Accurate identification of protein domain boundaries is useful for protein structure determination and prediction. However, predicting protein domain boundaries from a sequence is still very challenging and largely unsolved.We developed a new method to integrate the classification power of machine learning with evolutionary signals embedded in protein families in order to improve protein domain boundary prediction. The method first extracts putative domain boundary signals from a multiple sequence alignment between a query sequence and its homologs. The putative sites are then classified and scored by support vector machines in conjunction with input features such as sequence profiles, secondary structures, solvent accessibilities around the sites and their positions. The method was evaluated on a domain benchmark by 10-fold cross-validation and 60% of true domain boundaries can be recalled at a precision of 60%. The trade-off between the precision and recall can be adjusted according to specific needs by using different decision thresholds on the domain boundary scores assigned by the support vector machines.http://sysbio.rnet.missouri.edu/dobo/.The good prediction accuracy and the flexibility of selecting domain boundary sites at different precision and recall values make our method a useful tool for protein structure determination and modelling. The method is available at  It has been well over thirty years since Wetlaufer formally introduced what he termed structural regions of a protein chain. Such regions were portions of the peptide sequence which assumed a compact structure . In modeDue to the large amounts of data being generated by today's technology, human experts can no longer keep up. It is simply not possible to visually identify and annotate such a large number of domains. Thus, computational approaches are needed to fill the gap.ab-initio. Most template-based approaches attempt to find homologous sequences in one of the many existing domain databases and then infer from these sequences the domain(s) of the protein in question. Of course the drawback to this approach is that it will only work if a domain is conserved and has already been deposited in a database. A few template based methods [Ab-initio methods make predictions based solely on the primary sequence of a protein and therefore work regardless of the novelty of the protein at hand. Traditional methods for this type of approach include sequence comparison, neural networks and statistical analysis [ab-initio approaches construct an ensemble of 3D models via de novo modelling techniques which are then analyzed and parsed for domain boundaries[ab-initio approaches into one comprehensive package [At present, computational methods for protein domain prediction can be roughly dichotomized as either template-based or  methods ,6 take aanalysis -13. Someoundaries,14. Fina package ,16.ab-initio approaches are the only choice. Unfortunately, the accuracy of their domain boundary predictions is still too low for general, practical use [ab-initio methods can be classified into two sub-categories: comparative sequence analysis [For proteins without homology to known structures, ical use ,12,17,18analysis -24 and danalysis . Most coanalysis -24. The analysis -28. Thisanalysis ,12,17 whanalysis , SCOP [3analysis  and DALIanalysis ,32. Stilab-initio method we have developed to exploit evolutionary domain boundary signals embedded in homologous proteins. This reduces the search space of domain boundaries and in turn improves domain boundary prediction. It is well known that during evolution genes may undergo recombination to produce complex domain architectures via gene fusion [Here we present DoBo, a new e fusion , gene fie fusion ,34, domae fusion . Thus eve fusion . Some exe fusion ,41. Whene fusion ), proteiThe starting point for our dataset was a collection of proteins curated for the DOMpro package . From thTo detect putative domain boundary signals for a protein, PSI-BLAST  is used When extracting domain boundary signals from a multiple sequence alignment, each sequence from the msa is processed in order of increasing PSI-BLAST e-value. The location of each domain boundary signal is noted and aggregated to a list of all the signals for the protein.The collection of domain boundary signals stops when all of the sequences in the msa have been processed or whenever signals have been generated at 35 unique residue locations, whichever comes first. It is worth noting that these parameter values used to generate putative domain boundary sites are adjustable and may have some impact on the sensitivity and specificity of domain boundary prediction.Domain boundary signals are classified as one of three possible types: false boundary, near boundary, or away boundary. False boundary signals are those generated from a single domain protein. Near boundary signals are those which occur within 20 residues of any domain boundary in a multi-domain protein. The remaining signals come from multi-domain proteins and correspond to away boundary signals as they take place more than 20 residues away from a true domain boundary. The 20-residue threshold is in accordance with previous research ,17,45.To predict domain boundaries, each domain boundary signal was classified using a support vector machine (SVM) . As suppTo determine if a protein is single domain or multi-domain, we first classify all domain boundary signals as false signals or near/away signals. If a protein has one or more near/away signals, it is classified as a multi-domain protein. Those proteins which only generate signals classified as false signals or do not generate any signal at all are classified as single domain proteins. Domain boundaries are predicted based directly on the output of SVMlight. For each domain boundary signal, a set of features is fed into SVMlight and output is generated. Generally speaking, for Task-2 if the output is positive, i.e. greater than 0, then a domain boundary is predicted at that signal site. It is also possible to set a different decision threshold and determine predicted domain boundaries with respect to that new threshold.Both Task-1 and Task-2 SVM predictors were trained using the SVM light package . The feaFor both Task-1 and Task-2 SVM predictors, we used a radial basis kernel function and set gamma to "0.015" according to a leaving one out cross validation (LOOCV) procedure. For the purposes of training and evaluation we performed 10 fold cross validation, splitting the proteins up into 10 set of approximately equal size. For Task-1 we used all proteins in our dataset while for Task-2 we limited ourselves to those targets known to be multi-domain proteins.To ascertain the usefulness of domain boundary signals generated by multiple sequence alignments, we calculated the percentage of domain boundaries which had a signal within 20 residues. When calculating this value, we excluded the domain boundary closest to each terminal end of the protein sequence . For our dataset, there were 462 such boundaries and we found that 391 had a domain boundary signal within 20 residues. Thus, 84.6% of domain boundaries had a signal nearby. Figure Table One key application of domain boundary prediction is to select positions to cut a large protein into foldable units for structure determination or prediction. In order to facilitate this application, we study how the precision and recall of domain boundary predictions change according to decision thresholds on domain boundary scores predicted by the support vector machines. Figure Table ab-initio domain boundary predictors on the targets from the Critical Assessment of Techniques for Protein Structure Prediction (CASP9). The additional predictors considered were DOMPro and PPRODO [As an additional assessment of our method, we evaluated its performance along with that of two additional d PPRODO ,13. The d PPRODO . To evalOne immediate benefit of this new domain boundary prediction process is the combination of the strengths of machine learning and evolutionary signals. Evolutionary signals embedded in multiple sequence alignments help significantly reduce the search space. As mentioned, the domain boundary signal embedded in the primary sequence is very weak. Any reduction in the search space which does not eliminate domain boundary sites will likely increase overall accuracy of domain boundary prediction as it will reduce the chance of false positives. For our dataset, the average sequence length is 210 residues while the average number of domain boundary signals generated per protein is 23. This is a significant reduction in the number of sites that must be classified. Remarkably, this 10-fold reduction in search space does not severely hamper the search for domain boundaries as the number of domain boundaries which have a signal nearby is still quite high, at slightly under 85%.We have also demonstrated that not only are signals generated near domain boundary sites, but they also contain useful information which can be used to classify them. The machine learning method, which incorporates sequence profiles, secondary structures, relative solvent accessibilities and positional information of putative boundary sites, can produce scores to rank, select and classify the largely reduced set of putative domain boundary sites. Our two-tiered classification approach allows proteins to be classified as single or multi-domain and the boundary signals in multi-domain proteins can be further processed in a task specific way. When classifying signals as near or away boundary signals, our method allows the user to specify a threshold to meet his or her needs. The threshold can be decreased to boost recall or it can be raised to better precision. This is a stark contrast to many other methods which fix the threshold and do not allow for application specific use.Figure In addition to the decision threshold, there are a number of other parameters that can be set and modified. With respect to the signal generation process, it is possible to vary the e-value threshold of the PSI-BLAST search, the minimum signal gap, minimum domain length and unique signal site limit. Overall, we found that the method is quite robust within a range of reasonable parameter values and the tuning of these parameters usually involves some minor trade-offs between different prediction objectives. For instance, we set a shorter minimum domain and signal gap length, and used an older version of the NCBI non-redundant database when generating domain boundary signals and this yielded a slightly higher overall accuracy for Task 1 and Task 2 (i.e. ~85% and ~77% respectively using a LOOCV procedure), but a lower precision and recall at the break-even point (i.e. ~53%). The final values used for these parameters were chosen empirically based on coverage of domain boundaries by signals, the average number of signal sites per protein and the break-even point. Another parameter that can be set is the number of sequences to be considered from the multiple sequence alignment. We found that considering all sequences in a multiple sequence alignment can sometimes be detrimental to the overall performance. While it is true that allowing more sequences for consideration often increases the number of signals and hence increases the coverage of domain boundaries, it does so at the cost of enlarging the search space. We also found that number of signal generated for a protein has no direct bearing on performance. The precision and recall of domain boundary predictions for proteins generating fewer than 10 signals is comparable to that of proteins which generate many more signals.A drawback to our approach is that by limiting the search space by means of evolutionary signals, our method is dependent on the generation of those signals. That is to say if no signals are generated then domain boundary predictions cannot be made. We have found that when signals are not generated, the most common cause is that the length of the protein is too short. For a domain boundary signal to be generated it must occur at least 45 residues from the N or C terminal and the resulting domain must be at least 45 residues long. This effectively means that proteins less than 90 residues in length are incapable of generating signals. In practice, this limitation does not pose any serious problem as such proteins are likely to be single domain and hence there are no boundaries to detect. Another reason that signals might not be generated is if a significant number of homologs cannot be identified during the PSI-BLAST search. This does occasionally happen and in this case the method will not work.http://sysbio.rnet.missouri.edu/dobo/.We developed a two-step procedure to integrate machine learning and domain evolutionary signals to improve domain boundary prediction. The evolutionary domain signals extracted from multiple sequence alignments of query proteins and their homologs can reduce the space of the domain boundary search by about 10 fold while retaining the majority of true domain boundaries. The further application of support vector machines together with other sequence-derived features can effectively score and classify these putative boundaries in order to identify true domain boundaries. The numerical scores assigned to the predicted domain boundaries make it possible to select domain boundaries at different precision and recall values. This flexibility and the good prediction accuracy make this method a valuable tool for protein structure determination and prediction. It is available at JC designed and implemented the first version of the method and conducted the initial experiments. JE implemented the second version of the method, added some new features, and carried out the remaining experiments. XD converted some programs from Perl into C++. JE and JC drafted the manuscript. All the authors read, edited and approved the final manuscript.


