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 217 records · Page 12Linked to original sources

PubMed2003

Solution structure, dynamics, and thermodynamics of the native state ensemble of the Sem-5 C-terminal SH3 domain.

Although the high-resolution structure of a protein may provide significant insight into which regions are important for function, it is well-known that proteins undergo significant conformational fluctuations, even under native conditions. This suggests that the static structure alone may not provide sufficient information for elucidation of the thermodynamic determinants of biological function and that an accurate molecular-level description of function requires knowledge of the nature and energetics of the conformational states that constitute the native state ensemble. Here the native state ensemble of the C-terminal src homology domain-3 (C-SH3) from Caenorhabditis elegans Sem-5 has been studied using a variety of high-resolution biophysical techniques. In addition to determining the first solution structure of the unliganded protein, we have performed (15)N relaxation and native state hydrogen-deuterium exchange. It is observed that the regions of greatest structural variabilility also show low protection and order parameters, suggesting a higher degree of conformational diversity. These flexible regions also coincide with those regions of Sem-5 that have been predicted by the COREX algorithm to be unfolded in many of the most probable conformational states within the native state ensemble. The implications of this agreement and the potential role of conformational heterogeneity of the observed biophysical properties are discussed.

Algorithms↗

PubMed1999

Structural transitions in the protein L denatured state ensemble.

We use a broad array of biophysical methods to probe the extent of structure and time scale of structural transitions in the protein L denatured state ensemble. Measurement of amide proton exchange protection during the first several milliseconds following initiation of refolding in 0.4 M sodium sulfate revealed weak protection in the first beta-hairpin and helix. A tryptophan residue was introduced into the first beta-hairpin to probe the extent of structure formation in this part of the protein; the intrinsic fluorescence of this tryptophan was found to deviate from that expected given its local sequence context in 2-3 M guanidine, suggesting some partial ordering of this region in the unfolded state ensemble. To further probe this partial ordering, dansyl groups were introduced via cysteine residues at three sites in the protein. It was found that fluorescence energy transfer from the introduced tryptophan to the dansyl groups decreased dramatically upon unfolding. Stopped-flow fluorescence studies showed that the recovery of dansyl fluorescence upon refolding occurred on a submillisecond time scale. To probe the interactions responsible for the residual structure observed in the denatured state ensemble, the conformation of a peptide corresponding to the first beta-hairpin and helix of protein L was studied using circular dichroism spectroscopy and compared to that of full-length protein L and previously characterized peptides corresponding to the isolated helix and second beta-hairpin.

Amino Acid Sequence↗

PubMedDate not supplied

On the use of neural network ensembles in QSAR and QSPR.

Despite their growing popularity among neural network practitioners, ensemble methods have not been widely adopted in structure-activity and structure-property correlation. Neural networks are inherently unstable, in that small changes in the training set and/or training parameters can lead to large changes in their generalization performance. Recent research has shown that by capitalizing on the diversity of the individual models, ensemble techniques can minimize uncertainty and produce more stable and accurate predictors. In this work, we present a critical assessment of the most common ensemble technique known as bootstrap aggregation, or bagging, as applied to QSAR and QSPR. Although aggregation does offer definitive advantages, we demonstrate that bagging may not be the best possible choice and that simpler techniques such as retraining with the full sample can often produce superior results. These findings are rationalized using Krogh and Vedelsby's decomposition of the generalization error into a term that measures the average generalization performance of the individual networks and a term that measures the diversity among them. For networks that are designed to resist over-fitting, the benefits of aggregation are clear but not overwhelming.

Computer Simulation↗

PubMedDate not supplied

Using ensembles to classify compounds for drug discovery.

This paper introduces Signal, a novel method for classifying activity against a small molecule drug target. Signal creates an ensemble, or collection, of meaningful descriptors chosen from a much larger property space. The method works with a variety of descriptor types, including fingerprints that represent four-point pharmacophores or shape descriptors. It also exploits information from both active and inactive compounds and generates predictive models suitable for high throughput screening data analysis. Given the fingerprints and activity data for a set of compounds, Signal is a two step process. The first step is to Evaluate the Descriptors: for each descriptor in the fingerprint, quantify and rank the correlation between the activity of the compounds and the presence of that descriptor. The second step is to Create an Ensemble Model: use the high ranking descriptors to create a model of activity against the biological target. For the first step, two possible ranking strategies were investigated: mutual information and chi-square. For the second step, two types of ensemble models were investigated: high ranking and a novel method called high ranking set cover. Of the four possible pairings, the combination of chi-square and high ranking set cover performed the best on a Thrombin data set.

Algorithms↗

PubMedDate not supplied

Ensemble of linear models for predicting drug properties.

