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

Author(s): Tuszynska Irina | Bujnicki Janusz

Journal: BMC Bioinformatics
ISSN 1471-2105

Volume: 12;
Issue: 1;
Start page: 348;
Date: 2011;
Original page

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

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
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Tango Jona
Tangokurs Rapperswil-Jona