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PubMed1997

Influence of static and dynamic bends on the birefringence decay profile of RNA helices: Brownian dynamics simulations.

Bends in nucleic acid helices can be quantified in a transient electric birefringence (TEB) experiment from the ratio of the terminal decay times of the bent molecule and its fully duplex counterpart (tau-ratio method). The apparent bend angles can be extracted from the experimental tau-ratios through the application of static (equilibrium-ensemble) hydrodynamic models; however, such models do not properly address the faster component(s) of the birefringence decay profile, which can represent up to 80% of the total birefringence signal for large band angles. To address this latter issue, the relative amplitudes of the components in the birefringence decay profile have been analyzed through a series of Brownian dynamics (BD) simulations. Decay profiles have been simulated for three-, five-, and nine-bead models representing RNA molecules with central bends of 30 degrees, 60 degrees, and 90 degrees, and with various degrees of associated angle dispersion. The BD simulations are in close agreement with experimental results for the fractional amplitudes, suggesting that both amplitudes and terminal tau-ratios can be used as a measure of the magnitudes of bends in the helix axis. Although the current results indicate that it is generally not possible to distinguish between relatively fixed and highly flexible bends from single tau-ratio measurements, because they can lead to similar reductions in terminal decay time and amplitude, measurements of the dependence of the fractional amplitudes on helix length may afford such a distinction.

Algorithms↗
PubMed1999

Hydrogen-exchange stabilities of RNase T1 and variants with buried and solvent-exposed Ala --> Gly mutations in the helix.

Hydrogen-exchange rates were measured for RNase T1 and three variants with Ala --> Gly substitutions at a solvent-exposed (residue 21) and a buried (residue 23) position in the helix: A21G, G23A, and A21G + G23A. These results were used to measure the stabilities of the proteins. The hydrogen-exchange stabilities (DeltaG(HX)) for the most stable residues in each variant agree with the equilibrium conformational stability measured by urea denaturation (DeltaG(U)), if the effects of D(2)O and proline isomerization are included [Huyghues-Despointes, B. M. P., Scholtz, J. M., and Pace, C. N. (1999) Nat. Struct. Biol. 6, 210-212]. These residues also show similar changes in DeltaG(HX) upon Ala --> Gly mutations (DeltaDeltaG(HX)) as compared to equilibrium measurements (DeltaDeltaG(U)), indicating that the most stable residues are exchanging from the globally unfolded ensemble. Alanine is stabilizing compared to glycine by 1 kcal/mol at a solvent-exposed site 21 as seen by other methods for the RNase T1 protein and peptide helix [Myers, J. K., Pace, C. N., and Scholtz, J. M. (1997) Proc. Natl. Acad. Sci. U.S.A. 94, 3833-2837], while it is destabilizing at the buried site 23 by the same amount. For the A21G variant, only local NMR chemical shift perturbations are observed compared to RNase T1. For the G23A variant, large chemical shift changes are seen throughout the sequence, although X-ray crystal structures of the variant and RNase T1 are nearly superimposable. Ala --> Gly mutations in the helix of RNase T1 at both helical positions alter the native-state hydrogen-exchange stabilities of residues throughout the sequence.

Alanine↗
PubMedDate not supplied

Metric and multidimensional scaling: efficient tools for clustering molecular conformations.

The application of metric and multidimensional scaling to conformer ensembles was demonstrated in this work. An automated process was devised to cluster and assign group memberships and cluster representatives. The method allows rapid clustering, leading to intuitive results that can be visually inspected. Multidimensional scaling was found to be superior to metric scaling for clustering conformers. The performance of different hierarchical clustering algorithms was compared using multidimensional plots, and the group average method was found to perform best.

Amino Acid Sequence↗
PubMed1994

HIV-1 reverse transcriptase inhibitor design using artificial neural networks.

