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Background: Neutralization sensitivity of HIV-1 virus to antibodies and anti-sera varies greatly between the isolates. Significant role of V1/V2 domain as a global neutralization sensitivity regulator has been suggested. Recent X-ray structures revealed presence of well-defined tertiary structure within this domain but also demonstrated partial disorder and conformational heterogeneity. Methods: Correlations of neutralization sensitivity with the conformational propensities for beta-strand and alpha-helix formation over the entire folded V1/V2 domain as well as within sliding 5-residue window were investigated. Analysis was based on a set of neutralization data for 106 HIV isolates for which consistent neutralization sensitivity measurements against multiple pools of human immune sera have been previously reported. Results: Significant correlation between beta-sheet formation propensity of the folded segments of V1/V2 domain and neutralization sensitivity was observed. Strongest correlation peaks localized to the beta-strands B and C. Correlation persisted when subsets of HIV isolates belonging to clades B, C and circulating recombinant form BC where analyzed individually or in combinations. Conclusions: Observed correlations suggest that stability of the beta-sheet structure and/or degree of structural disorder in the V1/V2 domain is an important determinant of the global neutralization sensitivity of HIV-1 virus. While specific mechanism is to yet to be investigated, plausible hypothesis is that less ordered V1/V2s may have stronger masking effect on various neutralizing epitopes, perhaps effectively occupying larger volume and thereby occluding antibody access. © 2014 Maxim Totrov.


Ferreira P.A.,Duke University | Orry A.,Molsoft LLC
Journal of Neurogenetics | Year: 2012

Despite remarkable advances in human genetics and other genetic model systems, the fruit fly, Drosophila melanogaster, remains a powerful experimental tool to probe with ease the inner workings of a myriad of biological and pathological processes, even when evolutionary forces impart apparent divergences to some of such processes. The understanding of such evolutionary differences provides mechanistic insights into genotype-phenotype correlations underpinning biological processes across metazoans. The pioneering work developed by the William Pak laboratory for the past four decades, and the work of others, epitomize the notion of how the Drosophila system breaks new fertile ground or complements research fields of high scientific and medical relevance. Among the three major genetic complementation groups produced by the Pak's laboratory and impairing distinct facets of photoreceptor neuronal function, the nina group (ninaA, ...., ninaJ) selectively affects the biogenesis of G protein-coupled receptors (GPCRs), mediating the photoconversion and transduction of light stimuli. Among the nina genes identified, ninaA arguably assumes heightened significance for several reasons. First, it presents unique physiological selectivity toward the biogenesis of a subset of GPCRs, a standalone biological manifestation yet to be discerned for most mammalian homologues of NinaA. Second, NinaA belongs to a family of proteins, immunophilins, which are the primary targets for immunosuppressive drugs at the therapeutic forefront of a multitude of medical conditions. Third, NinaA closest homologue, cyclophilin B (CyPB/PPIB), is an immunophilin whose loss-of-function was found recently to cause osteogenesis imperfecta in the human. This report highlights advances made by studies on some members of immunophilins, the cyclophilins. Finally, it reexamines critically data and dogmas derived from past and recent genetic, structural, biological, and pathological studies on NinaA and few other cyclophilins that support some of such paradigms to be less than definite and advance our understanding of the roles of cyclophilins in cell function, disease, and therapeutic interventions. Copyright © 2012 Informa Healthcare USA, Inc.


Abagyan R.,Molsoft LLC | Bottegoni G.,Italian Institute of Technology
Journal of Chemical Information and Modeling | Year: 2010

The use of multiple X-ray protein structures has been reported to be an efficient alternative for the representation of the binding pocket flexibility needed for accurate small molecules docking. However, the docking performance of the individual single conformations varies widely, and adding certain conformations to an ensemble is even counterproductive. Here we used a very large and diverse benchmark of 1068 X-ray protein conformations of 99 therapeutically relevant proteins, first, to compare the performance of the ensemble and single-conformation docking and, second, to find the properties of the best-performing conformers that can be used to select a smaller set of conformers for ensemble docking. The conformer selection has been validated through retrospective virtual screening experiments aimed at separating known ligand binders from decoys. We found that the conformers cocrystallized with the largest ligands displayed high selectivity for binders, and when combined in ensembles they consistently provided better results than randomly chosen protein conformations. The use of ensembles encompassing between 3 and 5 experimental conformations consistently improved the docking accuracy and binders vs decoys separation. © 2010 American Chemical Society.


Totrov M.,Molsoft LLC
Methods in Molecular Biology | Year: 2012

Loop modeling is crucial for high-quality homology model construction outside conserved secondary structure elements. Dozens of loop modeling protocols involving a range of database and ab initio search algorithms and a variety of scoring functions have been proposed. Knowledge-based loop modeling methods are very fast and some can successfully and reliably predict loops up to about eight residues long. Several recent ab initio loop simulation methods can be used to construct accurate models of loops up to 12-13 residues long, albeit at a substantial computational cost. Major current challenges are the simulations of loops longer than 12-13 residues, the modeling of multiple interacting flexible loops, and the sensitivity of the loop predictions to the accuracy of the loop environment. © 2011 Springer Science+Business Media,LLC.


