Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes
1
Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
2
Bioinformatics Laboratory, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, ul. Umultowska 89, PL-61-614 Poznan, Poland
*
Author to whom correspondence should be addressed.
†
These authors have contributed equally and should be considered joint first authors.
Genes 2018, 9(9), 432; https://doi.org/10.3390/genes9090432
Received: 14 May 2018
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Revised: 26 July 2018
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Accepted: 21 August 2018
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Published: 25 August 2018
(This article belongs to the Special Issue Computational Analysis of RNA Structure and Function)
RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.
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Keywords:
ribonucleoprotein; RNP; macromolecular complex; computational modelling; structural bioinformatics
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MDPI and ACS Style
Nithin, C.; Ghosh, P.; Bujnicki, J.M. Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes. Genes 2018, 9, 432. https://doi.org/10.3390/genes9090432
AMA Style
Nithin C, Ghosh P, Bujnicki JM. Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes. Genes. 2018; 9(9):432. https://doi.org/10.3390/genes9090432
Chicago/Turabian StyleNithin, Chandran, Pritha Ghosh, and Janusz M. Bujnicki. 2018. "Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes" Genes 9, no. 9: 432. https://doi.org/10.3390/genes9090432
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