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2 2015

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Assigning NMR spectra of irregular RNAs by heuristic algorithms

100%

Szachniuk M.

tom Vol. 63, nr 1

329--338

Computer-aided analysis and preprocessing of spectral data is a prerequisite for any study of molecular structures by Nuclear Magnetic Resonance (NMR) spectroscopy. The data processing stage usually involves a considerable dedication of time and expert knowledge to cope with peak picking, resonance signal assignment and calculation of structure parameters. A significant part of the latter step is performed in an automated way. However, in peak picking and resonance assignment a multistage manual assistance is still essential. The work presented here is focused on the theoretical modeling and analyzing the assignment problem by applying heuristic approaches to the NMR spectra recorded for RNA structures containing irregular regions.

Evolutionary algorithm for a reconstruction of NOE paths in NMR spectra of RNA chains

51%

Błażewicz J. , Szachniuk M. , Wojtowicz A.

nr 3

Building the library of RNA 3D nucleotide conformations using the clustering approach

32%

Zok T. , Antczak M. , Riedel M. , Nebel D. , Villmann T. , Lukasiak P. , Blazewicz J. , Szachniuk M.

tom Vol. 25, no. 3

689--700

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.

Assigning NMR spectra of irregular RNAs by heuristic algorithms

23%

Szachniuk M.