DNA conformational kinetics

A model for the time dependence of DNA conformational state probabilities is formulated in the form of first-order differential equations. This model is applied to investigate the renaturation and denaturation rates for T2 and T7 DNA as reported in the series of experiments by Record and Zimm. Qualitative agreement is found in denaturation and for series of renaturation experiments with the same initial condition. However, partial agreement with series of renaturation experiments having the same final condition is obtained only by including an initial bimolecular step with properly matched pairs of strands. Comparison of all experiments with the calculated rates yields 5 × 10⁴ min^?1 as the step rate for melting a single base pair.     

Statistical measures on residue-level protein structural properties

The atomic-level structural properties of proteins, such as bond lengths, bond angles, and torsion angles, have been well studied and understood based on either chemistry knowledge or statistical analysis. Similar properties on the residue-level, such as the distances between two residues and the angles formed by short sequences of residues, can be equally important for structural analysis and modeling, but these have not been examined and documented on a similar scale. While these properties are difficult to measure experimentally, they can be statistically estimated in meaningful ways based on their distributions in known proteins structures. Residue-level structural properties including various types of residue distances and angles are estimated statistically. A software package is built to provide direct access to the statistical data for the properties including some important correlations not previously investigated. The distributions of residue distances and angles may vary with varying sequences, but in most cases, are concentrated in some high probability ranges, corresponding to their frequent occurrences in either α-helices or β-sheets. Strong correlations among neighboring residue angles, similar to those between neighboring torsion angles at the atomic-level, are revealed based on their statistical measures. Residue-level statistical potentials can be defined using the statistical distributions and correlations of the residue distances and angles. Ramachandran-like plots for strongly correlated residue angles are plotted and analyzed. Their applications to structural evaluation and refinement are demonstrated. With the increase in both number and quality of known protein structures, many structural properties can be derived from sets of protein structures by statistical analysis and data mining, and these can even be used as a supplement to the experimental data for structure determinations. Indeed, the statistical measures on various types of residue distances and angles provide more systematic and quantitative assessments on these properties, which can otherwise be estimated only individually and qualitatively. Their distributions and correlations in known protein structures show their importance for providing insights into how proteins may fold naturally to various residue-level structures.     

Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure prediction

Computational methods are rapidly gaining importance in the field of structural biology, mostly due to the explosive progress in genome sequencing projects and the large disparity between the number of sequences and the number of structures. There has been an exponential growth in the number of available protein sequences and a slower growth in the number of structures. There is therefore an urgent need to develop computational methods to predict structures and identify their functions from the sequence. Developing methods that will satisfy these needs both efficiently and accurately is of paramount importance for advances in many biomedical fields, including drug development and discovery of biomarkers. A novel method called fast learning optimized prediction methodology (FLOPRED) is proposed for predicting protein secondary structure, using knowledge-based potentials combined with structure information from the CATH database. A neural network-based extreme learning machine (ELM) and advanced particle swarm optimization (PSO) are used with this data that yield better and faster convergence to produce more accurate results. Protein secondary structures are predicted reliably, more efficiently and more accurately using FLOPRED. These techniques yield superior classification of secondary structure elements, with a training accuracy ranging between 83?% and 87?% over a widerange of hidden neurons and a cross-validated testing accuracy ranging between 81?% and 84?% and a segment overlap (SOV) score of 78?% that are obtained with different sets of proteins. These results are comparable to other recently published studies, but are obtained with greater efficiencies, in terms of time and cost.     

