Topic: Atomistic “Swimming” simulations of biomolecular binding as a tool of structural biology and drug discovery

Speaker: Prof. Yibing Shan

Time: 10:30 AM - 11:30 AM； Dec.13，2023 (Wednesday)

Venue: The Learning Studio (Starbucks, 2nd floor)

Abstract：

With leaps and bounds progress in computing technology, it is increasingly feasible to simulate the physical process of protein-protein and protein-small molecule interactions atomistically, and to predict bound structures of biomolecules.  Careful scrutiny of the simulation-generated structural models in light of existing biochemical, biophysical, and cellular data, can largely eliminate false models. As examples, we will discuss our modeling of the full-length structures of JAK2 kinase and the structure of Ras-Raf signalosome, both are important in cancer biology and drug discovery.  We will also show that such an approach can correctly recapitulate protein-small molecule binding in drug discovery settings. Recognizing that we are investigating complex systems and that the simulations are limited both in scale and in accuracy, we emphasize a soft and qualitative approach that integrates domain expertise, as oppose to a hard automatic protocol-based approach.  

Introduction：

Dr Yibing Shan has spent more than two decades in the field of biomolecular simulations, computational structural biology, and computer-aided drug discovery. He was a founding member of D. E. Shaw Research and played a key role in the conceptualization and design of Anton Supercomputer specialized for molecular dynamics simulations. He was twice a co-recipient of the Association of Computing Machinery (ACM) Gordon Bell Prize of Supercomputing.  Yibing initiated the “swimming” simulations of biomolecular association and developed it into a tool that has become integral to a number of state-of-the-art drug discovery platforms.  He earned his A.B. in engineering physics from Shanghai University of Science and Technology, a M.S. in computer science and a PhsD in physics from Drexel University.