We propose a new classification method for the prediction of drug properties, called random feature subset boosting for linear discriminant analysis (LDA). The main novelty of this method is the ability to overcome the problems with constructing ensembles of linear discriminant models based on generalized eigenvectors of covariance matrices. Such linear models are popular in building classification-based structure-activity relationships. The introduction of ensembles of LDA models allows for an analysis of more complex problems than by using single LDA, for example, those involving multiple mechanisms of action. Using four data sets, we show experimentally that the method is competitive with other recently studied chemoinformatic methods, including support vector machines and models based on decision trees. We present an easy scheme for interpreting the model despite its apparent sophistication. We also outline theoretical evidence as to why, contrary to the conventional AdaBoost ensemble algorithm, this method is able to increase the accuracy of LDA models.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2004

Determination of an ensemble of structures representing the denatured state of the bovine acyl-coenzyme a binding protein.

The denatured state of a protein contains important information about the determinants of the folding process. By combining site-directed spin-labeling NMR experiments and restrained computer simulations, we have determined ensembles of conformations that represent the denatured state of the bovine acyl-coenzyme A binding protein (ACBP) at three different concentrations of guanidine hydrochloride. As the experimentally determined distance information corresponds to weighted averages over a broad ensemble of structures, we applied the experimental restraints to a system of noninteracting replicas of the protein by using a Monte Carlo sampling scheme. This procedure permits us to sample ensembles of conformations that are compatible with the experimental data and thus to obtain information regarding the distribution of structures in the denatured state. Our results show that the denatured state of ACBP is highly heterogeneous. The high sensitivity of the computational method that we present, however, enabled us to identify long-range interactions between two regions, located near the N- and C-termini, that include both native and non-native elements. The preferential formation of these contacts suggests that the sequence-dependent patterns of helical propensity and hydrophobicity are important determinants of the structure in the denatured state of ACBP.

Acyl Coenzyme A↗

PubMed2004

Existence of specific "folds" in polyproline II ensembles of an "unfolded"alanine peptide detected by molecular dynamics.

Equilibrium ensembles of octaalanine (Ac-Ala8-NHMe) in water, prepared with MD, are analyzed for contributing microstates with an RMSD-based conformational clustering algorithm. The extracted ensemble-averaged properties are in excellent agreement with numerous spectroscopic measurements reported with small alanine model peptides in water. However, the dominantly polyproline II-like ensemble of the peptide is found to be populated with a handful of highly position-specific "folds", including beta-turns, beta-hairpins, and helix nuclei, which could be the "seeds" that initiate proteins along their folding pathways.

Dipeptides↗

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↗

PubMed2006

Quantifying polypeptide conformational space: sensitivity to conformation and ensemble definition.

Quantifying the density of conformations over phase space (the conformational distribution) is needed to model important macromolecular processes such as protein folding. In this work, we quantify the conformational distribution for a simple polypeptide (N-mer polyalanine) using the cumulative distribution function (CDF), which gives the probability that two randomly selected conformations are separated by less than a "conformational" distance and whose inverse gives conformation counts as a function of conformational radius. An important finding is that the conformation counts obtained by the CDF inverse depend critically on the assignment of a conformation's distance span and the ensemble (e.g., unfolded state model): varying ensemble and conformation definition (1 --> 2 A) varies the CDF-based conformation counts for Ala(50) from 10(11) to 10(69). In particular, relatively short molecular dynamics (MD) relaxation of Ala(50)'s random-walk ensemble reduces the number of conformers from 10(55) to 10(14) (using a 1 A root-mean-square-deviation radius conformation definition) pointing to potential disconnections in comparing the results from simplified models of unfolded proteins with those from all-atom MD simulations. Explicit waters are found to roughen the landscape considerably. Under some common conformation definitions, the results herein provide (i) an upper limit to the number of accessible conformations that compose unfolded states of proteins, (ii) the optimal clustering radius/conformation radius for counting conformations for a given energy and solvent model, (iii) a means of comparing various studies, and (iv) an assessment of the applicability of random search in protein folding.

Computer Simulation↗

PubMed2005

Measurement and interpretation of particle-particle and particle-wall interactions in levitated colloidal ensembles.

