Reduced Stability and Increased Dynamics in the Human Proliferating Cell Nuclear Antigen (PCNA) Relative to the Yeast Homolog

Proliferating Cell Nuclear Antigen (PCNA) is an essential factor for DNA replication and repair. PCNA forms a toroidal, ring shaped structure of 90 kDa by the symmetric association of three identical monomers. The ring encircles the DNA and acts as a platform where polymerases and other proteins dock to carry out different DNA metabolic processes. The amino acid sequence of human PCNA is 35% identical to the yeast homolog, and the two proteins have the same 3D crystal structure. In this report, we give evidence that the budding yeast (sc) and human (h) PCNAs have highly similar structures in solution but differ substantially in their stability and dynamics. hPCNA is less resistant to chemical and thermal denaturation and displays lower cooperativity of unfolding as compared to scPCNA. Solvent exchange rates measurements show that the slowest exchanging backbone amides are at the β-sheet, in the structure core, and not at the helices, which line the central channel. However, all the backbone amides of hPCNA exchange fast, becoming undetectable within hours, while the signals from the core amides of scPCNA persist for longer times. The high dynamics of the α-helices, which face the DNA in the PCNA-loaded form, is likely to have functional implications for the sliding of the PCNA ring on the DNA since a large hole with a flexible wall facilitates the establishment of protein-DNA interactions that are transient and easily broken. The increased dynamics of hPCNA relative to scPCNA may allow it to acquire multiple induced conformations upon binding to its substrates enlarging its binding diversity

Rosetta FlexPepDock ab-initio: Simultaneous Folding, Docking and Refinement of Peptides onto Their Receptors

Flexible peptides that fold upon binding to another protein molecule mediate a large number of regulatory interactions in the living cell and may provide highly specific recognition modules. We present Rosetta FlexPepDock ab-initio, a protocol for simultaneous docking and de-novo folding of peptides, starting from an approximate specification of the peptide binding site. Using the Rosetta fragments library and a coarse-grained structural representation of the peptide and the receptor, FlexPepDock ab-initio samples efficiently and simultaneously the space of possible peptide backbone conformations and rigid-body orientations over the receptor surface of a given binding site. The subsequent all-atom refinement of the coarse-grained models includes full side-chain modeling of both the receptor and the peptide, resulting in high-resolution models in which key side-chain interactions are recapitulated. The protocol was applied to a benchmark in which peptides were modeled over receptors in either their bound backbone conformations or in their free, unbound form. Near-native peptide conformations were identified in 18/26 of the bound cases and 7/14 of the unbound cases. The protocol performs well on peptides from various classes of secondary structures, including coiled peptides with unusual turns and kinks. The results presented here significantly extend the scope of state-of-the-art methods for high-resolution peptide modeling, which can now be applied to a wide variety of peptide-protein interactions where no prior information about the peptide backbone conformation is available, enabling detailed structure-based studies and manipulation of those interactions

Subtle Alterations in PCNA-Partner Interactions Severely Impair DNA Replication and Repair

Dynamic switching of PCNA-partner interactions is essential for normal DNA replication and repair in yeast

Vascular smooth muscle cells remodel collagen matrices by long-distance action and anisotropic interaction

While matrix remodeling plays a key role in vascular physiology and pathology, the underlying mechanisms have remained incompletely understood. We studied the remodeling of collagen matrices by individual vascular smooth muscle cells (SMCs), clusters and monolayers. In addition, we focused on the contribution of transglutaminase 2 (TG2), which plays an important role in the remodeling of small arteries. Single SMCs displaced fibers in collagen matrices at distances up to at least 300 μm in the course of 8–12 h. This process involved both ‘hauling up’ of matrix by the cells and local matrix compaction at a distance from the cells, up to 200 μm. This exceeded the distance over which cellular protrusions were active, implicating the involvement of secreted enzymes such as TG2. SMC isolated from TG2 KO mice still showed compaction, with changed dynamics and relaxation. The TG active site inhibitor L682777 blocked local compaction by wild type cells, strongly reducing the displacement of matrix towards the cells. At increasing cell density, cells cooperated to establish compaction. In a ring-shaped collagen matrix, this resulted in preferential displacement in the radial direction, perpendicular to the cellular long axis. This process was unaffected by inhibition of TG2 cross-linking. These results show that SMCs are capable of matrix remodeling by prolonged, gradual compaction along their short axis. This process could add to the 3D organization and remodeling of blood vessels based on the orientation and contraction of SMCs

