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.     

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 ().     

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%.

     

Sequence-dependent B<-->A transition in DNA evaluated with dimeric and trimeric scales.

Experimental data on the sequence-dependent B<-->A conformational transition in 24 oligo- and polymeric duplexes yield optimal dimeric and trimeric scales for this transition. The 10 sequence dimers and the 32 trimers of the DNA duplex were characterized by the free energy differences between the B and A forms in water solution. In general, the trimeric scale describes the sequence-dependent DNA conformational propensities more accurately than the dimeric scale, which is likely related to the trimeric model accounting for the two interfaces between adjacent base pairs on both sides (rather than only one interface in the dimeric model). The exceptional preference of the B form for the AA:TT dimers and AAN:N'TT trimers is consistent with the cooperative interactions in both grooves. In the minor groove, this is the hydration spine that stabilizes adenine runs in B form. In the major groove, these are hydrophobic interactions between the thymine methyls and the sugar methylene groups from the preceding nucleotides, occurring in B form. This interpretation is in accord with the key role played by hydration in the B<-->A transition in DNA. Importantly, our trimeric scale is consistent with the relative occurrences of the DNA trimers in A form in protein-DNA cocrystals. Thus, we suggest that the B/A scales developed here can be used for analyzing genome sequences in search for A-philic motifs, putatively operative in the protein-DNA recognition.     

Chronic hypoxia augments protein kinase G-mediated Ca2+ desensitization in pulmonary vascular smooth muscle through inhibition of RhoA/Rho kinase signaling

Pulmonary vascular smooth muscle (VSM) sensitivity to nitric oxide (NO) is enhanced in pulmonary arteries from rats exposed to chronic hypoxia (CH) compared with controls. Furthermore, in contrast to control arteries, relaxation to NO following CH is not reliant on a decrease in VSM intracellular free calcium ([Ca(2+)](i)). We hypothesized that enhanced NO-dependent pulmonary vasodilation following CH is a function of VSM myofilament Ca(2+) desensitization via inhibition of the RhoA/Rho kinase (ROK) pathway. To test this hypothesis, we compared the ability of the NO donor, spermine NONOate, to reverse VSM tone generated by UTP, the ROK agonist sphingosylphosphorylcholine, or the protein kinase C (PKC) activator phorbol 12-myristate 13-acetate in Ca(2+)-permeabilized, endothelium-denuded pulmonary arteries (150- to 300-microm inner diameter) from control and CH (4 wk at 0.5 atm) rats. Arteries were loaded with fura-2 AM to continuously monitor VSM [Ca(2+)](i). We further examined effects of NO on levels of GTP-bound RhoA and ROK membrane translocation as indexes of enzyme activity in arteries from each group. We found that spermine NONOate reversed Y-27632-sensitive Ca(2+) sensitization and inhibited both RhoA and ROK activity in vessels from CH rats but not control animals. In contrast, spermine NONOate was without effect on PKC-mediated vasoconstriction in either group. We conclude that CH mediates a shift in NO signaling to promote pulmonary VSM Ca(2+) desensitization through inhibition of RhoA/ROK.