Artificial neural networks were used to analyze and predict the human immunodeficiency virus type 1 reverse transcriptase inhibitors. The training and control sets included 44 molecules (most of them are well-known substances such as AZT, dde, etc.). The activities of the molecules were taken from literature. Topological indices were calculated and used as molecular parameters. The four most informative parameters were chosen and applied to predict activities of both new and control molecules. We used a network pruning algorithm and network ensembles to obtain the final classifier. Increasing of neural network generalization of the new data was observed, when using the aforementioned methods. The prognosis of new molecules revealed one molecule as possibly very active. It was confirmed by further biological tests.

Algorithms↗
PubMed2004

Energy controlled insertion of polar molecules in dense fluids.

We present a method to search low energy configurations of polar molecules in the complex potential energy surfaces associated with dense fluids. The search is done in the configurational space of the translational and rotational degrees of freedom of the molecule, combining steepest-descent and Newton-Raphson steps which embed information on the average sizes of the potential energy wells obtained from prior inspection of the liquid structure. We perform a molecular dynamics simulation of a liquid water shell which demonstrates that the method enables fast and energy-controlled water molecule insertion in aqueous environments. The algorithm finds low energy configurations of incoming water molecules around three orders of magnitude faster than direct random insertion. This method represents an important step towards dynamic simulations of open systems and it may also prove useful for energy-biased ensemble average calculations of the chemical potential.

Journal Article↗
PubMed2002

Early kinetic intermediate in the folding of acyl-CoA binding protein detected by fluorescence labeling and ultrarapid mixing.

Early conformational events during folding of acyl-CoA binding protein (ACBP), an 86-residue alpha-helical protein, were explored by using a continuous-flow mixing apparatus with a dead time of 70 micros to measure changes in intrinsic tryptophan fluorescence and tryptophan-dansyl fluorescence energy transfer. Although the folding of ACBP was initially described as a concerted two-state process, the tryptophan fluorescence measurements revealed a previously unresolved phase with a time constant tau = 80 micros, indicating formation of an intermediate with only slightly enhanced fluorescence of Trp-55 and Trp-58 relative to the unfolded state. To amplify this phase, a dansyl fluorophore was introduced at the C terminus by labeling an I86C mutant of ACBP with 5-IAEDANS [5-((((2-iodoacetyl)amino)ethyl)amino)naphthalene-1-sulfonic acid]. Continuous-flow refolding of guanidine HCl-denatured ACBP showed a major increase in tryptophan-dansyl fluorescence energy transfer, indicating formation of a partially collapsed ensemble of states on the 100-micros time scale. A subsequent decrease in dansyl fluorescence is attributed to intramolecular quenching of donor fluorescence on formation of the native state. The kinetic data are fully accounted for by three-state mechanisms with either on- or off-pathway intermediates. The intermediate accumulates to a maximum population of 40%, and its stability depends only weakly on denaturant concentration, which is consistent with a marginally stable ensemble of partially collapsed states with approximately 1/3 of the solvent-accessible surface buried. The findings indicate that ultrafast mixing methods combined with sensitive conformational probes can reveal transient accumulation of intermediate states in proteins with apparent two-state folding mechanisms.

Amino Acid Substitution↗
PubMed2004

Synchronization measures of bursting data: application to the electrocorticogram of an auditory event-related experiment.

Synchronization measures have become an important tool for exploring the relationships between time series. We review three recently proposed nonlinear synchronization measures and expand their definitions in a straightforward way to apply to an ensemble of measurements. We also develop a synchronization measure in which nearest neighbors are determined across the ensemble. We compare these four nonlinear synchronization measures and show that our measure succeeds in physically motivated examples where the other methods fail. We apply the synchronization measure to human electrocorticogram data collected during an auditory event-related potential experiment. The results suggest a crude model of cortical connectivity.

Action Potentials↗
PubMed2005

Self-gravitating Brownian systems and bacterial populations with two or more types of particles.

We study the thermodynamical properties of a self-gravitating gas with two or more types of particles. Using the method of linear series of equilibria, we determine the structure and stability of statistical equilibrium states in both microcanonical and canonical ensembles. We show how the critical temperature (Jeans instability) and the critical energy (Antonov instability) depend on the relative mass of the particles and on the dimension of space. We then study the dynamical evolution of a multicomponent gas of self-gravitating Brownian particles in the canonical ensemble. Self-similar solutions describing the collapse below the critical temperature are obtained analytically. We find particle segregation, with the scaling profile of the slowest collapsing particles decaying with a nonuniversal exponent that we compute perturbatively in different limits. These results are compared with numerical simulations of the two-species Smoluchowski-Poisson system. Our model of self-attracting Brownian particles also describes the chemotactic aggregation of a multi-species system of bacteria in biology.

