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The predicted profile for the conformational dynamics of the tyrosine kinase family is shown. Regions highlighted in red correspond to important structural elements involved in protein activation. Credit: CNIO Researchers from the Structural Biology Computational Group of the Spanish National Cancer Research Centre (CNIO), led by Alfonso Valencia, in collaboration with a group headed by Francesco Gervasio at the University College London (UK), have developed the first computational method based on evolutionary principles to predict protein dynamics, which explains the changes in the shape or dimensional structure that they experience in order to interact with other compounds or speed up chemical reactions. The study constitutes a major step forward in the computational study of protein dynamics (i.e. their movement), which is crucial for the design of drugs and for the research on genetic diseases, such as cancer, resulting in higher levels of complexity than allowed by current methods. The results have been published this week in the journal Proceedings of the National Academy of Sciences (PNAS). Proteins are macromolecules that are key to the thousands of cellular functions that take place in a living organism. They are formed by chains of smaller molecules called amino acids that fold forming a three-dimensional structure. It has recently been discovered that by studying the co-evolution of amino acids we can reconstruct the form or structure of these biological compounds in their natural surroundings. "A protein's amino acids can co-evolve, i.e. change in a coordinated way," says Alfonso Valencia. "By analysing the sequences of a given family of proteins, we can predict physical contacts between amino acids with great precision, in sufficient number to reconstruct the folding of a protein accurately and, therefore, its structure or form." However, this structure does not remain static; it goes through changes in such a way that, similar to a dance in which each of the dancers adapts to their partner, it interacts with other biological compounds or with drugs. This is known as protein dynamics, the study of which has proven to be very difficult both with experimental observations and using computational tools. The question addressed by the researchers at the beginning of the study, when Francesco Gervasio headed the Computational Biophysics Group at the CNIO, was more complex: can we use co-evolutionary studies to predict changes in the shape of proteins and, consequently, the language they establish with their environment? "We developed a model in which the amino acids that have a strong co-evolutionary relationship attracted each other, without further additional data," says Simone Marsili, researcher who has also participated in the project. "First, we simulate the folding process and then we can see how the simulations were able to predict the changes in shape of the proteins at different levels of complexity, including those required for kinases to function [these are key proteins in metabolic and cell signalling processes as well as in cell transport, amongst others]." This new computational method easily integrates experimental and genomic data through the use of the latest sequence analysis and 3D modelling technology. In addition, it demonstrates that genomic data can be a source of useful information to supplement the current tools used to study the structure and dynamics of proteins. "The ability to predict key features of proteins at this level of complexity will help to understand how the sequence of a protein determines its dynamics and, therefore, its functions," concludes Valencia. This field of knowledge is key to the study of genetic diseases, such as cancer, or the design of drugs, amongst other uses. More information: Ludovico Sutto et al. From residue coevolution to protein conformational ensembles and functional dynamics, Proceedings of the National Academy of Sciences (2015). DOI: 10.1073/pnas.1508584112


