1. High-resolution structures of microtubule based organelles
Our lab is interested in revealing the mystery of cell by using a range of advanced imaging techniques, with an emphasis on high-resolution structural study of macromolecular complexes up to hundreds of megadolton. We are particularly interested in microtubule-based complexes and organelles. To dissect the detailed structures, we employ a “divide and conquer” approach in which we can break the organelle into small pieces, determine the high-resolution structure of each piece by single-particle cryo-electron microscopy (cryo-EM), study the overall architecture by cryo-tomography (cryo-ET) and ultimately reconstitute an atomic model of the entire organelle.
2. Disease mechanisms at atomic detail
Structural studies of macromolecular complexes isolated from native source allow us to identify proteins de novo, providing a confident candidate list for the diagnosis of related diseases. Our previous work identified over 200 proteins from ciliary complexes, including 22 proteins whose mutants are associated with primary ciliary dyskinesia (PCD) and 18 proteins whose mutants are associated with multiple morphological abnormality of flagella (MMAF). Further structural and functional studies are required to understand the disease mechanism. We are interested in uncovering how gene mutations change the structure of protein complexes at an atomic detail. By complementing with genetic and functional studies, this project will help us understand these diseases and accelerate their diagnosis and potential treatment.
3. Methods for accurate and fast model building
How to build atomic model accurately and quickly, especially for medium- to low-resolution cryo-EM structures isolated from native source with unknown protein identity, remains a challenge. This also applies to cryo-ET since many sub-nanometer structures are being obtained. We will be working on developing methods for accurate and fast atomic model building by integrating deep-learning-based tertiary structure predictions with manual protein assignment, automatic model building and fold recognition. We hope this will accelerate the interpretation of large native complexes in our lab and benefit the community as well.