Miguel A. Andrade-Navarro

Prediction of protein secondary structure from circular dichroism using theoretically derived spectra

Circular dichroism (CD) is a spectroscopic technique commonly used to investigate the structure of proteins. Major secondary structure types, alpha‐helices and beta‐strands, produce distinctive CD spectra. Thus, by comparing the CD spectrum of a protein of interest to a reference set consisting of CD spectra of proteins of known structure, predictive methods can estimate the secondary structure of the protein. Currently available methods, including K2D2, use such experimental CD reference sets, which are very small in size when compared to the number of tertiary structures available in the Protein Data Bank (PDB). Conversely, given a PDB structure, it is possible to predict a theoretical CD spectrum from it. The methodological framework for this calculation was established long ago but only recently a convenient implementation called DichroCalc has been developed. In this study, we set to determine whether theoretically derived spectra could be used as reference set for accurate CD based predictions of secondary structure. We used DichroCalc to calculate the theoretical CD spectra of a nonredundant set of structures representing most proteins in the PDB, and applied a straightforward approach for predicting protein secondary structure content using these theoretical CD spectra as reference set. We show that this method improves the predictions, particularly for the wavelength interval between 200 and 240 nm and for beta‐strand content. We have implemented this method, called K2D3, in a publicly accessible web server at http://www. ogic.ca/projects/k2d3. Proteins 2012.

DNA methylation protects hematopoietic stem cell multipotency from myeloerythroid restriction

DNA methylation is a dynamic epigenetic mark that undergoes extensive changes during differentiation of self-renewing stem cells. However, whether these changes are the cause or consequence of stem cell fate remains unknown. Here, we show that alternative functional programs of hematopoietic stem cells (HSCs) are governed by gradual differences in methylation levels. Constitutive methylation is essential for HSC self-renewal but dispensable for homing, cell cycle control and suppression of apoptosis. Notably, HSCs from mice with reduced DNA methyltransferase 1 activity cannot suppress key myeloerythroid regulators and thus can differentiate into myeloerythroid, but not lymphoid, progeny. A similar methylation dosage effect controls stem cell function in leukemia. These data identify DNA methylation as an essential epigenetic mechanism to protect stem cells from premature activation of predominant differentiation programs and suggest that methylation dynamics determine stem cell functions in tissue homeostasis and cancer.

K2D2: Estimation of protein secondary structure from circular dichroism spectra

BackgroundCircular dichroism spectroscopy is a widely used technique to analyze the secondary structure of proteins in solution. Predictive methods use the circular dichroism spectra from proteins of known tertiary structure to assess the secondary structure contents of a protein with unknown structure given its circular dichroism spectrum.ResultsWe developed K2D2, a method with an associated web server to estimate protein secondary structure from circular dichroism spectra. The method uses a self-organized map of spectra from proteins with known structure to deduce a map of protein secondary structure that is used to do the predictions.ConclusionThe K2D2 server is publicly accessible at http://www.ogic.ca/projects/k2d2/. It accepts as input a circular dichroism spectrum and outputs the estimated secondary structure content (alpha-helix and beta-strand) of the corresponding protein, as well as an estimated measure of error.

Cargo-Selected Transport from the Mitochondria to Peroxisomes Is Mediated by Vesicular Carriers

Mitochondria and peroxisomes share a number of common biochemical processes, including the beta oxidation of fatty acids and the scavenging of peroxides. Here, we identify a new outer-membrane mitochondria-anchored protein ligase (MAPL) containing a really interesting new gene (RING)-finger domain. Overexpression of MAPL leads to mitochondrial fragmentation, indicating a regulatory function controlling mitochondrial morphology. In addition, confocal- and electron-microscopy studies of MAPL-YFP led to the observation that MAPL is also incorporated within unique, DRP1-independent, 70-100 nm diameter mitochondria-derived vesicles (MDVs). Importantly, vesicles containing MAPL exclude another outer-membrane marker, TOM20, and vesicles containing TOM20 exclude MAPL, indicating that MDVs selectively incorporate their cargo. We further demonstrate that MAPL-containing vesicles fuse with a subset of peroxisomes, marking the first evidence for a direct relationship between these two functionally related organelles. In contrast, a distinct vesicle population labeled with TOM20 does not fuse with peroxisomes, indicating that the incorporation of specific cargo is a primary determinant of MDV fate. These data are the first to identify MAPL, describe and characterize MDVs, and define a new intracellular transport route between mitochondria and peroxisomes.

