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.

Structural and mechanistic motifs in membrane proteins: the three-dimensional modelling of rhodopsin, band 3, and the nicotinic acetylcholine receptor

Because so little is known about the structure of membrane proteins, an attempt has been made in this work to develop techniques by which to model them in three dimensions. The procedures devised rely heavily upon the availability of several sequences of a given protein. The modelling procedure is composed of two parts. The first identifies transmembrane regions within the protein sequence on the basis of hydrophobicity, β-turn potential, and the presence of certain amino acid types, specifically, proline and basic residues. The second part of the procedure arranges these transmembrane helices within the bilayer based upon the evolutionary conservation of their residues. Conserved residues are oriented toward other helices and variable residues are positioned to face the surrounding lipids. Available structural information concerning the protein's helical arrangement, including the lengths of interhelical loops, is also taken into account. Rhodopsin, band 3, and the nicotinic acetylcholine receptor have all been modelled using this methodology, and mechanisms of action could be proposed based upon the resulting structures.

Specific residues in the rhodopsin and iodopsin sequences were identified, which may regulate the proteins' wavelength selectivities. A hinge-like motion of helices M3, M4, and M5 with respect to the rest of the protein was proposed to result in the activation of transducin, the G-protein associated with rhodopsin. A similar mechanism is also proposed for signal transduction by the muscarinic acetylcholine and β-adrenergic receptors.

The nicotinic acetylcholine receptor was modelled with four trans-membrane helices per subunit and with the five homologous M2 helices forming the cation channel. Putative channel-lining residues were identified and a mechanism of channel-opening based upon the concerted, tangential rotation of the M2 helices was proposed.

Band 3, the anion exchange protein found in the erythrocyte membrane, was modelled with 14 transmembrane helices. In general the pathway of anion transport can be viewed as a channel composed of six helices that contains a single hydrophobic restriction. This hydrophobic region will not allow the passage of charged species, unless they are part of an ion-pair. An arginine residue located near this restriction is proposed to be responsible for anion transport. When ion-paired with a transportable anion it rotates across the barrier and releases the anion on the other side of the membrane. A similar process returns it to its original position. This proposed mechanism, based on the three-dimensional model, can account for the passive, electroneutral, anion exchange observed for band 3. Dianions can be transported through a similar mechanism with the additional participation of a histidine residue. Both residues are located on M10.