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The protein, p27, is among the estimated one-third of human proteins called intrinsically disordered proteins (IDPs) that do not spontaneously fold into specific 3-D shapes. p27 helps to regulate cell division; reduced levels of the protein are associated with the spread of breast and other cancers. This study, however, was sparked by evidence of the possible benefits of inhibiting p27, particularly to aid regeneration of sensory hair cells of the inner ear to combat hearing loss. The results also raise broader hopes regarding drug development targeting disordered proteins. Disordered proteins are implicated in a wide range of diseases, including diabetes and neurodegenerative disorders, but so far drug-development efforts have failed. Most drugs work by binding to proteins' stable 3-D shape, which disordered proteins lack. The p27 protein works by binding to an enzyme and forming a complex that blocks cell division. This type of regulation is necessary to keep cells in check when not otherwise instructed to divide. In this study St. Jude researchers used NMR spectroscopy to identify 36 small molecules that bind to two different but partially overlapping regions of p27 where the protein binds to the enzyme. NMR spectroscopy uses magnetic properties of atoms to discover structural details of different molecules and especially how they interact with one another. Most drugs are small molecules. One of the small molecules in this study inhibited p27 function in biochemical assays, demonstrating in principle that small molecules can disrupt and possibly regulate function of disordered proteins. "The thought had been that small molecules would not bind specifically to disordered proteins," said co-corresponding author Richard Kriwacki, Ph.D., a member of the Department of Structural Biology. "This study demonstrates that small molecules identified by screening a library of compounds not only bind to a disordered protein, but sequester and inhibit the protein's activity." Scientists have begun work to engineer a compound that forms a stronger bond and encompasses the p27-enzyme binding site. "The concept of p27 inhibition as a possible strategy for hair cell regeneration has been around for more than 15 years, but until now no one has been able to do it," said co-corresponding author Jian Zuo, Ph.D., a member of the St. Jude Department of Developmental Neurobiology, who studies hair cell regeneration. "I knew Richard was an authority on intrinsically disordered proteins like p27 so I approached him; and he came up with the innovative, some would say crazy, idea of screening small molecules for inhibition of p27." Hair cells in the inner ear convert sound vibrations into electrical signals that travel to the brain via the auditory nerve. In chickens, fish and amphibians, hair cells regenerate from the surrounding cells called supporting cells, but human hair cells lost to injury, disease or age do not. Such damage is a leading cause of hearing loss. Laboratory experiments have shown that supporting cells from mice can be coaxed into becoming hair cells in part by blocking production of p27. Using NMR spectroscopy, researchers screened a library of small molecules for evidence of p27 binding that may disrupt protein function. Investigators were specially looking for small molecules that bind p27 where the protein normally binds its enzyme partner and blocks cell division. First author Luigi Iconaru, Ph.D., a St. Jude postdoctoral fellow, led the effort. Co-author Anang Shelat, Ph.D., assistant member of the St. Jude Department of Chemical Biology and Therapeutics, developed the small-molecule library using molecules that were slightly larger and more complex than traditionally used for drug- development screening. Surprisingly, researchers found two distinct groups of small molecules that bind distinct, but overlapping segments of p27. The small molecules provided insight into how disordered proteins bind, including the dynamic interaction between small molecules and short-lived binding sites created by different arrangements of the amino acids that make up p27. "The next step is to link the small molecules and binding sites identified in this study together to form larger compounds that bind p27 at multiple sites with greater affinity and specificity," Zuo said. "While small-molecule compounds are still a long way from the clinic, these results are another small step on the long road to a drug for hearing loss that could be infused into the cochlea to generate new hair cells." More information: Luigi I. Iconaru et al. Discovery of Small Molecules that Inhibit the Disordered Protein, p27Kip1, Scientific Reports (2015). DOI: 10.1038/srep15686


