Commit 7985d99e authored by Vladimir Reinharz's avatar Vladimir Reinharz
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update acknowledgement oliver + results rna-dna / rna-ligand

parent 7fe79c52
......@@ -507,7 +507,12 @@ It is important to recall that the set of positives and negatives is influenced
\label{fig:auc_remu}
\end{subfigure}
}
\caption{{\bf Overall performances of \soft using experimental and computationally-predicted structural disruption data.} For a set of extreme percentile cutoff of the \shape profile disruption in the first column (computational \remu disruption in the second column) $\delta$ and a minimal distance $\gamma$ from the mutation we show the average AUC. Average over the 4 RNA structures of 5S in the first row and those of c-di-GMP in the second row. For c-di-GMP the positive set is composed of the positions interacting with the protein in the PDBs as the known positions of the pocket.}
\caption{{\bf Overall performances of \soft using experimental and computationally-predicted structural disruption data.} For a set of extreme percentile cutoff of the \shape profile disruption in the first {\color{red} row} (computational \remu disruption in the second {\color{red} row $\delta$ and a minimal distance $\gamma$ from the mutation we show the average AUC.
5S positive set composed of the binding interfaces with other chains present in its four PDB models.
The tRNA positive set is divided between the anticodon positions and the A-$\psi$-C-G motif positions, obtained from the litterature.
The c-di-GMP, cobalamin and adenine riboswitches positive sets are composed of the positions at most 5$\AA$ from their ligands in their PDB structures. Four different models exist for
c-di-GMP and the AUC values are averaged.
}}
\label{fig:aucremumam}
\end{figure*}
......@@ -611,7 +616,7 @@ These observations implies that although a theoretical model do capture part of
\label{sec:conclusion}
We have presented a novel paradigm for analyzing non-coding RNA sequences combining the biochemical signal collected from structure probing experiments on RNA mutants, with the evolutionary information available in multiple sequence alignments. We applied this model using mutate-and-map and Rfam data, and show that the signal extracted with our technology yields promising performance for identifying nucleotides involved in molecular interfaces.
A broad range of methods have been produced to predict RNA-Protein interactions~\cite{puton2012computational} or RNA-RNA interactions~\cite{hofacker2009rna, zadeh2011nupack, wright2014coprarna}. Yet, the vast majority of these programs aim to identify potential molecular targets from a library, and predict the best fits. By contrast, \soft focuses on the sole biochemical and evolutionary properties of the RNA being analyzed. It enables, for the first time without prior knowledge of potential partners, the identification of {\em hot-spots} in RNA, involved in RNA-RNA and RNA-Protein interfaces, i.e. sets of critical nucleotides possibly implicated in the molecular functions. This information could then in turn be used to identify molecular targets or more realistically restrict the degree of freedom of molecular docking software~\cite{Lopes2013}.
A broad range of methods have been produced to predict RNA-Protein interactions~\cite{puton2012computational} or RNA-RNA interactions~\cite{hofacker2009rna, zadeh2011nupack, wright2014coprarna}. Yet, the vast majority of these programs aim to identify potential molecular targets from a library, and predict the best fits. By contrast, \soft focuses on the sole biochemical and evolutionary properties of the RNA being analyzed. It enables, for the first time without prior knowledge of potential partners, the identification of {\em hot-spots} in RNA, involved in RNA-RNA, RNA-Protein, {\color{red}RNA-DNA and RNA-ligand} interfaces, i.e. sets of critical nucleotides possibly implicated in the molecular functions. This information could then in turn be used to identify molecular targets or more realistically restrict the degree of freedom of molecular docking software~\cite{Lopes2013}.
This result illustrates the usefulness of the signal extracted by \soft, but the scope of application of these concepts should not remain limited to quaternary structures. For instance, we envision to use the nucleotide networks detected with \soft to predict non-canonical interactions and 3D motifs within an RNA molecules.
......@@ -622,6 +627,7 @@ The volume of applications of \soft is currently limited by the number of availa
\section{Acknowledgments}
\label{sec:acknowledgments}
{\color{red} The authors would like to thank Olivier Tremblay-Savard for his useful comments and suggestions about tRNA binding interfaces.}
This work was funded by a NSERC CGS fellowship (to VR), a French {\em Fondation pour la Recherche M\'edicale} grant (to YP), and a FQRNT team grant 239215 and NSERC Discovery grants 219671 \& 241015 (to JW).
\subsubsection{Conflict of interest statement.} None declared.
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