Arnautova Y.A.,Molsoft LLC | Abagyan R.A.,Molsoft LLC | Totrov M.,Molsoft LLC
Proteins: Structure, Function and Bioinformatics | Year: 2011

We report the development of internal coordinate mechanics force field (ICMFF), new force field parameterized using a combination of experimental data for crystals of small molecules and quantum mechanics calculations. The main features of ICMFF include: (a) parameterization for the dielectric constant relevant to the condensed state (ε = 2) instead of vacuum, (b) an improved description of hydrogen-bond interactions using duplicate sets of van der Waals parameters for heavy atom-hydrogen interactions, and (c) improved backbone covalent geometry and energetics achieved using novel backbone torsional potentials and inclusion of the bond angles at the C α atoms into the internal variable set. The performance of ICMFF was evaluated through loop modeling simulations for 4-13 residue loops. ICMFF was combined with a solvent-accessible surface area solvation model optimized using a large set of loop decoys. Conformational sampling was carried out using the biased probability Monte Carlo method. Average/median backbone root-mean-square deviations of the lowest energy conformations from the native structures were 0.25/0.21 Å for four residues loops, 0.84/0.46 Å for eight residue loops, and 1.16/0.73 Å for 12 residue loops. To our knowledge, these results are significantly better than or comparable with those reported to date for any loop modeling method that does not take crystal packing into account. Moreover, the accuracy of our method is on par with the best previously reported results obtained considering the crystal environment. We attribute this success to the high accuracy of the new ICM force field achieved by meticulous parameterization, to the optimized solvent model, and the efficiency of the search method. © 2010 Wiley-Liss, Inc.


A new binding site comparison algorithm using optimal superposition of the continuous pharmacophoric property distributions is reported. The method demonstrates high sensitivity in discovering both, distantly homologous and convergent binding sites. Good quality of superposition is also observed on multiple examples. Using the new approach, a measure of site similarity is derived and applied to clustering of ligand binding pockets in PDB. © 2011 Totrov; licensee BioMed Central Ltd.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 399.25K | Year: 2010

DESCRIPTION (provided by applicant): We propose to develop several essential components that would allow comprehensive modeling of glycoproteins in ICM: a carbohydrate extension of the ECEPP05 force-field, a library of common glycosylating moieties, tools for glycan construction on protein structures, and simulations of their stochastic dynamics and docking to glycan-binding proteins. Together these components will comprise the Glycosylation Modeling System. Carbohydrate moieties on the proteins (glycans) play an essential role in a range of physiological processes, including viral evasion from the immune system, blood anticoagulation, the progression of cancer, cell-cell recognition, and the correct folding and processing of proteins. Understanding of their function on the level of three-dimensional structure has been lagging behind that of the regular polypeptides. Flexibility, disorder and heterogeneity of glycans make experimental structure determination difficult. Molecular mechanics techniques increasingly allow in-silico simulation of essential biological processes at the atomic level. Molsoft's ICM (Internal Coordinate Mechanics) software platform is a particularly efficient modeling tool because of its use of internal variables, rather than traditional Cartesian coordinates, in the description of the molecular structure. This proposed development will allow to apply this approach to modeling of carbohydrates. Equipping structural biologists with the Glycoprotein Modeling System in ICM will help bring molecular simulations and structure predictions for glycans to the next level of accuracy. Successful structure modeling will ultimately help improve our understanding of molecular mechanisms underlying disease and help to accelerate structure-based drug discovery efforts. PUBLIC HEALTH RELEVANCE: Carbohydrate moieties on the proteins (glycans) play an essential role in a range of physiological processes, including viral evasion from the immune system, blood anticoagulation, the progression of cancer, cell-cell recognition, and the correct folding and processing of proteins. Equipping structural biologists with the Glycoprotein Modeling System in ICM will help bring molecular simulations and structure predictions for glycans to the next level of accuracy. Successful structure modeling will ultimately help improve our understanding of molecular mechanisms underlying disease and help accelerate structure-based drug discovery efforts.


The present invention is directed to a recombinant immunogenic polypeptide. The polypeptide includes a loop peptide inserted into an immunogenic scaffold protein. The loop polypeptide has an amino acid sequence which presents the 3074 mAb- or the 2219/2557 mAb-targeted epitope of the HIV gp120 protein and not other known epitopes of the HIV gp120 protein. When used as an immunogen, the polypeptide induces an antibody response which neutralizes heterologous HIV-1 viruses in a pattern similar to that observed for the 3074 mAb- or the 2219/2557 mAb-targeted epitope, respectively. Pharmaceutical compositions containing the immunogenic polypeptide as well as methods of making and using it are also disclosed.


A structured document file which includes graphical and non graphical information. The graphical information includes the vector and coordinate description of a representation of at least one three dimensional object and at least one of the following group of view attributes or features: rotation, translation, view angle, lighting, colors, specific graphical representations, labels and parameters. The vector and coordinate description are stored with or independently from the one or more view attributes or features and the vector and coordinate description and the one or more view attributes are stored in a single file which can be accessed and viewed by a structured document viewer. Related authoring tools and viewers are also disclosed.


