Structural biology of G protein‐coupled receptor signaling complexes

G protein‐coupled receptors (GPCRs) constitute the largest family of cell surface receptors that mediate numerous cell signaling pathways, and are targets of more than one‐third of clinical drugs. Thanks to the advancement of novel structural biology technologies, high‐resolution structures of GPCRs in complex with their signaling transducers, including G‐protein and arrestin, have been determined. These 3D complex structures have significantly improved our understanding of the molecular mechanism of GPCR signaling and provided a structural basis for signaling‐biased drug discovery targeting GPCRs. Here we summarize structural studies of GPCR signaling complexes with G protein and arrestin using rhodopsin as a model system, and highlight the key features of GPCR conformational states in biased signaling including the sequence motifs of receptor TM6 that determine selective coupling of G proteins, and the phosphorylation codes of GPCRs for arrestin recruitment. We envision the future of GPCR structural biology not only to solve more high‐resolution complex structures but also to show stepwise GPCR signaling complex assembly and disassembly and dynamic process of GPCR signal transduction.     

G‐protein‐coupled receptor structures were not built in a day

Among the most exciting recent developments in structural biology is the structure determination of G‐protein‐coupled receptors (GPCRs), which comprise the largest class of membrane proteins in mammalian cells and have enormous importance for disease and drug development. The GPCR structures are perhaps the most visible examples of a nascent revolution in membrane protein structure determination. Like other major milestones in science, however, such as the sequencing of the human genome, these achievements were built on a hidden foundation of technological developments. Here, we describe some of the methods that are fueling the membrane protein structure revolution and have enabled the determination of the current GPCR structures, along with new techniques that may lead to future structures.     

Representing and comparing protein folds and fold families using three‐dimensional shape‐density representations

The question of how best to compare and classify the (three‐dimensional) structures of proteins is one of the most important unsolved problems in computational biology. To help tackle this problem, we have developed a novel shape‐density superposition algorithm called 3D‐Blast which represents and superposes the shapes of protein backbone folds using the spherical polar Fourier correlation technique originally developed by us for protein docking. The utility of this approach is compared with several well‐known protein structure alignment algorithms using receiver‐operator‐characteristic plots of queries against the “gold standard” CATH database. Despite being completely independent of protein sequences and using no information about the internal geometry of proteins, our results from searching the CATH database show that 3D‐Blast is highly competitive compared to current state‐of‐the‐art protein structure alignment algorithms. A novel and potentially very useful feature of our approach is that it allows an average or “consensus” fold to be calculated easily for a given group of protein structures. We find that using consensus shapes to represent entire fold families also gives very good database query performance. We propose that using the notion of consensus fold shapes could provide a powerful new way to index existing protein structure databases, and that it offers an objective way to cluster and classify all of the currently known folds in the protein universe. Proteins 2012. © 2011 Wiley Periodicals, Inc.     

Comparison analysis of primary ligand‐binding sites in seven‐helix membrane proteins

Seven‐helix transmembrane proteins, including the G‐protein‐coupled receptors (GPCRs), mediate a broad range of fundamental cellular activities through binding to a wide range of ligands. Understanding the structural basis for the ligand‐binding selectivity of these proteins is of significance to their structure‐based drug design. Comparison analysis of proteins' ligand‐binding sites provides a useful way to study their structure‐activity relationships. Various computational methods have been developed for the binding‐site comparison of soluble proteins. In this work, we applied this approach to the analysis of the primary ligand‐binding sites of 92 seven‐helix transmembrane proteins. Results of the studies confirmed that the binding site of bacterial rhodopsins is indeed different from all GPCRs. In the latter group, further comparison of the binding sites indicated a group of residues that could be responsible for ligand‐binding selectivity and important for structure‐based drug design. Furthermore, unexpected binding‐site dissimilarities were observed among adrenergic and adenosine receptors, suggesting that the percentage of the overall sequence identity between a target protein and a template protein alone is not sufficient for selecting the best template for homology modeling of seven‐helix membrane proteins. These results provided novel insight into the structural basis of ligand‐binding selectivity of seven‐helix membrane proteins and are of practical use to the computational modeling of these proteins. © 2010 Wiley Periodicals, Inc. Biopolymers 95: 31–38, 2011.     

