PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “ensemble”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11Linked to original sources

Parametric and ensemble sequence alignment algorithms.

Recently algorithms for parametric alignment (Waterman et al., 1992, Natl Acad. Sci. USA 89, 6090-6093; Gusfield et al., 1992, Proceedings of the Third Annual ACM-SIAM Discrete Algorithms) find optimal scores for all penalty parameters, both for global and local sequence alignment. This paper reviews those techniques. Then in the main part of this paper dynamic programming methods are used to compute ensemble alignment, finding all alignment scores for all parameters. Both global and local ensemble alignments are studied, and parametric alignment is used to compute near optimal ensemble alignments.

Algorithms↗

Neural ensemble coding of target identity in echolocating bats.

Most insectivorous bats use echolocation to determine the identity of flying insects. Among the many target features that are so extracted, the insect's wingbeat pattern and frequency appear to serve as useful cues for identification. Biosonar pulses impinging on the fluttering wings of an insect are returned as echoes whose amplitudes vary with time, thus providing a characteristic signature of the insect. It has been shown previously that neurons in the inferior colliculus, a midbrain auditory nucleus, of the little brown bat respond to sound stimuli that mimic echoes from fluttering targets. To examine the manner in which target identity is represented in the inferior colliculus, an ensemble coding analysis using a filter-based approach was undertaken. The analysis indicates that a discrete subset of neurons in the inferior colliculus, the onset units, are strongly tuned to wingbeat frequencies of targets that the bat hunts, and that ensemble response reaches a maximum at a distinct phase of the prey capture maneuver: the late approach stage. On the basis of the analysis it is hypothesized that inferior colliculus neurons may play an important role in target detection-identification processing. Although ensemble coding of temporally sequenced information has not been analyzed in the auditory system so far, this study indicates that this method of coding may provide the information necessary to detect and identify targets during prey capture.

Acoustic Stimulation↗

Using ensemble classifier to identify membrane protein types.

Predicting membrane protein type is both an important and challenging topic in current molecular and cellular biology. This is because knowledge of membrane protein type often provides useful clues for determining, or sheds light upon, the function of an uncharacterized membrane protein. With the explosion of newly-found protein sequences in the post-genomic era, it is in a great demand to develop a computational method for fast and reliably identifying the types of membrane proteins according to their primary sequences. In this paper, a novel classifier, the so-called "ensemble classifier", was introduced. It is formed by fusing a set of nearest neighbor (NN) classifiers, each of which is defined in a different pseudo amino acid composition space. The type for a query protein is determined by the outcome of voting among these constituent individual classifiers. It was demonstrated through the self-consistency test, jackknife test, and independent dataset test that the ensemble classifier outperformed other existing classifiers widely used in biological literatures. It is anticipated that the idea of ensemble classifier can also be used to improve the prediction quality in classifying other attributes of proteins according to their sequences.

Algorithms↗

RNA unrestrained molecular dynamics ensemble improves agreement with experimental NMR data compared to single static structure: a test case.

Nuclear magnetic resonance (NMR) provides structural and dynamic information reflecting an average, often non-linear, of multiple solution-state conformations. Therefore, a single optimized structure derived from NMR refinement may be misleading if the NMR data actually result from averaging of distinct conformers. It is hypothesized that a conformational ensemble generated by a valid molecular dynamics (MD) simulation should be able to improve agreement with the NMR data set compared with the single optimized starting structure. Using a model system consisting of two sequence-related self-complementary ribonucleotide octamers for which NMR data was available, 0.3 ns particle mesh Ewald MD simulations were performed in the AMBER force field in the presence of explicit water and counterions. Agreement of the averaged properties of the molecular dynamics ensembles with NMR data such as homonuclear proton nuclear Overhauser effect (NOE)-based distance constraints, homonuclear proton and heteronuclear (1)H-(31)P coupling constant (J) data, and qualitative NMR information on hydrogen bond occupancy, was systematically assessed. Despite the short length of the simulation, the ensemble generated from it agreed with the NMR experimental constraints more completely than the single optimized NMR structure. This suggests that short unrestrained MD simulations may be of utility in interpreting NMR results. As expected, a 0.5 ns simulation utilizing a distance dependent dielectric did not improve agreement with the NMR data, consistent with its inferior exploration of conformational space as assessed by 2-D RMSD plots. Thus, ability to rapidly improve agreement with NMR constraints may be a sensitive diagnostic of the MD methods themselves.

Hydrogen Bonding↗

The assessment of LH surge for predicting ovulation time using clinical, hormonal, and ultrasonic indices in infertile women with an ensemble of neural networks.