News Article | January 13, 2016
Site: www.nature.com

No statistical methods were used to predetermine sample size. The investigators were not blinded to allocation during experiments and outcome assessment. A constitutively stabilized mutant of HIF2α (HIF2α-TM) was obtained from Christina Warnecke20. The HIF2α-TM (triple mutant) construct harbours the following mutations in the prolyl and asparagyl hydroxylation sites: P405A, P530G and N851A. Polypeptide fragments of DYRK1B were cloned into pcDNA3-HA and include DYRK1B N terminus, N-Ter (amino acids 1–110), DYRK1B kinase domain, KD (amino acids 111–431), and DYRK1B C terminus, C-Ter (amino acids 432–629). cDNAs for RBX1, Elongin B and Elongin C were kindly provided from Michele Pagano (New York University) and cloned into the pcDNA vector by PCR. HA-tagged HIF1α and HIF2α were obtained from Addgene. GFP-tagged DYRK1A and DYRK1B were cloned into pcDNA vector. pcDNA-HA-VHL was provided by Kook Hwan Kim (Sungkyunkwan University School of Medicine, Korea). Site-directed mutagenesis was performed using QuickChange or QuickChange Multi Site-Directed mutagenesis kit (Agilent) and resulting plasmids were sequence verified. Lentivirus was generated by co-transfection of the lentiviral vectors with pCMV-ΔR8.1 and pMD2.G plasmids into HEK293T cells as previously described9, 42. ShRNA sequences are: ID2-1: GCCTACTGAATGCTGTGTATACTCGAGTATACACAGCATTCAGTAGGC; ID2-2: CCCACTATTGTCAGCCTGCATCTCGAGATGCAGGCTGACAATAGTGGG; DYRK1A: CAGGTTGTAAAGGCATATGATCTCGAGATCATATGCCTTTACAACCTG; DYRK1B: GACCTACAAGCACATCAATGACTCGAGTCATTGATGTGCTTGTAGGTC. IMR-32 (ATCC CCL-127), SK-N-SH (ATCC HTB-11), U87 (ATCC HTB-14), NCI-H1299 (ATCC CRL-5803), HRT18 (ATCC CCL-244), and HEK293T (ATCC CRL-11268) cell lines were acquired through American Type Culture Collection. U251 (Sigma, catalogue number 09063001) cell line was obtained through Sigma. Cell lines were cultured in DMEM supplemented with 10% fetal bovine serum (FBS, Sigma). Cells were routinely tested for mycoplasma contamination using Mycoplasma Plus PCR Primer Set (Agilent, Santa Clara, CA) and were found to be negative. Cells were transfected with Lipofectamine 2000 (Invitrogen) or calcium phosphate. Mouse NSCs were grown in Neurocult medium (StemCell Technologies) containing 1× proliferation supplements (StemCell Technologies), and recombinant FGF-2 and EGF (20 ng ml−1 each; Peprotech). GBM-derived glioma stem cells were obtained by de-identified brain tumour specimens from excess material collected for clinical purposes at New York Presbyterian-Columbia University Medical Center. Donors (patients diagnosed with glioblastoma) were anonymous. Progressive numbers were used to label specimens coded in order to preserve the confidentiality of the subjects. Work with these materials was designated as IRB exempt under paragraph 4 and it is covered under IRB protocol #IRB-AAAI7305. GBM-derived GSCs were grown in DMEM:F12 containing 1× N2 and B27 supplements (Invitrogen) and human recombinant FGF-2 and EGF (20 ng ml−1 each; Peprotech). Cells at passage (P) 4 were transduced using lentiviral particle in medium containing 4 μg ml−1 of polybrene (Sigma). Cells were cultured in hypoxic chamber with 1% O (O Control Glove Box, Coy Laboratory Products, MI) for the indicated times or treated with a final concentration of 100–300 μM CoCl (Sigma) as specified in figure legends. Mouse neurosphere assay was performed by plating 2,000 cells in 35 mm dishes in collagen containing NSC medium to ensure that distinct colonies were derived from single cells and therefore clonal in origin43. We determined neurosphere formation over serial clonal passages in limiting dilution semi-solid cultures and the cell expansion rate over passages, which is considered a direct indication of self-renewing symmetric cell divisions44. For serial sub-culturing we mechanically dissociated neurospheres into single cells in bulk and re-cultured them under the same conditions for six passages. The number of spheres was scored after 14 days. Only colonies >100 μm in diameter were counted as spheres. Neurosphere size was determined by measuring the diameters of individual neurospheres under light microscopy. Data are presented as percent of neurospheres obtained at each passage (number of neurospheres scored/number of NSCs plated × 100) in three independent experiments. P value was calculated using a multiple t-test with Holm–Sidak correction for multiple comparisons. To determine the expansion rate, we plated 10,000 cells from 3 independent P1 clonal assays in 35 mm dishes and scored the number of viable cells after 7 days by Trypan Blue exclusion. Expansion rate of NSCs was determined using a linear regression model and difference in the slopes (P value) was determined by the analysis of covariance (ANCOVA) using Prism 6.0 (GraphPad). Limiting dilution assay (LDA) for human GSCs was performed as described previously45. Briefly, spheres were dissociated into single cells and plated into 96-well plates in 0.2 ml of medium containing growth factors at increasing densities (1–100 cells per well) in triplicate. Cultures were left undisturbed for 14 days, and then the percent of wells not containing spheres for each cell dilution was calculated and plotted against the number of cells per well. Linear regression lines were plotted, and we estimated the minimal frequency of glioma cells endowed with stem cell