Can one and two-dimensional solid-state NMR fingerprint zeolite framework topology?

In this contribution, we have explored the potential and strength of one-dimensional (1D) (29)Si and two-dimensional (2D) (29)S-(29)Si and (29)Si-(17)O NMR as invariants of non-oriented graph for fingerprinting zeolite frameworks. 1D and 2D (29)Si NMR can indeed provide indications on the graph vertices, edges and allow the construction of the adjacency matrix, i.e. the set of connections between the graph vertices. From the structural data, hypothetical 1D (29)Si and 2D (29)Si-(29)Si NMR signatures for 193 of the zeolite frameworks reported in the Atlas of Zeolite Structures have been generated. Comparison between all signatures shows that thanks to the 1D (29)Si NMR data only, almost 20% of the known zeolite frameworks could be distinguished. Further NMR signatures were generated by taking into account 2D (29)Si-(29)Si and (29)Si-(17)O correlations. By sorting and comparison of all the NMR data, up to 80% of the listed zeolites could be unambiguously discriminated. This work indicates that (i) solid-state NMR data indeed represent a rather strong graph invariant for zeolite framework, (ii) despite their difficulties and costs (isotopic labeling is often required, the NMR measurements can be long), (29)Si and (17)O NMR measurements are worth being investigated in the frame of zeolites structure resolution. This approach could also be generalized to other zeolite-related materials containing NMR-measurable nuclides.

An algorithmic game-theory approach for coarse-grain prediction of RNA 3D structure.

We present a new approach for the prediction of the coarse-grain 3D structure of RNA molecules. We model a molecule as being made of helices and junctions. Those junctions are classified into topological families that determine their preferred 3D shapes. All the parts of the molecule are then allowed to establish long-distance contacts that induce a 3D folding of the molecule. An algorithm relying on game theory is proposed to discover such long-distance contacts that allow the molecule to reach a Nash equilibrium. As reported by our experiments, this approach allows one to predict the global shape of large molecules of several hundreds of nucleotides that are out of reach of the state-of-the-art methods.

Automated prediction of three-way junction topological families in RNA secondary structures.

We present an algorithm for automatically predicting the topological family of any RNA three-way junction, given only the information from the secondary structure: the sequence and the Watson-Crick pairings. The parameters of the algorithm have been determined on a data set of 33 three-way junctions whose 3D conformation is known. We applied the algorithm on 53 other junctions and compared the predictions to the real shape of those junctions. We show that the correct answer is selected out of nine possible configurations 64% of the time. Additionally, these results are noticeably improved if homology information is used. The resulting software, Cartaj, is available online and downloadable (with source) at: http://cartaj.lri.fr.