Abstract
The function of the RNA molecule depends on its three-dimensional (3D) structure. Therefore, understanding the role of the RNA molecule requires detailed knowledge of its 3D structure. Initially, this task was performed experimentally using X-ray crystallography and NMR spectroscopy, but this technique remains limited to small molecules and becomes more expensive for large molecules. For this reason, the number of RNA 3D structures in databases increases in a difficult way. In the other hand, the number of RNA sequences increases rapidly, due to the high development of the sequencing tools. In order to remedy this shortcoming, a number of computational methods have been developed based on different aspects, such as dynamic molecular fragments or pattern and the coarse-grained potentials to predict the RNA 3D structure. In this paper we give a general overview of these methods, their categories, their advantages and their drawbacks.
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Chentoufi, A., Fatmi, A.E., Bekri, A., Benhlima, S., Sabbane, M. (2018). A General Overview of 3D RNA Structure Prediction Approaches. In: Ben Ahmed, M., Boudhir, A. (eds) Innovations in Smart Cities and Applications. SCAMS 2017. Lecture Notes in Networks and Systems, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-74500-8_45
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