Predicting glycosaminoglycan surface protein interactions and implications for studying axonal growth.

Cell-surface carbohydrates play important roles in numerous biological processes through their interactions with various protein-binding partners. These interactions are made possible by the vast structural diversity of carbohydrates and the diverse array of carbohydrate presentations on the cell surface. Among the most complex and important carbohydrates are glycosaminoglycans (GAGs), which display varied stereochemistry, chain lengths, and patterns of sulfation. GAG-protein interactions participate in neuronal development, angiogenesis, spinal cord injury, viral invasion, and immune response. Unfortunately, little structural information is available for these complexes; indeed, for the highly sulfated chondroitin sulfate motifs, CS-E and CS-D, there are no structural data. We describe here the development and validation of the GAG-Dock computational method to predict accurately the binding poses of protein-bound GAGs. We validate that GAG-Dock reproduces accurately (<1-Å rmsd) the crystal structure poses for four known heparin-protein structures. Further, we predict the pose of heparin and chondroitin sulfate derivatives bound to the axon guidance proteins, protein tyrosine phosphatase σ (RPTPσ), and Nogo receptors 1-3 (NgR1-3). Such predictions should be useful in understanding and interpreting the role of GAGs in neural development and axonal regeneration after CNS injury.

Characterizing and predicting the functional and conformational diversity of seven-transmembrane proteins.

The activation of seven-transmembrane receptors (7TMRs) allows cells to sense their environment and convert extracellular signals (like hormone binding) into intracellular signals (through G protein-coupled and/or ß arrestin-coupled pathways). A single 7TMR is capable of transducing a wide spectrum of physiological responses inside a cell by coupling to these pathways. This intracellular pleiotropic action is enabled by multiple conformations exhibited by these receptors. Developments in membrane protein structure determination technologies have led to a rapid increase in crystal structures for many 7TMRs. Majority of these receptors have been crystallized in their inactive conformation and, for some, one of the many active conformations has also been crystallized. Given the topological constraints of a lipid bilayer that results in a single fold of seven almost parallel TM helices connected by mostly unstructured loops, these structures exhibit a diversity of conformations not only across the receptors but also across the different functional forms for receptors with structures for one of the functionally active conformations. Here we present a method to characterize this conformational diversity in terms of transmembrane helix topology (TMHTOP) parameters and how to use these helix orientation parameters to predict functionally-distinct multiple conformations for these receptors. The TMHTOP parameters enable a quantification of the structural changes that underlie 7TMR activation and also sheds a unique mechanistic light on the pleiotropic nature of these receptors. It provides a common language to describe the 7TMR activation mechanisms as well as differences across many receptors in terms of visually intuitive structural parameters. Protein structure prediction methods can use these parameters to describe 7TMR conformational ensembles, which coupled to experimental data can be used to develop testable hypotheses for the structural basis of 7TMR functions.

Predicted 3D structures for adenosine receptors bound to ligands: comparison to the crystal structure.

G protein-coupled receptors (GPCRs) are therapeutic targets for many diseases, but progress in developing active and selective therapeutics has been severely hampered by the difficulty in obtaining accurate structures. We have been developing methods for predicting the structures for GPCR ligand complexes, but validation has been hampered by a lack of experimental structures with which to compare our predictions. We report here the predicted structures of the human adenosine GPCR subtypes (A(1), A(2A), A(2B), and A(3)) and the binding sites for adenosine agonist and eight antagonists to this predicted structure, making no use of structural data, and compare with recent experimental crystal structure for ZM241385 bound human A(2A) receptor. The predicted structure correctly identifies 9 of the 12 crystal binding site residues. Moreover, the predicted binding energies of eight antagonists to the predicted structure of A(2A) correlate quite well with experiment. These excellent predictions resulted when we used Monte Carlo techniques to optimize the loop structures, particularly the cysteine linkages. Ignoring these linkages led to a much worse predicted binding site (identifying only 3 of the 12 important residues). These results indicate that computational methods can predict the three-dimensional structure of GPCR membrane proteins sufficiently accurately for use in designing subtype selective ligands for important GPCR therapeutics targets.

Structure prediction of G protein-coupled receptors and their ensemble of functionally important conformations.

G protein-coupled receptors (GPCRs) are integral membrane proteins whose "pleiotropic" nature enables transmembrane (TM) signal transduction, amplification, and diversification via G protein-coupled and ß arrestin-coupled pathways. GPCRs appear to enable this by being structurally flexible and by existing in different conformational states with potentially different signaling and functional consequences. We describe a method for the prediction of the three-dimensional structures of these different conformations of GPCRs starting from their amino acid sequence. It combines a unique protocol of computational methods that first predict the TM regions of these receptors and TM helix shapes based on those regions, which is followed by a locally complete sampling of TM helix packings and their scoring that results in a few (~10-20) lowest energy conformations likely to play a role in binding to different ligands and signaling events. Prediction of the structures for multiple conformations of a GPCR is starting to enable the testing of multiple hypotheses related to GPCR activation and binding to ligands with different signaling profiles.