A multivariate twin study of hippocampal volume, self-esteem and well-being in middle-aged men

Self-esteem and well-being are important for successful aging, and some evidence suggests that self-esteem and well-being are associated with hippocampal volume, cognition and stress responsivity. Whereas most of this evidence is based on studies on older adults, we investigated self-esteem, well-being and hippocampal volume in 474 male middle-aged twins. Self-esteem was significantly positively correlated with hippocampal volume (0.09, P = 0.03 for left hippocampus, 0.10, P = 0.04 for right). Correlations for well-being were not significant (Ps > 0.05). There were strong phenotypic correlations between self-esteem and well-being (0.72, P < 0.001) and between left and right hippocampal volume (0.72, P < 0.001). In multivariate genetic analyses, a two-factor additive genetic and unique environmental (AE) model with well-being and self-esteem on one factor and left and right hippocampal volumes on the other factor fits the data better than Cholesky, independent pathway or common pathway models. The correlation between the two genetic factors was 0.12 (P = 0.03); the correlation between the environmental factors was 0.09 (P > 0.05). Our results indicate that largely different genetic and environmental factors underlie self-esteem and well-being on one hand and hippocampal volume on the other.     

P.R.E.S.S.--an R-package for exploring residual-level protein structural statistics

P.R.E.S.S. is an R-package developed to allow researchers to get access to and manipulate a large set of statistical data on protein residue-level structural properties such as residue-level virtual bond lengths, virtual bond angles, and virtual torsion angles. A large set of high-resolution protein structures is downloaded and surveyed. Their residue-level structural properties are calculated and documented. The statistical distributions and correlations of these properties can be queried and displayed. Tools are also provided for modeling and analyzing a given structure in terms of its residue-level structural properties. In particular, new tools for computing residue-level statistical potentials and displaying residue-level Ramachandran-like plots are developed for structural analysis and refinement. P.R.E.S.S. has been released in R as an open source software package, with a user-friendly GUI, accessible and executable by a public user in any R environment. P.R.E.S.S. can also be downloaded directly at http://www.math.iastate.edu/press/.     

Chronic hypoxia upregulates pulmonary arterial ASIC1: a novel mechanism of enhanced store-operated Ca2+ entry and receptor-dependent vasoconstriction

Acid-sensing ion channel 1 (ASIC1) is a newly characterized contributor to store-operated Ca(2+) entry (SOCE) in pulmonary vascular smooth muscle (VSM). Since SOCE is implicated in elevated basal VSM intracellular Ca(2+) concentration ([Ca(2+)](i)) and augmented vasoconstriction in chronic hypoxia (CH)-induced pulmonary hypertension, we hypothesized that ASIC1 contributes to these responses. To test this hypothesis, we examined effects of the specific pharmacologic ASIC1a inhibitor, psalmotoxin 1 (PcTX1), on vasoconstrictor and vessel wall [Ca(2+)](i) responses to UTP and KCl (depolarizing stimulus) in fura-2-loaded, pressurized small pulmonary arteries from control and CH (4 wk at 0.5 atm) Wistar rats. PcTX1 had no effect on basal vessel wall [Ca(2+)](i), but attenuated vasoconstriction and increases in vessel wall [Ca(2+)](i) to UTP in arteries from control and CH rats; normalizing responses between groups. In contrast, responses to the depolarizing stimulus, KCl, were unaffected by CH exposure or PcTX1. Upon examining potential Ca(2+) influx mechanisms, we found that PcTX1 prevented augmented SOCE following CH. Exposure to CH resulted in a significant increase in pulmonary arterial ASIC1 protein. This study supports a novel role of ASIC1 in elevated receptor-stimulated vasoconstriction following CH which is likely mediated through increased ASIC1 expression and SOCE.     

Protein misfolding, mitochondrial dysfunction and muscle loss are not directly dependent on soluble and aggregation state of mSOD1 protein in skeletal muscle of ALS