Bacteria↗
PubMed2006

Combinations of single nucleotide polymorphisms in neuroendocrine effector and receptor genes predict chronic fatigue syndrome.

OBJECTIVE: This paper asks whether the presence of chronic fatigue syndrome (CFS) can be more accurately predicted from single nucleotide polymorphism (SNP) profiles than would occur by chance. METHODS: Specifically, given SNP profiles for 43 CFS patients, together with 58 controls, we used an enumerative search to identify an ensemble of conjunctive rules that predict whether a patient has CFS. RESULTS: The accuracy of the rules reached 76.3%, with the highest accuracy rules yielding 49 true negatives, 15 false negatives, 28 true positives and nine false positives (odds ratio [OR] 8.94, p < 0.0001). Analysis of the SNPs used most frequently in the overall ensemble of rules gave rise to a list of 'most important SNPs', which was not identical to the list of 'most differentiating SNPs' that one would calculate via studying each SNP independently. The top three genes containing the SNPs accounting for the highest accumulated importances were neuronal tryptophan hydroxylase (TPH2), catechol-O-methyltransferase (COMT) and nuclear receptor subfamily 3, group C, member 1 glucocorticoid receptor (NR3C1). CONCLUSION: The fact that only 28 out of several million possible SNPs predict whether a person has CFS with 76% accuracy indicates that CFS has a genetic component that may help to explain some aspects of the illness.

Algorithms↗
PubMed1990

[Computer-assisted evaluation of emission data].

To estimate the hygienic relevance of a given air pollution situation the knowledge of the quantiles of an ensemble of measuring data is very important. A computer programme to determine the quantiles is presented. In addition to the usual method of ranking data it allows the calculation by use of the Weibull distribution function, which is important in the case of a small data set. First results of practising on this software are discussed.

Air Pollutants↗
PubMed2000

Matching protein beta-sheet partners by feedforward and recurrent neural networks.

Predicting the secondary structure (alpha-helices, beta-sheets, coils) of proteins is an important step towards understanding their three dimensional conformations. Unlike alpha-helices that are built up from one contiguous region of the polypeptide chain, beta-sheets are more complex resulting from a combination of two or more disjoint regions. The exact nature of these long distance interactions remains unclear. Here we introduce two neural-network based methods for the prediction of amino acid partners in parallel as well as anti-parallel beta-sheets. The neural architectures predict whether two residues located at the center of two distant windows are paired or not in a beta-sheet structure. Variations on these architecture, including also profiles and ensembles, are trained and tested via five-fold cross validation using a large corpus of curated data. Prediction on both coupled and non-coupled residues currently approaches 84% accuracy, better than any previously reported method.

Animals↗
PubMed2003

Dynamical mean-field theory of spiking neuron ensembles: response to a single spike with independent noises.

A semianalytical dynamical mean-field theory has been developed for a study of dynamics of an ensemble of N-unit FitzHugh-Nagumo neurons subject to white noises. Assuming weak noises and the Gaussian distribution of state variables, we have driven equations of motions for moments of local and global variables. Dynamical mean-field approximation (DMA) has replaced original, 2N-dimensional stochastic differential equations (DEs) by eight-dimensional deterministic DEs, whereas the conventional moment method yields N(2N+3)-dimensional deterministic DEs for local variables. We have discussed the dependence of the spike firing precision and the synchronization on the noise intensity, synaptic coupling, and the size of the neuron ensemble. The spike timing precision is shown to be improved by increasing the size of the neuron ensemble, even when there are no couplings among neurons. When the coupling is introduced, neurons in ensembles respond to an input spike with a partial synchronization. Results calculated by our DMA theory are in good agreement with those obtained by direct simulations. DMA theory is extended to a large cluster which can be divided into multiple subclusters according to their functions. A model calculation has demonstrated that when the noise intensity is moderate, the spike propagation with a fairly precise timing is possible among noisy subclusters with feed-forward couplings, as in the synfire chain. We have compared DMA theory with the conventional moment method, showing that the former may be alternatively derived from the latter by a reduction in the number of moments with the mean-field approximation.

