I am a Grad student at Prof. Vijay Pande's Lab. This is my first year at the lab and my second year as a Biophysics grad student.   My research involves the coarse-grain modeling of large bio-molecules.  Methodologically, I am using Normal Mode Analysis, which decomposes the complex fluctuations of proteins into a number of collective motions of atoms. This method has several immediate applications, which I am currently developing.   The first application is the building of atomistic models for experimental low-resolution data. This is relevant to the analysis of Cryo-EM data of Myosin and other large proteins that undergo conformational change upon binding of ligands, and for which crystallographic data is lacking or unavailable. Normal Mode offers a different coordinate system which is a natural basis to sample conformational change.  The software I am developing would be a natural  "Simbios app", since it would allow a researcher to build an atomistic model from experimental EM data; moreover, this is an application that we would use internally in the Myosin DBP.   The second application is the simulation of long-time scale dynamics of proteins that evolve between energetic minima corresponding to different conformations, with an emphasis on preserving the kinetics of the system.  Our goal is to run a Langvin dynamics simulation in the space of normal modes (rather than the space of individual atoms, as is currently done).  This would lead to a significant speed increase, allowing for the long time simulation of large biomolecules, with a reasonable fidelity to the true kinetics.   Myosin is a clear case study for both applications, since there is plenty of EM-data available as well as some kinetic data. Kinetic data is important to parametrize our coarse-grain dynamic model and enable us to make predictions.

E-mail: ppetrone@stanford.edu