Nathan Alexander

Interaction of a G protein with an activated receptor opens the interdomain interface in the alpha subunit

In G-protein signaling, an activated receptor catalyzes GDP/GTP exchange on the Gα subunit of a heterotrimeric G protein. In an initial step, receptor interaction with Gα acts to allosterically trigger GDP release from a binding site located between the nucleotide binding domain and a helical domain, but the molecular mechanism is unknown. In this study, site-directed spin labeling and double electron–electron resonance spectroscopy are employed to reveal a large-scale separation of the domains that provides a direct pathway for nucleotide escape. Cross-linking studies show that the domain separation is required for receptor enhancement of nucleotide exchange rates. The interdomain opening is coupled to receptor binding via the C-terminal helix of Gα, the extension of which is a high-affinity receptor binding element.

Conformation of receptor-bound visual arrestin

Arrestin-1 (visual arrestin) binds to light-activated phosphorylated rhodopsin (P-Rh*) to terminate G-protein signaling. To map conformational changes upon binding to the receptor, pairs of spin labels were introduced in arrestin-1 and double electron–electron resonance was used to monitor interspin distance changes upon P-Rh* binding. The results indicate that the relative position of the N and C domains remains largely unchanged, contrary to expectations of a “clam-shell” model. A loop implicated in P-Rh* binding that connects β-strands V and VI (the “finger loop,” residues 67–79) moves toward the expected location of P-Rh* in the complex, but does not assume a fully extended conformation. A striking and unexpected movement of a loop containing residue 139 away from the adjacent finger loop is observed, which appears to facilitate P-Rh* binding. This change is accompanied by smaller movements of distal loops containing residues 157 and 344 at the tips of the N and C domains, which correspond to “plastic” regions of arrestin-1 that have distinct conformations in monomers of the crystal tetramer. Remarkably, the loops containing residues 139, 157, and 344 appear to have high flexibility in both free arrestin-1 and the P-Rh*complex.

Energetic analysis of the rhodopsin–G-protein complex links the α5 helix to GDP release

We present a model of interaction of Gi protein with activated rhodopsin (R*) which pin-points energetic contributions to activation and reconciles the β2AR–Gs crystal structure with new and previously published experimental data. In silico analysis demonstrated energetic changes when the Gα C-terminal helix (α5) interacts with the R* cytoplasmic pocket, leading to displacement of the helical domain and GDP release. The model features a less dramatic domain opening than the crystal structure. The α5 helix undergoes a 63º rotation, accompanied by a 5.7Å translation, which reorganizes interfaces between α5 and α1 helices and between α5 and β6–α5. Changes in the β6–α5 loop displace αG. All of these movements lead to opening of the GDP binding pocket. The model creates a roadmap for experimental studies of receptormediated G protein activation.We present a model of interaction of Gi protein with the activated receptor (R*) rhodopsin, which pinpoints energetic contributions to activation and reconciles the β2 adrenergic receptor–Gs crystal structure with new and previously published experimental data. In silico analysis demonstrated energetic changes when the Gα C-terminal helix (α5) interacts with the R* cytoplasmic pocket, thus leading to displacement of the helical domain and GDP release. The model features a less dramatic domain opening compared with the crystal structure. The α5 helix undergoes a 63° rotation, accompanied by a 5.7-Å translation, that reorganizes interfaces between α5 and α1 helices and between α5 and β6-α5. Changes in the β6-α5 loop displace αG. All of these movements lead to opening of the GDP-binding pocket. The model creates a roadmap for experimental studies of receptor-mediated G-protein activation.

Algorithm for selection of optimized EPR distance restraints for de novo protein structure determination.