capacity (the number of cells required to generate at least one sphere in every well = the stem cell frequency) based on the Poisson distribution and the intersection at the 37% level using Prism 6.0 software. Data represent the means of three independent experiments performed in different days for the evaluation of the effects of ID2, ID2(T27A) in the presence or in the absence of DYRK1B. LDA for the undegradable HIF2α rescue experiment was performed by using three cultures transduced independently on the same day. To identify the sites of ID2 phosphorylation from IMR32 human neuroblastoma cells, the immunoprecipitated ID2 protein was excised, digested with trypsin, chymotrypsin and Lys-C and the peptides extracted from the polyacrylamide in two 30 μl aliquots of 50% acetonitrile/5% formic acid. These extracts were combined and evaporated to 25 μl for MS analysis. The LC–MS system consisted of a state-of-the-art Finnigan LTQ-FT mass spectrometer system with a Protana nanospray ion source interfaced to a self-packed 8 cm × 75 μm id Phenomenex Jupiter 10 μm C18 reversed-phase capillary column. 0.5–5 μl volumes of the extract were injected and the peptides eluted from the column by an acetonitrile/0.1 M acetic acid gradient at a flow rate of 0.25 μl min−1. The nanospray ion source was operated at 2.8 kV. The digest was analysed using the double play capability of the instrument acquiring full scan mass spectra to determine peptide molecular weights and product ion spectra to determine amino acid sequence in sequential scans. This mode of analysis produces approximately 1200 CAD spectra of ions ranging in abundance over several orders of magnitude. Tandem MS/MS experiments were performed on each candidate phosphopeptide to verify its sequence and locate the phosphorylation site. A signature of a phosphopeptide is the detection of loss of 98 daltons (the mass of phosphoric acid) in the MS/MS spectrum. With this method, three phosphopeptides were found to carry phosphorylations at residues Ser5, Ser14 and Thr27 of the ID2 protein. The anti-phospho-T27-ID2 antibody was generated by immunizing rabbits with a short synthetic peptide containing the phosphorylated T27 (CGISRSK-pT-PVDDPMS) (Yenzym Antibodies, LLC). A two-step purification process was applied. First, antiserum was cross-absorbed against the phospho-peptide matrix to purify antibodies that recognize the phosphorylated peptide. Then, the anti-serum was purified against the un-phosphorylated peptide matrix to remove non-specific antibodies. Cells were lysed in NP40 lysis buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1 mM EDTA, 1% NP40, 1.5 mM Na VO , 50 mM sodium fluoride, 10 mM sodium pyrophosphate, 10 mM β-glycerolphosphate and EDTA-free protease inhibitor cocktail (Roche)) or RIPA buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1 mM EDTA, 1% NP40, 0.5% sodium dexoycholate, 0.1% sodium dodecyl sulphate, 1.5 mM Na VO , 50 mM sodium fluoride, 10 mM sodium pyrophosphate, 10 mM β-glycerolphosphate and EDTA-free protease inhibitor cocktail (Roche)). Lysates were cleared by centrifugation at 15,000 r.p.m. for 15 min at 4 °C. For immunoprecipitation, cell lysates were incubated with primary antibody (hydroxyproline, Abcam, ab37067; VHL, BD, 556347; DYRK1A, Cell Signaling Technology, 2771; DYRK1B, Cell Signaling Technology, 5672) and protein G/A beads (Santa Cruz, sc-2003) or phospho-Tyrosine (P-Tyr-100) Sepharose beads (Cell Signaling Technology, 9419), HA affinity matrix (Roche, 11815016001), Flag M2 affinity gel (Sigma, F2426) at 4 °C overnight. Beads were washed with lysis buffer four times and eluted in 2× SDS sample buffer. Protein samples were separated by SDS–PAGE and transferred to polyvinyl difluoride (PVDF) or nitrocellulose (NC) membrane. Membranes were blocked in TBS with 5% non-fat milk and 0.1% Tween20, and probed with primary antibodies. Antibodies and working concentrations are: ID2 1:500 (C-20, sc-489), GFP 1:1,000 (B-2, sc-9996), HIF2α/EPAS-1 1:250 (190b, sc-13596), c-MYC (9E10, sc-40), and Elongin B 1:1,000 (FL-118, sc-11447), obtained from Santa Cruz Biotechnology; phospho-Tyrosine 1:1,000 (P-Tyr-100, 9411), HA 1:1,000 (C29F4, 3724), VHL 1:500 (2738), DYRK1A 1:1,000, 2771; DYRK1B 1:1,000, 5672) and RBX1 1:2,000 (D3J5I, 11922), obtained from Cell Signaling Technology; VHL 1:500 (GeneTex, GTX101087); β-actin 1:8000 (A5441), α-tubulin 1:8,000 (T5168), and Flag M2 1:500 (F1804) obtained from Sigma; HIF1α 1:500 (H1alpha67, NB100-105) and Elongin C 1:1,000 (NB100-78353) obtained from Novus Biologicals; HA 1:1000 (3F10, 12158167001) obtained from Roche. Secondary antibodies horseradish-peroxidase-conjugated were purchased from Pierce and ECL solution (Amersham) was used for detection. For in vitro binding assays, HA-tagged RBX1, Elongin B, Elongin C and VHL were in vitro translated using TNT quick coupled transcription/translation system (Promega). Active VHL protein complex was purchased from EMD Millipore. Purified His-VHL protein was purchased from ProteinOne (Rockville, MD). GST, GST–ID2 and Flag–ID2 proteins were bacterial expressed and purified using glutathione sepharose beads (GE healthcare life science). Active DYRK1B (Invitrogen) was used for in vitro phosphorylation of Flag-ID2 proteins. Biotinylated wild-type and modified (pT27 and T27W) ID2 peptides (amino acids 14–34) were synthesized by LifeTein (Somerset, NJ). In vitro binding