Mutations designed to destabilize the receptor-bound conformation increase MICA-NKG2D association rate and affinity

MICA is a major histocompatibility complex-like protein that undergoes a structural transition from disorder to order upon binding its immunoreceptor, NKG2D. We redesigned the disordered region of MICA with RosettaDesign to increase NKG2D binding. Mutations that stabilize this region were expected to increase association kinetics without changing dissociation kinetics, increase affinity of interaction, and reduce entropy loss upon binding. MICA mutants were stable in solution, and they were amenable to surface plasmon resonance evaluation of NKG2D binding kinetics and thermodynamics. Several MICA mutants bound NKG2D with enhanced affinity, kinetic changes were primarily observed during association, and thermodynamic changes in entropy were as expected. However, none of the 15 combinations of mutations predicted to stabilize the receptor-bound MICA conformation enhanced NKG2D affinity, whereas all 10 mutants predicted to be destabilized bound NKG2D with increased on-rates. Five of these had affinities enhanced by 0.9-1.8 kcal/mol over wild type by one to three non-contacting substitutions. Therefore, in this case, mutations designed to mildly destabilize a protein enhanced association and affinity.     

The interplay between effector binding and allostery in an engineered protein switch

The protein design rules for engineering allosteric regulation are not well understood. A fundamental understanding of the determinants of ligand binding in an allosteric context could facilitate the design and construction of versatile protein switches and biosensors. Here, we conducted extensive in vitro and in vivo characterization of the effects of 285 unique point mutations at 15 residues in the maltose‐binding pocket of the maltose‐activated β‐lactamase MBP317‐347. MBP317‐347 is an allosteric enzyme formed by the insertion of TEM‐1 β‐lactamase into the E. coli maltose binding protein (MBP). We find that the maltose‐dependent resistance to ampicillin conferred to the cells by the MBP317‐347 switch gene (the switch phenotype) is very robust to mutations, with most mutations slightly improving the switch phenotype. We identified 15 mutations that improved switch performance from twofold to 22‐fold, primarily by decreasing the catalytic activity in the absence of maltose, perhaps by disrupting interactions that cause a small fraction of MBP in solution to exist in a partially closed state in the absence of maltose. Other notable mutations include K15D and K15H that increased maltose affinity 30‐fold and Y155K and Y155R that compromised switching by diminishing the ability of maltose to increase catalytic activity. The data also provided insights into normal MBP physiology, as select mutations at D14, W62, and F156 retained high maltose affinity but abolished the switch's ability to substitute for MBP in the transport of maltose into the cell. The results reveal the complex relationship between ligand binding and allostery in this engineered switch.     

Action-at-a-distance interactions enhance protein binding affinity

The identification of protein mutations that enhance binding affinity may be achieved by computational or experimental means, or by a combination of the two. Sources of affinity enhancement may include improvements to the net balance of binding interactions of residues forming intermolecular contacts at the binding interface, such as packing and hydrogen-bonding interactions. Here we identify noncontacting residues that make substantial contributions to binding affinity and that also provide opportunities for mutations that increase binding affinity of the TEM1 beta-lactamase (TEM1) to the beta-lactamase inhibitor protein (BLIP). A region of BLIP not on the direct TEM1-binding surface was identified for which changes in net charge result in particularly large increases in computed binding affinity. Some mutations to the region have previously been characterized, and our results are in good correspondence with this results of that study. In addition, we propose novel mutations to BLIP that were computed to improve binding significantly without contacting TEM1 directly. This class of noncontacting electrostatic interactions could have general utility in the design and tuning of binding interactions.     

Exploring additivity effects of double mutations on the binding affinity of protein‐protein complexes

Additivity in binding affinity of protein‐protein complexes refers to the change in free energy of binding (ΔΔG_bind) for double (or multiple) mutations which is approximately equal to the sum of their corresponding single mutation ΔΔG_bind values. In this study, we have explored the additivity effect of double mutants, which shows a linear relationship between the binding affinity of double and sum of single mutants with a correlation of 0.90. However, the comparison of ΔΔG_bind values showed a mean absolute deviation of 0.86 kcal/mol, and 25.6% of the double mutants show a deviation of more than 1 kcal/mol, which are identified as non‐additive. The additivity effects have been analyzed based on the influence of structural features such as accessible surface area, long range order, binding propensity change, surrounding hydrophobicity, flexibility, atomic contacts between the mutations and distance between the 2 mutations. We found that non‐additive mutations tend to be closer to each other and have more contacts. We have also used machine learning methods to discriminate additive and non‐additive mutations using structure‐based features, which showed the accuracies in the range of 0.77–0.92 for protein‐protein complexes belonging to different functions. Further, we have compared the additivity effects of protein stability along with binding affinity and explored the similarities and differences between them. The results obtained in this study provide insights into the effects of various structural features on binding affinity of double mutants, and will aid the development of accurate methods to predict the binding affinity of double mutants.     