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No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. Human HOIP and HOIL-1L cDNA were purchased from Open Biosystems (cloneIDs 4653017 and 3877587, respectively). HOIP RBR (residues 696–1,072), HOIP RING2L (residues 853–1,072) and full-length HOIL-1L were cloned into the pET-NKI-6xHis-3C-LIC vector30 coding for an N-terminal 6×His tag with a 3C protease cleavage site. HOIP UBA–RBR (residues 475–1,072) was cloned into a pET-NKI-6×His-eGFP-3C-LIC vector that codes for a 3C-cleavable His-tagged enhanced green fluorescent protein (eGFP) followed by the HOIP sequence. Human UbcH5B and Cdc34 DNA were a gift from M. Petroski. Coding sequences for UbcH13 and Uev1a were extracted out of a human cDNA library (Agilent Megaman). For crystallization, UbcH5B (residues 2–147) with the mutations S22R (to prevent backside ubiquitin binding31) and C85K (to enable covalent ubiquitin linkage21) was cloned into the pET-NKI-6×His-3C-LIC vector. UbcH5B without S22R and C85K mutations (used for enzymatic assays), Cdc34, UbcH13 and Uev1a were cloned into the same vector. Untagged mono-ubiquitin with native N and C termini, used for crystallization and linear ubiquitination assays, was cloned into the pET29 vector (Novagen) using NdeI/XhoI restriction sites. N-terminally blocked mono-ubiquitin used for thioester assays was cloned in the pET-NKI-6×His-3C-LIC vector. Untagged linear di-ubiquitin was cloned with overlap extension PCR and ligated into the pET29 vector (Novagen) using NdeI/XhoI restriction sites. N- and C-terminally blocked di-ubiquitin with a N-terminal His tag and a C-terminal Ala–Ser sequence was cloned into the pET-NKI-6×His-3C-LIC vector. Human ubiquitin-activating enzyme E1 (Ube1) was cloned into a pET28 vector resulting in an N-terminal His tag. For NF-κB assays full-length HOIP with an N-terminal Flag tag and HOIL-1L with an N-terminal myc tag were cloned into pcDNA3.1(+) (Invitrogen) using EcoRI/NotI restriction sites. Mutations in UbcH5B, ubiquitin and HOIP were introduced using standard site-directed mutagenesis techniques. All proteins were expressed in BL21(DE3) E. coli after induction with 0.5 mM IPTG overnight at 20 °C. For expression of HOIP and HOIL-1L constructs, 0.5 mM ZnCl was added to the cultures before induction. Bacteria were harvested by centrifugation, lysed by addition of lysozyme and sonication in the presence of protease inhibitors (PMSF and leupeptin) and DNase. Lysates were cleared by centrifugation and His-tagged proteins were initially purified using Ni-NTA agarose (Qiagen). For HOIP RBR used for crystallization, and UbcH5B, Cdc34, UbcH13, Uev1a, wild-type ubiquitin to generate K48-linked di-ubiquitin and HOIL-1L His tags were removed by addition of 3C protease overnight at 4 °C. HOIP RBR and HOIL-1L were further purified using Superdex 200 10/300 GL or HiLoad 16/600 Superdex 200 pg size-exclusion chromatography columns (GE Healthcare) equilibrated in protein buffer (10 mM HEPES pH 7.9, 100 mM NaCl). UbcH5B used for biochemical assays was further purified on a Superdex 75 10/300 GL size-exclusion chromatography column (GE Healthcare) equilibrated in protein buffer. HOIP mutants for activity assays, and Cdc34, UbcH13 and Uev1a were desalted into protein buffer directly after Ni-NTA purification using PD MidiTrap G-25 desalting columns (GE Healthcare). Ube1 for biochemical assays was further purified using ion-exchange chromatography (Source Q) in 10 mM HEPES pH 7.9, 10 mM NaCl and eluted with a gradient from 10–500 mM NaCl. N-terminally His-tagged (di-)ubiquitin was purified