Late assembly (L)-domains are protein interaction motifs, whose dysfunction causes characteristic budding defects in enveloped viruses. Three different amino acid motifs, namely PT/SAP, PPXY and YPXnL have been shown to play a major role in the release of exogenous retroviruses. Although the L-domains of exogenous retroviruses have been studied comprehensively, little is known about these motifs in endogenous human retroviruses.A molecular clone of the human endogenous retrovirus K113 had been engineered to reverse the presumed non-synonymous postinsertional mutations in the major viral genes. Potential L-domains in the molecular clone expressing viral particles were inactivated and the release and budding phenotype analyzed by RT-assays and Western blot. Rescue experiments by Tsg101 and Alix overexpression and colocalisation studies were performed to substantiate the results.We identified three functional L-domains of the virus, all located in the Gag p15 protein. A consensus PTAP tetrapeptide serves as the main L-domain for the virus and its inactivation reduces virus release in HEK 293T cells by over 80%. Electron microscopy of cells expressing the PTAP mutant revealed predominantly late budding structures and budding chains at the plasma membrane. The fact that this motif determines subcellular colocalization with Tsg101, an ESCRT-I complex protein known to bind to the tetrapeptide, supports its role as an L-domain. Moreover, two YPXnL motifs providing additional L-domain function were identified in the p15 protein. One is adjacent to the PTAP sequence and the other is in the p15 N-terminus. Mutations in either motif diminishes virus release and induces an L-domain phenotype while inactivation of all three L-domains results in a complete loss of particle release in HEK 293T cells. The flexibility of the virus in the use of L-domains for gaining access to the ESCRT machinery is demonstrated by overexpression of Tsg101 which rescues the release of the YPXnL mutants. Similarly, overexpression of Alix not only enhances release of the PTAP mutant by a factor of four but also the release of a triple mutant, indicating that additional cryptic YPXnL domains with a low affinity for Alix may be present. No L-domain activity is provided by the proline-rich peptides at the Gag C-terminus.Our data demonstrate that HERV-K(HML-2) release is predominantly mediated through a consensus PTAP motif and two auxiliary YPXnL motifs in the p15 protein of the Gag precursor.


Using binding assays and X-ray crystallography, we now show that RVX-208 selectively binds to bromodomains of the BET  family, competing for a site bound by the endogenous ligand, acetylated lysine, and that this accounts for its pharmacological activity. siRNA experiments further suggest that induction of ApoA-I mRNA is mediated by BET family member BRD4. These data indicate that RVX-208 increases ApoA-I production through an epigenetic mechanism and suggests that BET inhibition may be a promising new approach to the treatment of atherosclerosis.Increased synthesis of Apolipoprotein A-I (ApoA-I) and HDL is believed to provide a new approach to treating atherosclerosis through the stimulation of reverse cholesterol transport. RVX-208 increases the production of ApoA-I in hepatocytes  Atherosclerosis continues to be a major cause of morbidity and mortality despite significant reductions in coronary events through the use of statins, which inhibit HMGCoA reductase and intracellular cholesterol synthesis. These drugs significantly lower LDL-C and can arrest plaque accumulation, but prevent only about 30–40% of cardiovascular events. In contrast to the linear correlation between decreases in low-density lipoprotein cholesterol (LDL-C) and cardiovascular risk While there are several approaches to the therapeutic modification of HDL currently in clinical development in vitro, and in vivo studies in monkeys have demonstrated that ApoA-I induction with RVX-208 leads to enhanced systemic capacity to promote cholesterol efflux RVX-208 is a small molecule undergoing clinical development as a potential therapy to enhance ApoA-I production In this report, we identify the molecular target of RVX-208 to be the BET proteins, and in particular the BET family member BRD4, which regulates ApoA-I expression through an epigenetic mechanism.+); 1H-NMR (DMSO-d6): δ 11.8 , 7.9 , 6.8 , 6.6 , 4.9 , 3.7–3.9 , 2.3 ; 13C-NMR (DMSO-d6): δ 164.9, 161.7, 160.4, 159.1, 153.8, 153.2, 131.5, 128.9, 127.9, 105.3, 101.8, 98.2, 74.7, 61.1, 56.6, 56.3, 16.8. JQ-1 was synthesized as described RVX-208 (2-(4-(2-hydroxyethoxy)-3,5-dimethylphenyl)-5,7-dimethoxyquinazolin-4(3H)-one) was synthesized by NAEJA Pharmaceuticals  and IRIX Pharmaceuticals  to support both non-clinical and clinical work. Synthetic procedures can be found in US Patents 8,114,995 Huh7 cells were plated at 23,000/well in a 96 well plate in DMEM +10% FBS before allowing to grow overnight. Cells were treated with compounds for 48 h in 0.1%DMSO with or without 5 µM Actinomycin D. U937 cells were differentiated for 3 days in 60 ng/mL PMA, 32,000 cells/well in 96-well format. Cells were then treated with compound in 0.1%DMSO in RPMI media +10%FBS, and after 1 h, lipopolysaccharide  was added to the cells at 1 µg/mL for 3 hours.Cells were harvested by mRNA Catcher PLUS Kit followed by real-time PCR using the RNA UltraSense One-Step qRT-PCR System. ApoA-I, IL-6 and TNFα mRNA levels were measured relative to the endogenous control serpin A1 or cyclophilin in the same sample. Data was acquired using the 7500 Real Time PCR System (Applied Biosystems).N-terminal His-tag based on Single bromodomain and dual domain BRD4[BD1BD2] constructs with an 5 µM of purified bromodomain protein was incubated with 5X SYPRO® Orange (Molecular Probes) at a final concentration of 20 mM HEPES pH 7.4, 100 mM NaCl in the presence of 100 µM compound or DMSO (0.2%) in a fast 96 well optical plate (Applied Biosytems). Samples were incubated at room temperature for 30 minutes and ramped from 25°C to 95°C in a ViiA7 real-time PCR machine (Applied Biosystems). The resulting fluorescence data was analyzed and melting temperatures calculated using Protein Thermal Shift™ Software v1.0 (Life Technologies).N-terminally His-tagged bromodomains or BRD4(BD1BD2) and 25–50 nM biotinylated tetra-acetylated histone H4 peptide (Millipore) were incubated in the presence of Europium Cryptate-labeled streptavidin (Cisbio Cat. #610SAKLB) and XL665-labeled monoclonal anti-His antibody (Cisbio Cat.#61HISXLB) in a white 96 well microtiter plate (Greiner). For inhibition assays, serially diluted compound was added to these reactions in a 0.2% final concentration of DMSO. Final buffer concentrations were 30 mM HEPES pH 7.4, 30 mM NaCl, 0.3 mM CHAPS, 20 mM PO4 pH 7.0, 320 mM KF, 0.08% BSA). After 2 h incubation at room temperature, the fluorescence by FRET was measured at 665 and 620 nm by a SynergyH4 plate reader (Biotek). IC50 values were determined from a dose response curve.200 nM 150 µM BRD2[BD1], BRD2[BD2], BRD4[BD1] or BRD4[BD2]in 50 mM HEPES pH 7.5, 150 mM sodium chloride and 0.05% DMSO was injected into a solution containing 10 µM RVX-208 in the same buffer at 25°C, and the associated change in heat measured in a Microcal Auto-ITC instrument.2, 1 mM EGTA, 2 mM ribonucleoside vanadyl complex) as previously described Soluble proteins were extracted using a buffer containing 0.5% Triton X-100 . All siRNAs were obtained from Dharmacon . The siRNA was prepared as per manufacturer's instructions (Dharmacon) and incubated with 23,000 cells/well Huh-7 cells in DMEM containing 10% FBS at a final concentration of 100 nM. 48 h post-transfection siRNA-containing media was replaced with fresh media containing 0.1% DMSO and incubated for a further 48 h. To track BRD expression, proteins were collected in 20 µL/well of 10 mM Hepes pH 7.4, 2% SDS, protease inhibitors' cocktail (Life Technologies) on treatment day or at the end of the experiment. mRNA was collected at 3 h and 48 h post transfection. In a double transfection, a second round of siRNA was added after the first 48 h siRNA incubation, and 24 h later was replaced with fresh medium containing 0.1% DMSO for a further 48 h.2SO4, and for BRD2[BD2]/RVX-208 with a solution of 25% PEG1500 (w/v). Selected monocrystals were quickly transferred into a cryo-protectant solution containing the well solution supplemented with 10% (v/v) ethylene glycol; a single crystal was harvested with a nylon loop; flash-cooled into liquid nitrogen; and stored at cryogenic temperature for later analyses at the synchrotron.The recombinant human BRD4[BD1] and human BRD2[BD2] protein domain constructs were each concentrated to 10 mg/mL in a buffer consisting of 10 mM HEPES pH 7.5, 150 mM NaCl, 0.5 mM TCEP. The compound RVX-208 was dissolved to 50 mM in DMSO and then added to the proteins to obtain a final 2 mM concentration; gently mixed; and incubated on ice for 30 minutes. Sparse matrix crystallization screens were evaluated at room temperature and yielded crystals for BRD4[BD1]/RVX-208 with the solution of 25% PEG 3350 (w/v), 0.1 M Bis-Tris pH 6.5, and 0.2 M LiHKL2000a = 37.278 Å, b = 44.623 Å, and c = 78.064 Å. The space group was determined to be P212121on the basis of both the systematic absences and the more favorable χ2 values of high ordered symmetry. The calculated Matthews coefficient of Vm = 2.4 Å3/Da suggests a solvent content of 49%,given one molecule of BRD4[BD1]/RVX-208 complex in the asymmetric unit. The crystal of BRD2[BD2] diffracted to 1.08 Å resolution and indexed with a C-centered orthorhombic unit cell having the dimensions of a = 51.100 Å, b = 66.350 Å, and c = 75.854 Å. The space group was determined to be C2221on the basis of the systematic absences. The calculated Matthews Coefficient of Vm = 2.4 Å3/Da suggests a solvent content of 48%, given one molecule of BRD2[BD2]/RVX-208 complex in the asymmetric unit. The