Animals↗
PubMed2005

Transition state ensemble for the folding of B domain of protein A: a comparison of distributed molecular dynamics simulations with experiments.

Folding pathways of the B domain of staphylococcal protein A have been sampled with a distributed computing approach. Starting from an extended structure, the method employs an index measuring topological similarity to the native structure to selectively sample trajectory branches leading to the native fold. Unperturbed and continuous folding trajectories are drawn on a physics-based atomic potential energy surface with an implicit solvent. The sampled folding trajectories demonstrate a similar sequence of events: the earlier stage involves a partial formation of helix 2 and to a less extent of helix 1 at their N terminals, followed by the hydrophobic collapse between residues F14, I17, and L18 on helix 1 and residues R28, F31, and I32 on helix 2, which results in the rigidification of the helix turn from R28 to I32. Helix 2 is then able to extend, allowing for the formation to turn 2. The above description explains one experimental result why a G30A mutant of the protein was observed to be the fastest folder among proteins of its size. And the ensemble of structures right before the final collapse is in good agreement with the transition state ensemble mapped by another recent experiment with Fersht Phi values. We emphasize that because the approach here does not provide quantifications of the free energy landscape, our model of the transition state ensemble emerges from comparisons of simulations and previous experimental results rather from the simulation results alone. On the other hand, as our approach does not rely on a low-dimensional free energy surface, it can complement methods based on the construction of free energy surfaces.

Amino Acid Sequence↗
PubMed2005

Clustering algorithms for identifying core atom sets and for assessing the precision of protein structure ensembles.

An important open question in the field of NMR-based biomolecular structure determination is how best to characterize the precision of the resulting ensemble of structures. Typically, the RMSD, as minimized in superimposing the ensemble of structures, is the preferred measure of precision. However, the presence of poorly determined atomic coordinates and multiple "RMSD-stable domains"--locally well-defined regions that are not aligned in global superimpositions--complicate RMSD calculations. In this paper, we present a method, based on a novel, structurally defined order parameter, for identifying a set of core atoms to use in determining superimpositions for RMSD calculations. In addition we present a method for deciding whether to partition that core atom set into "RMSD-stable domains" and, if so, how to determine partitioning of the core atom set. We demonstrate our algorithm and its application in calculating statistically sound RMSD values by applying it to a set of NMR-derived structural ensembles, superimposing each RMSD-stable domain (or the entire core atom set, where appropriate) found in each protein structure under consideration. A parameter calculated by our algorithm using a novel, kurtosis-based criterion, the epsilon-value, is a measure of precision of the superimposition that complements the RMSD. In addition, we compare our algorithm with previously described algorithms for determining core atom sets. The methods presented in this paper for biomolecular structure superimposition are quite general, and have application in many areas of structural bioinformatics and structural biology.

Algorithms↗
PubMed2005

Simultaneous determination of protein structure and dynamics.

We present a protocol for the experimental determination of ensembles of protein conformations that represent simultaneously the native structure and its associated dynamics. The procedure combines the strengths of nuclear magnetic resonance spectroscopy--for obtaining experimental information at the atomic level about the structural and dynamical features of proteins--with the ability of molecular dynamics simulations to explore a wide range of protein conformations. We illustrate the method for human ubiquitin in solution and find that there is considerable conformational heterogeneity throughout the protein structure. The interior atoms of the protein are tightly packed in each individual conformation that contributes to the ensemble but their overall behaviour can be described as having a significant degree of liquid-like character. The protocol is completely general and should lead to significant advances in our ability to understand and utilize the structures of native proteins.

Computer Simulation↗
PubMed1994

The effects of signal conditioning on the statistical analyses of gait EMG.