This paper reports measurements of particle-wall and particle-particle interactions in levitated colloidal ensembles using integrated total internal reflection microscopy (TIRM) and video microscopy (VM) techniques. In levitated colloidal ensembles with area fractions of phiA = 0.03-0.25, ensemble TIRM measured height distribution functions are used to interpret particle-wall interactions, and VM measured pair distribution functions are used to interpret particle-particle interactions using inverse Ornstein-Zernike (OZ) and three-dimensional inverse Monte Carlo (MC) analyses. An inconsistent finding is the observation of an anomalous long-range particle-particle attraction and recovery of the expected Derjaguin-Landau-Verwey-Overbeek (DLVO) particle-wall interactions for all concentrations examined. Because particle-wall and particle-particle potentials are expected to be consistent in several respects, the analytical and experimental methods employed in this investigation are examined for possible sources of error. Comparison of inverse OZ and three-dimensional inverse MC analyses are used to address uncertainties related to dimensionality, effects of particle concentration, and assumptions of the OZ theory and closure relations. The possible influence of charge heterogeneity and particle size polydispersity on measured distribution functions is discussed with regard to inconsistent particle-wall and particle-particle potentials. Ultimately, achieving a consistent understanding of particle-wall and particle-particle interactions in interfacial and confined colloidal systems is essential to numerous complex fluid and advanced material technologies.

Journal Article↗

PubMedDate not supplied

Redundancy and synergy arising from pairwise correlations in neuronal ensembles.

Multielectrode arrays allow recording of the activity of many single neurons, from which correlations can be calculated. The functional roles of correlations can be revealed by measures of the information conveyed by neuronal activity; a simple formula has been shown to discriminate the information transmitted by individual spikes from the positive or negative contributions due to correlations (Panzeri et al., 1999). Here, this analysis, previously applied to recordings from small ensembles, is developed further by considering a model of a large ensemble, in which correlations among the signal and noise components of neuronal firing are small in absolute value and entirely random in origin. Even such small random correlations are shown to lead to large possible synergy or redundancy, whenever the time window for extracting information from neuronal firing extends to the order of the mean interspike interval. In addition, a sample of recordings from rat barrel cortex illustrates the mean time window at which such "corrections" dominate when correlations are, as often in the real brain, neither random nor small. The presence of this kind of correlations for a large ensemble of cells restricts further the time of validity of the expansion.

Action Potentials↗

PubMed2003

The precision of NMR structure ensembles revisited.

Biomolecular structures provide the basis for many studies in research areas such as structure-based drug design and homology modeling. In order to use molecular coordinates it is important that they are reliable in terms of accurate description of the experimental data and in terms of the overall and local geometry. Besides these primary quality criteria an indication is needed for the uncertainty in the atomic coordinates that may arise from the dynamic behavior of the considered molecules as well as from experimental- and computational procedures. In contrast to the crystallographic B-factor, a good measure for the uncertainty in NMR-derived atomic coordinates is still not available. It has become clear in recent years that the widely used atomic Root Mean Square Deviation (RMSD), which is a measure for the precision of the data, overestimates the accuracy of NMR structure ensembles and therefore is a problematic measure for the uncertainty in the atomic coordinates. In this study we report a method that yields a more realistic estimate of the uncertainty in the atomic coordinates by maximizing the RMSD of an ensemble of structures, while maintaining the accordance with the experimentally derived data. The results indicate that the RMSD of most NMR structure ensembles can be significantly increased compromising neither geometric quality nor NMR data. This maximized RMSD therefore seems a better estimate of the true uncertainty in the atomic coordinates.

Crystallography, X-Ray↗

PubMed2000

Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task.

When an animal learns to make movements in response to different stimuli, changes in activity in the motor cortex seem to accompany and underlie this learning. The precise nature of modifications in cortical motor areas during the initial stages of motor learning, however, is largely unknown. Here we address this issue by chronically recording from neuronal ensembles located in the rat motor cortex, throughout the period required for rats to learn a reaction-time task. Motor learning was demonstrated by a decrease in the variance of the rats' reaction times and an increase in the time the animals were able to wait for a trigger stimulus. These behavioural changes were correlated with a significant increase in our ability to predict the correct or incorrect outcome of single trials based on three measures of neuronal ensemble activity: average firing rate, temporal patterns of firing, and correlated firing. This increase in prediction indicates that an association between sensory cues and movement emerged in the motor cortex as the task was learned. Such modifications in cortical ensemble activity may be critical for the initial learning of motor tasks.

Analysis of Variance↗

PubMed2005

Measurement-induced entanglement for excitation stored in remote atomic ensembles.

A critical requirement for diverse applications in quantum information science is the capability to disseminate quantum resources over complex quantum networks. For example, the coherent distribution of entangled quantum states together with quantum memory (for storing the states) can enable scalable architectures for quantum computation, communication and metrology. Here we report observations of entanglement between two atomic ensembles located in distinct, spatially separated set-ups. Quantum interference in the detection of a photon emitted by one of the samples projects the otherwise independent ensembles into an entangled state with one joint excitation stored remotely in 10(5) atoms at each site. After a programmable delay, we confirm entanglement by mapping the state of the atoms to optical fields and measuring mutual coherences and photon statistics for these fields. We thereby determine a quantitative lower bound for the entanglement of the joint state of the ensembles. Our observations represent significant progress in the ability to distribute and store entangled quantum states.