Affinity enhancement of an in vivo matured therapeutic antibody using structure-based computational design

Improving the affinity of a high-affinity protein-protein interaction is a challenging problem that has practical applications in the development of therapeutic biomolecules. We used a combination of structure-based computational methods to optimize the binding affinity of an antibody fragment to the I-domain of the integrin VLA1. Despite the already high affinity of the antibody (Kd approximately 7 nM) and the moderate resolution (2.8 A) of the starting crystal structure, the affinity was increased by an order of magnitude primarily through a decrease in the dissociation rate. We determined the crystal structure of a high-affinity quadruple mutant complex at 2.2 A. The structure shows that the design makes the predicted contacts. Structural evidence and mutagenesis experiments that probe a hydrogen bond network illustrate the importance of satisfying hydrogen bonding requirements while seeking higher-affinity mutations. The large and diverse set of interface mutations allowed refinement of the mutant binding affinity prediction protocol and improvement of the single-mutant success rate. Our results indicate that structure-based computational design can be successfully applied to further improve the binding of high-affinity antibodies.     

Computational design of second‐site suppressor mutations at protein–protein interfaces

The importance of a protein–protein interaction to a signaling pathway can be established by showing that amino acid mutations that weaken the interaction disrupt signaling, and that additional mutations that rescue the interaction recover signaling. Identifying rescue mutations, often referred to as second‐site suppressor mutations, controls against scenarios in which the initial deleterious mutation inactivates the protein or disrupts alternative protein–protein interactions. Here, we test a structure‐based protocol for identifying second‐site suppressor mutations that is based on a strategy previously described by Kortemme and Baker. The molecular modeling software Rosetta is used to scan an interface for point mutations that are predicted to weaken binding but can be rescued by mutations on the partner protein. The protocol typically identifies three types of specificity switches: knob‐in‐to‐hole redesigns, switching hydrophobic interactions to hydrogen bond interactions, and replacing polar interactions with nonpolar interactions. Computational predictions were tested with two separate protein complexes; the G‐protein Gα_i1 bound to the RGS14 GoLoco motif, and UbcH7 bound to the ubiquitin ligase E6AP. Eight designs were experimentally tested. Swapping a buried hydrophobic residue with a polar residue dramatically weakened binding affinities. In none of these cases were we able to identify compensating mutations that returned binding to wild‐type affinity, highlighting the challenges inherent in designing buried hydrogen bond networks. The strongest specificity switches were a knob‐in‐to‐hole design (20‐fold) and the replacement of a charge–charge interaction with nonpolar interactions (55‐fold). In two cases, specificity was further tuned by including mutations distant from the initial design. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Using collections of structural models to predict changes of binding affinity caused by mutations in protein–protein interactions

Protein–protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state‐of‐the‐art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state‐of‐art methods.     

NKG2D discriminates diverse ligands through selectively mechano‐regulated ligand conformational changes

Stimulatory immune receptor NKG2D binds diverse ligands to elicit differential anti‐tumor and anti‐virus immune responses. Two conflicting degeneracy recognition models based on static crystal structures and in‐solution binding affinities have been considered for almost two decades. Whether and how NKG2D recognizes and discriminates diverse ligands still remain unclear. Using live‐cell‐based single‐molecule biomechanical assay, we characterized the in situ binding kinetics of NKG2D interacting with different ligands in the absence or presence of mechanical force. We found that mechanical force application selectively prolonged NKG2D interaction lifetimes with the ligands MICA and MICB, but not with ULBPs, and that force‐strengthened binding is much more pronounced for MICA than for other ligands. We also integrated steered molecular dynamics simulations and mutagenesis to reveal force‐induced rotational conformational changes of MICA, involving formation of additional hydrogen bonds on its binding interface with NKG2D, impeding MICA dissociation under force. We further provided a kinetic triggering model to reveal that force‐dependent affinity determines NKG2D ligand discrimination and its downstream NK cell activation. Together, our results demonstrate that NKG2D has a discrimination power to recognize different ligands, which depends on selective mechanical force‐induced ligand conformational changes.     