Uracil DNA N-Glycosylase Promotes Assembly of Human Centromere Protein A

Uracil is removed from DNA by the conserved enzyme Uracil DNA N-glycosylase (UNG). Previously, we observed that inhibiting UNG in Xenopus egg extracts blocked assembly of CENP-A, a histone H3 variant. CENP-A is an essential protein in all species, since it is required for chromosome segregation during mitosis. Thus, the implication of UNG in CENP-A assembly implies that UNG would also be essential, but UNG mutants lacking catalytic activity are viable in all species. In this paper, we present evidence that UNG2 colocalizes with CENP-A and H2AX phosphorylation at centromeres in normally cycling cells. Reduction of UNG2 in human cells blocks CENP-A assembly, and results in reduced cell proliferation, associated with increased frequencies of mitotic abnormalities and rapid cell death. Overexpression of UNG2 induces high levels of CENP-A assembly in human cells. Using a multiphoton laser approach, we demonstrate that UNG2 is rapidly recruited to sites of DNA damage. Taken together, our data are consistent with a model in which the N-terminus of UNG2 interacts with the active site of the enzyme and with chromatin

Advances in tenascin-C biology

Tenascin-C is an extracellular matrix glycoprotein that is specifically and transiently expressed upon tissue injury. Upon tissue damage, tenascin-C plays a multitude of different roles that mediate both inflammatory and fibrotic processes to enable effective tissue repair. In the last decade, emerging evidence has demonstrated a vital role for tenascin-C in cardiac and arterial injury, tumor angiogenesis and metastasis, as well as in modulating stem cell behavior. Here we highlight the molecular mechanisms by which tenascin-C mediates these effects and discuss the implications of mis-regulated tenascin-C expression in driving disease pathology

A nonparametric data mining approach for risk prediction in car insurance: a case study from the Montenegrin market

For prediction of risk in car insurance we used the nonparametric data mining techniques such as clustering, support vector regression (SVR) and kernel logistic regression (KLR). The goal of these techniques is to classify risk and predict claim size based on data, thus helping the insurer to assess the risk and calculate actual premiums. We proved that used data mining techniques can predict claim sizes and their occurrence, based on the case study data, with better accuracy than the standard methods. This represents the basis for calculation of net risk premium. Also, the article discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such as Montenegrin

Computational Methods for Investment Portfolio: the Use of Fuzzy Measures and Constraint Programming for Risk Management

Summary. Computational intelligence techniques are very useful tools for solving problems that involve understanding, modeling, and analysis of large data sets. One of the numerous fields where computational intelligence has found an extremely important role is finance. More precisely, optimization issues of one’s financial investments, to guarantee a given return, at a minimal risk, have been solved using intelligent techniques such as genetic algorithm, rule-based expert system, neural network, and support-vector machine. Even though these methods provide good and usually fast approximation of the best investment strategy, they suffer some common drawbacks including the neglect of the dependence among among criteria characterizing investment assets (i.e. return, risk, etc.), and the assumption that all available data are precise and certain. To face these weaknesses, we propose a novel approach involving utility-based multi-criteria decision making setting and fuzzy integration over intervals.

Estimating Interest Rate Curves by Support Vector Regression

A model that seeks to estimate an interest rate curve should have two desirable capabilities in addition to the usual characteristics required from any function-estimation model: it should incorporate the bid-ask spreads of the securities from which the curve is extracted and restrict the curve shape. The goal of this article is to estimate interest rate curves by using Support Vector Regression (SVR), a method derived from the Statistical Learning Theory developed by Vapnik (1995). The motivation is that SVR features these extra capabilities at a low estimation cost. The SVR is specified by a loss function, a kernel function and a smoothing parameter. SVR models the daily U.S. dollar interest rate swap curves, from 1997 to 2001. As expected from a priori and sensibility analyses, the SVR equipped with the kernel generating a spline with an infinite number of nodes was the best performing SVR. Comparing this SVR with other models, it achieved the best cross-validation interpolation performance in controlling the bias-variance trade-off and generating the lowest error considering the desired accuracy fixed by the bid-ask spreads.Bid-ask spread, Interest rate curves, Interest rate swaps, Support Vector Regression,