experiments between ID2 and VCB–Cul2 were performed using 500 ng of Flag-ID2 and 500 ng of VCB–Cul2 complex or 500 ng VHL protein in binding buffer (50 mM Tris-Cl, pH 7.5, 100 mM NaCl, 1 mM EDTA, 10 mM β-glycerophosphate, 10 mM sodium pyrophosphate, 50 mM sodium fluoride, 1.5 mM Na VO , 0.2% NP40, 10% glycerol, 0.1 mg ml−1 BSA and EDTA-free protease inhibitor cocktail (Roche)) at 4 °C for 3 h. In vitro binding between ID2 peptides and purified proteins was performed using 2 μg of ID2 peptides and 200 ng of recombinant VCB–Cul2 complex or 200 ng recombinant VHL in binding buffer (50 mM Tris-Cl, pH 7.5, 100 mM NaCl, 1 mM EDTA, 10 mM β-glycerophosphate, 10 mM sodium pyrophosphate, 50 mM sodium fluoride, 1.5 mM Na VO , 0.4% NP40, 10% glycerol, 0.1 mg ml−1 BSA and EDTA-free protease inhibitor cocktail (Roche)) at 4 °C for 3 h or overnight. Protein complexes were pulled down using glutathione sepharose beads (GE Healthcare Life Science) or streptavidin conjugated beads (Thermo Fisher Scientific) and analysed by immunoblot. Cdk1, Cdk5, DYRK1A, DYRK1B, ERK, GSK3, PKA, CaMKII, Chk1, Chk2, RSK-1, RSK-2, aurora-A, aurora-B, PLK-1, PLK-2, and NEK2 were all purchased from Life Technology and ATM from EMD Millipore. The 18 protein kinases tested in the survey were selected because they are proline-directed S/T kinases (Cdk1, Cdk5, DYRK1A, DYRK1B, ERK) and/or because they were considered to be candidate kinases for Thr27, Ser14 or Ser5 from kinase consensus prediction algorithms (NetPhosK1.0, http://www.cbs.dtu.dk/services/NetPhosK/; GPS Version 3.0 http://gps.biocuckoo.org/#) or visual inspection of the flanking regions and review of the literature for consensus kinase phosphorylation motifs. 1 μg of bacterially purified GST-ID substrates were incubated with 10–20 ng each of the recombinant active kinases. The reaction mixture included 10 μCi of [γ-32P]ATP (PerkinElmer Life Sciences) in 50 μl of kinase buffer (25 mM Tris-HCl, pH 7.5, 5 mM β-glycerophosphate, 2 mM dithiothreitol (DTT), 0.1 mM Na VO , 10 mM MgCl , and 0.2 mM ATP). Reactions were incubated at 30 °C for 30 min. Reactions were terminated by addition of Laemmli SDS sample buffer and boiling on 95 °C for 5 min. Proteins were separated on SDS–PAGE gel and phosphorylation of proteins was visualized by autoradiography. Coomassie staining was used to document the amount of substrates included in the kinase reaction. In vitro phosphorylation of Flag– ID2 proteins by DYRK1B (Invitrogen) was performed using 500 ng of GST–DYRK1B and 200 ng of bacterially expressed purified Flag–ID2 protein. In vivo kinase assay in GSCs and glioma cells was performed using endogenous or exogenously expressed DYRK1A and DYRK1B. Cell lysates were prepared in lysis buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1 mM EDTA, 1% NP40, 1.5 mM Na VO , 50 mM sodium fluoride, 10 mM sodium pyrophosphate, 10 mM β-glycerolphosphate and EDTA-free protease inhibitor cocktail (Roche)). DYRK1 kinases were immunoprecipitated using DYRK1A and DYRK1B antibodies (for endogenous DYRK1 proteins) or GFP antibody (for exogenous GFP–DYRK1 proteins) from 1 mg cellular lysates at 4 °C. Immunoprecipitates were washed with lysis buffer four times followed by two washes in kinase buffer as described above and incubated with 200 ng purified Flag–ID2 protein in kinase buffer for 30 min at 30 °C. Kinase reactions were separated by SDS–PAGE and analysed by western blot using p-T27-ID2 antibody. HIF2α half-life was quantified using ImageJ processing software (NIH). Densitometry values were analysed by Prism 6.0 using the linear regression function. Stoichiometric quantification of ID2 and VHL in U87 cells was obtained using recombinant Flag–ID2 and His-tagged-VHL as references. The chemiluminescent signal of serial dilutions of the recombinant proteins was quantified using ImageJ, plotted to generate a linear standard curve against which the densitometric signal generated by serial dilutions of cellular lysates (1 × 106 U87 cells) was calculated. Triplicate values ± s.e.m. were used to estimate the ID2:VHL ratio per cell. The stoichiometry of pT27-ID2 phosphorylation was determined as described46. Briefly, SK-N-SH cells were plated at density of 1 × 106 in 100 mm dishes. Forty-eight hours later 1.5 mg of cellular lysates from cells untreated or treated with CoCl during the previous 24 h were prepared in RIPA buffer and immunoprecipitated using 4 μg of pT27-ID2 antibody or rabbit IgG overnight at 4 °C. Immune complexes were collected with TrueBlot anti-rabbit IgG beads (Rockland), washed 5 times in lysis buffer, and eluted in SDS sample buffer. Serial dilutions of cellular lysates, IgG and pT27-ID2 immunoprecipitates were loaded as duplicate series for SDS–PAGE and western blot analysis using ID2 or p-T27-ID2 antibodies. Densitometry quantification of the chemiluminescent signals was used to determine (1) the efficiency of the immunoprecipitation using the antibody against p-ID2-T27 and (2) the ratio between efficiency of the immunoprecipitation evaluated by western blot for p-T27-ID2 and total ID2 antibodies. This represents the percent of phosphorylated Thr27 of ID2 present in the cell preparation. Cellular ID2 complexes were purified from the cell line NCI-H1299 stably engineered to express Flag-HA–ID2. Cellular lysates were prepared in 50 mM Tris-HCl, 250 mM NaCl, 0.2% NP40, 1 mM EDTA, 10% glycerol, protease and phosphatase