Journal Article↗

PubMed2005

Design and synthesis of heterobimetallic donor-acceptor chemodosimetric ensembles for the detection of sulfhydryl-containing amino acids and peptides.

A competitive indicator displacement assay has been successfully developed for the ratiometric determination of sulfhydryl-containing amino acids and peptides using heterobimetallic donor-acceptor complexes as chemodosimetric ensembles. Chromotropic cis-[ML2(CN)2](M = FeII, RuII, OsII; L = diimine) are used as signaling indicators and PtII(DMSO)Cl2 acceptor moiety is used as the receptor for the sulfhydryl-containing analytes. A series of three heterobimetallic donor-acceptor complexes: cis-FeII(bpy)2[CN-PtII(DMSO)Cl2]2 (1), cis-Ru(II)(bpy)2[CN-PtII(DMSO)Cl2]2 (2) and cis-Os(II)(bpy)2[CN-PtII(DMSO)Cl2]2 (3) are synthesized and characterized by X-ray crystallography. All the three ensembles are able to produce specific colorimetric/fluorimetric responses to sulfhydryl-containing amino acids (cysteine, homocysteine and methionine) as well as the sulfhydryl-containing small peptide glutathione. The mechanism of the competitive displacement assay is evaluated by examining the thermodynamics of formation of the donor-acceptor linkage and adducts between the acceptor metal and the sulfhydryl-containing analytes as well as by systematic variation of the donor and acceptor metals in the chemodosimetric ensembles.

Binding, Competitive↗

PubMed2004

Determination of solvation free energies by adaptive expanded ensemble molecular dynamics.

A new method of calculating absolute free energies is presented. It was developed as an extension to the expanded ensemble molecular dynamics scheme and uses probability density estimation to continuously optimize the expanded ensemble parameters. The new method is much faster as it removes the time-consuming and expertise-requiring step of determining balancing factors. Its efficiency and accuracy are demonstrated for the dissolution of three qualitatively very different chemical species in water: methane, ionic salts, and benzylamine. A recently suggested optimization scheme by Wang and Landau [Phys. Rev. Lett. 86, 2050 (2001)] was also implemented and found to be computationally less efficient than the proposed adaptive expanded ensemble method.

Journal Article↗

PubMed2004

The canonical ensemble via symplectic integrators using Nosé and Nosé-Poincaré chains.

Simulations that sample from the canonical ensemble can be generated by the addition of a single degree of freedom, provided that the system is ergodic, as described by Nosé with subsequent modifications by Hoover to allow sampling in real time. Nosé-Hoover dynamics is not ergodic for small or stiff systems and the addition of auxiliary thermostats is needed to overcome this deficiency. Nosé-Hoover dynamics, like its derivatives, does not have a Hamiltonian structure, precluding the use of symplectic integrators which are noted for their long term stability and structure preservation. As an alternative to Nosé-Hoover, the Hamiltonian Nosé-Poincaré method was proposed by Bond, Laird, and Leimkuhler [J. Comput. Phys. 151, 114 (1999)], but the straightforward addition of thermostatting chains does not sample from the canonical ensemble. In this paper a method is proposed whereby additional thermostats can be applied to a Hamiltonian system while retaining sampling from the canonical ensemble. This technique has been used to construct thermostatting chains for the Nosé and Nosé-Poincaré methods.

Journal Article↗

PubMed2004

Exploring protein-folding ensembles: a variable-barrier model for the analysis of equilibrium unfolding experiments.

Recent theoretical and experimental results point to the existence of small barriers to protein folding. These barriers can even be absent altogether, resulting in a continuous folding transition (i.e., downhill folding). With small barriers, the detailed properties of folding ensembles may become accessible to equilibrium experiments. However, further progress is hampered because folding experiments are interpreted with chemical models (e.g., the two-state model), which assume the existence of well defined macrostates separated by arbitrarily high barriers. Here we introduce a phenomenological model based on the classical Landau theory for critical transitions. In this physical model the height of the thermodynamic free energy barrier and the general properties of the folding ensemble are directly obtained from the experimental data. From the analysis of differential scanning calorimetry data alone, our model identifies the presence of a significant (>35 kJ/mol) barrier for the two-state protein thioredoxin and the absence of a barrier for BBL, a previously characterized downhill folding protein. These results illustrate the potential of our approach for extracting the general features of protein ensembles from equilibrium folding experiments.

Calorimetry, Differential Scanning↗