Aleksey Porollo

p62 is a key regulator of nutrient sensing in the mTORC1 pathway.

The signaling adaptor p62 is a critical mediator of important cellular functions, owing to its ability to establish interactions with various signaling intermediaries. Here, we identify raptor as an interacting partner of p62. Thus, p62 is an integral part of the mTORC1 complex and is necessary to mediate amino acid signaling for the activation of S6K1 and 4EBP1. p62 interacts in an amino acid-dependent manner with mTOR and raptor. In addition, p62 binds the Rags proteins and favors formation of the active Rag heterodimer that is further stabilized by raptor. Interestingly, p62 colocalizes with Rags at the lysosomal compartment and is required for the interaction of mTOR with Rag GTPases in vivo and for translocation of the mTORC1 complex to the lysosome, a crucial step for mTOR activation.

Combining prediction of secondary structure and solvent accessibility in proteins.

Owing to the use of evolutionary information and advanced machine learning protocols, secondary structures of amino acid residues in proteins can be predicted from the primary sequence with more than 75% per‐residue accuracy for the 3‐state (i.e., helix, β‐strand, and coil) classification problem. In this work we investigate whether further progress may be achieved by incorporating the relative solvent accessibility (RSA) of an amino acid residue as a fingerprint of the overall topology of the protein. Toward that goal, we developed a novel method for secondary structure prediction that uses predicted RSA in addition to attributes derived from evolutionary profiles. Our general approach follows the 2‐stage protocol of Rost and Sander, with a number of Elman‐type recurrent neural networks (NNs) combined into a consensus predictor. The RSA is predicted using our recently developed regression‐based method that provides real‐valued RSA, with the overall correlation coefficients between the actual and predicted RSA of about 0.66 in rigorous tests on independent control sets. Using the predicted RSA, we were able to improve the performance of our secondary structure prediction by up to 1.4% and achieved the overall per‐residue accuracy between 77.0% and 78.4% for the 3‐state classification problem on different control sets comprising, together, 603 proteins without homology to proteins included in the training. The effects of including solvent accessibility depend on the quality of RSA prediction. In the limit of perfect prediction (i.e., when using the actual RSA values derived from known protein structures), the accuracy of secondary structure prediction increases by up to 4%. We also observed that projecting real‐valued RSA into 2 discrete classes with the commonly used threshold of 25% RSA decreases the classification accuracy for secondary structure prediction. While the level of improvement of secondary structure prediction may be different for prediction protocols that implicitly account for RSA in other ways, we conclude that an increase in the 3‐state classification accuracy may be achieved when combining RSA with a state‐of‐the‐art protocol utilizing evolutionary profiles. The new method is available through a Web server at http://sable.cchmc.org. Proteins 2005.

Prediction‐based fingerprints of protein–protein interactions

The recognition of protein interaction sites is an important intermediate step toward identification of functionally relevant residues and understanding protein function, facilitating experimental efforts in that regard. Toward that goal, the authors propose a novel representation for the recognition of protein–protein interaction sites that integrates enhanced relative solvent accessibility (RSA) predictions with high resolution structural data. An observation that RSA predictions are biased toward the level of surface exposure consistent with protein complexes led the authors to investigate the difference between the predicted and actual (i.e., observed in an unbound structure) RSA of an amino acid residue as a fingerprint of interaction sites. The authors demonstrate that RSA prediction‐based fingerprints of protein interactions significantly improve the discrimination between interacting and noninteracting sites, compared with evolutionary conservation, physicochemical characteristics, structure‐derived and other features considered before. On the basis of these observations, the authors developed a new method for the prediction of protein–protein interaction sites, using machine learning approaches to combine the most informative features into the final predictor. For training and validation, the authors used several large sets of protein complexes and derived from them nonredundant representative chains, with interaction sites mapped from multiple complexes. Alternative machine learning techniques are used, including Support Vector Machines and Neural Networks, so as to evaluate the relative effects of the choice of a representation and a specific learning algorithm. The effects of induced fit and uncertainty of the negative (noninteracting) class assignment are also evaluated. Several representative methods from the literature are reimplemented to enable direct comparison of the results. Using rigorous validation protocols, the authors estimated that the new method yields the overall classification accuracy of about 74% and Matthews correlation coefficients of 0.42, as opposed to up to 70% classification accuracy and up to 0.3 Matthews correlation coefficient for methods that do not utilize RSA prediction‐based fingerprints. The new method is available at http://sppider.cchmc.org. Proteins 2007.