The SUMO protease SENP5 is required to maintain mitochondrial morphology and function

Mitochondria are dynamic organelles that undergo regulated fission and fusion events that are essential to maintain metabolic stability. We previously demonstrated that the mitochondrial fission GTPase DRP1 is a substrate for SUMOylation. To further understand how SUMOylation impacts mitochondrial function, we searched for a SUMO protease that may affect mitochondrial dynamics. We demonstrate that the cytosolic pool of SENP5 catalyzes the cleavage of SUMO1 from a number of mitochondrial substrates. Overexpression of SENP5 rescues SUMO1-induced mitochondrial fragmentation that is partly due to the downregulation of DRP1. By contrast, silencing of SENP5 results in a fragmented and altered morphology. DRP1 was stably mono-SUMOylated in these cells, suggesting that SUMOylation leads to increased DRP1 mediated fission. In addition, the reduction of SENP5 levels resulted in a significant increase in the production of free radicals. Reformation of the mitochondrial tubules by expressing the dominant interfering DRP1 or by RNA silencing of endogenous DRP1 protein rescued both the morphological aberrations and the increased production of ROS induced by downregulation of SENP5. These data demonstrate the importance of SENP5 as a new regulator of SUMO1 proteolysis from mitochondrial targets, impacting mitochondrial morphology and metabolism.

Identification of LRRC8 Heteromers as an Essential Component of the Volume-Regulated Anion Channel VRAC

One Swell Ion Channel When mammalian cells are faced with osmotic challenges, they need to swell or shrink. The molecular characterization of the volume-regulated anion channel (VRAC) remains unknown, although many candidate proteins have been proposed. Voss et al. (p. 634, published online 10 April; see the Perspective by Mindell) used a genome-wide screen to identify a group of leucine-rich repeat–containing (LRRC) proteins necessary for forming VRAC. Suppression of LRRC8A nearly eliminated the presence of VRAC in mammalian cells. A heterooligomer of LRRC proteins appears to form VRAC. Identification of VRAC components is an essential step forward in the understanding of swelling-activated ion channels and provides opportunities for understanding both the mechanism of the channel and its role in physiology. Components of an elusive swelling-activated anion channel are identified and form a structurally new class of channel. [Also see Perspective by Mindell] Regulation of cell volume is critical for many cellular and organismal functions, yet the molecular identity of a key player, the volume-regulated anion channel VRAC, has remained unknown. A genome-wide small interfering RNA screen in mammalian cells identified LRRC8A as a VRAC component. LRRC8A formed heteromers with other LRRC8 multispan membrane proteins. Genomic disruption of LRRC8A ablated VRAC currents. Cells with disruption of all five LRRC8 genes required LRRC8A cotransfection with other LRRC8 isoforms to reconstitute VRAC currents. The isoform combination determined VRAC inactivation kinetics. Taurine flux and regulatory volume decrease also depended on LRRC8 proteins. Our work shows that VRAC defines a class of anion channels, suggests that VRAC is identical to the volume-sensitive organic osmolyte/anion channel VSOAC, and explains the heterogeneity of native VRAC currents.

A Directed Protein Interaction Network for Investigating Intracellular Signal Transduction