using Ni-NTA as described above followed by size-exclusion chromatography using a Superdex 75 10/300 GL column (GE Healthcare) equilibrated in protein buffer or buffer exchange into protein buffer using PD MidiTrap G-25 desalting columns. To purify untagged mono- or di-ubiquitin, 0.5 mM EDTA and 100 mM sodium acetate pH 4.5 were added to the bacterial lysates and lysates were cleared by centrifugation, diluted sevenfold with 50 mM sodium acetate pH 4.5 and applied to a Source S 10/100 ion exchange column (GE Healthcare) equilibrated in 50 mM sodium acetate pH 4.5. Ubiquitin was eluted with a 0–500 mM NaCl gradient and further purified by size-exclusion chromatography on a Superdex 75 10/300 GL column (GE Healthcare) equilibrated in protein buffer. His-eGFP-HOIP was purified using size-exclusion chromatography as described for HOIP RBR, followed by 3C cleavage and removal of His-eGFP via a second round of size-exclusion chromatography. All proteins were generally flash frozen in liquid nitrogen in small aliquots and stored at −80 °C. UbcH5B~ubiquitin linkage was performed based on published methods21. Briefly, Ube1, UbcH5B(S22R/C85K) and ubiquitin were mixed and buffer exchanged into 50 mM Tris pH 10, 150 mM NaCl using PD-10 desalting columns (GE Healthcare). 10 mM MgCl , 5 mM ATP and 1 mM TCEP were added and the protein solution was incubated at 37 °C for 16 h. The completeness of the reaction was monitored using SDS–PAGE and covalently linked UbcH5B~ubiquitin was purified from unreacted proteins and Ube1 using a Superdex 75 10/300 GL size-exclusion chromatography column (GE Healthcare) equilibrated in protein buffer. HOIP RBR was mixed with a 1.3-fold molar excess of UbcH5B~ubiquitin and applied to a Superdex 200 10/300 GL size-exclusion chromatography column equilibrated in protein buffer. Complex formation and purity was confirmed using SDS–PAGE, and complex containing fractions were pooled and concentrated to ~12 mg ml−1 for crystallization. Crystallization was performed using the vapour diffusion technique in sitting drop MRC 96-well plates (Molecular Dimensions). Initial crystals were obtained mixing HOIP/UbcH5B~ubiquitin complex solution with an equimolar amount of free ubiquitin in the Morpheus Screen (Molecular Dimensions). Subsequently, 2 μl of the protein complex were mixed with 0.6 μl reservoir solution (0.1 M Morpheus Buffer 3 pH 8.5 (Tris/Bicine), 0.12 M Morpheus Alcohols Mix (0.02 M each of 1,6-hexanediol; 1-butanol; 1,2-propanediol (racemic); 2-propanol; 1,4-butanediol; 1,3-propanediol), 30% Morpheus P550MME_P20K mix (20% PEG550MME, 10% PEG20K) and 8% glycerol) in MRC 48-well plates (Molecular Dimensions). Crystals appeared after about one week at 12 °C and were cryo-cooled, and evaluated on a rotating anode X-ray generator (Rigaku FR-E superbright). Seeding and dehydration of the crystals was performed to improve crystal diffraction. For successful dehydration, reservoir was slowly added to the protein drop (3 × 0.5 μl within ~2 h) and subsequently equilibrated overnight at 12 °C against a reservoir solution with increased P550MME_P20K concentration by adding 11 μl 60% Morpheus P550MME_P20K stock solution to 50 μl reservoir solution. The new reservoir solution was then slowly added to the protein drop (3 × 0.5 μl, followed by 2 × 1 μl with removal of 1 μl each in the last steps). After further overnight equilibration, crystals were harvested from the drop and directly cryo-cooled in a cryogenic nitrogen stream at 100 K. Crystals diffracted in-house to 4–6 Å. Complete diffraction data were measured at 100 K at beamline 23ID-D of the General Medical Sciences and Cancer Institutes Structural Biology Facility at the Advanced Photon Source (GM/CA @ APS), Argonne National Laboratory. Despite their size (common dimensions of ~200 × 140 × 100 μm3) crystals exhibited substantial inhomogeneity resulting in split and smeared diffraction spots. Using raster scans32, a suitable region for data collection could be identified at the edge of the crystal. Using a small (20 μm diameter) beam, split spots could be separated to allow reliable indexing and integration. Utilization