Accurate prediction of solvent accessibility using neural networks–based regression

Accurate prediction of relative solvent accessibilities (RSAs) of amino acid residues in proteins may be used to facilitate protein structure prediction and functional annotation. Toward that goal we developed a novel method for improved prediction of RSAs. Contrary to other machine learning–based methods from the literature, we do not impose a classification problem with arbitrary boundaries between the classes. Instead, we seek a continuous approximation of the real‐value RSA using nonlinear regression, with several feed forward and recurrent neural networks, which are then combined into a consensus predictor. A set of 860 protein structures derived from the PFAM database was used for training, whereas validation of the results was carefully performed on several nonredundant control sets comprising a total of 603 structures derived from new Protein Data Bank structures and had no homology to proteins included in the training. Two classes of alternative predictors were developed for comparison with the regression‐based approach: one based on the standard classification approach and the other based on a semicontinuous approximation with the so‐called thermometer encoding. Furthermore, a weighted approximation, with errors being scaled by the observed levels of variability in RSA for equivalent residues in families of homologous structures, was applied in order to improve the results. The effects of including evolutionary profiles and the growth of sequence databases were assessed. In accord with the observed levels of variability in RSA for different ranges of RSA values, the regression accuracy is higher for buried than for exposed residues, with overall 15.3–15.8% mean absolute errors and correlation coefficients between the predicted and experimental values of 0.64–0.67 on different control sets. The new method outperforms classification‐based algorithms when the real value predictions are projected onto two‐class classification problems with several commonly used thresholds to separate exposed and buried residues. For example, classification accuracy of about 77% is consistently achieved on all control sets with a threshold of 25% RSA. A web server that enables RSA prediction using the new method and provides customizable graphical representation of the results is available at http://sable.cchmc.org. Proteins 2004.

Granulocyte Macrophage-Colony Stimulating Factor Induced Zn Sequestration Enhances Macrophage Superoxide and Limits Intracellular Pathogen Survival

Macrophages possess numerous mechanisms to combat microbial invasion, including sequestration of essential nutrients, like zinc (Zn). The pleiotropic cytokine granulocyte macrophage-colony stimulating factor (GM-CSF) enhances antimicrobial defenses against intracellular pathogens such as Histoplasma capsulatum, but its mode of action remains elusive. We have found that GM-CSF-activated infected macrophages sequestered labile Zn by inducing binding to metallothioneins (MTs) in a STAT3 and STAT5 transcription-factor-dependent manner. GM-CSF upregulated expression of Zn exporters, Slc30a4 and Slc30a7; the metal was shuttled away from phagosomes and into the Golgi apparatus. This distinctive Zn sequestration strategy elevated phagosomal H⁺ channel function and triggered reactive oxygen species generation by NADPH oxidase. Consequently, H. capsulatum was selectively deprived of Zn, thereby halting replication and fostering fungal clearance. GM-CSF mediated Zn sequestration via MTs in vitro and in vivo in mice and in human macrophages. These findings illuminate a GM-CSF-induced Zn-sequestration network that drives phagocyte antimicrobial effector function.