Ensemble averaged EMG profiles generated for leg muscles during gait have been used to clinically assess disease or injury. Several of the methods that have been reported for conditioning gait EMG signals were compared using data collected from clinically normal subjects walking on a treadmill. Specifically investigated were the effects of filtering and the quantity of data averaged upon several statistical tests that measure the variability of, or differences between, EMG profiles. Our results suggest that the variance ratio (VR) provides a reasonable test of data variability because of its modest sensitivity to both the degree of filtering and the amount of data averaged. They also suggest that of the comparison statistics: Pearson's r, the Kolmogorov-Smirnov T test and the ANOVA F ratio, the T test was the most reliable in detecting differences between given profiles for all test conditions. However, recognition of this ability of the T test must be tempered by the knowledge that while obvious EMG signal differences did exist, observable functional differences in gait did not. The relationship between statistically similar/dissimilar EMG patterns and clinically functional/dysfunctional gait patterns needs to be established. In addition, since all of the test statistics studied were affected to some degree by filtering and averaging, care should be used when comparing statistical results from separate studies unless it is known that the studies were conducted under similar conditions, including data processing. To that end, we recommend that at least 20 strides be used in the averaging process since the statistics we tested have reached or are asymptotically approaching their final values by this point.

Analysis of Variance↗
PubMed2006

Personal noise ranking of road traffic: subjective estimation versus physiological parameters under laboratory conditions.

OBJECTIVE: To evaluate the subjective estimation of noise-induced discomfort and its correlation to psychoacoustic and physiological parameters under laboratory conditions. To establish an effective description of sound qualities of road traffic noise, supplementing the current standards and calculation specifications. METHODS: Pass-by vehicle noise samples were binaurally recorded with a dummy head measurement system, and synthetically composed to six vehicle ensembles considering different road beds, varying speed profiles and noise barriers. Fifty-one persons were selected and tested under laboratory conditions. Study participants were exposed to defined acoustic stimuli, alternating with neutral phases lacking acoustic content in a listening room. Concomitant recording of electrocardiogram (ECG) and respiratory rate was performed. Subjective estimation of noise-induced discomfort of assigned vehicle ensembles was rated on a personal ranking scale (PRS) by the study subjects. Subjective ratings were combined with objective psychoacoustic parameters by multiple regression analysis. RESULTS: Heart rate was increased during all noise exposure phases compared to neutral phases; the increase of heart rate differed among vehicle ensembles and was statistically significant in two cases (p<0.01). Respiratory rate remained unaffected. Personal rankings also differed among vehicle ensembles and correlated well with objective psychoacoustic parameters (p<0.0001); e.g., loudness combined with roughness describes the correlation with subjective estimation of noise-induced discomfort better than the A-weighted sound level. Vehicle ensembles rated more unpleasant caused higher increases in heart rate as well (p<0.0001). CONCLUSIONS: The sound quality of road traffic noise as it is described by various psychoacoustic parameters not only determines the subjective estimation of noise-induced discomfort but in addition affects physiological parameters like heart rate. This should be considered for future perspectives in road- and traffic planning and therefore may serve construction engineers as well as traffic planner as a supplemental tool.

Austria↗
PubMed2006

Homology modeling using parametric alignment ensemble generation with consensus and energy-based model selection.

The accuracy of a homology model based on the structure of a distant relative or other topologically equivalent protein is primarily limited by the quality of the alignment. Here we describe a systematic approach for sequence-to-structure alignment, called 'K*Sync', in which alignments are generated by dynamic programming using a scoring function that combines information on many protein features, including a novel measure of how obligate a sequence region is to the protein fold. By systematically varying the weights on the different features that contribute to the alignment score, we generate very large ensembles of diverse alignments, each optimal under a particular constellation of weights. We investigate a variety of approaches to select the best models from the ensemble, including consensus of the alignments, a hydrophobic burial measure, low- and high-resolution energy functions, and combinations of these evaluation methods. The effect on model quality and selection resulting from loop modeling and backbone optimization is also studied. The performance of the method on a benchmark set is reported and shows the approach to be effective at both generating and selecting accurate alignments. The method serves as the foundation of the homology modeling module in the Robetta server.

Amino Acid Sequence↗