of a small beam necessitated higher flux to retain reliable diffraction. To mitigate the radiation damage, the total dose was distributed over a 100-μm stretch of the crystal by using the ‘helical’ mode of ‘vector’ data collection as implemented in JBluIce33. Data were measured at 1.282 Å wavelength with a Pilatus3 6M pixel array detector with a 1-mm-thick sensor (Dectris). Data were collected from a single crystal and indexed, integrated and scaled in XDS/XSCALE34. Data were further processed using AIMLESS35 from the CCP4 suite36 with a resolution cut-off of 3.48 Å, resulting in an and CC1/2 = 0.648 in the highest resolution shell. Phasing was carried out in Phaser37 using an MR-SAD protocol as implemented in PHENIX38. For this, independent molecular replacement searches were initially performed for the RING2L domain of HOIP (from PDB: 4LJP (ref. 14)), UbcH5B (from PDB: 3A33 (ref. 39)), and ubiquitin (from PDB: 4LJP (ref. 14)) with the four C-terminal residues deleted. Various ambiguous solutions were identified that could not be separated, and Zn2+ sites could not be identified using MR-SAD due to incompleteness of resultant models. However, manual inspection revealed that some MR solutions contained ubiquitin oriented near identically to the symmetry-related donor ubiquitin observed in the HOIP RING2L/ubiquitin-ubiquitin transfer complex (PDB: 4LJP (ref. 14)). Based on this observation, a trimmed search model was created that contained a complex of the core of HOIP RING2L (with residues 906–924 and 949–999 removed) and C-terminally truncated ubiquitin. An MR search using this model found a single solution for two copies of the complex. After successful iterative searches for two UbcH5B molecules and two further ubiquitin molecules, MR-SAD using Phaser identified 15 distinct Zn2+ sites including the known Zn2+ sites in the RING2L domain of HOIP. Further molecular replacement in Phaser using a single unit of the initial HOIP RING2L/UbcH5B~ubiquitin complex (without the additional second ubiquitin), and the NMR structure of HOIP IBR (zinc atoms removed, deposited in Protein Data Bank40 under PDB accession number 2CT7, unpublished) correctly placed a single HOIP IBR domain, which was then manually copied to the other NCS-related HOIP in the asymmetric unit. For molecular replacement of the HOIP RING1, Sculptor41 was used to generate a search model based on the structure of the RING1 domain of HHARI (PDB: 4KBL (ref. 11)). However, Phaser was not able to correctly place this domain, probably owing to the low sequence conservation of only 27% identity. However, since mutational analysis of HOIP suggested that the RING/E2 interaction is preserved between RING-type E3 ligases and RBR-type E3 ligases5, we overlaid the E2 of the published RNF4–RING/UbcH5A~ubiquitin structure (PDB: 4AP4 (ref. 21)) with the E2 in our structure and then used this overlay to add the RING1 model generated by Sculptor. This overlay placed the HOIP RING1 Zn2+-coordinating residues near the last remaining free Zn2+ ions found earlier by Phaser MR-SAD, indicating correct placement of the RING1 domain. In the final round of molecular replacement, the two additional ubiquitin (Ub ) molecules were reinstated at the RING1–IBR interface. At this stage, Refmac42 was used for refinement using settings optimized for low-resolution refinement43 including ‘jelly body refinement’ and Babinet scaling. ProSMART44 was used to generate external restraints against high-resolution structures (PDB: 4LJO (ref. 14) for HOIP RING2L and ubiquitin, and PDB: 2ESK (ref. 45) for UbcH5B). After this, clear extra electron density became visible for the unmodelled helical linker regions of the RING1–IBR and IBR–RING2L transitions and for other regions omitted in the initial search models. Further model building and refinement was manually performed in Coot46 and Refmac. During refinement additional clear positive difference map electron density became visible and Phaser was used to place one additional UbcH5B molecule (UbcH5B ) into this density. TLS restraints were generated using the TLSMD server47 and NCS restraints were used