3Drefine: Consistent protein structure refinement by optimizing hydrogen bonding network and atomic‐level energy minimization

One of the major limitations of computational protein structure prediction is the deviation of predicted models from their experimentally derived true, native structures. The limitations often hinder the possibility of applying computational protein structure prediction methods in biochemical assignment and drug design that are very sensitive to structural details. Refinement of these low‐resolution predicted models to high‐resolution structures close to the native state, however, has proven to be extremely challenging. Thus, protein structure refinement remains a largely unsolved problem. Critical assessment of techniques for protein structure prediction (CASP) specifically indicated that most predictors participating in the refinement category still did not consistently improve model quality. Here, we propose a two‐step refinement protocol, called 3Drefine, to consistently bring the initial model closer to the native structure. The first step is based on optimization of hydrogen bonding (HB) network and the second step applies atomic‐level energy minimization on the optimized model using a composite physics and knowledge‐based force fields. The approach has been evaluated on the CASP benchmark data and it exhibits consistent improvement over the initial structure in both global and local structural quality measures. 3Drefine method is also computationally inexpensive, consuming only few minutes of CPU time to refine a protein of typical length (300 residues). 3Drefine web server is freely available at http://sysbio.rnet.missouri.edu/3Drefine/ . Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Ligand–protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED‐SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED‐SMA is an automated and fast method to classify binding sites. It is based on MED‐SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora‐A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED‐SMA approach is demonstrated as it gathers binding sites of proteins with similar structure‐activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target‐based drug design and prediction of cross‐reactivity and therefore potential toxic side effects.     

Non‐interacting surface solvation and dynamics in protein–protein interactions

Protein–protein interactions control a plethora of cellular processes, including cell proliferation, differentiation, apoptosis, and signal transduction. Understanding how and why proteins interact will inevitably lead to novel structure‐based drug design methods, as well as design of de novo binders with preferred interaction properties. At a structural and molecular level, interface and rim regions are not enough to fully account for the energetics of protein–protein binding, even for simple lock‐and‐key rigid binders. As we have recently shown, properties of the global surface might also play a role in protein–protein interactions. Here, we report on molecular dynamics simulations performed to understand solvent effects on protein–protein surfaces. We compare properties of the interface, rim, and non‐interacting surface regions for five different complexes and their free components. Interface and rim residues become, as expected, less mobile upon complexation. However, non‐interacting surface appears more flexible in the complex. Fluctuations of polar residues are always lower compared with charged ones, independent of the protein state. Further, stable water molecules are often observed around polar residues, in contrast to charged ones. Our analysis reveals that (a) upon complexation, the non‐interacting surface can have a direct entropic compensation for the lower interface and rim entropy and (b) the mobility of the first hydration layer, which is linked to the stability of the protein–protein complex, is influenced by the local chemical properties of the surface. These findings corroborate previous hypotheses on the role of the hydration layer in shielding protein–protein complexes from unintended protein–protein interactions. Proteins 2015; 83:445–458. © 2014 Wiley Periodicals, Inc.     

Use of a structural alphabet to find compatible folds for amino acid sequences

The structural annotation of proteins with no detectable homologs of known 3D structure identified using sequence‐search methods is a major challenge today. We propose an original method that computes the conditional probabilities for the amino‐acid sequence of a protein to fit to known protein 3D structures using a structural alphabet, known as “Protein Blocks” (PBs). PBs constitute a library of 16 local structural prototypes that approximate every part of protein backbone structures. It is used to encode 3D protein structures into 1D PB sequences and to capture sequence to structure relationships. Our method relies on amino acid occurrence matrices, one for each PB, to score global and local threading of query amino acid sequences to protein folds encoded into PB sequences. It does not use any information from residue contacts or sequence‐search methods or explicit incorporation of hydrophobic effect. The performance of the method was assessed with independent test datasets derived from SCOP 1.75A. With a Z‐score cutoff that achieved 95% specificity (i.e., less than 5% false positives), global and local threading showed sensitivity of 64.1% and 34.2%, respectively. We further tested its performance on 57 difficult CASP10 targets that had no known homologs in PDB: 38 compatible templates were identified by our approach and 66% of these hits yielded correctly predicted structures. This method scales‐up well and offers promising perspectives for structural annotations at genomic level. It has been implemented in the form of a web‐server that is freely available at http://www.bo‐protscience.fr/forsa .     

