind any shape of ligands (An, Hicks, Zorine, et al, Nat. Struct. Mol. Biol, 2023).

With high-quality proteins, I then worked on the actual ligand binding problems. I first computationally identified the most shape complementary protein docking to the ligand, then in the laboratory, using high-throughput methods to identify the ones who are actually suitable to the ligand. From the privileged protein-ligand pair, with computational sampling of local structures and docking conformations, I was able to quickly improve the binding affinity to nanomolar range. This pipeline stably generated binders to diverse ligands, including polar and flexible ligands, such as methotrexate, for the first time! We easily converted the de novo binders into sensors, and showed their usage in ligand-specific nanopore and chemical induced dimerization. (An, Science, 2024).

More recently, with my colleagues, we successfully implemented the whole idea to Ca-Diffusion (unpublished), and we demonstrated it carried over its success in more ligands stably, and stayed specific in cells.

Another direction I am pushing forward is, using the small-molecule protein manipulation pipeline I developed to engineer enzymes. Stay tuned for all interesting results! Ultimately, I want to build my team and/or form a team with others, in both acamedia and industry, tackle questions of sensing and enzyme domestication!

Currently, I am in the job market. Do let me know if you are interested!