Analysis of allosteric signal transduction mechanisms in an engineered fluorescent maltose biosensor

We previously reported the construction of a family of reagentless fluorescent biosensor proteins by the structure-based design of conjugation sites for a single, environmentally sensitive small molecule dye, thus providing a mechanism for the transduction of ligand-induced conformational changes into a macroscopic fluorescence observable. Here we investigate the microscopic mechanisms that may be responsible for the macroscopic fluorescent changes in such Fluorescent Allosteric Signal Transduction (FAST) proteins. As case studies, we selected three individual cysteine mutations (F92C, D95C, and S233C) of Escherichia coli maltose binding protein (MBP) covalently labeled with a single small molecule fluorescent probe, N-((2-iodoacetoxy)ethyl)-N-methyl)amino-7-nitrobenz-2-oxa-1,3-diazole (NBD), each giving rise to a robust FAST protein with a distinct maltose-dependent fluorescence response. The fluorescence emission intensity, anisotropy, lifetime, and iodide-dependent fluorescence quenching were determined for each conjugate in the presence and absence of maltose. Structure-derived solvent accessible surface areas of the three FAST proteins are consistent with experimentally observed quenching data. The D95C protein exhibits the largest fluorescence change upon maltose binding. This mutant was selected for further characterization, and residues surrounding the fluorophore coupling site were mutagenized. Analysis of the resulting mutant FAST proteins suggests that specific hydrogen-bonding interactions between the fluorophore molecule and two tyrosine side-chains, Tyr171 and Tyr176, in the open state but not the closed, are responsible for the dramatic fluorescence response of this construct. Taken together these results provide insights that can be used in future design cycles to construct fluorescent biosensors that optimize signaling by engineering specific hydrogen bonds between a fluorophore and protein.     

Review: Multipoint binding and heterogeneity in immobilized metal affinity chromatography

Studies carried out using engineered proteins clearly demonstrate that adsorption to derivatized surfaces involves multiple interactions between functional groups on the protein and complementary sites distributed on the surface. The fact that adsorption involves multipoint interactions has important implications for the design of separations processes and for the interpretation of heterogeneity in biological recognition phenomena. Increasing the density of surface metal sites (immobilized copper ions) is found to be functionally equivalent to increasing the number of metal-coordinating groups on the protein (histidines and deporotonated amines), m in that both processes increase the likelihood of simultaneous interactions between the protein and the surface. A consequence of multiple-site interactions is a significant in crease in protein binding affinity that depends on the arrangement of surface sites. A protein will show the highest affinity for arrangements of surface sites which best match its own pattern of functioal groups and will show lower affinity for less optimal arrangements, resulting in binding that is inherently heterogeneous. We have found that reversible protein adsorption in immobilized metal affinity chromatography (IMAC) is described by the Temikin model, which characterizes binding heterogeneity by a uniform distribution of binding energies over the population of surface binding sites. (c) 1995 John Wiley & Sons, Inc.     

Free Energy Perturbation Calculations of Mutation Effects on SARS-CoV-2 RBD::ACE2 Binding Affinity

The strength of binding between human angiotensin converting enzyme 2 (ACE2) and the receptor binding domain (RBD) of viral spike protein plays a role in the transmissibility of the SARS-CoV-2 virus. In this study we focus on a subset of RBD mutations that have been frequently observed in infected individuals and probe binding affinity changes to ACE2 using surface plasmon resonance (SPR) measurements and free energy perturbation (FEP) calculations. Our SPR results are largely in accord with previous studies but discrepancies do arise due to differences in experimental methods and to protocol differences even when a single method is used. Overall, we find that FEP performance is superior to that of other computational approaches examined as determined by agreement with experiment and, in particular, by its ability to identify stabilizing mutations. Moreover, the calculations successfully predict the observed cooperative stabilization of binding by the Q498R N501Y double mutant present in Omicron variants and offer a physical explanation for the underlying mechanism. Overall, our results suggest that despite the significant computational cost, FEP calculations may offer an effective strategy to understand the effects of interfacial mutations on protein–protein binding affinities and, hence, in a variety of practical applications such as the optimization of neutralizing antibodies.     