Versatile annotation and publication quality visualization of protein complexes using POLYVIEW-3D

BackgroundMacromolecular visualization as well as automated structural and functional annotation tools play an increasingly important role in the post-genomic era, contributing significantly towards the understanding of molecular systems and processes. For example, three dimensional (3D) models help in exploring protein active sites and functional hot spots that can be targeted in drug design. Automated annotation and visualization pipelines can also reveal other functionally important attributes of macromolecules. These goals are dependent on the availability of advanced tools that integrate better the existing databases, annotation servers and other resources with state-of-the-art rendering programs.ResultsWe present a new tool for protein structure analysis, with the focus on annotation and visualization of protein complexes, which is an extension of our previously developed POLYVIEW web server. By integrating the web technology with state-of-the-art software for macromolecular visualization, such as the PyMol program, POLYVIEW-3D enables combining versatile structural and functional annotations with a simple web-based interface for creating publication quality structure rendering, as well as animated images for Powerpoint™, web sites and other electronic resources. The service is platform independent and no plug-ins are required. Several examples of how POLYVIEW-3D can be used for structural and functional analysis in the context of protein-protein interactions are presented to illustrate the available annotation options.ConclusionPOLYVIEW-3D server features the PyMol image rendering that provides detailed and high quality presentation of macromolecular structures, with an easy to use web-based interface. POLYVIEW-3D also provides a wide array of options for automated structural and functional analysis of proteins and their complexes. Thus, the POLYVIEW-3D server may become an important resource for researches and educators in the fields of protein science and structural bioinformatics. The new server is available at http://polyview.cchmc.org/polyview3d.html.

Control of Nutrient Stress-Induced Metabolic Reprogramming by PKCζ in Tumorigenesis

Tumor cells have high-energetic and anabolic needs and are known to adapt their metabolism to be able to survive and keep proliferating under conditions of nutrient stress. We show that PKCζ deficiency promotes the plasticity necessary for cancer cells to reprogram their metabolism to utilize glutamine through the serine biosynthetic pathway in the absence of glucose. PKCζ represses the expression of two key enzymes of the pathway, PHGDH and PSAT1, and phosphorylates PHGDH at key residues to inhibit its enzymatic activity. Interestingly, the loss of PKCζ in mice results in enhanced intestinal tumorigenesis and increased levels of these two metabolic enzymes, whereas patients with low levels of PKCζ have a poor prognosis. Furthermore, PKCζ and caspase-3 activities are correlated with PHGDH levels in human intestinal tumors. Taken together, this demonstrates that PKCζ is a critical metabolic tumor suppressor in mouse and human cancer.

Effects of Parabens on Adipocyte Differentiation

Parabens are a group of alkyl esters of p-hydroxybenzoic acid that include methylparaben, ethylparaben, propylparaben, butylparaben, and benzylparaben. Paraben esters and their salts are widely used as preservatives in cosmetics, toiletries, food, and pharmaceuticals. Humans are exposed to parabens through the use of such products from dermal contact, ingestion, and inhalation. However, research on the effects of parabens on health is limited, and the effects of parabens on adipogenesis have not been systematically studied. Here, we report that (1) parabens promote adipogenesis (or adipocyte differentiation) in murine 3T3-L1 cells, as revealed by adipocyte morphology, lipid accumulation, and mRNA expression of adipocyte-specific markers; (2) the adipogenic potency of parabens is increased with increasing length of the linear alkyl chain in the following potency ranking order: methyl- < ethyl- < propyl- < butylparaben. The extension of the linear alkyl chain with an aromatic ring in benzylparaben further augments the adipogenic ability, whereas 4-hydroxybenzoic acid, the common metabolite of all parabens, and the structurally related benzoic acid (without the OH group) are inactive in promoting 3T3-L1 adipocyte differentiation; (3) parabens activate glucocorticoid receptor and/or peroxisome proliferator-activated receptor γ in 3T3-L1 preadipocytes; however, no direct binding to, or modulation of, the ligand binding domain of the glucocorticoid receptor by parabens was detected by glucocorticoid receptor competitor assays; and lastly, (4) parabens, butyl- and benzylparaben in particular, also promote adipose conversion of human adipose-derived multipotent stromal cells. Our results suggest that parabens may contribute to obesity epidemic, and the role of parabens in adipogenesis in vivo needs to be examined further.

