February Monthly Meeting

Spectroscopy Society of Pittsburgh Monthly Meeting

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WHEN: Wednesday – February 19, 2014
RSVP BY: Thursday – February 13, 2014

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Technology Forum Speaker’s Presentation & Social Hour: 5:30 PM – Wolfe Lecture Hall (located in Bayer Hall)
Social Hour: 5:30 PM – City View Café

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(6th Floor of Union)
Dinner: 6:30 PM – City View Café (6th Floor of Union)
Business Meeting: 8:00 PM – Laura Falk Hall (located in Bayer Hall)
Technical Program Speaker’s Presentation: 8:15 PM – Laura Falk Hall

Dr. Angela Gronenborn, University of Pittsburgh School of Medicine

“Synergy between NMR, cryo-EM and large-scale MD simulations – Novel Findings for HIV Capsid Function”

HIV-1 particles contain a conical-shaped capsid that encloses the viral RNA genome and performs essential functions in the virus life cycle. Previous structural analysis of two- and three-dimensional arrays provided a molecular model of the capsid protein (CA) hexamer and revealed three interfaces in the lattice. Using the high-resolution NMR structure of the CA C-terminal domain (CTD) dimer and

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in particular the unique interface identified, it was possible to reconstruct a model for a tubular assembly of CA protein that fit extremely well into the cryoEM density map. A novel CTD-CTD interface

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at the local three-fold axis in the cryoEM map was confirmed by mutagenesis to be essential for function. More recently, the cryo-EM structure of the tube was solved at 8Å resolution and this cryo-EM structure allowed unambiguous modeling and refinement by large-scale molecular dynamics (MD) simulation, resulting in all-atom models for the hexamer-of-hexamer and pentamer-of-hexamer elements of spheroidal capsids. Furthermore, the 3D structure of a native HIV-1 core was determined by cryo-electron tomography (Cryo-ET), which in combination with MD simulations permitted the construction of a realistic all-atom model for the entire capsid, based on the 3D authentic core structure.

Dr. Nathan Clark, University of Pittsburgh

“DNA Sequence Evolution: Resurrecting the Past to See the Future”
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predict relationships between genes so that we can piece together the genetic networks that compose all organisms. We demonstrate how rate covariation has been successfully employed to discover new genes in medical genetics, thereby revealing the genetic basis of disease.