A hybrid protein structure determination approach combining sparse Electron Paramagnetic Resonance (EPR) distance restraints and Rosetta de novo protein folding has been previously demonstrated to yield high quality models (Alexander et al. (2008)). However, widespread application of this methodology to proteins of unknown structures is hindered by the lack of a general strategy to place spin label pairs in the primary sequence. In this work, we report the development of an algorithm that optimally selects spin labeling positions for the purpose of distance measurements by EPR. For the α-helical subdomain of T4 lysozyme (T4L), simulated restraints that maximize sequence separation between the two spin labels while simultaneously ensuring pairwise connectivity of secondary structure elements yielded vastly improved models by Rosetta folding. 54% of all these models have the correct fold compared to only 21% and 8% correctly folded models when randomly placed restraints or no restraints are used, respectively. Moreover, the improvements in model quality require a limited number of optimized restraints, which is determined by the pairwise connectivities of T4L α-helices. The predicted improvement in Rosetta model quality was verified by experimental determination of distances between spin labels pairs selected by the algorithm. Overall, our results reinforce the rationale for the combined use of sparse EPR distance restraints and de novo folding. By alleviating the experimental bottleneck associated with restraint selection, this algorithm sets the stage for extending computational structure determination to larger, traditionally elusive protein topologies of critical structural and biochemical importance.

BCL::Fold - De Novo Prediction of Complex and Large Protein Topologies by Assembly of Secondary Structure Elements

Computational de novo protein structure prediction is limited to small proteins of simple topology. The present work explores an approach to extend beyond the current limitations through assembling protein topologies from idealized α-helices and β-strands. The algorithm performs a Monte Carlo Metropolis simulated annealing folding simulation. It optimizes a knowledge-based potential that analyzes radius of gyration, β-strand pairing, secondary structure element (SSE) packing, amino acid pair distance, amino acid environment, contact order, secondary structure prediction agreement and loop closure. Discontinuation of the protein chain favors sampling of non-local contacts and thereby creation of complex protein topologies. The folding simulation is accelerated through exclusion of flexible loop regions further reducing the size of the conformational search space. The algorithm is benchmarked on 66 proteins with lengths between 83 and 293 amino acids. For 61 out of these proteins, the best SSE-only models obtained have an RMSD100 below 8.0 Å and recover more than 20% of the native contacts. The algorithm assembles protein topologies with up to 215 residues and a relative contact order of 0.46. The method is tailored to be used in conjunction with low-resolution or sparse experimental data sets which often provide restraints for regions of defined secondary structure.

BCL::MP-Fold: Folding Membrane Proteins through Assembly of Transmembrane Helices

Membrane protein structure determination remains a challenging endeavor. Computational methods that predict membrane protein structure from sequence can potentially aid structure determination for such difficult target proteins. The de novo protein structure prediction method BCL::Fold rapidly assembles secondary structure elements into three-dimensional models. Here, we describe modifications to the algorithm, named BCL::MP-Fold, in order to simulate membrane protein folding. Models are built into a static membrane object and are evaluated using a knowledge-based energy potential, which has been modified to account for the membrane environment. Additionally, a symmetry folding mode allows for the prediction of obligate homomultimers, a common property among membrane proteins. In a benchmark test of 40 proteins of known structure, the method sampled the correct topology in 34 cases. This demonstrates that the algorithm can accurately predict protein topology without the need for large multiple sequence alignments, homologous template structures, or experimental restraints.

Bcl::Cluster: A method for clustering biological molecules coupled with visualization in the Pymol Molecular Graphics System

Clustering algorithms are used as data analysis tools in a wide variety of applications in Biology. Clustering has become especially important in protein structure prediction and virtual high throughput screening methods. In protein structure prediction, clustering is used to structure the conformational space of thousands of protein models. In virtual high throughput screening, databases with millions of drug-like molecules are organized by structural similarity, e.g. common scaffolds. The tree-like dendrogram structure obtained from hierarchical clustering can provide a qualitative overview of the results, which is important for focusing detailed analysis. However, in practice it is difficult to relate specific components of the dendrogram directly back to the objects of which it is comprised and to display all desired information within the two dimensions of the dendrogram. The current work presents a hierarchical agglomerative clustering method termed bcl::Cluster. bcl::Cluster utilizes the Pymol Molecular Graphics System to graphically depict dendrograms in three dimensions. This allows simultaneous display of relevant biological molecules as well as additional information about the clusters and the members comprising them.