CYP63A2, a Catalytically Versatile Fungal P450 Monooxygenase Capable of Oxidizing Higher-Molecular-Weight Polycyclic Aromatic Hydrocarbons, Alkylphenols, and Alkanes

ABSTRACT Cytochrome P450 monooxygenases (P450s) are known to oxidize hydrocarbons, albeit with limited substrate specificity across classes of these compounds. Here we report a P450 monooxygenase (CYP63A2) from the model ligninolytic white rot fungus Phanerochaete chrysosporium that was found to possess a broad oxidizing capability toward structurally diverse hydrocarbons belonging to mutagenic/carcinogenic fused-ring higher-molecular-weight polycyclic aromatic hydrocarbons (HMW-PAHs), endocrine-disrupting long-chain alkylphenols (APs), and crude oil aliphatic hydrocarbon n-alkanes. A homology-based three-dimensional (3D) model revealed the presence of an extraordinarily large active-site cavity in CYP63A2 compared to the mammalian PAH-oxidizing (CYP3A4, CYP1A2, and CYP1B1) and bacterial aliphatic-hydrocarbon-oxidizing (CYP101D and CYP102A1) P450s. This structural feature in conjunction with ligand docking simulations suggested potential versatility of the enzyme. Experimental characterization using recombinantly expressed CYP63A2 revealed its ability to oxidize HMW-PAHs of various ring sizes, including 4 rings (pyrene and fluoranthene), 5 rings [benzo(a)pyrene], and 6 rings [benzo(ghi)perylene], with the highest enzymatic activity being toward the 5-ring PAH followed by the 4-ring and 6-ring PAHs, in that order. Recombinant CYP63A2 activity yielded monohydroxylated PAH metabolites. The enzyme was found to also act as an alkane ω-hydroxylase that oxidized n-alkanes with various chain lengths (C9 to C12 and C15 to C19), as well as alkyl side chains (C3 to C9) in alkylphenols (APs). CYP63A2 showed preferential oxidation of long-chain APs and alkanes. To our knowledge, this is the first P450 identified from any of the biological kingdoms that possesses such broad substrate specificity toward structurally diverse xenobiotics (PAHs, APs, and alkanes), making it a potent enzyme biocatalyst candidate to handle mixed pollution (e.g., crude oil spills).

Mitochondria-specific transgenic overexpression of connexin-43 simulates preconditioning-induced cytoprotection of stem cells

AIMS We previously reported that preconditioning of stem cells with insulin-like growth factor-1 (IGF-1) translocated connexin-43 (Cx-43) into mitochondria, causing cytoprotection. We posit that these preconditioning effects could be simulated by mitochondria-specific overexpression of Cx-43. METHODS AND RESULTS During IGF-1-induced preconditioning of C57black/6 mouse bone marrow stem cell antigen-1(+) (Sca-1(+)) cells, Cx-43 was mainly translocated onto the mitochondrial inner membrane, which was abrogated by an extracellular signal-regulated kinases 1 and 2 (ERK1/2) blocker PD98059. To investigate the role of mitochondrial Cx-43, we successfully designed a vector coding for full-length mouse Cx-43 with a mitochondria-targeting sequence (mito-Cx-43) and cloned into a shuttle vector (pShuttle-IRES-hrGFP-1) for mitochondria-specific overexpression of Cx-43 (mito-Cx-43). Sca-1(+) cells with mito-Cx-43 reduced cytosolic accumulation of cytochrome c, lowered caspase-3 activity, and improved survival during index oxygen-glucose deprivation as determined by terminal deoxynucleotidyl transferase dUTP nick-end labelling and lactate dehydrogenase assays. Computational analysis revealed a B-cell lymphoma-2 (Bcl-2) homology domain-3 (BH3) motif in Cx-43 with a conserved pattern of amino acids consistent with the Bcl-2 family that regulated cytochrome c release. Moreover, computational secondary structure prediction indicated an extended α-helix in this region, a known condition for BH3-driven protein-protein interactions. CONCLUSION Cx-43 translocation into mitochondria during preconditioning was ERK1/2-dependent. Expression of mito-Cx-43 simulated the cytoprotective effects of preconditioning in stem cells. Structural features of Cx-43 were shared with the Bcl-2 family as determined by computational analysis.