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.     

Conformational Effects of DNA Stretching

DNA is an extensible molecule, and an extended conformation of DNA is involved in some biological processes. We have examined the effect of elongation stress on the conformational properties of DNA base pairs by conformational analysis. The calculations show that stretching does significantly affect the conformational properties and flexibilities of base pairs. In particular, we have found that the propeller twist in base pairs reverses its sign upon stretching. The energy profile analysis indicates that electrostatic interactions make a major contribution to the stabilization of the positive-propeller-twist configuration in stretched DNA. This stretching also results in a monotonic decrease in the helical twist angle, tending to unwind the double helix. Fluctuations in most variables initially increase upon stretching, because of unstacking of base pairs, but then the fluctuations decrease as DNA is stretched further, owing to the formation of specific interactions between base pairs induced by the positive propeller twist. Thus, the stretching of DNA has particularly significant effects upon DNA flexibility. These changes in both the conformation and flexibility of base pairs probably have a role in functional interactions with proteins.     

Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable

Background

By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Naïve Bayes and other machine learning algorithms we are able to distinguish between two classes of protein sequences: those folding to highly-designable conformations, or those folding to poorly- or non-designable conformations.

Results

First, we generate all possible compact lattice conformations for the specified shape (a hexagon or a triangle) on the 2D triangular lattice. Then we generate all possible binary hydrophobic/polar (H/P) sequences and by using a specified energy function, thread them through all of these compact conformations. If for a given sequence the lowest energy is obtained for a particular lattice conformation we assume that this sequence folds to that conformation. Highly-designable conformations have many H/P sequences folding to them, while poorly-designable conformations have few or no H/P sequences. We classify sequences as folding to either highly – or poorly-designable conformations. We have randomly selected subsets of the sequences belonging to highly-designable and poorly-designable conformations and used them to train several different standard machine learning algorithms.

Conclusion

By using these machine learning algorithms with ten-fold cross-validation we are able to classify the two classes of sequences with high accuracy – in some cases exceeding 95%.

     

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.     

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/.     

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.     

Inferring ideal amino acid interaction forms from statistical protein contact potentials

We have analyzed 29 different published matrices of protein pairwise contact potentials (CPs) between amino acids derived from different sets of proteins, either crystallographic structures taken from the Protein Data Bank (PDB) or computer-generated decoys. Each of the CPs is similar to 1 of the 2 matrices derived in the work of Miyazawa and Jernigan (Proteins 1999;34:49-68). The CP matrices of the first class can be approximated with a correlation of order 0.9 by the formula e(ij) = h(i) + h(j), 1 相似文献   

GOR V server for protein secondary structure prediction

SUMMARY: We have created the GOR V web server for protein secondary structure prediction. The GOR V algorithm combines information theory, Bayesian statistics and evolutionary information. In its fifth version, the GOR method reached (with the full jack-knife procedure) an accuracy of prediction Q3 of 73.5%. Although GOR V has been among the most successful methods, its online unavailability has been a deterrent to its popularity. Here, we remedy this situation by creating the GOR V server.