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DARS-RNP and QUASI-RNP: New statistical potentials for protein-RNA docking

Irina Tuszynska1 and Janusz M Bujnicki12*

Author Affiliations

1 Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland

2 Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, ul. Umultowska 89, PL-61-614 Poznan, Poland

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BMC Bioinformatics 2011, 12:348  doi:10.1186/1471-2105-12-348

Published: 18 August 2011

Abstract

Background

Protein-RNA interactions play fundamental roles in many biological processes. Understanding the molecular mechanism of protein-RNA recognition and formation of protein-RNA complexes is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes is tedious and difficult, both by X-ray crystallography and NMR. For many interacting proteins and RNAs the individual structures are available, enabling computational prediction of complex structures by computational docking. However, methods for protein-RNA docking remain scarce, in particular in comparison to the numerous methods for protein-protein docking.

Results

We developed two medium-resolution, knowledge-based potentials for scoring protein-RNA models obtained by docking: the quasi-chemical potential (QUASI-RNP) and the Decoys As the Reference State potential (DARS-RNP). Both potentials use a coarse-grained representation for both RNA and protein molecules and are capable of dealing with RNA structures with posttranscriptionally modified residues. We compared the discriminative power of DARS-RNP and QUASI-RNP for selecting rigid-body docking poses with the potentials previously developed by the Varani and Fernandez groups.

Conclusions

In both bound and unbound docking tests, DARS-RNP showed the highest ability to identify native-like structures. Python implementations of DARS-RNP and QUASI-RNP are freely available for download at http://iimcb.genesilico.pl/RNP/ webcite

Keywords:
RNA; protein; RNP; macromolecular docking; complex modeling; structural bioinformatics