An ensemble of independently trained neural networks (NN) is proposed for the assessment of luteinizing hormone (LH) surge for predicting ovulation time in infertile but ovulating women. The proposed ensemble involves a number of parallel NN modules. Each pair of the NNs learn specific data that are previously collected for monitoring timing function of LH estradiol (pg ml-1), and follicle diameter (mm) are used to train NN pairs to approximate the function of the LH values. A reasonable and accurate estimation places ovulation approximately 10-12 h after the LH peak. The double-valued (bi-phasic) regions of training data are separated into two single-valued (bi-phasic) regions of training data are separated into two single-valued parts (not exactly preovulatory, postovulatory division) that can be learned by each module of the NN pair. During testing, after the initial decision to have single-valued sides, the assessment is obtained by a linear opinion pool (consensus rule) using the decisions of NNs on the corresponding side without waiting. The network ensemble has various desirable properties: high assessment accuracy of a double-valued multisource data, minimized learning and recall times, and a parallel structure. The ovulation time can be predicted through the assessment of LH peak with a better precision and fewer number of tests.

Diagnosis, Computer-Assisted↗

Ensemble averaging and digital filtering in chromatography and electrophoresis.

Ensemble averaging and digital filtering were implemented for signal-to-noise ratio improvement in the separation techniques of size-exclusion chromatography, immunoaffinity chromatography, capillary zone electrophoresis and capillary ion analysis. Results of ensemble averaging were always greater than statistically predicted. Techniques included five to nine replicate separations and yielded signal-to-noise improvement factors of 2.5 to 9.3. Running-average and time constant (RC)-convolution digital filters yielded increases in the signal-to-noise ratio ranging from zero to twelve. This paper will discuss and illustrate the usage of ensemble averaging and digital filtering in liquid-phase separation techniques.

Algorithms↗

A novel ensemble machine learning for robust microarray data classification.

Microarray data analysis and classification has demonstrated convincingly that it provides an effective methodology for the effective diagnosis of diseases and cancers. Although much research has been performed on applying machine learning techniques for microarray data classification during the past years, it has been shown that conventional machine learning techniques have intrinsic drawbacks in achieving accurate and robust classifications. This paper presents a novel ensemble machine learning approach for the development of robust microarray data classification. Different from the conventional ensemble learning techniques, the approach presented begins with generating a pool of candidate base classifiers based on the gene sub-sampling and then the selection of a sub-set of appropriate base classifiers to construct the classification committee based on classifier clustering. Experimental results have demonstrated that the classifiers constructed by the proposed method outperforms not only the classifiers generated by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods (bagging and boosting).

Algorithms↗

Increasing stability reduces conformational heterogeneity in a protein folding intermediate ensemble.

A multi-site, time-resolved fluorescence resonance energy transfer methodology has been used to study structural heterogeneity in a late folding intermediate ensemble, IL, of the small protein barstar. Four different intra-molecular distances have been measured within the structural components of IL. The IL ensemble is shown to consist of different sub-populations of molecules, in each of which one or more of the four distances are native-like and the remaining distances are unfolded-like. In very stable conditions that favor formation of IL, all four distances are native-like in most molecules. In less stable conditions, one or more distances are unfolded-like. As stability is decreased, the proportion of molecules with unfolded-like distances increases. Thus, the results show that protein folding intermediates are ensembles of different structural forms, and they demonstrate that conformational entropy increases as structures become less stable. These observations provide direct experimental evidence in support of a basic tenet of energy landscape theory for protein folding, that available conformational space, as represented by structural heterogeneity in IL, becomes restricted as the stability is increased. The results also vindicate an important prediction of energy landscape theory, that different folding pathways may become dominant under different folding conditions. In more stable folding conditions, uniformly native-like compactness is achieved during folding to IL, whereas in less stable conditions, uniformly native-like compactness is achieved only later during the folding of IL to N.

Bacterial Proteins↗

Testing the efficiency of sensory coding with optimal stimulus ensembles.

According to Barlow's seminal "efficient coding hypothesis," the coding strategy of sensory neurons should be matched to the statistics of stimuli that occur in an animal's natural habitat. Using an automatic search technique, we here test this hypothesis and identify stimulus ensembles that sensory neurons are optimized for. Focusing on grasshopper auditory receptor neurons, we find that their optimal stimulus ensembles differ from the natural environment, but largely overlap with a behaviorally important sub-ensemble of the natural sounds. This indicates that the receptors are optimized for peak rather than average performance. More generally, our results suggest that the coding strategies of sensory neurons are heavily influenced by differences in behavioral relevance among natural stimuli.

Acoustic Stimulation↗

Ensemble forecasting of species distributions.

Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.

Ecosystem↗

Thermodynamics and kinetics of a molecular motor ensemble.