throughout refinement. One overall B-factor was refined in Refmac. In later rounds of refinement the PDB_REDO server48 was used for refinement optimization and MolProbity49 was used for structure validation. Data processing and refinement statistics are summarized in Extended Data Fig. 2b. Ramachandran statistics were calculated using MolProbity and 94.8% of all residues are in favoured regions, 4.9% in allowed regions and 0.3% are outliers. The final structure has a MolProbity score of 1.75 (100th percentile). In the final structure the two HOIP RBR molecules (see also Extended Data Fig. 3) are defined by electron density from residues 699 to 707, 711 to 948, 969 to 991, and 996 to 1,011 (chain A) and 699 to 754, 760 to 957, 967 to 1,015, 1,019 to 1,035 and 1,054 to 1,066 (chain B). The catalytic UbcH5B~ubiquitin conjugates are defined from UbcH5B residues 3 to 147 and ubiquitin residues 1 to 76 (chains C and E), and UbcH5B residues 2 to 147 and ubiquitin residues 1 to 76 (chains D and F). The allosteric ubiquitin chains (chains G and H) are defined from residues 1 to 76 and the additional UbcH5B (chain I) is defined from residues 2 to 146. PHENIX was used to calculate simulated annealing (SA) composite omit maps and feature enhanced maps (FEM). All molecular figures were prepared in PyMOL (Schrödinger, LLC). K48-linked and K63-linked ubiquitin chains were formed through a linkage-specific enzymatic reaction using Cdc34 and UbcH13/Uev1a E2 ubiquitin-conjugating enzymes, respectively, as described in the literature50. Ubiquitin chains were separated using ion-exchange chromatography as described above for purification of mono-ubiquitin. Purified K48-linked di-ubiquitin was directly desalted into protein buffer using PD-10 desalting columns, whereas K63-linked di-ubiquitin was further purified on a Superdex 75 10/300 GL size-exclusion chromatography column equilibrated in protein buffer. Native ubiquitin without additional residues was used to generate di-ubiquitin chains for ITC experiments, whereas N-terminally blocked ubiquitin was used to form K48-linked di-ubiquitin for testing allosteric activation of HOIP RBR. Linear ubiquitin formation assays were performed in 50 mM HEPES pH 7.9, 100 mM NaCl, 10 mM MgCl and 0.6 mM DTT using 200 nM E1, 1 μM UbcH5B, 1 μM HOIP RBR or HOIP RING2L and 40 μM untagged ubiquitin. Reactions were started by addition of 10 mM ATP and were incubated at 30 °C for 2 h. Samples were taken at the indicated time points and treated with 50 mM sodium acetate pH 4.5 as described previously6, mixed with SDS sample buffer and analysed by SDS–PAGE using 12% Bolt Bis-Tris gels (Life Technologies). Proteins were visualized with Coomassie Brilliant blue dye. To test the activating effect of linear di-ubiquitin on auto-inhibited HOIP UBA–RBR, 5 μM HOIP UBA–RBR was pre-incubated with N- and C-terminally blocked linear di-ubiquitin or HOIL-1L at the indicated concentrations before addition of the remaining assay components. Samples were taken after 60 min and subsequently treated as described above. To monitor HOIP~ubiquitin thioester ubiquitin transfer from UbcH5B to HOIP, Ube1 (100 nM), UbcH5B (4 μM) and N-terminally blocked ubiquitin (32 μM) were mixed in 50 mM HEPES pH 7.9, 100 mM NaCl, 10 mM MgCl and 5 mM ATP and incubated at 25 °C for 5 min when 2 μM HOIP RBR was added. Samples were taken 10 s after HOIP addition, quenched by addition of pre-heated SDS protein-loading buffer without DTT, and run on a 12% SDS–PAGE gel (Life Technologies). The 10-s time point used was empirically determined with a time-course experiment (Extended Data Fig. 9g). Gels were stained with Coomassie Brilliant blue dye and scanned on a Li-COR Odyssey scanner using the 700 nm (red) channel. For the thioester transfer assay shown in Fig. 3d, 200 nM Ube1, 2 μM UbcH5B, 8 μM HOIP RBR, 8 μM N-terminally blocked ubiquitin and 10 mM ATP were used and samples taken after 30 s. Furthermore, proteins were transferred to a PVDF membrane and ubiquitin was visualized on a LI-COR Odyssey scanner at 800 nm using an anti-ubiquitin antibody (P4D1, Santa Cruz, 1:200 dilution in TBST (50 mM Tris pH 7.4, 