Research Description
How can a single mutation disrupt protein function and propagate throughout scales, from cells to tissues and the whole organism causing diseases like cancer? How can the change of just a few atoms in an oncogene trigger the formation of a tumor? How is the sequence of a protein related to its shape and function?
We aim to answer these fundamental questions by exploring the molecular basis of mendelian diseases and cancer at the deepest level, in terms of protein structures and their motions at the atomic scale. In the same way as animal shapes are selected by evolution to fly, run or swim, each protein fold has been evolutionarily selected to perform certain motions, the so-called 鈥渃onformational-changes鈥, which dictate biological function. In cancer cells, proteins also mutate and quickly evolve their 鈥減henotypes鈥 - their conformational dynamics and resulting cellular function - in order to adapt to their environment and promote oncogenic growth.
To reveal the key dynamic information contained in cancer mutation patterns, we develop simulation methods (; ) and servers () and integrate them with structural determination techniques (SAXS, cryoEM), in vitro and in vivo experiments. Using this interdisciplinary approach, we discovered that EGFR mutations in brain tumors converge to acquire a similar conformation, which is antagonistic from mutations in lung cancer and respond to different drugs. Our mechanistic insights set a rational basis for synergistic drug combinations that trap this glioblastoma-specific EGFR conformation, triggering tumor regression in animal tumor models (; ).
Our goal is to go beyond conventional oncogenes like EGFR and perform a complete conformational p