Abstract of the Talk
In this talk, I will discuss our works on biomolecular flexibility and normal mode analysis. Based on the intrinsic structural properties, flexibility and rigidity index (FRI) is proposed. FRI has demonstrated great advantages in both efficiency and accuracy. More recently, a multiscale virtual particle based elastic network model (MVP-ENM) is proposed for biomolecular normal mode analysis. Unlike the previous ENMs that use a constant spring constant, a particle-dependent spring parameter is used in MVP-ENM. Two independent models, i.e., multiscale virtual particle based Gaussian network model (MVP-GNM) and multiscale virtual particle based anisotropic network model (MVP-ANM), are proposed. Even with a rather coarse grid and a low resolution, the MVP-GNM is able to predict the Debye-Waller factors (B-factors) with considerable good accuracy. Similar properties have also been observed in MVP-ANM. Further, it is found that MVP-ANM can deliver a very consistent low-frequency eigenmodes in various scales. This demonstrates the great potential of MVP-ANM in the deformation analysis of low resolution data.
Dr. Kelin Xia obtained his Ph.D. degree from the Chinese Academy of Sciences in Jan 2013. He was a visiting scholar in the Department of Mathematics, Michigan State University from Dec 2009-Dec 2012. From Jan 2013 to May 2016, he worked as a visiting assistant professor at Michigan State University. He joined Nanyang Technological University at Jun 2016. His research focused on scientific computation, mathematical molecular biology, and topological data analysis (TDA), particularly complex data in biomolecular systems.
Bi-Weekly Seminar Series by the NYU-ECNU Center for Computational Chemistry at NYU Shanghai