Protein structure refinement algorithms

Protein structure prediction has remained a major challenge in structural biology for more than half a century. Accelerated and cost efficient sequencing technologies have allowed researchers to sequence new organisms and discover new protein sequences. Novel protein structure prediction technologies will allow researchers to study the structure of proteins and to determine their roles in the underlying biology processes and develop novel therapeutics.

Difficulty of the problem stems from two folds: (a) describing the energy landscape that corresponds to the protein structure, commonly referred to as force field problem; and (b) sampling of the energy landscape, trying to find the lowest energy configuration that is hypothesized to be the native state of the structure in solution. The two problems are interweaved and they have to be solved simultaneously. This thesis is composed of three major contributions. In the first chapter we describe a novel high-resolution protein structure refinement algorithm called GRID. In the second chapter we present REMCGRID, an algorithm for generation of low energy decoy sets. In the third chapter, we present a machine learning approach to ranking decoys by incorporating coarse-grain features of protein structures.

Investigating the Role of the GET3-GET4/GET5 Interaction During Tail-Anchor Protein Targeting

The proper targeting of membrane proteins is essential to the viability of all cells. Tail-anchored (TA) proteins, defined as having a single transmembrane helix at their C-terminus, are post-translationally targeted to the endoplasmic reticulum (ER) membrane by the GET pathway (Guided Entry of TA proteins). In the yeast pathway, the handover of TA substrates is mediated by the heterotetrameric Get4/Get5 (Get4/5) complex, which tethers the co-chaperone Sgt2 to the central targeting factor, the Get3 ATPase. Although binding of Get4/5 to Get3 is critical for efficient TA targeting, the mechanisms by which Get4 regulates Get3 are unknown. To understand the molecular basis of Get4 function, we used a combination of structural biology, biochemistry, and cell biology. Get4/5 binds across the Get3 dimer interface, in an orientation only compatible with a closed Get3, providing insight into the role of nucleotide in complex formation. Additionally, this structure reveals two functionally distinct binding interfaces for anchoring and ATPase regulation, and loss of the regulatory interface leads to strong defects in vitro and in vivo. Additional crystal structures of the Get3-Get4/5 complex give rise to an alternate conformation, which represents an initial binding interaction mediated by electrostatics that facilitates the rate of subsequent inhibited complex formation. This interface is supported by an in-depth kinetic analysis of the Get3-Get4/5 interaction confirming the two-step complex formation. These results allow us to generate a refined model for Get4/5 function in TA targeting.