Mutant superoxide dismutase 1 (mSOD1) is often found as aggregates at the outer-membrane of mitochondria in motor neurons of various mouse models and familial amyotrophic lateral sclerosis (f-ALS) patients. It has been postulated that disruption of mitochondrial function by physical association of misfolded mSOD1 aggregates may actually be the trigger for initiation of degeneration of motor neurons in ALS. However, it was not clear if the same mechanism is involved in muscle degeneration and mitochondrial dysfunction in skeletal muscles of ALS. Recent study from our laboratory show that two skeletal muscle proteins, namely creatine kinase (CK) and glyceraldehydes-3-phosphate dehydrogenase (GAPDH) undergo major conformational and functional changes in the f-ALS mouse model of ALS (G93A). In this paper, we report two intriguing observations which are as follows:(i) G93A protein does not form aggregates in skeletal muscle at any stages of disease process probably due to high chymotrypsin-like activity of proteasome and thus G93A protein aggregates have no direct effects on progressive loss of muscle mass and global changes in protein conformation in ALS, and (ii) the soluble G93A protein does not have direct effects on mitochondrial dysfunction as determined by quantifying the release of reactive oxygen species (ROS) in skeletal muscle mitochondria; instead, the proteins affected by G93A possibly affect mitochondrial ROS release. These data strongly suggest for the first time that unlike in motor neurons, the soluble and aggregation states of the G93A protein do not have direct effects on protein misfolding and mitochondrial dysfunction in skeletal muscle during ALS.     

Distance matrix-based approach to protein structure prediction

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r _ij ² ] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r _ij is greater or less than a cutoff value r _cutoff. We have performed spectral decomposition of the distance matrices $ {\mathbf{D}} = \sum {\lambda_{k} {\mathbf{v}}_{k} {\mathbf{v}}_{k}^{T} } Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r _ij²] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r _ij is greater or less than a cutoff value r _cutoff. We have performed spectral decomposition of the distance matrices , in terms of eigenvalues and the corresponding eigenvectors and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r ²—the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r ² from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 ?, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 ?. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the dynamics. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM) that is based on the contact matrix C (related to D), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to atomic molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide a similar sampling of conformations. Finally, we use distance constraints from databases of known protein structures for structure refinement. We use the distributions of distances of various types in known protein structures to obtain the most probable ranges or the mean-force potentials for the distances. We then impose these constraints on structures to be refined or include the mean-force potentials directly in the energy minimization so that more plausible structural models can be built. This approach has been successfully used by us in 2006 in the CASPR structure refinement ().     

Antibodies designed as effective cancer vaccines

Antigen/antibody complexes can efficiently target antigen presenting cells to allow stimulation of the cellular immune response. Due to the difficulty of manufacture and their inherent instability complexes have proved inefficient cancer vaccines. However, anti-idiotypic antibodies mimicking antigens have been shown to stimulate both antibody and T cell responses. The latter are due to T cell mimotopes expressed within the complementarity-determining regions (CDRs) of antibodies that are efficiently presented to dendritic cells in vivo. Based on this observation we have designed a DNA vaccine platform called ImmunoBody™, where cytotoxic T lymphocyte (CTL) and helper T cell epitopes replace CDR regions within the framework of a human IgG1 antibody. The ImmunoBody™ expression system has a number of design features which allow for rapid production of a wide range of vaccines. The CDR regions of the heavy and light chain have been engineered to contain unique restriction endonuclease sites, which can be easily opened, and oligonucleotides encoding the T cell epitopes inserted. The variable and constant regions of the ImmunoBody™ are also flanked by restriction sites, which permit easy exchange of other IgG subtypes. Here we show a range of T cell epitopes can be inserted into the ImmunoBody™ vector and upon immunization these T cell epitopes are efficiently processed and presented to stimulate high frequency helper and CTL responses capable of anti-tumor activity.Key words: DNA vaccines, cancer vaccines, melanoma, CTL, helper T cells     

Pervasive properties of the genomic signature

Background  

The dinucleotide relative abundance profile can be regarded as a genomic signature because, despite diversity between species, it varies little between 50 kilobase or longer windows on a given genome. Both the causes and the functional significance of this phenomenon could be illuminated by determining if it persists on smaller scales. The profile is computed from the base step "odds ratios" that compare dinucleotide frequencies to those expected under the assumption of stochastic equilibrium (thorough shuffling). Analysis is carried out on 22 sequences, representing 19 species and comprised of about 53 million bases all together, to assess stability of the signature in windows ranging in size from 50 kilobases down to 125 bases.