inhibitors. Flag-HA–ID2 immunoprecipitates were recovered first with anti-Flag antibody-conjugated M2 agarose (Sigma) and washed with lysis buffer containing 300 mM NaCl and 0.3% NP40. Bound polypeptides were eluted with Flag peptide and further affinity purified by anti-HA antibody-conjugated agarose (Roche). The eluates from the HA beads were analysed directly on long gradient reverse phase LC–MS/MS. A specificity score of proteins interacting with ID2 was computed for each polypeptide by comparing the number of peptides identified from mass spectrometry analysis to those reported in the CRAPome database that includes a list of potential contaminants from affinity purification-mass spectrometry experiments (http://www.crapome.org). The specificity score is computed as [(#peptide*#xcorr)/(AveSC*MaxSC* # of Expt.)], #peptide, identified peptide count; #xcorr, the cross-correlation score for all candidate peptides queried from the database; AveSC, averaged spectral counts from CRAPome; MaxSC, maximal spectral counts from CRAPome; and # of Expt., the total found number of experiments from CRAPome. U87 cells were transfected with pcDNA3-HA-HIFα (HIF1α or HIF2α), pcDNA3-Flag–ID2 (WT or T27A), pEGFP-DYRK1B and pcDNA3-Myc-Ubiquitin. 36 h after transfection, cells were treated with 20 μM MG132 (EMD Millipore) for 6 h. After washing with ice-cold PBS twice, cells were lysed in 100 μl of 50 mM Tris-HCl pH 8.0, 150 mM NaCl (TBS) containing 2% SDS and boiled at 100 °C for 10 min. Lysates were diluted with 900 μl of TBS containing 1% NP40. Immunoprecipitation was performed using 1 mg of cellular lysates. Ubiquitylated proteins were immunoprecipitated using anti-Myc antibody and analysed by western blot using HA antibody. A previously described47, highly accurate flexible peptide docking method implemented in ICM software (Molsoft LLC, La Jolla CA) was used to dock ID2 peptides to VCB or components thereof. A series of overlapping peptides of varying lengths were docked to the complex of VHL and Elongin C (EloC), or VHL or EloC alone, from the recent crystallographic structure22 of the VHL-CRL ligase. Briefly, an all-atom model of the peptide was docked into grid potentials derived from the X-ray structure using a stochastic global optimization in internal coordinates with pseudo-Brownian and collective ‘probability-biased’ random moves as implemented in the ICM program. Five types of potentials for the peptide-receptor interaction energy — hydrogen van der Waals, non-hydrogen van der Waals, hydrogen bonding, hydrophobicity and electrostatics — were precomputed on a rectilinear grid with 0.5 Å spacing that fills a 34 Å × 34 Å × 25 Å box containing the VHL-EloC (V-C) complex, to which the peptide was docked by searching its full conformational space within the space of the grid potentials. The preferred docking conformation was identified by the lowest energy conformation in the search. The preferred peptide was identified by its maximal contact surface area with the respective receptor. ab initio folding and analysis of the peptides was performed as previously described48, 49. ab initio folding of the ID2 peptide and its phospho-T27 mutant showed that both strongly prefer an α-helical conformation free (unbound) in solution, with the phospho-T27 mutant having a calculated free energy almost 50 kcal-equivalent units lower than the unmodified peptide. Total RNA was prepared with Trizol reagent (Invitrogen) and cDNA was synthesized using SuperScript II Reverse Transcriptase (Invitrogen) as described42, 50. Semi-quantitative RT–PCR was performed using AccuPrime Taq DNA polymerase (Invitrogen) and the following primers: for HIF2A Fw 5′_GTGCTCCCACGGCCTGTA_3′ and Rv 5′_TTGTCACACCTATGGCATATCACA_3′; GAPDH Fw 5′_AGAAGGCTGGGGCTCATTTG_3′ and Rv 5′_AGGGGCCATCCACAGTCTTC_3′. The quantitative RT–PCR was performed with a Roche480 thermal cycler, using SYBR Green PCR Master Mix from Applied Biosystem. Primers used in qRT–PCR are: SOX2 Fw 5′_TTGCTGCCTCTTTAAGACTAGGA_3′ and Rv 5′_CTGGGGCTCAAACTTCTCTC_3′; NANOG Fw 5′_ATGCCTCACACGGAGACTGT_3′ and Rv 5′_AAGTGGGTTGTTTGCCTTTG_3′; POU5F1 Fw 5′_GTGGAGGAAGCTGACAACAA_3′ and Rv 5′_ATTCTCCAGGTTGCCTCTCA_3′; FLT1 Fw 5′_AGCCCATAAATGGTCTTTGC_3′ and Rv 5′_GTGGTTTGCTTGAGCTGTGT_3′; PIK3CA Fw 5′_TGCAAAGAATCAGAACAATGCC_3′ and 5′_CACGGAGGCATTCTAAAGTCA_3′; BMI1 Fw 5′_AATCCCCACCTGATGTGTGT_3′ and Rv 5′_GCTGGTCTCCAGGTAACGAA_3′; GAPDH Fw 5′_GAAGGTGAAGGTCGGAGTCAAC_3′ and Rv 5′_CAGAGTTAAAAGCAGCCCTGGT_3′; 18S Fw 5′_CGCCGCTAGAGGTGAAATTC_3′ and Rv 5′_CTTTCGCTCTGGTCCGTCTT_3′. The relative amount of specific mRNA was normalized to 18S or GAPDH. Results are presented as the mean ± s.d. of three independent experiments each performed in triplicate (n = 9). Statistical significance was determined by Student’s t-test (two-tailed) using GraphPad Prism 6.0 software. Mice were housed in pathogen-free animal facility. All animal studies were approved by the IACUC at Columbia University (numbers AAAE9252; AAAE9956). Mice were 4–6-week-old male athymic nude (Nu/Nu, Charles River Laboratories). No statistical method was used to pre-determine sample size. No method of randomization was used to allocate animals to experimental groups. Mice in the same cage were generally part of the same treatment. The investigators were not blinded during outcome assessment. In none of the experiments did tumours exceed the maximum volume allowed according to our IACUC protocol, specifically 20 mm in the maximum diameter. 