The statistical analysis of density dependence.

The distrubution of the reciprocal of von Neumann's ratio is considered under the random walk model, x(t+1) rwis;d xt + et, and it is shown how this quantity can be used to test for density dependence in animal populations. Another test is described which is robust under superimposed errors of measurement. The methods are used to analyze data on Canadian fur-bearing mammals.     

The PRESAGE database for structural genomics.

The PRESAGE database is a collaborative resource for structural genomics. It provides a database of proteins to which researchers add annotations indicating current experimental status, structural predictions and suggestions. The database is intended to enhance communication among structural genomics researchers and aid dissemination of their results. The PRESAGE database may be accessed at http://presage.stanford.edu/     

The structural dependence of amino acid hydrophobicity parameters

Amino acid hydrophobicity parameters, G_hp log P (partition coefficient) values, free energies of solution, G_sol and hydration numbers, are well correlated by equations derived from the relationship O_X = Aα_X + Lσ_IX + Sν_X + I_iX + H₁n_HX + H₂n_nX + b₀ where O is the quantity correlated; X denotes the amino acid side chain; α is a polarizability parameter; σ_I, a localized electrical effect parameter; ν, a steric parameter; i, an indicator variable which accounts for an ionic X ; n_H and n_n the number of OH or NH bonds and of full nonbonding orbitals in X, respectively, and b₀ is the intercept. The equation is based on the assumption that Δ_hp log P and ΔG_sol are all functions of the difference in intermolecular forces between the amino acid and some medium, and the amino acid and water. The parameters were chosen to model the intermolecular forces of interest.Generally the most important factor is α_x. This is followed by ν, i, and n_H. Least important is σ_I. ΔG_sol depends on α, n_H and n_n. Hydration numbers depend on i, n_H and n_n. The hydrophobicity of amino acid side chains is the result of a preference for a nonpolar medium as a increases and for a polar medium as i, n_H and σ_I increase. It is quantitatively accounted for by the model, and no special “hydrophobic bond” need be involved. The results show that log P values for amino acids are composite quantities whose composition is variable.     

The dependence of late evoked cortical potentials on a complex of cognitive factors]

The formation of late evoked cortical responses, N150 and P300, correlates with some factors determining cognitive activity (selective attention, different types of memory, decision making process). Influence of different significance stimuli in a particular situation results in creation of an integral "psychonervous image" reflecting a context of these stimuli, whole complex of cognitive tasks to be solved. It influences significantly on the magnitude of cortical evoked reactions to different stimuli that allows to make a correspondence with the "psychological set" phenomenon and its role in the evaluation of the perceived stimuli.     

Some statistical properties of the miniature endplate potentials

I will demonstrate that series of miniature endplate potentials (m.e.p.ps) showing a high proportion of so-called giant m.e.p.ps (g.m.e.p.ps) have different statistical structures from series where the proportion of g.m.e.p.ps is low. The nature of the different structures will be discussed on the basis of two statistical models, one for the distribution of the m.e.p.p. amplitudes and one for the series of point events occurring in time.     

Development of unified statistical potentials describing protein-protein interactions

A residue-based and a heavy atom-based statistical pair potential are developed for use in assessing the strength of protein-protein interactions. To ensure the quality of the potentials, a nonredundant, high-quality dimer database is constructed. The protein complexes in this dataset are checked by a literature search to confirm that they form multimers, and the pairwise amino acid preference to interact across a protein-protein interface is analyzed and pair potentials constructed. The performance of the residue-based potentials is evaluated by using four jackknife tests and by assessing the potentials' ability to select true protein-protein interfaces from false ones. Compared to potentials developed for monomeric protein structure prediction, the interdomain potential performs much better at distinguishing protein-protein interactions. The potential developed from homodimer interfaces is almost the same as that developed from heterodimer interfaces with a correlation coefficient of 0.92. The residue-based potential is well suited for genomic scale protein interaction prediction and analysis, such as in a recently developed threading-based algorithm, MULTIPROSPECTOR. However, the more time-consuming atom-based potential performs better in identifying near-native structures from docking generated decoys.     

The structural basis of the carcinogenic and mutagenic potentials of phytoalexins

CASE, a structure-activity relational system, was used to predict the proportion of substances to be carcinogenic and mutagenic among plant pesticides (phytoalexins) and other natural products compared to that of randomly selected chemicals. There were no significant differences between phytoalexins and other natural products. On the other hand, the natural products, as a group, were predicted to be less mutagenic and carcinogenic than randomly selected chemicals. 37% of natural products are predicted to be rodent carcinogens.     

Development of novel statistical potentials for protein fold recognition

The need to perform large-scale studies of protein fold recognition, structure prediction and protein-protein interactions has led to novel developments of residue-level minimal models of proteins. A minimum requirement for useful protein force-fields is that they be successful in the recognition of native conformations. The balance between the level of detail in describing the specific interactions within proteins and the accuracy obtained using minimal protein models is the focus of many current protein studies. Recent results suggest that the introduction of explicit orientation dependence in a coarse-grained, residue-level model improves the ability of inter-residue potentials to recognize the native state. New statistical and optimization computational algorithms can be used to obtain accurate residue-dependent potentials for use in protein fold recognition and, more importantly, structure prediction.     