CONFOLD: Residue‐residue contact‐guided ab initio protein folding

Predicted protein residue–residue contacts can be used to build three‐dimensional models and consequently to predict protein folds from scratch. A considerable amount of effort is currently being spent to improve contact prediction accuracy, whereas few methods are available to construct protein tertiary structures from predicted contacts. Here, we present an ab initio protein folding method to build three‐dimensional models using predicted contacts and secondary structures. Our method first translates contacts and secondary structures into distance, dihedral angle, and hydrogen bond restraints according to a set of new conversion rules, and then provides these restraints as input for a distance geometry algorithm to build tertiary structure models. The initially reconstructed models are used to regenerate a set of physically realistic contact restraints and detect secondary structure patterns, which are then used to reconstruct final structural models. This unique two‐stage modeling approach of integrating contacts and secondary structures improves the quality and accuracy of structural models and in particular generates better β‐sheets than other algorithms. We validate our method on two standard benchmark datasets using true contacts and secondary structures. Our method improves TM‐score of reconstructed protein models by 45% and 42% over the existing method on the two datasets, respectively. On the dataset for benchmarking reconstructions methods with predicted contacts and secondary structures, the average TM‐score of best models reconstructed by our method is 0.59, 5.5% higher than the existing method. The CONFOLD web server is available at http://protein.rnet.missouri.edu/confold/ . Proteins 2015; 83:1436–1449. © 2015 Wiley Periodicals, Inc.     

Exhaustive comparison and classification of ligand‐binding surfaces in proteins

Many proteins function by interacting with other small molecules (ligands). Identification of ligand‐binding sites (LBS) in proteins can therefore help to infer their molecular functions. A comprehensive comparison among local structures of LBSs was previously performed, in order to understand their relationships and to classify their structural motifs. However, similar exhaustive comparison among local surfaces of LBSs (patches) has never been performed, due to computational complexity. To enhance our understanding of LBSs, it is worth performing such comparisons among patches and classifying them based on similarities of their surface configurations and electrostatic potentials. In this study, we first developed a rapid method to compare two patches. We then clustered patches corresponding to the same PDB chemical component identifier for a ligand, and selected a representative patch from each cluster. We subsequently exhaustively as compared the representative patches and clustered them using similarity score, PatSim. Finally, the resultant PatSim scores were compared with similarities of atomic structures of the LBSs and those of the ligand‐binding protein sequences and functions. Consequently, we classified the patches into ～2000 well‐characterized clusters. We found that about 63% of these clusters are used in identical protein folds, although about 25% of the clusters are conserved in distantly related proteins and even in proteins with cross‐fold similarity. Furthermore, we showed that patches with higher PatSim score have potential to be involved in similar biological processes.     

Critical analysis of the successes and failures of homology models of G protein‐coupled receptors

We present a critical assessment of the performance of our homology model refinement method for G protein‐coupled receptors (GPCRs), called LITICon that led to top ranking structures in a recent structure prediction assessment GPCRDOCK2010. GPCRs form the largest class of drug targets for which only a few crystal structures are currently available. Therefore, accurate homology models are essential for drug design in these receptors. We submitted five models each for human chemokine CXCR4 (bound to small molecule IT1t and peptide CVX15) and dopamine D3DR (bound to small molecule eticlopride) before the crystal structures were published. Our models in both CXCR4/IT1t and D3/eticlopride assessments were ranked first and second, respectively, by ligand RMSD to the crystal structures. For both receptors, we developed two types of protein models: homology models based on known GPCR crystal structures, and ab initio models based on the prediction method MembStruk. The homology‐based models compared better to the crystal structures than the ab initio models. However, a robust refinement procedure for obtaining high accuracy structures is needed. We demonstrate that optimization of the helical tilt, rotation, and translation is vital for GPCR homology model refinement. As a proof of concept, our in‐house refinement program LITiCon captured the distinct orientation of TM2 in CXCR4, which differs from that of adrenoreceptors. These findings would be critical for refining GPCR homology models in future. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Improving computational protein design by using structure‐derived sequence profile

