Journal of Molecular Biology
Evolution of Class-Specific Peptides Targeting a Hot Spot of the Gαs Subunit
Introduction
The heterotrimeric G-protein signaling pathway plays a central role in both biology and human therapies. In this pathway, heterotrimeric G-protein subunits (denoted Gα, β, and γ) route information from cell surface G-protein-coupled receptors (GPCRs) to specific intracellular effectors. Presently, GPCRs comprise the largest family of receptor drug targets in humans, accounting for 25% of marketed pharmaceuticals.1 Routing of a signal from a GPCR to corresponding effectors is dictated by the identity of the G-protein α and βγ heterodimer subunits associated with the GPCR. In humans, there is substantial combinatorial diversity in the routing partners, with 16 distinct Gα subunit genes categorized into 4 classes (i/o, q/11, s, and 12/13) corresponding to their effector coupling, 5 β subunits, and 12 receptor-specific γ subunits.2 Combinations of these components enable differentiated cells to respond uniquely to extracellular signals.3
While drugs targeting effector proteins do exist, so far, only one therapy, suramin, appears to target the G-protein routers themselves, inhibiting dissociation of nucleotide from Gα·guanosine 5′-diphosphate (GDP).4, 5 One reason for this paucity of drugs arises from the difficulty of targeting large protein–protein binding interfaces with traditional drug-like molecules.6 Towards this end, our laboratory is interested in exploring whether peptides that recognize such surfaces with protein-like affinity and selectivity can be designed. We have investigated this concept in the context of the G-protein α subunits, with the goal of determining whether Gα class- and state-specific peptide ligands that target the protein-binding surface of this router can be generated.
Gα subunits comprise two domains: a unique helical domain and a GTPase domain containing the guanosine-nucleotide-binding pocket of the protein (Fig. 1a). The identity of the nucleotide bound within this pocket [guanosine 5′-triphosphate (GTP) versus GDP] is coupled to the conformation of the subunit's binding surface via three malleable switch elements in the GTPase domain (SI, SII, and SIII). This conformational “switching” provides the basis for Gα router activity, allowing the subunit to bind different partners in its “on” (Gα·GTP) and “off” (Gα·GDP) states.
In the “off” state, Gα·GDP binds Gβγ, forming a heterotrimer associated with the cytosolic face of the GPCR. Signal activation of the GPCR triggers exchange of GDP for GTP in Gα and causes dissociation of Gβγ. Both activated Gα·GTP and Gβγ are then capable of regulating effector response for a period of time, dependent upon the GTPase rate of Gα. GTP hydrolysis returns Gα to its “off” state, resulting in reformation of the Gα·GDP-Gβγ heterotrimer and termination of signal.9 Beyond Gβγ, which binds the “off” state of Gα, and various effector proteins that bind the “on” state, additional regulatory proteins also interact with the binding surface of Gα in a conformation-specific manner. These include G-protein-regulatory (GPR) [Gα(i/o)-Loco (GoLoco)] motifs, which are thought to sequester Gα·GDP and regulator of G-protein signaling (RGS) proteins that accelerate the rate of Gα GTPase activity.10
The Gα binding interfaces of these proteins have been shown to overlap at a convergent binding surface defined by switch II and α-helix 3 elements of the subunit (SII/α3).11, 12, 13 Attributes of convergent binding surfaces, or binding “hot spots,” were originally characterized by Clackson and Wells and have since been identified in a growing number of proteins.14, 15 Such sites generally exhibit a hydrophobic character with a high degree of sequence conservation across different members of a protein family, as well as structural plasticity. Sequence conservation of the SII/α3 convergent binding site across various classes of the Gα family is illustrated in Fig. 1b. This primary sequence identity results in highly conserved Gα protein structures with a backbone RMSD of ∼ 1 Å in the GTPase domains of Gαi1, Gαq, Gαs(s), and Gα12 subunits.8, 11, 16