final crystallographic data reduction statistics are summarized in Single-crystal X-ray diffraction was obtained at Beam line X29 of the National Synchrotron Light Source, Brookhaven National Laboratory, using an automated sample mount system. The X-ray diffraction data were reduced using R/Rfree of 23%/25% for BRD4[BD1] and to 23%/26%for BRD2[BD2]. Inspection of the initial electron density maps showed unambiguous density for the compound RVX-208 in each of the two structures. Several rounds of model building and restrained refinement were performed in the absence of RVX-208. Solvent molecules were then positioned in each of the two structures in order to improve the clarity of the residual element of density corresponding to the ligand. The RVX-208 compound and some solute molecules were then included with the structure and refined. The RVX-208 compound bound to BRD4[BD1] with two alternate conformers for the di-methyl phenoxy ethyl alcohol moiety. However, binding of RVX-208 to BRD2[BD2] showed only one stable binding mode. Given the high resolution of these two structures, individual anisotropic temperature factors were also refined. The BRD4[BD1]/RVX-208 structure refined to R/Rfree of 11.0%/14.5% using the data extending to 1.24 Å resolution; the BRD2[BD2]/RVX-208 structure refined to R/Rfree of 12.7%/15.9% at 1.08 Å resolution. The final refinement statistics are listed in The structures were solved by molecular replacement with data from 10-3.0 Å resolution using PHASER . The BRD4 structure (RCSB entry 3MXF) RVX-208, which is a quinazolin-4-one derivative , inducesAn epigenetic mechanism for regulating transcription involving the BET proteins was recently described for small molecules with a benzodiazepine core structure, JQ-1 and I-BET, distinct from RVX-208 m for BET bromodomain denaturation in the presence of ligand is proportional to its affinity m of all BET bromodomains, indicative of binding  for the BETs expressed in hepatocytes  and evaluated binding of RVX-208 by thermal denaturation. In this technique, the increase in the T binding . HoweverD∼5–30 nM) over BD1 (KD∼2–3 µM)  . JQ-1 do∼2–3 µM) .To determine the pharmacological relationship between RVX-208 binding and ApoA-I induction, we evaluated 254 compounds structurally related to RVX-208 for their binding to BRD4[BD1]and ability to induce ApoA-I mRNA in Huh7 cells. 180 (71%) compounds were positive and 45 (18%) were negative in both assays, supporting a requirement for BET bromodomain binding for induction ApoA-I mRNA. In contrast 26 (10%) compounds bound to BRD4[BD1] but did not induce ApoA-I, consistent with an inability to accumulate in cells, whereas only 3 (1%) compounds demonstrated induction of ApoA-I in the absence of BRD4[BD1] binding, which could be attributed to aberrant data or the possibility that these compounds worked by an alternate mechanism.To further explore this structure-activity relationship, we determined the IC50s of multiple compounds for binding to a fragment of BRD4 containing both BD1 and BD2. Comparison with their EC170s for induction of ApoA-I mRNA in Huh7 cells (EC170 is the dose required to give a 70% increase in ApoA-I mRNA)  indicateIf the displacement of acetylated histone peptide regions from BET bromodomains by RVX-208 is relevant to gene regulation, we would expect to see displacement of BET proteins from chromatin in cells. When Huh7 cells are incubated with RVX-208 or JQ-1, BRDs2, 3 and 4 are partially released from chromatin as indicated by the increase in these proteins in the soluble fraction of suitably prepared lysates . This ocZ, αA, αB, αC) and two interconnecting loops (ZA and BC), and is virtually identical to the published structure of BRD4 with JQ-1 (RCSB entry 3MXF) with an RMSD of 0.111 Å. RVX-208 binds with the plane of the quinazolin-4-one oriented parallel to the alpha helices that make up the bromodomain with the dimethyl-phenyl region of RVX-208 existing in two distinct conformations resulting from rotation around the carbon-carbon linker with the quinazolin-4-one. RVX-208 binds BRD4[BD1] in a mostly hydrophobic pocket where it is flanked by Val87, Leu92 and Leu94 side chains on one side of the compound and Pro82 and Leu146 side chain on the other. It is also within van der Waals distances to the four aromatic residues Tyr97, Tyr139, Phe83 and Trp81. RVX-208 quinazolin-4-one also forms a bidentate set of two hydrogen bond interactions with the conserved Asn140 amide side chain, the amino acid recognized by acetylated lysine of the natural substrate, as well as an extensive water network. The quinazolin-4-one scaffold ketone and methoxy oxygens both form a water-mediated interaction with the Tyr97 hydroxyl group. The other compound methoxy oxygen also forms a water-mediated interaction with Gln85. Several of the amino acids in the binding pocket, Trp81-Phe83, Asn140, Val87, Leu92, and Leu94 are conserved among all bromodomains.To determine how RVX-208 binds to bromodomains, we selected two purified bromodomains: BD1 of BRD4 and BD2 of BRD2. Within the BET family the BD1 bromodomains are more homologous to each other than they are to the BD2 domains and similarly for the BD2 domains. A 1.24 Å resolution crystal structure of RVX-208 bound to BRD4[BD1] was obtained . BRD4, Trp370-Phe372, Asn429, Val376, Leu381, and Leu383 are shared with BET BD1 domains . HoweverRVX-208 binds to BRD4[BD1] and BRD2[BD2] quite differently than does JQ-1, which is based on a triazolodiazepine core scaffold . RVX-208To develop a fuller picture of why just two amino acid changes in the binding pocket between BD1 and BD2 could so substantially affect the binding affinity of RVX-208, we wanted to explore the enthalpy and entropy using isothermal titration calorimetry (ITC). Low concentrations of DMSO were used to decrease the influence of DMSO binding to the BET bromodomains on the thermodynamic parameters. This technique recapitulated the preference of RVX-208 for BD2 versus BD1 in both BRD2 and BRD4 observed previously . The datRVX-208 binds to the bromodomains of all three BET proteins expressed in human liver cells  leading to their dissociation from chromatin. To determine which of these proteins might drive ApoA-I expression, we treated Huh7 cells with siRNAs directed against all three BRDs, which might be considered to partially mimic the effect of RVX-208 in dissociating of BET proteins from chromatin. The results indicate that siRNAs directed to BRD4, but not BRD2 or BRD3, led to an increase in ApoA-I mRNA .RVX-208, a compound that raises ApoA-I and HDL in humans, was originally discovered in a screen for inducers of ApoA-I mRNA in hepatocyte cell cultures The results presented here contain several lines of evidence to firmly establish that this mechanism leads to induction of ApoA-I expression. First, we have shown that RVX-208 binds to BET bromodomains via several different techniques, including X-ray crystallography, isothermal calorimetry, thermal denaturation and HTRF-FRET and displaces bound acetylated histone peptide both in vitro and in cells. Consequently, RVX-208 now joins two other, distinct chemical scaffolds that bind to the bromodomains of BET proteins and induce ApoA-I The co-crystal structure data of RVX-208 with a BD1 and BD2 domain demonstrate that RVX-208 binds in a unique manner compared to other published BET inhibitors. Furthermore, binding and calorimetric data indicate that this results in a preference for binding to BD2 over BD1 domains in the BET family. While the current data suggest that this difference may have an entropic component possibly due to a stacking interaction of the dimethyl-phenyl ring of RVX-208 with His433 not seen with Asp144 in BD1, we cannot rule out a pH dependent effect of the His433. The impact of differences in binding to BD1 and BD2 on biological activity is not known, although the energy of RVX-208 binding the two tandem domains in vitro appears to be an average of the two individual bromodomains.Whereas the preceding studies detail the ApoA-I and resultant HDL raising effects of RVX-208, our data point to an additional activity of interest in treating atherosclerosis. BET antagonism using RVX-208 and JQ-1 regulates inflammatory cytokines by suppressing IL-6 but not TNFα in the U937 macrophage-like cell line. This is consistent with data indicating that treatment with siRNAs against the BET proteins also suppresses IL-6 in macrophages (The interaction between BET proteins and the amino terminus of histones represents one of a growing class of protein-protein interactions (PPIs) that is amenable to small molecule drug discovery. Historically, these targets have been considered a challenge for small molecule inhibition due to the typically large surface areas involved at the interface between two proteins The discovery of the molecular target of RVX-208 emphasizes the power of using phenotypic screens for the identification of new drugs The finding that RVX-208 regulates transcription through an epigenetic mechanism mediated by the BET proteins further extends the promise of epigenetics as a potential source of new therapies to address several unmet medical needs. The first epigenetically targeted therapies to enter clinical practice include the histone deacetylase (HDAC) and DNA methyltransferase inhibitors Figure S1Thermal denaturation of RVX-208 binding to BET bromodomains. 5 µM of purified bromodomain protein and SYPRO® Orange were heated in the presence of 100 µM RVX-208, JQ1 or buffer control, and the fluorescence measured as a function of temperature.(TIF)Click here for additional data file.Figure S2Isothermal calorimetry of RVX-208 binding BRD2 and BRD4 bromodomains. Individual bromodomains at 150 µM, BRD2[BD1], BRD2[BD2], BRD4[BD1], or BRD4[BD2] were titrated into 10 µM RVX-208 and an integrated curve fit to data with background correction and modeled for single site binding.(TIF)Click here for additional data file.


