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The VPG is designed to calculate ensemble average rigidity properties using a single network by tracing probabilities for pebble placement, rather than moving the pebbles themselves.
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Thirteen enzyme configurations from the TS ensemble were used to calculate ensemble-averaged transmission coefficients and their standard deviations (Γ = 0.75 ± 0.26, κ = 3.13 ± 1.29, γ = 2.54 ± 1.61).
Motivated by this, we used PANDA [ 21] to calculate ensembles of networks based on jack-knifed sets of samples drawn from our initial male and female subject populations.
Subsequently, we calculated ensemble median values and presented these results together with the modelling spread.
Furthermore, comparison of different approaches for extracting a single, most probable structure from the calculated ensemble reveals the power of the PCR method and provides insights into the nature of the energetic landscape.
We devised three complementary methods for calculating ensemble similarities.
The final method for calculating ensemble similarities is based on a dimensionality reduction approach and consists of two steps.
However, these secondary structures are considerably less favourable than the endogenous stem loop secondary structure, with calculated ensemble free energies of folding 9.8 and 12.7 kcal/mol higher than the endogenous sequence.
Random Forests [ 6] (hereafter abbreviated as RF), is an ensemble learning method which calculates ensemble predicted value by aggregating a collection of ntree≥1 randomly grown trees.
Distance comparison (DC) maps were created by plotting the root-mean-square (rms) distance between two residues, i and j, normalized by the rms distance predicted for a purely random coil: 9 The rms inter-residue distances for the calculated ensemble were calculated as 10where Nstruct is the number of structures in the calculated ensemble.
Again, however, the residue-specific variations in the experimental data are for the most part not accurately reproduced in either of the calculated ensembles.
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