Abstract
We propose an approach for transforming the sampling of a molecular conformation distribution into an analytical model based on Hidden Markov Models. The model describes the sampled shape density as a mixture of multivariate unimodal densities. Thus, it delivers an interpretation of the sampled density as a set of typical shapes that appear with different probabilities and are characterized by their geometry, their variability and transition probabilities between the shapes. The gained model is used to identify atom groups of constant shape that are connected by metastable torsion angles. Based on this description an alignment for the original sampling is computed. As it takes into account the different shapes contained in the sampled set, this alignment allows to compute reasonable average shapes and meaningful shape density plots. Furthermore, it enables us to visualize typical conformations.
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Schmidt-Ehrenberg, J., Hege, HC. (2005). Visual Analysis of Molecular Conformations by Means of a Dynamic Density Mixture Model. In: R. Berthold, M., Glen, R.C., Diederichs, K., Kohlbacher, O., Fischer, I. (eds) Computational Life Sciences. CompLife 2005. Lecture Notes in Computer Science(), vol 3695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11560500_21
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DOI: https://doi.org/10.1007/11560500_21
Publisher Name: Springer, Berlin, Heidelberg
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