150 mM NaCl, 0.05% Tween-20)) followed by an IRDye 800CW secondary antibody (LI-COR, 1:10,000 dilution in TBST). All quantitative experiments shown in graphs were performed in triplicates and band intensities were quantified using the ImageStudio software (LI-COR). HOIP thioester transfer activity was calculated as the fraction of HOIP~ubiquitin to total HOIP for each mutant and normalized against thioester transfer activity of wild-type HOIP. Data were analysed in GraphPad Prism using two-tailed unpaired Student’s t-test or one-way ANOVA followed by Tukey’s post hoc test. To test the allosteric activation of HOIP RBR by linear di-ubiquitin, a modified ubiquitin transfer assay was performed. HOIP RBR was pre-incubated with N- and C-terminally blocked linear di-ubiquitin at the indicated final concentrations for 5 min at 25 °C. At the same time, Ube1, UbcH5B, ubiquitin and ATP were premixed and incubated for 5 min at 25 °C, resulting in fully loaded UbcH5B~ubiquitin. Both mixtures were subsequently mixed together, resulting in final concentrations of 100 nM Ube1, 2 μM UbcH5B, 8 μM N-terminally blocked ubiquitin and 2 μM HOIP RBR in the final buffer described for the standard ubiquitin transfer assay. Samples were taken after 30 s and further treated as described for the standard transfer assay. A 30-s time point was determined to give the best results in this assay, in which lower E2 and mono-ubiquitin concentrations were used, resulting in an overall slower reaction rate. The experiments comparing the effects of linear versus K48-linked di-ubiquitin (Extended Data Fig. 9e) were performed similarly, with the difference that all samples were incubated with apyrase (Sigma) for 5 min to deplete ATP before addition of HOIP/di-ubiquitin and prevent E2-loading of K48-linked di-ubiquitin, which features a free C terminus on one of the ubiquitin units. Sedimentation equilibrium experiments were performed in a ProteomeLab XL-I (Beckman Coulter) analytical ultracentrifuge. HOIP RBR/UbcH5B~ubiquitin as used for crystallization was loaded into a 6-channel equilibrium cell at 5.0, 2.5 and 1.25 μM concentration and centrifuged at 10,000 r.p.m., 20 °C in an An-50 Ti 8-place rotor until equilibrium was achieved. Data were analysed using HeteroAnalysis software (J. L. Cole and J. W. Lary, University of Connecticut; http://www.biotech.uconn.edu/auf/). ITC experiments were performed on an ITC200 calorimeter (Microcal). Aliquots (2 μl each) of 500–650 μM UbcH5B~ubiquitin or di-ubiquitin solution were injected into the cell containing 40–50 μM HOIP RBR or HOIP RBR/di-ubiquitin complexes. The experiments were performed at 23 °C in buffer containing 10 mM HEPES pH 7.9, 100 mM NaCl. For titrations of UbcH5B~ubiquitin into HOIP RBR/di-ubiquitin complexes, HOIP RBR was pre-incubated with an equimolar amount of di-ubiquitin before the ITC experiments. Data were analysed using the Origin software (Microcal). Human embryonic kidney (HEK) 293T cells (ATCC) were co-transfected with NF-κB-luc reporter plasmid that contains an NF-κB response element upstream of the promoter driving the luciferase reporter gene, pGL4.74[hRluc/TK] control vector (Promega) and epitope tagged Flag-HOIP or myc-HOIL-1L pcDNA3.1(+) plasmids in 6-well plates in triplicates using Lipofectamine 2000 transfection reagent. Since this assay could be carried out in a variety of cellular contexts, HEK293T cells were used because they are easy to transfect and suitable for the assay. The cells tested negative for mycoplasma contamination. Empty pcDNA3.1(+) vector was used as control. After 36 h, cells were lysed and 20 μl cell lysates were used to measure firefly luciferase and Renilla luciferase (transfection control) signals using the dual luciferase reporter assay system according to the manufacturer’s protocol (Promega). Data were analysed in GraphPad Prism and one-way ANOVA followed by Tukey’s post hoc tests were used for statistical analysis. Immunoblotting was performed with anti-Flag (clone M2, Sigma-Aldrich) and anti-myc (clone 9E10, Sigma-Aldrich) antibodies, to confirm equivalent wild-type and mutant protein expression levels.


