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

Irina Tuszynska,Janusz M Bujnicki

doi:10.1186/1471-2105-12-348

Abstract

BackgroundProtein-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.ResultsWe 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.ConclusionsIn 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/

Highlights

Protein-RNA interactions play fundamental roles in many biological processes
In this article we introduce two new, medium-resolution, knowledge-based potentials for scoring proteinRNA models: the quasi-chemical potential (QUASI-RNP) and the Decoys As the Reference State [19] potential (DARS-RNP)
The false models were not selected by the GRAMM scores, as we found that the GRAMM score alone is a poor predictor of the complex native-likeness

Summary

Introduction

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. For many interacting proteins and RNAs the individual structures are available, enabling computational prediction of complex structures by computational docking. Defects of protein-RNA interactions are responsible for many human diseases ranging from neurological disorders to cancer [2]. The understanding of these processes improves as new structures of protein-RNA complexes are solved and the molecular details of interactions analyzed. While there is a Docking methods are widely used to predict structures of macromolecular complexes, starting from structures of the individual components [7]. All docking methods face two main challenges: to search the space of possible orientations and conformations (poses) of the components and to identify near-native structures among the alternative complex models (decoys) generated. Conformational changes are either modeled explicitly at the atomic level (which makes such analyses computationally very demanding), or a certain level of ‘fuzziness’ is introduced e.g. by allowing for some extent of steric overlaps between atoms or by ‘coarse-graining’ of the representation i.e. by neglecting some atoms or grouping them into ‘united atoms’ to be considered jointly (reviews: [7,8])

Results

Discussion

Conclusion