PCA-MutPred: Prediction of Binding Free Energy Change Upon Missense Mutation in Protein-carbohydrate Complexes

Protein-carbohydrate interactions play an important role in several biological processes. The mutation of amino acid residues in carbohydrate-binding proteins may alter the binding affinity, affect the functions and lead to diseases. Elucidating the factors influencing the binding affinity change (ΔΔG) of protein-carbohydrate complexes upon mutation is a challenging task. In this work, we have collected the experimental data for the binding affinity change of 318 unique mutants and related with sequence and structural features of amino acid residues at the mutant sites. We found that accessible surface area, secondary structure, mutation preference, conservation score, hydrophobicity and contact energies are important to understand the binding affinity change upon mutation. We have developed multiple regression equations for predicting the binding affinity change upon mutation and our method showed an average correlation of 0.74 and a mean absolute error of 0.70 kcal/mol between experimental and predicted ΔΔG on a 10-fold cross-validation. Further, we have validated our method using an independent test data set of 124 (62 unique) mutations, which showed a correlation and MAE of 0.79 and 0.56 kcal/mol, respectively. We have developed a web server PCA-MutPred, Protein-CArbohydrate complex Mutation affinity Predictor, for predicting the change in binding affinity of protein–carbohydrate complexes and it is freely accessible at https://web.iitm.ac.in/bioinfo2/pcamutpred. We suggest that the method could be a useful resource for designing protein-carbohydrate complexes with desired affinities.     

Enhancement and destruction of antibody function by somatic mutation: unequal occurrence is controlled by V gene combinatorial associations.

We examined the positive and negative effects of somatic mutation on antibody function using saturation mutagenesis in vitro to mimic the potential of the in vivo process to diversify antibodies. Identical mutations were introduced into the second complementarity determining region of two anti-phosphocholine antibodies, T15 and D16, which share the same germline VH gene sequence. T15 predominates in primary responses and does not undergo affinity maturation. D16 is representative of antibodies that co-dominate in memory responses and do undergo affinity maturation. We previously reported that > 50% of T15 mutants had decreased antigen binding capacity. To test if this high frequency of binding loss was unique to T15 or a consequence of random point mutations applicable to other combining sites, we analyzed the same mutations in D16. We show that D16 suffers a similar loss of function, indicating an equally high potential for B-cell wastage. However, only D16 displayed the capacity for somatic mutation to improve antigen binding, which should enhance its persistence in memory responses. Mutation of residues contacting the haptenic group, as determined by molecular modeling, did not improve binding. Instead, productive mutations occurred in residues that either contacted carrier protein or were distant from the antigen binding site, possibly increasing binding site flexibility through long-range effects. Targeting such residues for mutation should aid in the rational design of improved antibodies.     

Generation, affinity maturation, and characterization of a human anti-human NKG2D monoclonal antibody with dual antagonistic and agonistic activity

In humans, NKG2D is an activating receptor on natural killer (NK) cells and a costimulatory receptor on certain T cells and plays a central role in mediating immune responses in autoimmune diseases, infectious diseases, and cancer. Monoclonal antibodies that antagonize or agonize immune responses mediated by human NKG2D are considered to be of broad and potent therapeutic utility. Nonetheless, monoclonal antibodies to NKG2D that are suitable for clinical investigations have not been published yet. Here, we describe the generation, affinity maturation, and characterization of a fully human monoclonal antibody to human NKG2D. Using phage display technology based on a newly generated naïve human Fab library in phage display vector pC3C followed by a tandem chain shuffling process designed for minimal deviation from natural human antibody sequences, we selected a human Fab, designated KYK-2.0, with high specificity and affinity to human NKG2D. KYK-2.0 Fab blocked the binding of the natural human NKG2D ligands MICA, MICB, and ULBP2 as potently as a commercially available mouse anti-human NKG2D monoclonal antibody in immunoglobulin G (IgG) format. Conversion of KYK-2.0 Fab to IgG1 resulted in subnanomolar avidity for human NKG2D. KYK-2.0 IgG1 was found to selectively recognize defined subpopulations of human lymphocytes known to express NKG2D, that is, the majority of human CD8+, CD16+, and CD56+ cells as well as a small fraction of human CD4+ cells. In solution, KYK-2.0 IgG1 interfered with the cytolytic activity of ex vivo expanded human NK cells. By contrast, immobilized KYK-2.0 IgG1 was found to strongly induce human NK cell activation. The dual antagonistic and agonistic activity promises a wide range of therapeutic applications for KYK-2.0 IgG1 and its derivatives.     