The design of specific ligands that target convergent binding surfaces presents an interesting problem due to the sequence conservation and dynamic topography of these sites. Combinatorial approaches such as in vitro selection provide a powerful solution for targeting these molecularly compliant surfaces.17 Using such methods, our laboratory and others have previously developed a number of peptides that bind the SII/α3 site of Gα in a conformation- and state-specific manner.13, 18, 19, 20 In these selection experiments, a library of randomized peptide sequences is selected for the ability to bind the G-protein target, typically using affinity chromatography. Functional sequences that bind the target are retained, are amplified, and eventually come to dominate the pool after iterative rounds of selection. The selection technique most often employed by our laboratory is mRNA display, wherein each peptide in a library is covalently coupled to its encoding mRNA as an mRNA–peptide fusion (Fig. 2a).21 This approach allows libraries to be created entirely in vitro, that are strictly monovalent, and have sequence diversities of > 1013 independent polypeptide sequences, the most of any available method.22 Generally, mRNA display selections result in peptides that bind protein or nucleic acid targets with nanomolar to picomolar affinities.23, 24, 25, 26, 27, 28
In the present work, we have set out to determine whether it is possible to generate peptide modulators towards the SII/α3 site that are capable of discriminating the highly conserved subunit classes (i/o, q/11, s, and 12/13). Taking as our starting point a short peptide known to bind the SII/α3 site of Gαi1, we have selected peptides targeting the short isoform of Gαs (Gαs(s)). Notably, our directed evolution strategy required both positive and negative selection steps, with the first step improving affinity for Gαs and with the second step removing binding to other Gα subunits. The resulting peptides are remarkably specific, showing no binding to the other three classes of Gα and even discriminating between the long and the short isoforms of Gαs. These peptides indicate that it is possible to modulate the SII/α3 site of Gα with class-specific ligands and illustrate a selection-based evolution of peptide binding specificity at a convergent binding site.
Section snippets
Results and Discussion
The immediate goal of our present work has been to select specific peptide ligands for the therapeutically relevant Gαs(s) protein target. More broadly, we are interested in exploring the nature and evolvability of molecular recognition between nominally structured peptide sequences and protein-binding surfaces. To this end, we have chosen the core peptide R6A-1, which binds the SII/α3 site of Gα with a 250-fold preference for the Gαi1 target over Gαs(s),29, 30 as a starting point for our
Conclusions
We have reported the directed evolution of subclass-specific peptides targeting the highly conserved SII/α3 convergent binding surface of Gα. A remarkable aspect of this work is our finding that the SII/α3 site, which is composed of nearly identical amino acids in the Gαi1 and Gαs(s) subunits, elicits an 8000-fold range of discrimination by related peptide ligands. This significant range of specificity argues that the surface conformation of the SII/α3 site, rather than its amino-acid identity,
Materials
The Escherichia coli strains BL21, BL21(DE3), and BL21-gold were obtained from Novagen (Madison, WI). The G-protein expression vector, NpT7-5-H6-TEV-Gαi1, was generously provided by Prof. Roger K. Sunahara (University of Michigan). The in vivo biotinylation vector, pDW363, was kindly supplied by Dr. David S. Waugh (National Cancer Institute, Frederick, MD). Human cDNA clones encoding G proteins with the pcDNA3.1 + vector (Invitrogen) were obtained from the UMR cDNA Resource Center†.
Acknowledgements
We would like to thank N.O. Artemyev for providing the Gαi1/Gαs(s) chimeras; R.T. Sunahara for the TEV protein expression vector; D.S. Waugh for the original pDW363 vector; and P.J. Bjorkman for the use of the Biacore 2000 instrument. Thanks to T.T. Takahashi for helpful discussions and to anonymous reviewers for their criticisms. This work was supported by grants to R.W.R. from the National Institutes of Health (R01 GM 60416) and the Charles Lee Powell Foundation.
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