If, contrary to conventional models of muscle, it is assumed that molecular forces equilibrate among rather than within molecular motors, an equation of state and an expression for energy output can be obtained for a near-equilibrium, coworking ensemble of molecular motors. These equations predict clear, testable relationships between motor structure, motor biochemistry, and ensemble motor function, and we discuss these relationships in the context of various experimental studies. In this model, net work by molecular motors is performed with the relaxation of a near-equilibrium intermediate step in a motor-catalyzed reaction. The free energy available for work is localized to this step, and the rate at which this free energy is transferred to work is accelerated by the free energy of a motor-catalyzed reaction. This thermodynamic model implicitly deals with a motile cell system as a dynamic network (not a rigid lattice) of molecular motors within which the mechanochemistry of one motor influences and is influenced by the mechanochemistry of other motors in the ensemble.

Animals↗

Weighted-ensemble Brownian dynamics simulations for protein association reactions.

A new method, weighted-ensemble Brownian dynamics, is proposed for the simulation of protein-association reactions and other events whose frequencies of outcomes are constricted by free energy barriers. The method features a weighted ensemble of trajectories in configuration space with energy levels dictating the proper correspondence between "particles" and probability. Instead of waiting a very long time for an unlikely event to occur, the probability packets are split, and small packets of probability are allowed to diffuse almost immediately into regions of configuration space that are less likely to be sampled. The method has been applied to the Northrup and Erickson (1992) model of docking-type diffusion-limited reactions and yields reaction rate constants in agreement with those obtained by direct Brownian simulation, but at a fraction of the CPU time (10(-4) to 10(-3), depending on the model). Because the method is essentially a variant of standard Brownian dynamics algorithms, it is anticipated that weighted-ensemble Brownian dynamics, in conjunction with biophysical force models, can be applied to a large class of association reactions of interest to the biophysics community.

Algorithms↗

Native states of adenylate kinase are two active sub-ensembles.

There are two kinds of conformational forms of adenylate kinase (AK) in equilibrium in solution with different ANS-binding properties. Furthermore, the nature of AP(5)A inhibition suggests also that the native forms of AK for binding with different substrates pre-exist in the absence of substrates. In the present study, a kinetics approach was used to explore the native forms distinguished by ANS-binding properties and by the nature of AP(5)A inhibition. The results revealed that the native forms distinguished by ANS probe are two conformational sub-ensembles. Both sub-ensembles are active and consist of a series of forms, which pre-exist in solution and can bind with different substrates. The K(m) values of N(1) for AMP, ADP and MgATP are larger than that of N(2), indicating that the N(2) sub-ensemble is more specific for binding substrates. This is consistent with the previous observation that the activity of N(2) is about 1.8-fold of that of N(1).

Adenylate Kinase↗

Effects of the difference in the unfolded-state ensemble on the folding of Escherichia coli dihydrofolate reductase.

The unfolded state of a protein is an ensemble of a large number of conformations ranging from fully extended to compact structures. To investigate the effects of the difference in the unfolded-state ensemble on protein folding, we have studied the structure, stability, and folding of "circular" dihydrofolate reductase (DHFR) from Escherichia coli in which the N and C-terminal regions are cross-linked by a disulfide bond, and compared the results with those of disulfide-reduced "linear" DHFR. Equilibrium studies by circular dichroism, difference absorption spectra, solution X-ray scattering, and size-exclusion chromatography show that whereas the native structures of both proteins are essentially the same, the unfolded state of circular DHFR adopts more compact conformations than the unfolded state of the linear form, even with the absence of secondary structure. Circular DHFR is more stable than linear DHFR, which may be due to the decrease in the conformational entropy of the unfolded state as a result of circularization. Kinetic refolding measurements by stopped-flow circular dichroism and fluorescence show that under the native conditions both proteins accumulate a burst-phase intermediate having the same structures and both fold by the same complex folding mechanism with the same folding rates. Thus, the effects of the difference in the unfolded state of circular and linear DHFRs on the refolding reaction are not observed after the formation of the intermediate. This suggests that for the proteins with close termini in the native structure, early compaction of a protein molecule to form a specific folding intermediate with the N and C-terminal regions in close proximity is a crucial event in folding. If there is an enhancement in the folding reflecting the reduction in the breadth of the unfolded-state ensemble for circular DHFR, this acceleration must occur in the sub-millisecond time-range.

Circular Dichroism↗

Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins.