Automating human intuition for protein design

In the design of new enzymes and binding proteins, human intuition is often used to modify computationally designed amino acid sequences prior to experimental characterization. The manual sequence changes involve both reversions of amino acid mutations back to the identity present in the parent scaffold and the introduction of residues making additional interactions with the binding partner or backing up first shell interactions. Automation of this manual sequence refinement process would allow more systematic evaluation and considerably reduce the amount of human designer effort involved. Here we introduce a benchmark for evaluating the ability of automated methods to recapitulate the sequence changes made to computer‐generated models by human designers, and use it to assess alternative computational methods. We find the best performance for a greedy one‐position‐at‐a‐time optimization protocol that utilizes metrics (such as shape complementarity) and local refinement methods too computationally expensive for global Monte Carlo (MC) sequence optimization. This protocol should be broadly useful for improving the stability and function of designed binding proteins. Proteins 2014; 82:858–866. © 2013 Wiley Periodicals, Inc.     

Making sense of the diverse ligand recognition by NKG2D

NKG2D recognizes multiple diverse ligands. Despite recent efforts in determining the crystal structures of NKG2D-ligand complexes, the principle governing this receptor-ligand recognition and hence the criteria for identifying unknown ligands of NKG2D remain central issues to be resolved. Here we compared the molecular recognition between NKG2D and three of the known ligands, UL16 binding protein (ULBP), MHC class I-like molecule, and retinoic acid early inducible gene as observed in the ligand-complexed crystal structures. The comparison shows that while the receptor uses a common interface region to bind the three diverse ligands, each ligand forms a distinct, but overlapping, set of hydrogen bonds, hydrophobic interactions, and salt bridges, illustrating the underlying principle of NKG2D-ligand recognition being the conservation in overall shape complementarity and binding energy while permitting variation in ligand sequence through induced fit recognition. To further test this hypothesis and to distinguish between diverse recognition and promiscuous ligand binding, four ULBP3 interface mutations, H21A, E76A, R82M, and D169A, were generated to each disrupt a single hydrogen bond or salt bridge. All mutant ULBP3 displayed reduced receptor binding, suggesting a specific, rather than promiscuous, receptor-ligand recognition. Mutants with severe loss of binding affect the receptor interactions that are mostly buried. Finally, a receptor-ligand recognition algorithm was developed to assist the identification of diverse NKG2D ligands based on evaluating the potential hydrogen bonds, hydrophobic interactions, and salt bridges at the receptor-ligand interface.     

Structure of the HCMV UL16-MICB Complex Elucidates Select Binding of a Viral Immunoevasin to Diverse NKG2D Ligands

The activating immunoreceptor NKG2D promotes elimination of infected or malignant cells by cytotoxic lymphocytes through engagement of stress-induced MHC class I-related ligands. The human cytomegalovirus (HCMV)-encoded immunoevasin UL16 subverts NKG2D-mediated immune responses by retaining a select group of diverse NKG2D ligands inside the cell. We report here the crystal structure of UL16 in complex with the NKG2D ligand MICB at 1.8 Å resolution, revealing the molecular basis for the promiscuous, but highly selective, binding of UL16 to unrelated NKG2D ligands. The immunoglobulin-like UL16 protein utilizes a three-stranded β-sheet to engage the α-helical surface of the MHC class I-like MICB platform domain. Intriguingly, residues at the center of this β-sheet mimic a central binding motif employed by the structurally unrelated C-type lectin-like NKG2D to facilitate engagement of diverse NKG2D ligands. Using surface plasmon resonance, we find that UL16 binds MICB, ULBP1, and ULBP2 with similar affinities that lie in the nanomolar range (12–66 nM). The ability of UL16 to bind its ligands depends critically on the presence of a glutamine (MICB) or closely related glutamate (ULBP1 and ULBP2) at position 169. An arginine residue at this position however, as found for example in MICA or ULBP3, would cause steric clashes with UL16 residues. The inability of UL16 to bind MICA and ULBP3 can therefore be attributed to single substitutions at key NKG2D ligand locations. This indicates that selective pressure exerted by viral immunoevasins such as UL16 contributed to the diversification of NKG2D ligands.     