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Enrico Di Cera, M.D., chair of biochemistry and molecular biology at Saint Louis University, is an author on the paper and says that the Structural Biology Grid Consortium has developed a repository, the Structural Biology Data Grid, to deposit, search and download structural biology data sets. In the current study, researchers found that the repository was effective in allowing researchers to reproduce earlier findings, letting work in the field progress. "This is a transformative development in the field," said Di Cera. "Finally, we may take full advantage of the enormous amounts of data being generated by structural biologists." X-ray crystallography, one of the most powerful tools in structural biology, allows researchers to determine the structure of proteins, nucleic acids and other small molecules at atomic level resolution. Understanding a protein's structure opens the door to understanding the molecular basis of diseases and developing new therapeutic strategies of intervention. Crystallographers share their findings in academic journals and currently use standard repositories of processed datasets like the Protein Data Bank. The Structural Biology Data Grid supports archiving of raw experimental datasets using a distribution model of computing clusters. Benefits include rapid access of the original experimental data for general use and validation. With the data collection process becoming increasingly streamlined, archiving through the Structural Biology Data Grid will become mainstream. In order to better leverage the breakthrough findings coming out of laboratories around the world, structural biologists created the Structural Biology Grid Consortium. The consortium's strategies include: curating and supporting a collection of data processing software; managing raw, experimental data sets; establishing a publication system for data sets; and integrating the storage resources of multiple research groups and institutions. In the current study, researchers conducted a pilot study, analyzing data from the repository collection. They found that the repository was effective in allowing researchers to reprocess data from earlier experiments, offering the opportunity to reproduce earlier findings, improve existing models, and catch possible mistakes earlier. "The Grid started as a joint effort of top structural biology labs around the world. We are proud to be part of a great initiative that uses big data for the benefits of the entire scientific community," said Di Cera. More information: Peter A. Meyer et al. Data publication with the structural biology data grid supports live analysis, Nature Communications (2016). DOI: 10.1038/ncomms10882


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ST. LOUIS — A new data sharing consortium is helping scientists more quickly share and benefit from findings in their field. Enrico Di Cera, M.D., chair of biochemistry and molecular biology at Saint Louis University, is an author on a paper recently published in Nature Communications and says that the Structural Biology Grid Consortium has developed a repository, the Structural Biology Data Grid, to deposit, search and download structural biology data sets. In the current study, researchers found that the repository was effective in allowing researchers to reproduce earlier findings, letting work in the field progress. “This is a transformative development in the field,” said Di Cera. “Finally, we may take full advantage of the enormous amounts of data being generated by structural biologists.” X-ray crystallography, one of the most powerful tools in structural biology, allows researchers to determine the structure of proteins, nucleic acids and other small molecules at atomic level resolution. Understanding a protein’s structure opens the door to understanding the molecular basis of diseases and developing new therapeutic strategies of intervention. Crystallographers share their findings in academic journals and currently use standard repositories of processed datasets like the Protein Data Bank. The Structural Biology Data Grid supports archiving of raw experimental datasets using a distribution model of computing clusters. Benefits include rapid access of the original experimental data for general use and validation. With the data collection process becoming increasingly streamlined, archiving through the Structural Biology Data Grid will become mainstream. In order to better leverage the breakthrough findings coming out of laboratories around the world, structural biologists created the Structural Biology Grid Consortium. The consortium’s strategies include: In the current study, researchers conducted a pilot study, analyzing data from the repository collection. They found that the repository was effective in allowing researchers to reprocess data from earlier experiments, offering the opportunity to reproduce earlier findings, improve existing models, and catch possible mistakes earlier. “The Grid started as a joint effort of top structural biology labs around the world. We are proud to be part of a great initiative that uses big data for the benefits of the entire scientific community,” said Di Cera. Enrico Di Cera, M.D., is the Alice A. Doisy professor and chairman of the department of biochemistry and molecular biology at Saint Louis University. He has devoted many years to the study of blood-clotting, a life-saving biological process that prevents excessive bleeding after injury, but which also has the potential to cause harm when triggered in the wrong conditions, as with deep vein thrombosis. Distinguishing himself throughout his career as a biophysicist, biochemist, structural biologist and protein engineer, Di Cera recently succeeded in crystalizing the key coagulation factor prothrombin — a feat that had eluded scientists for four decades. Established in 1836, Saint Louis University School of Medicine has the distinction of awarding the first medical degree west of the Mississippi River. The school educates physicians and biomedical scientists, conducts medical research, and provides health care on a local, national and international level. Research at the school seeks new cures and treatments in five key areas: cancer, liver disease, heart/lung disease, aging and brain disease and infectious diseases.

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