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Paper details
Number 3 - September 2015
Volume 25 - 2015
Building the library of RNA 3D nucleotide conformations using the clustering approach
Tomasz Zok, Maciej Antczak, Martin Riedel, David Nebel, Thomas Villmann, Piotr Lukasiak, Jacek Blazewicz, Marta Szachniuk
Abstract
An increasing number of known RNA 3D structures contributes to the recognition of various RNA families and identification
of their features. These tasks are based on an analysis of RNA conformations conducted at different levels of detail.
On the other hand, the knowledge of native nucleotide conformations is crucial for structure prediction and understanding
of RNA folding. However, this knowledge is stored in structural databases in a rather distributed form. Therefore, only
automated methods for sampling the space of RNA structures can reveal plausible conformational representatives useful
for further analysis. Here, we present a machine learning-based approach to inspect the dataset of RNA three-dimensional
structures and to create a library of nucleotide conformers. A median neural gas algorithm is applied to cluster nucleotide
structures upon their trigonometric description. The clustering procedure is two-stage: (i) backbone- and (ii) ribose-driven.
We show the resulting library that contains RNA nucleotide representatives over the entire data, and we evaluate its quality
by computing normal distribution measures and average RMSD between data points as well as the prototype within each
cluster.
Keywords
RNA nucleotides, conformer library, torsion angles, clustering, neural gas