Designing a protein sequence that will fold into a predefined structure is of both practical and fundamental interest. Many successful, computational designs in the last decade resulted from improved understanding of hydrophobic and polar interactions between side chains of amino acid residues in stabilizing protein tertiary structures. However, the coupling between main‐chain backbone structure and local sequence has yet to be fully addressed. Here, we attempt to account for such coupling by using a sequence profile derived from the sequences of five residue fragments in a fragment library that are structurally matched to the five‐residue segments contained in a target structure. We further introduced a term to reduce low complexity regions of designed sequences. These two terms together with optimized reference states for amino‐acid residues were implemented in the RosettaDesign program. The new method, called RosettaDesign‐SR, makes a 12% increase (from 34 to 46%) in fraction of proteins whose designed sequences are more than 35% identical to wild‐type sequences. Meanwhile, it reduces 8% (from 22% to 14%) to the number of designed sequences that are not homologous to any known protein sequences according to psi‐blast. More importantly, the sequences designed by RosettaDesign‐SR have 2–3% more polar residues at the surface and core regions of proteins and these surface and core polar residues have about 4% higher sequence identity to wild‐type sequences than by RosettaDesign. Thus, the proteins designed by RosettaDesign‐SR should be less likely to aggregate and more likely to have unique structures due to more specific polar interactions. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

RosettaSurf—A surface-centric computational design approach

Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins.     

Binding hotspots on K‐ras: Consensus ligand binding sites and other reactive regions from probe‐based molecular dynamics analysis

We have used probe‐based molecular dynamics (pMD) simulations to search for interaction hotspots on the surface of the therapeutically highly relevant oncogenic K‐Ras G12D. Combining the probe‐based query with an ensemble‐based pocket identification scheme and an analysis of existing Ras‐ligand complexes, we show that (i) pMD is a robust and cost‐effective strategy for binding site identification, (ii) all four of the previously reported ligand binding sites are suitable for structure‐based ligand design, and (iii) in some cases probe binding and expanded sampling of configurational space enable pocket expansion and increase the likelihood of site identification. Furthermore, by comparing the distribution of hotspots in nonpocket‐like regions with known protein‐ and membrane‐interacting interfaces, we propose that pMD has the potential to predict surface patches responsible for protein‐biomolecule interactions. These observations have important implications for future drug design efforts and will facilitate the search for potential interfaces responsible for the proposed transient oligomerization or interaction of Ras with other biomolecules in the cellular milieu. Proteins 2015; 83:898–909. © 2015 Wiley Periodicals, Inc.     

Crystal structures of ASK1‐inhibtor complexes provide a platform for structure‐based drug design

ASK1, a member of the MAPK Kinase Kinase family of proteins has been shown to play a key role in cancer, neurodegeneration and cardiovascular diseases and is emerging as a possible drug target. Here we describe a ‘replacement‐soaking’ method that has enabled the high‐throughput X‐ray structure determination of ASK1/ligand complexes. Comparison of the X‐ray structures of five ASK1/ligand complexes from 3 different chemotypes illustrates that the ASK1 ATP binding site is able to accommodate a range of chemical diversity and different binding modes. The replacement‐soaking system is also able to tolerate some protein flexibility. This crystal system provides a robust platform for ASK1/ligand structure determination and future structure based drug design.     

The presence of non‐native helical structure in the unfolding of a beta‐sheet protein MPT63

MPT63, a major secreted protein from Mycobacterium tuberculosis, has been shown to have immunogenic properties and has been implicated in virulence. MPT63 is a β‐sandwich protein containing 11 β strands and a very short stretch of 3₁₀ helix. The detailed experimental and computational study reported here investigates the equilibrium unfolding transition of MPT63. It is shown that in spite of being a complete β‐sheet protein, MPT63 has a strong propensity toward helix structures in its early intermediates. Far UV‐CD and FTIR spectra clearly suggest that the low‐pH intermediate of MTP63 has enhanced helical content, while fluorescence correlation spectroscopy suggests a significant contraction. Molecular dynamics simulation complements the experimental results indicating that the unfolded state of MPT63 traverses through intermediate forms with increased helical characteristics. It is found that this early intermediate contains exposed hydrophobic surface, and is aggregation prone. Although MPT63 is a complete β‐sheet protein in its native form, the present findings suggest that the secondary structure preferences of the local interactions in early folding pathway may not always follow the native conformation. Furthermore, the Gly25Ala mutant supports the proposed hypothesis by increasing the non‐native helical propensity of the protein structure.     