Reston ebolavirus VP30 C-terminal domain shows a rotated interface in comparison to the previous structure of the Zaire ebolavirus VP30 C-terminal domain.The crystal structure of the  Reston ebolavirus is presented. Reston VP30 and Ebola VP30 both form homodimers, but the dimeric interfaces are rotated relative to each other, suggesting subtle inherent differences or flexibility in the dimeric interface.The ebolaviruses can cause severe hemorrhagic fever. Essential to the ebolavirus life cycle is the protein VP30, which serves as a transcriptional cofactor. Here, the crystal structure of the C-terminal, NP-binding domain of VP30 from  However, Reston ebolavirus (RESTV) is uniquely Asian in origin. RESTV has been identified in bats and primates , VP30, VP35 and the RNA-dependent RNA polymerase (L) . Expression was performed in E. coli BL21 (DE3) cells in TB medium (Teknova) at 37°C with 50 µg ml−1 kanamycin. Cells were induced at an OD600 of 0.7 with 1 mM IPTG and incubated overnight at 25°C.RESTV (strain Reston-89) VP30 CTD, residues 142–266, was cloned using polymerase incomplete primer extension phosphine (TCEP), 10 mM imidazole, 0.5% glycerol, 125 U Benzonase, 0.71 mg ml−1 lysozyme and one EDTA-free complete protease inhibitor tablet (Roche)] and lysed via sonication. The lysate was clarified by centrifugation and filtration. The protein was eluted from HiTrap Ni2+ Chelating columns  with an imidazole step gradient. Pooled fractions were incubated for 18 h with Ulp-specific protease 1 (Ulp-1), intended to cleave the Smt domain from VP30. However, SDS–PAGE analysis showed that the His-Smt tag was unable to be removed . The protein was dialyzed against 25 mM Tris pH 8.0, 200 mM NaCl, 1 mM TCEP, 1% glycerol for 12 h and was concentrated to 18 mg ml−1 for crystallization. Macromolecule-production information is given in Table 1Cells were resuspended in lysis buffer [25 med Fig. 1a. The p2.2.M HEPES pH 7.0 in 2–3 weeks using 0.4 µl protein solution and 0.4 µl reservoir solution at 16°C. The rod-shaped crystals were cryoprotected with 20% ethylene glycol prior to flash-cooling in liquid nitrogen. Details are given in Table 2Crystals grew by vapor diffusion in sitting-drop trays in 10% PEG 6000, 100 m2.3.XDS  as a search model. The structure was refined with REFMAC . The VP30 domains retain their Smt fusion domains from purification . One of the Smt domains helps facilitate crystal-packing interactions, whereas the second Smt domain is disordered. The two copies of RESTV VP30 in the asymmetric unit are nearly identical in structure and superimpose with an r.m.s.d. of 0.76 Å.In the crystal structure, there are two copies of the RESTV VP30 CTD in the asymmetric unit Fig. 1b. The Von Fig. 1a. One oα backbones of the EBOV and RESTV structures. Like EBOV VP30 . Dimerization in both EBOV and RESTV VP30 is also facilitated by similar hydrophobic interactions between the globular domains and a conserved hydrogen bond between the side chain of Lys180 in helix 2 and the main chain of Cys251 in the linker between helices 6 and 7.The fold of RESTV VP30 CTD is helical and closely resembles that of EBOV VP30, with an r.m.s.d. of 1.15–2.14 Å separating the Ca). The domain rotation lowers the buried surface area on each monomer  but maintains a similar surface complementarity , suggesting that the RESTV VP30 CTD dimer is a relevant conformation . However, there is no apparent structural reason for the Smt domain to constrain the VP30 conformation and the Smt domain and VP30 CTD are not expected to form a stable complex in solution , which are instead solvent-exposed in the EBOV structure. These residues are important in VP30 function and their mutation is detrimental to VP30 oligomerization, NP binding and transcription initiation . While we are unable to rule out the influence of the Smt on the conformation of the RESTV dimer, the observation of two dimer conformations in the available structures suggests that there may be inherent differences between the African and Asian viral species. Alternatively, the VP30 CTD could adopt multiple conformations in solution: structural changes in VP30 may reflect its role in modulation of RNA synthesis or another role in the ebolavirus lifecycle.EBOV and RESTV VP30 have 84% sequence identity within the CTD and conserve both the overall structure and hydrophobic interfaces Fig. 1c. WhileReston ebolavirus VP30 C-terminal domain, 3v7oPDB reference:


PDZ domains recognise short sequence motifs at the extreme C-termini of proteins. A model based on microarray data has been recently published for predicting the binding preferences of PDZ domains to five residue long C-terminal sequences. Here we investigated the potential of this predictor for discovering novel protein interactions that involve PDZ domains. When tested on real negative data assembled from published literature, the predictor displayed a high false positive rate (FPR). We predicted and experimentally validated interactions between four PDZ domains derived from the human proteins MAGI1 and SCRIB and 19 peptides derived from human and viral C-termini of proteins. Measured binding intensities did not correlate with prediction scores, and the high FPR of the predictor was confirmed. Results indicate that limitations of the predictor may arise from an incomplete model definition and improper training of the model. Taking into account these limitations, we identified several novel putative interactions between PDZ domains of MAGI1 and SCRIB and the C-termini of the proteins FZD4, ARHGAP6, NET1, TANC1, GLUT7, MARCH3, MAS, ABC1, DLL1, TMEM215 and CYSLTR2. These proteins are localised to the membrane or suggested to act close to it and are often involved in G protein signalling. Furthermore, we showed that, while extension of minimal interacting domains or peptides toward tandem constructs or longer peptides never suppressed their ability to interact, the measured affinities and inferred specificity patterns often changed significantly. This suggests that if protein fragments interact, the full length proteins are also likely to interact, albeit possibly with altered affinities and specificities. Therefore, predictors dealing with protein fragments are promising tools for discovering protein interaction networks but their application to predict binding preferences within networks may be limited. Many of the protein interactions that function in cellular regulation and signalling are mediated by linear motifs that bind to globular domains. Such interactions are often specific, yet transient and therefore of low affinity Approaches for predicting domain-linear motif interactions have very often focussed on PDZ-peptide interactions. PDZs are a very abundant class of globular domains with 267 occurrences in the human proteome PDZs are implicated in the regulation of cell polarity, cell adhesion and intercellular communication et al.C. elegans and 54 H. sapiens PDZ domains using 10 billion random peptides. Stiffler et al.et al. were used by Chen et al.Until recently, only specific case studies had been published on the specificity of PDZ-peptide interactions, and the iSPOT tool et al.et al.et al.A very critical point for the development of protein interaction predictors is the availability of real negative interaction datasets The numerous existing predictors for PDZ-peptide interaction specificities focus on the core PDZ domain or binding pocket of the PDZ and mostly on four or five residue long peptides et al.in silico by using test datasets assembled by ourselves that consisted of real positive and negative interaction data for various PDZ domains. Then, by concentrating on PDZ domains of MAGI1 and SCRIB, we performed proteome-wide interaction predictions and experimentally validated a subset of those, allowing us to also assess the prediction quality in vitro. We also assessed how binding was influenced by extended protein fragments, i.e. peptides and PDZ constructs longer than those considered by the predictor. Finally, discovered interactors for MAGI1 and SCRIB were analysed with regard to new biological functions that can be linked to MAGI1 and SCRIB and that might be perturbed in tumours induced by oncoviruses or other factors. In total, this analysis allowed to highlight the power and limits of PPI network predictions involving PDZ domains, to uncover possible ways of improvements, and to obtain further insights into the mechanisms that define affinity and specificity of PDZ-peptide interactions.Based on these observations, several questions are raised: First of all, how correct are PDZ interaction predictors in theory and in practice? Second, to which extent can specificity predictions based on protein fragments be transferred to full length proteins and how much influence do extensions of protein fragments have on affinity and specificity of the corresponding interaction? Third, can existing PDZ interaction predictors be used to extend our knowledge on PPI networks mediated by PDZ-peptide interactions? Here, we attempted to answer these questions by focussing on the well studied predictor published by Chen et al.et al. Therefore, we included in the datasets interactions that we expect to occur between human proteins although they were originally described in the literature using rat and mouse PDZ domains.We aimed at assessing the performance of the PDZ interaction predictor published by Chen et al.The second and third test dataset contain negative interaction data that were assembled from published literature as follows. We took advantage of the particular characteristic of PDZ domains to occur as repeats within proteins  et al. (24%). Furthermore, the FPR obtained for non-interactions without PDZ-binding motifs is about 26%, which represents a weak performance with regard to the relatively straightforward task to discriminate between peptides that bear a prototypical PDZ-binding motif or not. We then analysed separately, within our test datasets, the data involving human PDZ domains that are either orthologous or not orthologous to the mouse PDZ domains present in the training set of Chen et al. Sensitivity and FPR of these subsets show that the predictor tends to be over-optimistic for PDZ domains that are orthologous to domains present in the training data, and over-pessimistic for PDZ domains that are not orthologous to any domain present in the training data  to all amino acids that were never seen at particular peptide positions. Whereas this strategy is sound when applying the predictor to peptides matching the general PDZ-binding consensus, it may lead to the selection of irrelevant peptides when querying an entire proteome. To take this issue into account, we applied an additional filter to accept only peptides ending with either C, Y, F, L, I, M, V, W or A, i.e. residues that were observed at position p0 in artificial or natural PDZ-binding peptides. This filter rejected 20 to 60% of the initial hits . For instance, domain positions Leu37 (α1 helix) and Thr74 ( α2–β5 loop) in α1-syntrophin  within