Effective prediction of the direction of signal flow in an interaction network enables modeling of signaling dynamics and identification of regulatory proteins. Finding More Pieces to the Signaling Puzzle Even well-studied pathways are likely to be incomplete in terms of our knowledge of all the components and their relationships, and the larger interconnected network that represents the true cellular regulatory landscape remains woefully unknown. Vinayagam et al. used an automated yeast two-hybrid interaction mating assay to identify protein-protein interactions (PPIs) among human proteins and then integrated that PPI data set with previously published data to create an undirected human PPI network connecting 9832 proteins through 39,641 interactions. The authors then applied a Bayesian learning strategy to assign direction to the interactions among the proteins. The resulting directed network enabled them to evaluate growth factor–induced protein phosphorylation dynamics and to identify previously unknown modulators of the extracellular signal–regulated protein kinase pathway, of which 18 were validated with cell-based assays. This strategy should prove useful in completing the puzzle of the cellular regulatory network. Cellular signal transduction is a complex process involving protein-protein interactions (PPIs) that transmit information. For example, signals from the plasma membrane may be transduced to transcription factors to regulate gene expression. To obtain a global view of cellular signaling and to predict potential signal modulators, we searched for protein interaction partners of more than 450 signaling-related proteins by means of automated yeast two-hybrid interaction mating. The resulting PPI network connected 1126 proteins through 2626 PPIs. After expansion of this interaction map with publicly available PPI data, we generated a directed network resembling the signal transduction flow between proteins with a naïve Bayesian classifier. We exploited information on the shortest PPI paths from membrane receptors to transcription factors to predict input and output relationships between interacting proteins. Integration of directed PPI with time-resolved protein phosphorylation data revealed network structures that dynamically conveyed information from the activated epidermal growth factor and extracellular signal–regulated kinase (EGF/ERK) signaling cascade to directly associated proteins and more distant proteins in the network. From the model network, we predicted 18 previously unknown modulators of EGF/ERK signaling, which we validated in mammalian cell-based assays. This generic experimental and computational approach provides a framework for elucidating causal connections between signaling proteins and facilitates the identification of proteins that modulate the flow of information in signaling networks.

HIPPIE: Integrating protein interaction networks with experiment based quality scores.

Protein function is often modulated by protein-protein interactions (PPIs) and therefore defining the partners of a protein helps to understand its activity. PPIs can be detected through different experimental approaches and are collected in several expert curated databases. These databases are used by researchers interested in examining detailed information on particular proteins. In many analyses the reliability of the characterization of the interactions becomes important and it might be necessary to select sets of PPIs of different confidence levels. To this goal, we generated HIPPIE (Human Integrated Protein-Protein Interaction rEference), a human PPI dataset with a normalized scoring scheme that integrates multiple experimental PPI datasets. HIPPIEs scoring scheme has been optimized by human experts and a computer algorithm to reflect the amount and quality of evidence for a given PPI and we show that these scores correlate to the quality of the experimental characterization. The HIPPIE web tool (available at http://cbdm.mdc-berlin.de/tools/hippie) allows researchers to do network analyses focused on likely true PPI sets by generating subnetworks around proteins of interest at a specified confidence level.

The FTO (fat mass and obesity associated) gene codes for a novel member of the non-heme dioxygenase superfamily

BackgroundGenetic variants in the FTO (fat mass and obesity associated) gene have been associated with an increased risk of obesity. However, the function of its protein product has not been experimentally studied and previously reported sequence similarity analyses suggested the absence of homologs in existing protein databases. Here, we present the first detailed computational analysis of the sequence and predicted structure of the protein encoded by FTO.ResultsWe performed a sequence similarity search using the human FTO protein as query and then generated a profile using the multiple sequence alignment of the homologous sequences. Profile-to-sequence and profile-to-profile based comparisons identified remote homologs of the non-heme dioxygenase family.ConclusionOur analysis suggests that human FTO is a member of the non-heme dioxygenase (Fe(II)- and 2-oxoglutarate-dependent dioxygenases) superfamily. Amino acid conservation patterns support this hypothesis and indicate that both 2-oxoglutarate and iron should be important for FTO function. This computational prediction of the function of FTO should suggest further steps for its experimental characterization and help to formulate hypothesis about the mechanisms by which it relates to obesity in humans.

The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text

BackgroundDetermining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them.ResultsA total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthews Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89% and the best AUC iP/R was 68%. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing systems against manually generated annotations done by curators from the BioGRID and MINT databases. The highest AUC iP/R achieved by any run was 53%, the best MCC score 0.55. In case of competitive systems with an acceptable recall (above 35%) the macro-averaged precision ranged between 50% and 80%, with a maximum F-Score of 55%.ConclusionsThe results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging. Nevertheless, text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results. Detecting associations between full text articles and interaction detection method PSI-MI terms (IMT) is more difficult than might be anticipated. This is due to the variability of method term mentions, errors resulting from pre-processing of articles provided as PDF files, and the heterogeneity and different granularity of method term concepts encountered in the ontology. However, combining the sophisticated techniques developed by the participants with supporting evidence strings derived from the articles for human interpretation could result in practical modules for biological annotation workflows.