The folding pathway of ubiquitin from all-atom molecular dynamics simulations

The folding (unfolding) pathway of ubiquitin is probed using all-atom molecular dynamics simulations. We dissect the folding pathway using two techniques: first, we probe the folding pathway of ubiquitin by calculating the evolution of structural properties over time and second, we identify the rate determining transition state for folding. The structural properties that we look at are hydrophobic solvent accessible surface area (SASA) and Calpha-root-mean-square deviation (rmsd). These properties on their own tell us relatively little about the folding pathway of ubiquitin; however, when plotted against each other, they become powerful tools for dissecting ubiquitin's folding mechanism. Plots of Calpha-rmsd against SASA serve as a phase space trajectories for the folding of ubiquitin. In this study, these plots show that ubiquitin folds to the native state via the population of an intermediate state. This is shown by an initial hydrophobic collapse phase followed by a second phase of secondary structure arrangement. Analysis of the structure of the intermediate state shows that it is a collapsed species with very little secondary structure. In reconciling these observations with recent experimental data, the transition that we observe in our simulations from the unfolded state (U) to the intermediate state (I) most likely occurs in the dead-time of the stopped flow instrument. The folding pathway of ubiquitin is probed further by identification of the rate-determining transition state for folding. The method used for this is essential dynamics, which utilizes a principal component analysis (PCA) on the atomic fluctuations throughout the simulation. The five transition state structures identified in silico are in good agreement with the experimentally determined transition state. The calculation of phi-values from the structures generated in the simulations is also carried out and it shows a good correlation with the experimentally measured values. An initial analysis of the denatured state shows that it is compact with fluctuating regions of nonnative secondary structure. It is found that the compactness in the denatured state is due to the burial of some hydrophobic residues. We conclude by looking at a correlation between folding kinetics and residual structure in the denatured state. A hierarchical folding mechanism is then proposed for ubiquitin.     

The effect of motor training on evoked sensorimotor cortex potentials in rats during ontogenesis]

Investigation into the influence of motor training on the functional activity of the rat sensorimotor cortex in ontogenesis has shown that three to four-month training, starting at the age of four weeks, leads to a statistically significant enhancement of sensorimotor cortex activity both by latencies and recovery cycles durations. A similar six to seven-month locomotor training produces the same statistically significant results. The differences in the shifts of functional activity after motor training observed between two age groups are not statistically significant. The probability of changes in the average definitive electrophysiological parameters of functional activity after motor training observed between two age groups are not statistically significant. The probability of changes in the average definitive electrophysiological parameters of functional activity of the sensorimotor cortex is suggested in rats aged more than a month, as a result of individual experience.     

Fast optimization of statistical potentials for structurally constrained phylogenetic models

Background  

Statistical approaches for protein design are relevant in the field of molecular evolutionary studies. In recent years, new, so-called structurally constrained (SC) models of protein-coding sequence evolution have been proposed, which use statistical potentials to assess sequence-structure compatibility. In a previous work, we defined a statistical framework for optimizing knowledge-based potentials especially suited to SC models. Our method used the maximum likelihood principle and provided what we call the joint potentials. However, the method required numerical estimations by the use of computationally heavy Markov Chain Monte Carlo sampling algorithms.     

Computational modeling of protein mutant stability: analysis and optimization of statistical potentials and structural features reveal insights into prediction model development

Background  

Understanding and predicting protein stability upon point mutations has wide-spread importance in molecular biology. Several prediction models have been developed in the past with various algorithms. Statistical potentials are one of the widely used algorithms for the prediction of changes in stability upon point mutations. Although the methods provide flexibility and the capability to develop an accurate and reliable prediction model, it can be achieved only by the right selection of the structural factors and optimization of their parameters for the statistical potentials. In this work, we have selected five atom classification systems and compared their efficiency for the development of amino acid atom potentials. Additionally, torsion angle potentials have been optimized to include the orientation of amino acids in such a way that altered backbone conformation in different secondary structural regions can be included for the prediction model. This study also elaborates the importance of classifying the mutations according to their solvent accessibility and secondary structure specificity. The prediction efficiency has been calculated individually for the mutations in different secondary structural regions and compared.     

3Dee: a database of protein structural domains

The 3Dee database is a repository of protein structural domains. It stores alternative domain definitions for the same protein, organises domains into sequence and structural hierarchies, contains non-redundant set(s) of sequences and structures, multiple structure alignments for families of domains, and allows previous versions of the database to be regenerated. AVAILABILITY: 3Dee is accessible on the World Wide Web at the URL http://barton.ebi.ac.uk/servers/3Dee.html.