X‐ray vs. NMR structures as templates for computational protein design

Certain protein‐design calculations involve using an experimentally determined high‐resolution structure as a template to identify new sequences that can adopt the same fold. This approach has led to the successful design of many novel, well‐folded, native‐like proteins. Although any atomic‐resolution structure can serve as a template in such calculations, most successful designs have used high‐resolution crystal structures. Because there are many proteins for which crystal structures are not available, it is of interest whether nuclear magnetic resonance (NMR) templates are also appropriate. We have analyzed differences between using X‐ray and NMR templates in side‐chain repacking and design calculations. We assembled a database of 29 proteins for which both a high‐resolution X‐ray structure and an ensemble of NMR structures are available. Using these pairs, we compared the rotamericity, χ₁‐angle recovery, and native‐sequence recovery of X‐ray and NMR templates. We carried out design using RosettaDesign on both types of templates, and compared the energies and packing qualities of the resulting structures. Overall, the X‐ray structures were better templates for use with Rosetta. However, for ～20% of proteins, a member of the reported NMR ensemble gave rise to designs with similar properties. Re‐evaluating RosettaDesign structures with other energy functions indicated much smaller differences between the two types of templates. Ultimately, experiments are required to confirm the utility of particular X‐ray and NMR templates. But our data suggest that the lack of a high‐resolution X‐ray structure should not preclude attempts at computational design if an NMR ensemble is available. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

Optimization of 3D Poisson–Nernst‐Planck model for fast evaluation of diverse protein channels

We show the accuracy and applicability of our fast algorithmic implementation of a three‐dimensional Poisson–Nernst–Planck (3D‐PNP) flow model for characterizing different protein channels. Due to its high computational efficiency, our model can predict the full current‐voltage characteristics of a channel within minutes, based on the experimental 3D structure of the channel or its computational model structure. Compared with other methods, such as Brownian dynamics, which currently needs a few weeks of the computational time, or even much more demanding molecular dynamics modeling, 3D‐PNP is the only available method for a function‐based evaluation of very numerous tentative structural channel models. Flow model tests of our algorithm and its optimal parametrization are provided for five native channels whose experimental structures are available in the protein data bank (PDB) in an open conductive state, and whose experimental current‐voltage characteristics have been published. The channels represent very different geometric and structural properties, which makes it the widest test to date of the accuracy of 3D‐PNP on real channels. We test whether the channel conductance, rectification, and charge selectivity obtained from the flow model, could be sufficiently sensitive to single‐point mutations, related to unsignificant changes in the channel structure. Our results show that the classical 3D‐PNP model, under proper parametrization, is able to achieve a qualitative agreement with experimental data for a majority of the tested characteristics and channels, including channels with narrow and irregular conductivity pores. We propose that although the standard PNP model cannot provide insight into complex physical phenomena due to its intrinsic limitations, its semiquantitative agreement is achievable for rectification and selectivity at a level sufficient for the bioinformatical purpose of selecting the best structural models with a great advantage of a very short computational time. Proteins 2013; 81:1802–1822. © 2013 Wiley Periodicals, Inc.     

Competing G protein‐coupled receptor kinases balance G protein and β‐arrestin signaling

Seven‐transmembrane receptors (7TMRs) are involved in nearly all aspects of chemical communications and represent major drug targets. 7TMRs transmit their signals not only via heterotrimeric G proteins but also through β‐arrestins, whose recruitment to the activated receptor is regulated by G protein‐coupled receptor kinases (GRKs). In this paper, we combined experimental approaches with computational modeling to decipher the molecular mechanisms as well as the hidden dynamics governing extracellular signal‐regulated kinase (ERK) activation by the angiotensin II type 1A receptor (AT_1AR) in human embryonic kidney (HEK)293 cells. We built an abstracted ordinary differential equations (ODE)‐based model that captured the available knowledge and experimental data. We inferred the unknown parameters by simultaneously fitting experimental data generated in both control and perturbed conditions. We demonstrate that, in addition to its well‐established function in the desensitization of G‐protein activation, GRK2 exerts a strong negative effect on β‐arrestin‐dependent signaling through its competition with GRK5 and 6 for receptor phosphorylation. Importantly, we experimentally confirmed the validity of this novel GRK2‐dependent mechanism in both primary vascular smooth muscle cells naturally expressing the AT_1AR, and HEK293 cells expressing other 7TMRs.