the et al.'s study. The selection of residues of the et al. may influence binding specificity.In the prediction model of Chen ntrophin , chosen oach see  selected binding . Altogetet al. we selected 17 human and three viral peptides for interaction measurements against five PDZ constructs: the four single PDZ domains MAGI1-2/6, MAGI1-3/6, SCRIB-3/4, SCRIB-4/4, and the tandem construct SCRIB-34/4 . The other peptide, called “short”, encompassed the last five wild type amino acids of the protein preceded by a GSGAG sequence (e.g. GSGAGSETSV for VANG2). This GSGAG sequence, composed of small neutral residues, was included to prevent the biotin tag N-terminally attached to the peptides to influence the binding to the PDZ domain. The “short” peptides, in which only the last five residues vary and correspond to natural proteins, would allow us to experimentally validate interaction predictions obtained with the predictor of Chen et al. that considers the last five residues in the prediction model. The long peptides (as well as the tandem PDZ construct) would allow us to address changes in binding affinity and specificity that might occur when using extended protein fragments.For each of these 19 proteins two peptides were designed, We opted for the surface plasmon resonance (SPR) method to measure these 190 (19 proteins×2 peptide versions×5 PDZ constructs) interactions. In SPR various concentrations of “analytes” ) flow over surfaces presenting attached “ligands” . The amount of analyte interacting with the ligand is measured and quantified in response units (RU). The intensity of this signal is proportional to the binding strength of the assayed interaction . K were heat map . Table SNine out of nine published interactions (including 16E6L/V) were confirmed by our experimental data, of which three out of four published Ket al. that we have previously noticed , we did not observe such correlation  (Chen  = 0.74) . In partet al.SCRIB-4/4 was found to significantly bind to only one peptide, TAX1, despite of the fact that SCRIB-4/4 was predicted to bind to 15 out of the 19 peptides tested . RemarkaThe “GLGF-loop”, which precedes the et al.Our interaction data reveal different binding preferences of the PDZ domains for specific hydrophobic amino acids at peptide position p0 . These observations indicate that SCRIB-34/4 may represent a supramodule as defined by Feng and Zhang The predictor of Chen Analysis of structures of PDZ-peptide complexes from the PDB showed that peptide residues upstream of p-4 are proximal to the versus short peptide constructs or for single versus tandem PDZ domains, protein fragment extensions were never found to change an experimentally significant interaction into a non-interaction, nor vice-versa. Therefore, we hypothesise that whenever an interaction is detected between minimal fragments, it is likely that the full length proteins will also interact, albeit possibly with different affinities. Unfortunately, affinity measurements could not be undertaken with full length proteins to provide more evidence for this hypothesis due to experimental limitations in handling large proteins in vitro.Taken together, our results suggest that extensions of protein fragments may lead to changes in affinity and specificity. However, when comparing binding intensities obtained for long et al.Our experimental data showed that many peptides bound weakly, with affinities much weaker than 20 Based on the results presented here we suggest FZD4, TMEM215 and ARHGAP6 as new interactors for MAGI1; TANC1, GLUT7, DLL1, MAS and NET1 as new interactors for SCRIB; and ABC1, MARCH3 and CYSLTR2 as new interactors for both MAGI1 and SCRIB. Remarkably, several of these proteins are proven or putative membrane proteins  while the three remaining ones  are involved in activities localised to the membrane. Indeed, SCRIB and MAGI1 were already known to localise to the membrane where they interact with numerous proteins involved in signal transmission, and more particularly in G protein mediated signalling. On the one hand, MAGI1 had been shown to interact with NET1 In line with these published findings, several of the novel putative interactors of MAGI1 and SCRIB that we identified are also involved in G protein signalling. FZD4, CYSLTR2 and MAS are GPCRs; NET1 is a GEF; ARHGAP6 is a GAP (G protein activating protein); ABC1 is a membrane transporter known to recruit two GEFs (PDZRhoGEF and LARG) involved in Cdc42 and RhoA signalling in vivo functions of MAGI1 and SCRIB that might be perturbed upon infection with HPV.MAGI1 and SCRIB are known to participate to the regulation of neuronal synapses via interaction with numerous proteins In this work, we showed that inferring protein interaction networks from predictions based on interacting protein fragments should involve at least two very distinct steps. The first step requires accurate prediction of interactions between the isolated protein fragments considered by the predictor. The predictor we used here for completing this step turned out to be rather inaccurate. There is much room for improving this step, in particular by integrating the wealth of structural information recently accumulated about protein domains, especially PDZs. The second step requires correct extrapolation of predicted fragment interactions to interactions between full length proteins. Our data indicate that such an extrapolation may be possible qualitatively, but not necessarily quantitatively. Therefore, while inferring protein interaction networks from minimal interacting fragment predictions appears as a reasonable perspective, more refined predictions addressing binding specificities in these networks remain a challenging, yet fascinating prospect.www.python.org), biopython www.gnuplot.info) and PyMOL (www.pymol.org). We used the same human proteome as described in Luck et al.The programming and data analysis was done using python . The predictor returns a score for each PDZ-peptide pair, which can be used to estimate the likeliness that the PDZ domain will bind the respective peptide. The higher the score, the more likely the interaction. Here, we used a score cutoff of 0.5, which should yield a sensitivity of 76% and FPR of 24% as specified by Chen et al.Chen et al. in order to define the 16 amino acid positions on which predictions are based. Mafft Each of the 95 human PDZ domains in the test datasets were added to the alignment of mouse PDZ domains provided by Chen et al. was not provided in the publication. The set of peptides from the training data was reconstructed as described by Chen et al. taking every peptide that was seen at least once in an interaction with a PDZ domain in the experimental data obtained by Stiffler et al.The training set containing 93 peptides of Chen et al., who wish to follow our developed protocol.In et al.Available structures of PDZ-ligand complexes were analysed in order to assess important domain residues for ligand recognition. A keyword search with “PDZ” in the PDB The PDZ sequences shown in The structure of the PDZ domain SCRIB-3/4 was modelled using the program Modeller 9v7. The structure template was obtained by querying the PDB with the sequence of SCRIB-3/4 (using the BLAST option) and choosing the structure with the best sequence match . Modeller was run using the automodel routine and default options. Model quality was assessed using the output information of Modeller and visual inspection. A model of SCRIB-3/4 of intermediate quality was sufficient for the purpose of this study.The cDNA encoding residues 448–572 and 613–752 of mouse MAGI-1 (UniProt acc.: Q6RHR9-1) encoding for MAGI1-2/6  and MAGI1-3/6  PDZ domains, respectively, were inserted into the NcoI/KpnI sites of the pETM-41 expression vector (EMBL) containing a 6×His-MBP tag followed by a TEV protease cleavage site. A similar cloning strategy was adopted for cDNA bearing residues 997–1093, 1097–1193 and 997–1193 of human SCRIB (Uniprot acc.: Q14160-1) encoding for SCRIB-3/4, SCRIB-4/4 PDZ domains and SCRIB-34/4 tandem PDZ construct, respectively.Escherichia coli cells in 300 ml of M9 minimal medium supplemented with Bacterial over-expression of PDZ domains was performed using BL21 DE3 Bacterial expression of The synthetic peptide 16E6L/V (RSSRTRRETQV), corresponding to the last 11 C-terminal residues of HPV16 E6 with the last residue L mutated to V, was synthesised by the Chemical Peptide Synthesis Service, IGBMC, France. Lyophilised peptide was re-suspended in water, passed on a NAP-5 desalting column  in order to remove residual contaminants. The desalted peptide was lyophilised prior to its dilution into buffer A. The peptide was checked by homonuclear 2D NMR experiments and its concentration estimated to be at 6 mM by measuring the peptide bond absorption at 205 nm as described previously et al.Data were collected on a Biacore 2000 instrument  at 25The steady-state binding signal Click here for additional data file.Dataset S2Prediction results of proteome-wide screen for MAGI1 and SCRIB PDZ-binding ligands using the predictor of Chen et al.. The prediction results were performed in binary mode using a cutoff of 0.5 and are provided without any additional filtering. No result file is provided for the PDZ domain MAGI1-1/6 because the screen did not reveal any peptides for this domain.(BZ2)Click here for additional data file.Dataset S3Alignment of human PDZ domains. The archive contains an alignment in fasta format of 95 PDZ domains. These include all PDZ domains that occur in the three test datasets as well as all MAGI1 and SCRIB PDZ domains. Additionally, a file is provided containing a translation between the PDZ domain names used in the test datasets and the PDZ domain names used in the alignment.(BZ2)Click here for additional data file.Dataset S4Implementation of the predictor of Chen et al.. The archive contains data files and python scripts necessary to launch the predictor. The only prerequisite for running the program is an installed python version. Check the README.txt for more information.(BZ2)Click here for additional data file.Table S1Diversity of amino acids at last five positions of PDZ-binding peptides in the training data of Chen et al..(PDF)Click here for additional data file.Table S2Filtered numbers of proteins predicted to bind to 1, 2, 3, … or all PDZ domains of MAGI1 (6 PDZs) or SCRIB (4 PDZs).(PDF)Click here for additional data file.Table S3Annotations for all proteins tested experimentally in this work for interaction to MAGI1 and SCRIB. The table contains UniProt IDs and information about biological functions of the proteins with regard to PDZ domain binding as well as published information on interactions with PDZ domain-containing proteins.(PDF)Click here for additional data file.Table S4Experimental data for all interactions measured. The table contains “double referenced” and normalised R(PDF)Click here for additional data file.Text S1Recommendations for application of the predictor of Chen et al..(TXT)Click here for additional data file.


