丁香园AV

Synopsis

Determining the orientation and conformation of an adsorbed biomolecule at an inorganic interface remains a grand challenge in studies of interest to materials science, medicine and biotechnology. The theme of this talk will be to share recent advances from our group in fundamental science and engineering of interfacial phenomena of complex peptides. Through the lens of understanding structure-function relationships and making predictions of adsorbed-peptide structure, this talk will also introduce a suite of computational tools that can be used to study these complex systems.

The first part of this talk will highlight how we are using simulations to understand the dominant driving forces that lead to unique orientation and conformation of peptides at the bio/nano interface with an illustration of how the surface/peptide interface strongly dictates the dominant contributions to binding energetics. I will also discuss the challenge of accurately predicting biomolecule structure at interfaces and how a marriage of simulation and sparse experimental data can provide deep insight. Following this I will share several examples related to the formation of biosilica in two systems: peptides based on marine diatoms, and the recognition domain of the salivary protein statherin.

Bio: Jim Pfaendtner is the Bindra Career Development Professor and Associate Professor & Chair of Chemical Engineering at the University of Washington. He holds a B.S. in Chemical Engineering (Georgia Tech, 2001) and a PhD in Chemical Engineering (Northwestern University, 2007). He also serves as Associate Vice Provost for Research Computing at the UW. Jim鈥檚 research focus is computational molecular science and his recent teaching interests are in the area of teaching data science skills to grad students in chemical and materials science and engineering. Jim is a recipient of an NSF CAREER the University of Washington Distinguished Teaching Award. Jim is currently the director of an NSF graduate training program (NRT) at the intersection of data science and molecular science.