RosettaEPR: rotamer library for spin label structure and dynamics.

An increasingly used parameter in structural biology is the measurement of distances between spin labels bound to a protein. One limitation to these measurements is the unknown position of the spin label relative to the protein backbone. To overcome this drawback, we introduce a rotamer library of the methanethiosulfonate spin label (MTSSL) into the protein modeling program Rosetta. Spin label rotamers were derived from conformations observed in crystal structures of spin labeled T4 lysozyme and previously published molecular dynamics simulations. Rosetta’s ability to accurately recover spin label conformations and EPR measured distance distributions was evaluated against 19 experimentally determined MTSSL labeled structures of T4 lysozyme and the membrane protein LeuT and 73 distance distributions from T4 lysozyme and the membrane protein MsbA. For a site in the core of T4 lysozyme, the correct spin label conformation (Χ1 and Χ2) is recovered in 99.8% of trials. In surface positions 53% of the trajectories agree with crystallized conformations in Χ1 and Χ2. This level of recovery is on par with Rosetta performance for the 20 natural amino acids. In addition, Rosetta predicts the distance between two spin labels with a mean error of 4.4 Å. The width of the experimental distance distribution, which reflects the flexibility of the two spin labels, is predicted with a mean error of 1.3 Å. RosettaEPR makes full-atom spin label modeling available to a wide scientific community in conjunction with the powerful suite of modeling methods within Rosetta.

BCL::Align¯Sequence alignment and fold recognition with a custom scoring function online

BCL::Align is a multiple sequence alignment tool that utilizes the dynamic programming method in combination with a customizable scoring function for sequence alignment and fold recognition. The scoring function is a weighted sum of the traditional PAM and BLOSUM scoring matrices, position-specific scoring matrices output by PSI-BLAST, secondary structure predicted by a variety of methods, chemical properties, and gap penalties. By adjusting the weights, the method can be tailored for fold recognition or sequence alignment tasks at different levels of sequence identity. A Monte Carlo algorithm was used to determine optimized weight sets for sequence alignment and fold recognition that most accurately reproduced the SABmark reference alignment test set. In an evaluation of sequence alignment performance, BCL::Align ranked best in alignment accuracy (Cline score of 22.90 for sequences in the Twilight Zone) when compared with Align-m, ClustalW, T-Coffee, and MUSCLE. ROC curve analysis indicates BCL::Aligns ability to correctly recognize protein folds with over 80% accuracy. The flexibility of the program allows it to be optimized for specific classes of proteins (e.g. membrane proteins) or fold families (e.g. TIM-barrel proteins). BCL::Align is free for academic use and available online at http://www.meilerlab.org/.

BCL::Fold--protein topology determination from limited NMR restraints.

When experimental protein NMR data are too sparse to apply traditional structure determination techniques, de novo protein structure prediction methods can be leveraged. Here, we describe the incorporation of NMR restraints into the protein structure prediction algorithm BCL::Fold. The method assembles discreet secondary structure elements using a Monte Carlo sampling algorithm with a consensus knowledge‐based energy function. New components were introduced into the energy function to accommodate chemical shift, nuclear Overhauser effect, and residual dipolar coupling data. In particular, since side chains are not explicitly modeled during the minimization process, a knowledge based potential was created to relate experimental side chain proton–proton distances to Cβ–Cβ distances. In a benchmark test of 67 proteins of known structure with the incorporation of sparse NMR restraints, the correct topology was sampled in 65 cases, with an average best model RMSD100 of 3.4 ± 1.3 Å versus 6.0 ± 2.0 Å produced with the de novo method. Additionally, the correct topology is present in the best scoring 1% of models in 61 cases. The benchmark set includes both soluble and membrane proteins with up to 565 residues, indicating the method is robust and applicable to large and membrane proteins that are less likely to produce rich NMR datasets. Proteins 2014; 82:587–595.