The use of nucleic acid derived aptamers has rapidly expanded since the introduction of SELEX in 1990. Nucleic acid aptamers have demonstrated their ability to target a broad range of molecules in ways that rival antibodies, but advances have been very uneven for different biochemical classes of targets, and clinical applications have been slow to emerge. What sets different aptamers apart from each other and from rivaling molecular recognition platforms, specifically proteins? What advantages do aptamers as a reagent class offer, and how do the chemical properties and selection procedures of aptamers influence their function? Do the building blocks of nucleic acid aptamers dictate inherent limitations in the nature of molecular targets, and do existing aptamers give us insight in how these challenges might be overcome? This review is written as an introduction for potential endusers of aptamer technology who are evaluating the advantages of aptamers as a versatile, affordable, yet highly expandable platform to target a broad range of biological processes or interactions. Aptamers can rival antibodies in affinity and specificity, providing low nanomolar or even picomolar affinity, yet they are also uniquely distinct from proteins in their mode of binding. New applications of aptamers are literally emerging every day, as are improved methods of selection. The current review will instead introduce, or reintroduce, some basic questions with respect to the fundamental molecular recognition properties of aptamers and their relation to the basic building blocks used for aptamer creation. This overview can by its very nature not be exhaustive. While some areas, such as aptamer based in vitro diagnostics or technical aspects of selection and minimization, are covered in great abundance in the literature, it can be much harder for a putative enduser to find at least an entry level discussion of other questions. Can aptamers target carbohydrate targets with antibody-like affinity? Is it possible to target a basic protein or peptide and still retain specificity? Can targets with negative net charge be effectively selected against while retaining specificity? The purpose of this review is to raise some of these questions and to initiate a discussion of these questions for potential users. In raising these questions, we are particularly mindful of the large divide that often seems to exist between the perceptions about aptamers in the scientific community at large and the views among established aptamer users. We also regard the relative simplicity of conventional SELEX as one of its major advantages that opens up aptamer selection to a broad audience of users. Our discussion is therefore geared more towards classic SELEX methodology that most laboratories with a primary focus on questions of biochemistry, molecular recognition or cell biology would be able to implement in a relatively cost effective manner.Aptamers, small peptides or nucleic acid sequences with unique and usually selected functional properties, have rapidly taken an important place in the available repertoire of pharmacological or diagnostic reagents. The term “aptamer”  may in its general sense apply to peptides, nucleic acids or chimera. However, it is most commonly associated with nucleic acid aptamers which are uniquely suited for SELEX . Initially employed for the study of regulatory mRNA in phage  and the Once non-essential sequence components are removed, aptamers tend to be approximately 1/10 the size of the average IgG antibody, another rapidly expanding class of therapeutics. This size of 7–15 kD and the phosphodiester backbone places aptamers in many ways in a niche between small molecule drugs and antibody therapeutics. Unlike most small molecule drugs, aptamers cannot readily penetrate cells unless equipped with specific uptake enhancing features that may facilitate cellular uptake. Compared to antibodies, their smaller size gives aptamers enhanced tissue penetration properties but also faster renal clearance. Modifications such as cholesterol or PEG attachment are therefore often applied to enhance serum half-life . For aptin vitro applications. This complexity stands in contrast to antibodies. For therapeutic antibodies, reagent class specific questions on safety, purity, or production have been addressed. This allows evaluations to focus more rapidly on target specific studies. However, with the introduction of improved coupling methods and decreasing production costs for the most common forms of aptamers [Arguably some of the strongest aspects of aptamers are the relative ease and low cost of classic SELEX, the ease of transition from selection to synthetic production, and the versatile repertoire of defined chemical modifications for the synthetic end product. These aspects have been the driving forces for the extremely innovative research in a large number of relatively small research labs. While this has resulted in a broad spectrum of analytical applications or early studies of therapeutic usages, it stands in contrast to the relatively modest number of established clinical applications. The therapeutic application of aptamers has in part been held back by the challenges involved in the cost effective upscaling of aptamer synthesis from the laboratory scale to one that meets the reagent requirements of clinical trials. Few roadmaps existed for the large scale GMP grade synthesis and purification of nucleic acid based biopolymers. New chemical modifications continue to improve basic features of aptamers, but each new modification creates additional questions that have to be addressed in preparation for preclinical and clinical tests. As a consequence, existing therapeutic aptamers do not reflect the diversity of chemical platforms that is represented in aptamers , we are At present, neither peptide based approaches nor aptamers can compete with most small molecule drugs when it comes to cellular uptake. Some success has been obtained through the fusion of entry mediating peptides  or the tChimeric aptamers that combine distinct selected functions may initially encounter ambiguous folding as well. However, if the problematic sequence components cannot be readily identified the required optimization of fused properties can be addressed directly by SELEX itself. This concept was demonstrated early by Burke and Willis who optimized fused aptamers against very divergent cytoplasmic small molecule targets . Anotherin vitro diagnostics, but greatly enhances the utility and evaluation of therapeutic aptamers through the introduction of fluorescent probes in mechanistic cell culture studies or the introduction of tracers in pharmacokinetic and imaging applications.The analysis of aptamer properties in biological systems is in most cases a more time consuming and costly undertaking than the initial selection. The ability to retroactively enhance the functionality of aptamers already under investigation is therefore of considerable advantage. In contrast to small molecule drug optimization, ongoing improvements in stabilization, pharmacokinetics or label attachment are largely substance class specific and will not have to be investigated exhaustively by each end user of aptamer SELEX methodology. Aptamers are generally considered non-immunogenic. Despite the ability of short stretches of double stranded RNA to activate Toll-like receptors, aptamers are poor activators of an innate immune response, and this response is further reduced by chemically modified RNAs ,20,21,22A survey of aptamer selections over the years reveals a strong initial bias towards ribonucleotides as building blocks despite the inherent enzymatic and chemical instability of unmodified RNA. This initial preference for RNA over DNA largely reflects the believe that the expanded ability of RNA to form complex three dimensional structures is more likely to mimic the structural repertoire of proteins. Due to the subsequent introduction of modified and more nuclease RNA, current RNA derived aptamers exceed DNA in serum stability. However, DNA aptamers have become more popular in recent years, largely due to a selection procedure that does not require an intermediate reverse transcription step, the relatively high intrinsic serum stability of DNA without the need for modified deoxyribonucleotides, and the low cost and high yield in the synthetic production of final products. In addition, the documented ability of select DNA aptamers to rival their RNA counterparts in affinity demonstrates that the lower structural complexity of DNA may be much less of an inherent limitation than was initially believed. At present, existing DNA aptamers exhibit a strong bias towards quadruplex structures or three-way junctions. Whether the apparently more restricted structural space of DNA aptamers will ultimately translate into a more limited molecular recognition space remains to be seen. So far, this has not emerged as an obvious limitation.O-methyl modified ribonucleotides  in E. coli [O-methyl modification to achieve serum stabilization. The drawback of this approach is the significantly higher cost and technical challenge involved in the chemical synthesis of the 2'-fluoro-substituted RNA end product. The latter is no significant impediment for 2'-O-methyl modified RNA. T7 polymerase mutants have been described that produce 2'-O-methyl RNA [O-methyl modified aptamer against VEGF [O-Me accepting polymerase reaction has until recently remained a limiting aspect to its widespread use.A subset of nucleases with exceptionally high ability to digest even modified RNAs is found in various strains of mycoplasm unless 2'-leotides . In retr E. coli , and the E. coli . Interesthyl RNA  as has tnst VEGF . HoweverRNA or DNA Spiegelmers are an alternative solution to the problem of aptamer stabilization, conceptually akin to anti-idiotype antibodies. Spiegelmers (mirror images) are the synthetic enantiomers  of conventional aptamers that were initially selected against the mirror image of the final target. Spiegelmers against L-arginine were an early examples . Since mde novo identification of biomarkers on live cells [in vivo optimized reagents need to be tackled.In addition to targeting well established biomarkers such as PSMA for cargo delivery ,33, aptave cells ,34,35, ove cells . The latve cells , BrdU [1ve cells  or thio-ve cells . These avice versa. In addition, the biophysical properties of the enzymatically incorporated 2'-fluoro and 2'-O-methyl modified aptamers are fundamentally different on various functionally relevant levels, maybe most importantly their hybridization energy. Average melting temperatures of homo duplexes increase in the order DNA < RNA < 2'-O-methyl-RNA < 2' F-RNA [Whether or not functionality in a cellular setting is ultimately desired has important consequences in the choice of the selection platform. The introduction of 2'-ribose modifications does alter folding, such that aptamers selected with standard nucleotides cannot be readily translated into their corresponding counterparts post selection, or 2' F-RNA ,39. This2' F-RNA . On the 2' F-RNA . The polHence, additional means of fine tuning hybridization could give aptamers a broader range of conformational dynamics, much as proteins can evolve to accommodate a broad range of demands on their intrinsic flexibility depending on their operating environment. However, the functional impact of these modifications on aptamer functions other than nuclease resistance is at present rarely exploited in a systematic manner, largely due the difficulties involved in optimizing the enzymatic incorporation of modified building blocks during classic, amplification dependent SELEX.in vitro diagnostics, most aptamers used in a biological setting represent or incorporate targeting modalities aimed at non-nucleic acid ligands. Small molecule binding aptamers and the control of mRNA processing are combined when the aptamer is introduced into the UTR region of mRNA. In such chimeric constructs, the aptamer can directly regulate translation [ds of 100 nM and 600 nM but discriminated against the closely related caffeine 104-fold. More importantly, this represented a more than a 10-fold improvement in discriminating power compared to antibodies [The challenges aptamers face in molecular recognition are obviously heavily dependent on the reagent class to be targeted. Many applications of aptamers make use of their intrinsic ability to base pair to nucleic acid targets. The rules governing specificity in these cases are relatively well understood. For the previously mentioned use of aptamers as tools in siRNA delivery, sequence components providing siRNA functionality must strike a balance between stability and processing by the cellular machinery. However, while hybridization driven functionality is a very prominent feature for aptamers, especially for aptamers used for nslation  or can cnslation , reviewenslation ). More rnslation  or tetranslation  induced nslation ). One oftibodies . While tin vivo setting, aptamers have also been selected against both alkaloids and steroids. The latter are largely variations of the well studied and conformational switch capable cocaine binding DNA aptamers, an example of a three way junction. In its most basic form, three way junctions provide a hydrophobic binding pocket at the central branching point (D approx. 