2 × 105 U87 cells stably expressing a doxycycline inducible lentiviral vector coding for DYRK1B or the empty vector were injected subcutaneously in the right flank in 100 μl volume of saline solution (7 mice per each group). Mice carrying 150–220 mm3 subcutaneous tumours (21 days after injection) generated by cells transduced with DYRK1B were treated with vehicle or doxycycline by oral gavage (Vibramycin, Pfizer Labs; 8 mg ml−1, 0.2 ml per day)51; mice carrying tumours generated by cells transduced with the empty vector were also fed with doxycycline. Tumour diameters were measured daily with a caliper and tumour volumes estimated using the formula: width2 × length/2 = V (mm3). Mice were euthanized after 5 days of doxycycline treatment. Tumours were dissected and fixed in formalin for immunohistochemical analysis. Data are means ± s.d. of  7 mice in each group. Statistical significance was determined by ANCOVA using GraphPad Prism 6.0 software package (GraphPad). Orthotopic implantation of glioma cells was performed as described previously using 5 × 104 U87 cells transduced with pLOC-vector, pLOC-DYRK1B (WT) or pLOC-DYRK1B-K140R mutant in 2 μl phosphate buffer42. In brief, 5 days after lentiviral infection, cells were injected 2 mm lateral and 0.5 mm anterior to the bregma, 2.5 mm below the skull of 4–6-week-old athymic nude (Nu/Nu, Charles River Laboratories) mice. Mice were monitored daily for abnormal ill effects according to AAALAS guidelines and euthanized when neurological symptoms were observed. Tumours were dissected and fixed in formalin for immunohistochemical analysis and immunofluorescence using V5 antibody (Life technologies, 46-0705) to identify exogenous DYRK1B and an antibody against human vimentin (Sigma, V6630) to identify human glioma cells. A Kaplan–Meier survival curve was generated using the GraphPad Prism 6.0 software package (GraphPad). Points on the curves indicate glioma related deaths (n = 7 animals for each group, p was determined by log rank analysis). We did not observe non-glioma related deaths. Mice injected with U87 cells transduced with pLOC-DYRK1B(WT) that did not show neurological signs on day 70 were euthanized for histological evaluation and shown as tumour-free mice in Fig. 5g. Intracranial injection of H-Ras-V12-IRES-Cre-ER-shp53 lentivirus was performed in 4-week-old Id1Flox/Flox, Id2Flox/Flox, Id3−/− mice (C57Bl6/SV129). Briefly, 1.3 µl of purified lentiviral particles in PBS were injected 1.45 mm lateral and 1.6 mm anterior to the bregma and 2.3 mm below the skull using a stereotaxic frame. Tamoxifen was administered for 5 days at 9 mg per 40 g of mouse weight by oral gavage starting 30 days after surgery. Mice were killed 2 days later and brains dissected and fixed for histological analysis. Tissue preparation and immunohistochemistry on tumour xenografts were performed as previously described42, 50, 52. Antibodies used in immunostaining are: HIF2α, mouse monoclonal, 1:200 (Novus Biological, NB100-132); Olig2, rabbit polyclonal, 1:200 (IBL International, JP18953); human Vimentin 1:50 (Sigma, V6630), Bromodeoxyuridine, mouse monoclonal 1:500 (Roche, 11170376001), V5 1:500 (Life technologies, 46-0705). Sections were permeabilized in 0.2% tritonX-100 for 10 min, blocked with 1% BSA-5% goat serum in PBS for 1 h. Primary antibodies were incubated at 4 °C overnight. Secondary antibodies biotinylated (Vector Laboratories) or conjugated with Alexa594 (1:500, Molecular Probes) were used. Slides were counterstained with haematoxylin for immunohistochemistry and DNA was counterstained with DAPI (Sigma) for immunofluorescence. Images were acquired using an Olympus 1X70 microscope equipped with digital camera and processed using Adobe Photoshop CS6 software. BrdU-positive cells were quantified by scoring the number of positive cells in five 4 × 10−3 mm2 images from 5 different mice from each group. Blinding was applied during histological analysis. Data are presented as means of five different mice ± standard deviation (s.d.) (two-tailed Student’s t-test, unequal variance). To infer if ID2 modulates the interactions between HIF2α and its transcriptional targets we used a modified version of MINDy53 algorithm, called CINDy25. CINDy uses adaptive partitioning method to accurately estimate the full conditional mutual information between a transcription factor and a target gene given the expression or activity of a signalling protein. Briefly, for every pair of transcription factor and target gene of interest, it estimates the mutual information that is, how much information can be inferred about the target gene when the expression of the transcription factor is known, conditioned on the expression/activity of the signalling protein. It estimates this conditional mutual information by estimating the multi-dimensional probability densities after partitioning the sample distribution using adaptive partitioning method. We applied CINDy algorithm on gene expression data for 548 samples obtained from The Cancer Genome Atlas (TCGA). Since the activity level and not the gene expression of ID2 is the determinant of its modulatory function that is, the extent to which it modulates the transcriptional network of HIF2α, we used an algorithm called Virtual Inference of Protein-activity by Enriched Regulon analysis (VIPER) to infer the activity of ID2 protein