Osteogenic properties of a short BMP‐2 chimera peptide

Bone morphogenetic proteins (BMPs) play a key role in bone and cartilage formation. For these properties, BMPs are employed in the field of tissue engineering to induce bone regeneration in damaged tissues. To overcome drawbacks due to the use of entire proteins, synthetic peptides derived from their parent BMPs have come out as promising molecules for biomaterial design. On the structural ground of the experimental BMP‐2 receptor complexes reported in the literature, we designed three peptides, reproducing the BMP‐2 region responsible for the binding to the type II receptor, ActRIIB. These peptides were characterized by NMR, and the structural features of the peptide–receptor binding interface were highlighted by docking experiments. Peptide–receptor binding affinities were analyzed by means of ELISA and surface plasmon resonance techniques. Furthermore, cellular assays were performed to assess their osteoinductive properties. A chimera peptide, obtained by combining the sequence portions 73–92 and 30–34 of BMP‐2, shows the best affinity for ActRIIB in the series and represents a good starting point for the design of new compounds able to reproduce osteogenic properties of the parent BMP‐2. Copyright © 2015 European Peptide Society and John Wiley & Sons, Ltd.     

Human IgG1 antibodies antagonizing activating receptor NKG2D on natural killer cells

NKG2D is a surface receptor expressed on NK cells but also on CD8+ T cells, γδ T cells, and auto-reactive CD4+/CD28- T cells of patients with rheumatoid arthritis. Various studies suggested that NKG2D plays a critical role in autoimmune diseases, e.g., in diabetes, celiac disease and rheumatoid arthritis (RA), rendering the activating receptor a potential target for antibody-based therapies. Here, we describe the generation and characteristics of a panel of human, high-affinity anti-NKG2D IgG1 monoclonal antibodies (mAbs) derived by phage display. The lead molecule mAb E4 bound with an affinity (KD) of 2.7 ± 1.4 x 10-11 M to soluble and membrane-bound human NKG2D, and cross-reacted with NKG2D from cynomolgus macaque, indicating potential suitability for studies in a relevant primate model. MAb E4 potently antagonized the cytolytic activity of NKL cells against BaF/3-MICA cells expressing NKG2D ligand, and blocked the NKG2D ligand-induced secretion of TNFα, IFNγ and GM-CSF, as well as surface expression of CRTAM by NK cells cultured on immobilized MICA or ULBP-1 ligands. The antibody did not show a detectable loss of binding to NKG2D after 7 days in human serum at 37°C, and resisted thermal inactivation up to 70°C. Based on these results, anti-human NKG2D mAb E4 provides an ideal candidate for development of a novel therapeutic agent antagonizing a key receptor of NK and cytotoxic T cells with implications in autoimmune diseases.     

Regulation and Gene Expression Profiling of NKG2D Positive Human Cytomegalovirus-Primed CD4(+) T-Cells

NKG2D is a stimulatory receptor expressed by natural killer (NK) cells, CD8(+) T-cells, and γδ T-cells. NKG2D expression is normally absent from CD4(+) T-cells, however recently a subset of NKG2D(+) CD4(+) T-cells has been found, which is specific for human cytomegalovirus (HCMV). This particular subset of HCMV-specific NKG2D(+) CD4(+) T-cells possesses effector-like functions, thus resembling the subsets of NKG2D(+) CD4(+) T-cells found in other chronic inflammations. However, the precise mechanism leading to NKG2D expression on HCMV-specific CD4(+) T-cells is currently not known. In this study we used genome-wide analysis of individual genes and gene set enrichment analysis (GSEA) to investigate the gene expression profile of NKG2D(+) CD4(+) T-cells, generated from HCMV-primed CD4(+) T-cells. We show that the HCMV-primed NKG2D(+) CD4(+) T-cells possess a higher differentiated phenotype than the NKG2D(-) CD4(+) T-cells, both at the gene expression profile and cytokine profile. The ability to express NKG2D at the cell surface was primarily determined by the activation or differentiation status of the CD4(+) T-cells and not by the antigen presenting cells. We observed a correlation between CD94 and NKG2D expression in the CD4(+) T-cells following HCMV stimulation. However, knock-down of CD94 did not affect NKG2D cell surface expression or signaling. In addition, we show that NKG2D is recycled at the cell surface of activated CD4(+) T-cells, whereas it is produced de novo in resting CD4(+) T-cells. These findings provide novel information about the gene expression profile of HCMV-primed NKG2D(+) CD4(+) T-cells, as well as the mechanisms regulating NKG2D cell surface expression.