10−15 M) which bind biotin in a buried binding site through a channel that maximizes interactions with the hydrophobic linker.While proteins have a series of side chains at their disposal for contacts with hydrophobic targets, nucleic acids are more limited in their repertoire of available interactions. A therefore somewhat surprising example for the formation of an exclusively hydrophobic ligand binding pocket was found in an aptamer that binds the acyl component of the phosphoglycolipid antibiotic moenomycin A with high (400 nM) affinity . While fng point C, left [ng point C right. ng point B through−6 and 10−4 M. These include challenging proof of principle targets such as the selective binding of various monosacharides [D of 10−7 M. While most affinities remain relatively modest compared to other aptamer targets, selectivity is in most cases very high providing good potential for catalytic aptamers or multivalent homo or bifunctional aptamers that may bind complex carbohydrate polymers with high affinity. Low micromolar binding affinities also have to be viewed in the context of relevant biological concentrations of metabolites and the affinities against single carbohydrate units generated by proteins. Glucose binding protein, first found in the periplasmic space of Pseudomonas aeruginosa, is considered to be a very sensitive and selective sensor of glucose in the extracellular milieu. It binds glucose with a KD of 0.35 μM and withstands challenges by access competing carbohydrates in 10-fold but not 100-molar excess [D = 4.9 μM) and provided an important proof of principle for expanded SELEX with charge modified nucleotides [SELEX has generated various examples of selective binding to carbohydrates (reviewed in ). As wascharides  or cellucharides  for whicr excess . Given tleotides .A 2'-fluoropyrimidine modified RNA aptamer (NX1838) against VEGF ,68, intrin vivo [Factors that control blood clotting are of great importance for the acute treatment of a large number of cases of thrombosis and have been a significant emphasis of aptamer research over the years, providing several in depth comparative studies. High affinity (20 nM) thrombin aptamers, reported in 1992 , were amin vivo .The initial selection against thrombin capitalized on the presentation of positively charged epitopes representing the sites binding of fibrinogen and heparin respectively. Since many RNA binding proteins utilize arginine or lysine residues to interact with their respective targets, it is tempting to extrapolate from our knowledgebase of nucleic acid binding proteins to predict the mode of binding and limitations in specificity for aptamers. However, a structural analysis of arginine directed aptamers surprisingly reveals an avoidance of charge based interaction and instead shows a reliance on hydrogen bonding in a complex with a fully engulfed arginine side chain. This mode of recognition is in stark contrast to the reverse interaction exhibited by RNA binding proteins. It has been suggested that this reflects the intrinsically different selection pressure for aptamers that allows exclusive emphasis on selectivity and affinity without the more complex functional requirements imposed on proteins . RegardlD = 3.5 nM) with specificity in an in vitro and cellular setting [The selection of aptamers against transcription factors or other nucleic acid binding proteins can be a challenge for target regions other than the nucleic acid binding portion. Positively charged surfaces can steer aptamers towards an epitope during selection, even if the final binding mode does not directly capitalize on charge interactions. But does such a favorable attraction undermine specificity? A direct evaluation of this question in 2011 revealed that aptamers can readily discriminate a panel of basic proteins , thereby setting . While NWhile this represents one viable approach to overcome the challenge imposed by very pronounced negative surface charges, an alternative and potentially more universal approach was outlined in principle by the earlier mentioned selection of modified aptamers against Sialyllactose. A similar selection using arginyl dUTP during SELEX yielded high affinity DNA aptamers against glutamic acid  as well A particularly interesting challenge of molecular recognition by aptamers and their ability to rival proteins in functionality involves the quest for nucleic acid GFP equivalents. A unique feature of fluorescent proteins is the spontaneous oxidation and cyclization chemistry of several core amino acids. In the case of GFP the final fluorophore resulting from this intramolecular reaction is 4-hydroxybenzlidene imidazolinone, shielded in the core of the GFP beta barrel. The limited chemical diversity of aptamers restricts options for similar chemistry, but fluorophores and other modifiers are readily attached to synthetic aptamers carrying amino or thiol functionalities. The enzymatic site directed incorporation of intrinsically fluorescent nucleotide analogs has been achieved using T7 polymerase and DNA templates in which modified bases flank the site of insertion . While t40 or 1024 possible sequences is already beyond the maximum experimentally achievable complexity, which is more often around 1013 molecules per screen. Some recent protocols operate with randomized sequences of around 20 nucleotides and thus can at least in principle be fully represented in the experimentally accessible population of sequences carrying 420 or 1012 permutations. In addition, the need for reamplification adds non randomized flanks that can serve as both primer sites for amplification and unique tags that can be highly beneficial if multiple SELEX procedures are being carried out in parallel. Thus, initial aptamers derived from SELEX range frequently from 50–80 nucleotides. An important first step in further analysis is the identification of the minimum size needed for function. Since the non-randomized flanking reagents can take part in the overall base pairing, it is not necessarily clear whether the randomized core does indeed represent the functional unit, and a variety of tools have emerged over the years to stream line this process of minimization .While classic approaches to aptamer minimization relied mainly on the reiterative use of biochemical methods and algorithms that evaluate the energetics of alternative two dimensional folding options, more advanced approaches include direct predictions of three dimensional folds ,90,91. HBeyond minimization approaches, one of the more profound shifts in the day to day business of aptamer selection has serendipitously arisen from the rapidly dropping costs of high throughput sequencing. This makes it for the first time possible to follow aptamer “enrichment” throughout the selection cycle on a sequence level. Data derived from such efforts are readily processed by an array of parsimony based methods, initially designed to deconstruct protein and gene evolution. As more studies are emerging, it becomes clear that many previously held believes about the underlying driving forces of selection and readouts of selection progress may have to be revisited. In addition, such studies have demonstrated the contribution of the amplification methodology to drifts in the aptamer population . Such drifts occurs in the complete absence of biochemical enrichment or partitioning steps . One exaet al. [The ability to follow the early rounds of selection directly for the emergence of sequence clusters instead of driving selection towards complete convergence has also coincided with technical advances in enrichment and detection methodology that bypass reamplification steps. While in principle already represented in the before mentioned direct selection of anti-thrombin DNA aptamers from defibrotide in 1994, a systematic exploitation of consecutive rounds of enrichment without reamplification was formally introduced by Berezovski et al. . This “net al.  while onWhat does the future hold for aptamers? Traditionally, the role of nucleic acids in therapeutics has been relegated to the coding of delivered genes, possibly supplemented by the enhanced delivery of fused aptamers. An area of rapidly increasing importance is the delivery of nucleic acids as regulatory entities. In the near future, aptamers are likely to capitalize on the advances made in stabilization and targeting to deliver highly potent regulatory cargo with functions such as siRNAs in ways that are unique to nucleic acid carriers. In the more distant future, aptamers with improved catalytic functions may provide new paradigms for enzyme replacement therapy by not only enhancing delivery but by directly representing the catalytic entity itself. The question whether nucleic acids ultimately remain dominant as the basic building block in aptamer creation is directly linked to advances in enrichment, detection, characterization, and cost effective multiplexing in non-SELEX approaches. In the meantime, the classic SELEX procedure has undergone major improvements over the years but it continues to remain readily accessible to most research labs. In addition, nucleic acid aptamers are likely to profit, much like the field of antibody therapeutics did, from the fact that many critical issues, such as the fine tuning of serum stability, pharmacokinetics, or the evaluation of chemical modifications, can be evaluated for aptamers as a compound class. Hence, the current rapid growth in the use of nucleic acid aptamers is unlikely to slow down in the near future.


