We used nuclear magnetic resonance data to determine ensembles of conformations representing the structure and dynamics of calmodulin (CaM) in the calcium-bound state (Ca2+-CaM) and in the state bound to myosin light chain kinase (CaM-MLCK).
For artificial neural network ensembles (ANNEs) using two different methods for determining ensemble classification – one using vote tallies and the other averaging individual network outputs – we have found that the distribution of predictions across positive vote tallies can be reasonably well-modeled as a beta binomial distribution, as can the distribution of errors.
Instruments based on thermal desorption are mainly configured for determining ensemble particle properties averaged over defined time periods, whereas the laser-based instruments are primarily used for single-particle measurements.
The resulting distances were used as restraints in the structure determination of GB1, and the goal is then to examine which of the determined ensembles is most similar to the reference ensemble.
We performed a series of molecular dynamics simulations in which Φ and Ψ values were used as ensemble-averaged structural restraints to determine an ensemble of structures representing the TS of ubiquitin.
The results were used to determine the ensemble average, as well as the extreme values, of quantities that indicate the importance of the turbulence radiation interaction.
Most of the examples described involve models where the ensemble decision is determined by voting: decision thresholds are established independently for each network and the classification "votes" that result are tallied to determine the ensemble classification.
To determine the ensemble of genes affected by knockdown of miR-132 we performed a microarray analysis in PC12 (ATCC) cells expressing the miR-132 sponge.
It is difficult, both theoretically and experimentally, to determine the ensemble of substates that a folded protein adopts.
Processing of the wire-mesh sensor data was applied to carry out a statistical analysis of characteristic parameters of downward slug flow, such as bubble and liquid slug length distributions, as well as to determine the ensemble-averaged shapes of the bubble nose, liquid film and bubble tail.