from its gene expression profile26. VIPER method allows the computational inference of protein activity, on an individual sample basis, from gene expression profile data. It uses the expression of genes that are most directly regulated by a given protein, such as the targets of a transcription factor (TF), as an accurate reporter of its activity. We defined the targets of ID2 by running ARACNe algorithm on 548 gene expression profiles and use the inferred 106 targets to determine its activity (Supplementary Table 3). We applied CINDy on 277 targets of HIF2α represented in Ingenuity pathway analysis (IPA) and for which gene expression data was available (Supplementary Table 4). Of these 277 targets, 77 are significantly modulated by ID2 activity (P value ≤ 0.05). Among the set of target genes whose expression was significantly positively correlated (P value ≤ 0.05) with the expression of HIF2α irrespective of the activity of ID2, that is, correlation was significant for samples with both high and low activity of ID2, the average expression of target genes for a given expression of HIF2α was higher when the activity of ID2 was high. The same set of target gene were more correlated in high ID2 activity samples compared to any set of random genes of same size (Fig. 5a), whereas they were not in ID2 low activity samples (Fig. 5b). We selected 25% of all samples with the highest/lowest ID2 activity to calculate the correlation between HIF2α and its targets. To determine whether regulation of ID2 by hypoxia might impact the correlation between high ID2 activity and HIF2α shown in Fig. 5a, b we compared the effects of ID2 activity versus ID2 expression for the transcriptional connection between HIF2α and its targets. We selected 25% of all patients (n = 548) in TCGA with high ID2 activity and 25% of patients with low ID2 activity and tested the enrichment of significantly positively correlated targets of HIF2α in each of the groups. This resulted in significant enrichment (P value < 0.001) in high ID2 activity but showed no significant enrichment (P value = 0.093) in low ID2 activity samples. Moreover, the difference in the enrichment score (∆ES) in these two groups was statistically significant (P value < 0.05). This significance is calculated by randomly selecting the same number of genes as the positively correlated targets of HIF2α, and calculating the ∆ES for these randomly selected genes, giving ∆ES . We repeated this step 1,000 times to obtain 1,000 ∆ES that are used to build the null distribution (Extended Data Fig. 9b). We used the null distribution to estimate P value calculated as (number of ∆ES > ∆ES )/1,000. Enrichment was observed only when ID2 activity was high but not when ID2 activity was low, thus suggesting that ID2 activity directionally impacts the regulation of targets of HIF2α by HIF2α. Consistently, the significant ∆ES using ID2 activity suggests that ID2 activity is determinant of correlation between HIF2α and its targets. Conversely, when we performed similar analysis using ID2 expression instead of ID2 activity, we found significant enrichment of positively correlated targets of HIF2α both in samples with high expression (P value = 0.025) and low expression of ID2 (P value = 0.048). Given the significant enrichment in both groups, we did not observe any significant difference in the enrichment score in the two groups (P value of ∆ES = 0.338). Thus, while the determination of the ID2 activity and its effects upon the HIF2α-targets connection by VIPER and CINDy allowed us to determine the unidirectional positive link between high ID2 activity and HIF2α transcription, a similar analysis performed using ID2 expression contemplates the dual connection between ID2 and HIF2α. To test if expression of DYRK1A and DYRK1B is a predictor of prognosis, we divided the patients into two cohorts based on their relative expression compared to the mean expression of all patients in GBM. First cohort contained the patients with high expression of both DYRK1A and DYRK1B (n = 101) and the other cohort contained patients with low expression (n = 128). We used average expression for both DYRK1A and DYRK1B, which individually divide the patient cohort into half and half. However, when we use the condition that patients should display higher or lower average expression of both these genes, then we select approximately 19% for high expression and 24% for low expression. Selection of these patients was entirely dependent on the overall expression of these genes in the entire cohort rather than a predefined cutoff. Kaplan–Meier survival analysis showed the significant survival benefit for the patients having the high expression of both DYRK1A and DYRK1B (P value = 0.004) compared to the patients with low expression. When similar analysis was performed using only the expression of DYRK1A or DYRK1B alone, the prediction was either non-significant (DYRK1A) or less significant (DYRK1B, P value = 0.008) when compared to the predictions using the expression of both genes. Results in graphs are expressed as means ± s.d. or means ± s.e.m., as indicated in figure legends, for the indicated number of observations. Statistical significance was determined by the Student’s t-test (two-tailed, unequal variance). P value < 0.05 is considered significant and is indicated in figure legends.

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