We present a prototype of a new approach to the folding problem of polypeptide chains. This approach is based on the analysis of known protein structures. It derives the energy potentials for the atomic interactions of all amino acid residue pairs as a function of the distance between the involved atoms. These potentials are then used to calculate the energies of all conformations that exist in the data base with respect to a given sequence. Then, by using only the most stable conformations, clusters of the most probable conformations for the given sequence are obtained. To discuss the results properly we introduce a new classification of segments based on their conformational stability. Special care is taken to allow for sparse data sets. The use of the method is demonstrated in the discussion of the identical oligopeptide sequences found in different conformations in unrelated proteins. VNTFV, for example, adopts a beta-strand in ribonuclease but it is found in an alpha-helical conformation in erythrocruorin. In the case of VNTFV the ensemble obtained consists of a single cluster of beta-strand conformations, indicating that this may be the preferred conformation for the pentapeptide. When the flanking residues are included in the calculation the hepapeptide P-VNTFV-H (ribonuclease) again yields an ensemble of beta-strands. However, in the ensemble of D-VNTFV-A (erythrocruorin) the major cluster is of alpha-helical type. In the present study we concentrate on the local aspects of protein conformations. However, the theory presented is quite general and not restricted to oligopeptides. We indicate extensions of the approach to the calculation of global conformations of proteins as well as conceivable applications to a number of molecular systems.

Amino Acid Sequence↗

Protein folding funnels: the nature of the transition state ensemble.

BACKGROUND: Energy landscape theory predicts that the folding funnel for a small fast-folding alpha-helical protein will have a transition state half-way to the native state. Estimates of the position of the transition state along an appropriate reaction coordinate can be obtained from linear free energy relationships observed for folding and unfolding rate constants as a function of denaturant concentration. The experimental results of Huang and Oas for lambda repressor, Fersht and collaborators for C12, and Gray and collaborators for cytochrome c indicate a free energy barrier midway between the folded and unfolded regions. This barrier arises from an entropic bottleneck for the folding process. RESULTS: In keeping with the experimental results, lattice simulations based on the folding funnel description show that the transition state is not just a single conformation, but rather an ensemble of a relatively large number of configurations that can be described by specific values of one or a few order parameters (e.g. the fraction of native contacts). Analysis of this transition state or bottleneck region from our lattice simulations and from atomistic models for small alpha-helical proteins by Boczko and Brooks indicates a broad distribution for native contact participation in the transition state ensemble centered around 50%. Importantly, however, the lattice-simulated transition state ensemble does include some particularly hot contacts, as seen in the experiments, which have been termed by others a folding nucleus. CONCLUSIONS: Linear free energy relations provide a crude spectroscopy of the transition state, allowing us to infer the values of a reaction coordinate based on the fraction of native contacts. This bottleneck may be thought of as a collection of delocalized nuclei where different native contacts will have different degrees of participation. The agreement between the experimental results and the theoretical predictions provides strong support for the landscape analysis.

Animals↗

Phase synchronization of an ion ensemble by frequency sweep excitation in Fourier transform ion cyclotron resonance.

The Fourier transform ion cyclotron resonance (FT-ICR) signal is produced by the coherent motion of a population of ions. The ability to produce a well-defined ion packet by excitation of an initially random ion ensemble is a major limiting factor of high mass FT-ICR. Ions must be both resonant and in phase with the applied radio frequency excitation field to be accelerated to radii suitable for detection by FT-ICR. Synchronization of the phase angles of an ensemble of ions occurs by off-resonant acceleration during frequency swept excitation. Results from computer-simulated ion trajectories suggest that phase synchronization of the ion packet prior to resonant excitation results in better spatial definition of the ion ensemble.

Computer Simulation↗

Band-trajectory model for temperature-programmed series-coupled column ensembles with pressure-tunable selectivity.

A model and a spreadsheet algorithm is described for the prediction of solute-band migration trajectories in a series-coupled combination of two capillary GC columns with pressure-tunable and -programmable selectivity and operated under temperature-programmed conditions. The model takes into account the acceleration of carrier gas in the two columns as a result of decompression effects, the deceleration of carrier gas as a result of the increase in viscosity during temperature programming, the decrease in solute retention factors with increasing temperature during the temperature program, the differences in retention factors for the two columns, and programmed changes in the carrier-gas flow rates in the two columns during selectivity programming. In the model, the 20-meter-long column ensemble is divided into 1-cm-long intervals, and the carrier-gas velocity and column temperature are assummed to be constant in any interval. Migration times for all of the mixture solutes are computed for each column interval, and the solute-band positons in the column ensemble are plotted versus the running sum of these migration times to obtain band trajectory plots. The sum of these migration times for all 2,000 intervals gives the ensemble retention times for the solutes. Isothermal retention factors (k) for all of the mixture components at various column temperatures (Tc) are used as imput to the algorithm. Slope and intercept values of In(k) vs 1/Tc plots are used in the algorithm. General features of the model are tested using a mixture of C12-C24 normal alkanes. A mixture of polar and nonpolar compounds is used to test the utility of the model for the predicition of peak separations and retention times with pressure-tunable and -programmable selectivity. Good agreement is observed in all cases.

Journal Article↗