Polycomb repressive complex 2 (PRC2) is an important regulator of cellular differentiation and cell type identity. Overexpression or activating mutations of EZH2, the catalytic component of the PRC2 complex, are linked to hyper-trimethylation of lysine 27 of histone H3 (H3K27me3) in many cancers. Potent EZH2 inhibitors that reduce levels of H3K27me3 kill mutant lymphoma cells and are efficacious in a mouse xenograft model of malignant rhabdoid tumors. Unlike most SET domain methyltransferases, EZH2 requires PRC2 components, SUZ12 and EED, for activity, but the mechanism by which catalysis is promoted in the PRC2 complex is unknown. We solved the 2.0 Å crystal structure of the EZH2 methyltransferase domain revealing that most of the canonical structural features of SET domain methyltransferase structures are conserved. The site of methyl transfer is in a catalytically competent state, and the structure clarifies the structural mechanism underlying oncogenic hyper-trimethylation of H3K27 in tumors harboring mutations at Y641 or A677. On the other hand, the I-SET and post-SET domains occupy atypical positions relative to the core SET domain resulting in incomplete formation of the cofactor binding site and occlusion of the substrate binding groove. A novel CXC domain N-terminal to the SET domain may contribute to the apparent inactive conformation. We propose that protein interactions within the PRC2 complex modulate the trajectory of the post-SET and I-SET domains of EZH2 in favor of a catalytically competent conformation. Enhancer of zeste homolog 2 (EZH2) is the catalytic component of polycomb repressive complex 2 (PRC2), an epigenetic regulator of stem cell pluripotency, and expression of tissue-specific genes involved in cellular differentiation and developmental programs ,2,3,4,5.Overexpression of EZH2 has been found in a number of human cancers ,14. IncrBecause of the important role of EZH2 in tumorigenesis, much effort has been put into discovery of inhibitors of EZH2 catalytic activity. Recently, several groups reported novel inhibitors of EZH2 ,30,31,32To better understand the molecular mechanism of EZH2 function, we solved the crystal structure of the C-terminal region of EZH2 containing a novel CXC domain and the catalytic I-SET, SET, and post-SET domains. Although the majority of canonical SET domain characteristics were observed, our structure reveals significant differences between the arrangement of the I-SET and post-SET regions compared to that of other SET-domain containing proteins. An unusual conformation of the post-SET domain suggests a potential mechanism of activation of EZH2 by the other core PRC2 subunits. Our structure also provides insight into the gain-of-function mutations in lymphomas.A DNA fragment encoding residues 520-746 of human EZH2 (Uniprot isoform 1) was sub-cloned from cDNA (GenBank BC010858) into the baculovirus expression vector pFBOH-LIC (GenBank EF456740).  The pFBOH-LIC N-terminal Hexa-His tag was removed by inserting the gene at an upstream Nco1 restriction site and a C-terminal Hexa-His tag introduced by PCR. The resulting expressed protein product is expected to have the AA sequence: MYQPCDHPRQPCDSSCPCVIAQNFCEKFCQCSSECQNRFPGCRCKAQCNTKQCPCYLAVRECDPDLCLTCGAADHWDSKNVSCKNCSIQRGSKKHLLLAPSDVAGWGIFIKDPVQKNEFISEYCGEIISQDEADRRGKVYDKYMCSFLFNLNNDFVVDATRKGNKIRFANHSVNPNCYAKVMMVNGDHRIGIFAKRAIQTGEELFFDYRYSQADALKYVGIEREMEIPHHHHHH. The protein was expressed in Sf9 cells (Invitrogen). The harvested cells were resuspended in lysis buffer containing 20 mM Tris-HCl, pH 8.0, 500 mM NaCl, 5 mM imidazol, 2 mM β-mercaptoethanol, 5% glycerol, 0.6% NP-40, protease inhibitor cocktail (Roche), 3000 U of benzonase (Novagen). Cells were lysed by brief sonication. The clarified lysate was loaded onto a 2 mL TALON column (Clonetech). The column was washed with 50 column volumes of 20 mM Tris-HCl buffer, pH 8.0, containing 500 mM NaCl, 5% glycerol and 5 mM imidazole. The bound protein was eluted with elution buffer containing 20 mM Tris-HCl, pH 8.0, 500 mM NaCl, 5% glycerol, 250 mM imidazole. The eluted protein was further purified to homogeneity on a Superdex200 column , equilibrated with 20 mM Tris-HCl buffer, pH 8.0, and 500 mM NaCl.Purified EZH2 (10 mg/mL) was mixed with SAM at 1:10 molar ratio of protein:SAM and crystallized using sitting drop vapor diffusion method at 20 °C by mixing 1 µl of the protein solution with 1 µl of the reservoir solution containing 20% PEG 3,350, 0.1 M HEPES, pH 7.5, 0.2 M Li SO4. The crystals were frozen in liquid nitrogen using 10% ethylene glycol as cryo protectant.An initial 2.24 Å diffraction data was collected at the Canadian Light Source (CLS) beamline CMCF 08ID-1 at the Zn absorption edge and used to solve the structure of EZH2 by the single-wavelength anomalous dispersion (SAD) phasing method. A second data set (2.0 Å) collected at beam line 19ID of Advanced Photon Source (APS), Argonne National Laboratory was used to further refine of the structure. All data sets collected at a temperature of 100K and were processed using the HKL-3000 suite . REFMAC 3H-labeled methyl group into a peptide corresponding to residues 21 to 44 of histone H3 [H3(21-44)] using Scintillation Proximity Assay (SPA). The enzymatic reactions were conducted in triplicate at 23 °C with 0.5 hour incubation of 20 µl reaction mixture in 20 mM Tris-HCl, pH 8.0 , 8 µM cold-SAM, 2 µM H3(21-44) peptide) and various concentrations of enzymes. Enzymatic reactions were stopped by adding 7.5 M Guanidine hydrochloride followed by 180 µl of buffer , mixing and then transferring to a 96-well FlashPlate . After mixing, the reaction mixtures in Flash-plates were incubated for 2 hour and the CPM were measured using Topcount plate reader . Methyltransferase activity assays for trimeric EZH2 complex (EZH2-EED-SUZ12) and EZH2 (520-746) were performed by monitoring the incorporation of The kinetic parameters for trimeric EZH2 complex were determined using 20 nM of EZH2 at a fixed concentration of peptide (5 µM) and varying concentrations of SAM (up to 20 μM), or at a fixed concentration of SAM (10 µM) and varying concentrations of peptide (up to 5 μM) and therefore kinetic parameters are considered apparent values at the above mentioned conditions. Assays were performed in 20 mM Tris-HCl pH 8, 0.01% Triton X-100, 5 mM DTT. The reaction mixtures were incubated for 30 min at 23 °C. To stop the reactions, 7.5 M Guanidine hydrochloride was added and mixed. A total of 10 µl of the reaction mixture was spotted onto SAM2® Biotin Capture Membrane  and placed at room temperature for 5 minutes. Membranes were washed with 2 M NaCl and deionized water at least twice each and were dried. Scintillation liquid was added and counts per minute (CPM) were measured. Experiments were performed in triplicate. Purified EZH2 (520-746) was dialyzed for binding assay with SAM and peptide in 50 mM Tris-HCl buffer, pH 8.0 and 250 or 500 mM NaCl respectively. Solutions of 1 mM SAM or 0.62 mM histone peptide H3 (residues 21-44) in dialysis buffer were injected into the sample cell containing approximately 1.4 ml of 0.05 mM protein solution. ITC titrations were performed on a VP-ITC Micro Calorimeter from GE Healthcare at 25 °C by using 10 μl injections with a total of 25 injections.Analysis of crystallographic structures was conducted with ICM . “Distances” between the two edges of the substrate binding groove were calculated as the distance between the backbone carbonyl of the catalytic tyrosine, at the C-terminal extremity of the SET domain, and the backbone nitrogen of the I-SET domain which forms a conserved hydrogen bond with the backbone carbonyl of the substrate lysine in all available ternary SET domain methyltransferase structures. These tWe crystallized and solved the 2.0 Å structure of the C-terminus of EZH2 , PRDM2 (2QPW) , PRMD4 . PRDMs aTogether, these results indicate structural features underlying suboptimal binding or positioning of both cofactor and substrate in isolated EZH2, which are expected to preclude catalysis. To test whether the specific structural features observed in our structure contribute to lack of substrate or cofactor binding we designed several EZH2 mutations that would be expected to disrupt or ‘relieve’ the apparent inactive conformations of the I-SET, post-SET and CXC domains. We mutated post-SET residue Ser729  to an aspartate to destabilize the observed conformation of the post-SET domain. We also mutated I-SET residue Phe667  into a leucine to remove interactions that stabilize the shifted conformation of the I-SET domain . NeitherFinally, given the recently reported potent SAM-competitive inhibitors of EZH2 ,30,31,32The crystal structure presented here reveals that EZH2 adopts a canonical SET domain methyltransferase fold in the absence of binding partners, and that the catalytic site is well formed . We findOur EZH2 structure contributes to the growing evidence for conformational plasticity of the I-SET and post-SET domains of SET domain methyltransferases and hints at potential mechanisms of regulation of catalytic activity. The post-SET domain participates in the formation of both cofactor and substrate binding sites, and is expected to adopt a catalytically competent conformation only when bound to both SAM and peptide Click here for additional data file.Figure S2EZH2, EHMT1 and vSET catalytic sites. The catalytic site of EZH2 is structurally closer to that of the human H3K9 dimethylase EHMT1 than the viral H3K27 trimethylase vSET .(TIF)Click here for additional data file.Figure S3Electrostatic potential. The expected location of the substrate peptide binding site of EZH2, at the interface of the post-SET (blue) and I-SET (cyan) domains, is electronegative. Bottom: electrostatic potential color coding. Blue: electropositive; red: electronegative.(TIF)Click here for additional data file.Figure S4Post-SET domain conformation. The post-SET domain (blue) of cofactor-bound SET domain methyltransferases is structurally diverse but always participates in the formation of the cofactor site. In the EZH2 structure, it projects away from its expected position and the cofactor is absent. When present, cofactor is shown as CPK and substrate is in green.(TIF)Click here for additional data file.Figure S5EZH2’s secondary pocket. A mesh representation of EZH2 (color-coding as in other figures) with the cofactor of a superimposed EHMT1/GLP structure (conserved hydrogen-bonds are highlighted), reveals the existence of a secondary pocket, juxtaposed to the cofactor site.(TIF)Click here for additional data file.Figure S6EZH2’s dimeric state in solution. EZH2 elutes both as a monomer and dimer out of a gel filtration column.(TIF)Click here for additional data file.Figure S7Interactions between the post-SET and I-SET domains. The altered orientation of the post-SET domain, resulting in incomplete formation of the cofactor site, is associated with a buried conformation of Ser 729. The shifted orientation of the I-SET domain, resulting in closure of the substrate-binding groove, is stabilized by a hydrogen-bond between the backbone of N668 and Y726, and orthogonal pi-stacking between Phe 667 and Phe 724. Color coding as in other figures.(TIF)Click here for additional data file.Figure S8Post-SET domain in PRDM structures. The post-SET domain in all human PRDM structures (blue) is oriented away from the putative cofactor site, and the cofactor is absent from all these structures. In a mouse PRDM9 structure crystallized in complex with SAH (green sticks), the post-SET domain (green ribbon) is folded on the cofactor. Mesh representation of human PRDM9 where the post-SET domain was truncated. Post-SET domain of human PRDM1 (PDB code 3DAL), PRDM2 (2QPW Wu 20084102), PRMD4 (3DB5), PRDM9 (4IJD), PRDM10 (3IHX), PRDM11 (3RAY), and PRDM12 (3EP0), and mouse PRDM9 (4C1Q).(TIF)Click here for additional data file.Figure S9Conserved, but incomplete folding of the cofactor-binding site. The cofactor site of EZH2 is in a conformational state that is compatible with the formation of 4 out of 6 hydrogen bonds (black) between the SET domain and the cofactor that are conserved across all available structures of cofactor-bound SET-domain methyltransferases. Preserved hydrogen bonds are shown in cyan. Lost hydrogen bonds are shown in magenta. The EZH2 structure (color coding as in other figures) is superimposed with cofactor-bound EHMT1/GLP (beige - PDB code 2RFI). Top-right: same view, with a mesh representation of EZH2, where the EHMT1/GLP ribbon was removed.(TIF)Click here for additional data file.Figure S10Atypical conformations of the I-SET and post-SET domains. Superimposition of the EZH2 structure  with ternary complexes of EHMT1/GLP (PDB code 2RFI), SETD7 (PDB code 1O9S) and SETD8 (PDB code 1ZKK) bound to cofactor  and substrate (no shown) shows that the I-SET domain of EZH2 is shifted towards the post-SET domain, resulting in hydrogen-bonding between Asn 668 and Tyr 726.(TIF)Click here for additional data file.