Progress in understanding of the molecular basis underlying functional diversification of cyclic di-nucleotide turnover proteins

Cyclic di-GMP was the first cyclic di-nucleotide second messenger described, presaging the discovery of additional cyclic di-nucleotide messengers in bacteria and eukaryotes. The GGDEF diguanylate cyclase (DGC) and EAL and HD-GYP phosphodiesterase (PDE) domains conduct the turnover of cyclic di-GMP. These three unrelated domains belong to superfamilies that exhibit significant variations in function, to include both enzymatically active and inactive members with a subset involved in synthesis and degradation of other cyclic di-nucleotides. Here we summarize current knowledge of sequence and structural varitions that underpin the functional diversification of cyclic di-GMP turnover proteins. Moreover, we highlight that superfamily diversification is not restricted to cyclic di-GMP signaling domains, as particular DHH/DHHA1 domain and HD domain proteins have been shown to act as cyclic di-AMP phosphodiesterases. We conclude with a consideration of the current limitations that such diversity of action places on bioinformatic prediction of the roles of GGDEF, EAL and HD-GYP domain proteins.

Refugium for surface life on Snowball Earth in a nearly enclosed sea? A numerical solution for sea-glacier invation through a narrow strait

Abstract Where photosynthetic eukaryotic organisms survived during the Snowball Earth events of the Neoproterozoic remains unclear. Our previous research tested whether a narrow arm of the ocean, similar to the modern Red Sea, could have been a refugium for photosynthetic eukaryotes during the Snowball Earth. Using an analytical ice-flow model, we demonstrated that a limited range of climate conditions could restrict sea-glacier flow sufficiently to allow an arm of the sea to remain partially free from sea-glacier penetration, a necessary condition for it to act as a refugium. Here we expand on the previous study, using a numerical ice-flow model, with the ability to capture additional physics, to calculate sea-glacier penetration, and to explore the effect of a channel with a narrow entrance. The climatic conditions are made selfconsistent by linking sublimation rate to surface temperature. As expected, the narrow entrance allows parts of the nearly enclosed sea to remain safe from sea-glacier penetration for a wider range of climate conditions.

Nickel dependent carcinogenesis and infections: structural and biophysical characterization of NDRG1 and SgSrnR

In prokaryotic organisms, lower eukaryotes and plants, some important biological reactions are catalyzed by nickel-dependent enzymes, making this metal ion essential microelement for their life. On the other hand, excessive concentration of nickel into the cell, or prolonged exposure to nickel compounds, has toxic effects in living organisms. In addition, nickel has been classified by IARC as Group I human carcinogen, because of the correlation between its inhalation and increased incidence of nasal and lung cancers. The aim of this work was to investigate the nickel impact on human health, considering both its direct role on human cells and its indirect effect as essential element for human important bacteria. In humans, nickel induces N-myc downstream regulated gene 1 (NDRG1) expression, recently proposed as new target in cancer therapy. CD, light scattering and ITC were applied on the recombinant full-length protein and its C-terminal intrinsically disordered domain, for studying the NDRG1 structural and functional properties. In particular, the fold and dynamics of the C-terminal region were examined by NMR spectroscopy and site-directed spin labeling coupled to EPR, showing the features of an intrinsically disordered region. In nickel-dependent bacteria, nickel metabolism is strictly regulated, through the activity of different transcription factors. In Streptomyces griseus the expression of two superoxide dismutases (SODs) is antagonistically regulated by nickel thanks to the transcriptional complex SgSrnR/SgSrnQ. The SgSrnR protein was heterologously expressed and its activity as possible nickel sensor studied. DNaseI footprinting and β-galactosidase gene reporter assays revealed that SgSrnR functions as transcriptional activator, prompting the hypothesis of a new model to describe the activity of this complex. In addition, ITC, NMR and X-ray crystallography demonstrated that SgSrnR presents the fold typical of ArsR/SmtB transcription factors and low metal binding affinity, non compatible with a role as a nickel-sensor, function probably played by its partner SgSrnQ.

Machine learning tools for protein annotation: the cases of transmembrane β-barrel and myristoylated proteins

Biology is now a “Big Data Science” thanks to technological advancements allowing the characterization of the whole macromolecular content of a cell or a collection of cells. This opens interesting perspectives, but only a small portion of this data may be experimentally characterized. From this derives the demand of accurate and efficient computational tools for automatic annotation of biological molecules. This is even more true when dealing with membrane proteins, on which my research project is focused leading to the development of two machine learning-based methods: BetAware-Deep and SVMyr. BetAware-Deep is a tool for the detection and topology prediction of transmembrane beta-barrel proteins found in Gram-negative bacteria. These proteins are involved in many biological processes and primary candidates as drug targets. BetAware-Deep exploits the combination of a deep learning framework (bidirectional long short-term memory) and a probabilistic graphical model (grammatical-restrained hidden conditional random field). Moreover, it introduced a modified formulation of the hydrophobic moment, designed to include the evolutionary information. BetAware-Deep outperformed all the available methods in topology prediction and reported high scores in the detection task. Glycine myristoylation in Eukaryotes is the binding of a myristic acid on an N-terminal glycine. SVMyr is a fast method based on support vector machines designed to predict this modification in dataset of proteomic scale. It uses as input octapeptides and exploits computational scores derived from experimental examples and mean physicochemical features. SVMyr outperformed all the available methods for co-translational myristoylation prediction. In addition, it allows (as a unique feature) the prediction of post-translational myristoylation. Both the tools here described are designed having in mind best practices for the development of machine learning-based tools outlined by the bioinformatics community. Moreover, they are made available via user-friendly web servers. All this make them valuable tools for filling the gap between sequential and annotated data.