protein-RNA Docking Research Articles

Protein-RNA Interaction Interface Prediction and Design

RNA-protein interactions play key roles in many biological processes. The three dimensional (3D) structure of protein-RNA complexes can be determined experimentally by structural biologists. The recognition between protein and RNA can be understood from the 3D atomic structure. However, the structure determination of protein-RNA complexes by experimental methods is often difficult and costly, and limited to the binding strength. Thus, the prediction and design of protein-RNA complex structures is important in biological medical research. In this review, we will discuss the recent progress in protein-RNA interface prediction and design, which includes the following aspects: (1) protein-RNA docking and the conformational change on binding; (2) the recognition mechanism of protein-RNA binding; (3) the molecular design based on the protein-RNA interface. Improvement of the protein-RNA docking algorithm will help us annotate a large number of proteins and RNA with unknown function, and molecular design based on macromolecular interactions will be useful in drug design.

Computational methods for prediction of protein–RNA interactions

Understanding the molecular mechanism of protein-RNA recognition and complex formation is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes by X-ray crystallography and nuclear magnetic resonance spectroscopy (NMR) is tedious and difficult. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental observations, computational predictions can be sufficiently accurate to prompt functional hypotheses and guide experiments, e.g. to identify individual amino acid or nucleotide residues. In this article we review 10 methods for predicting protein-RNA interactions, seven of which predict RNA-binding sites from protein sequences and three from structures. We also developed a meta-predictor that uses the output of top three sequence-based primary predictors to calculate a consensus prediction, which outperforms all the primary predictors. In order to fully cover the software for predicting protein-RNA interactions, we also describe five methods for protein-RNA docking. The article highlights the strengths and shortcomings of existing methods for the prediction of protein-RNA interactions and provides suggestions for their further development.

A new residue‐nucleotide propensity potential with structural information considered for discriminating protein‐RNA docking decoys

Understanding the key factors that influence the preferences of residue-nucleotide interactions in specific protein-RNA interactions has remained a research focus. We propose an effective approach to derive residue-nucleotide propensity potentials through considering both the types of residues and nucleotides, and secondary structure information of proteins and RNAs from the currently largest nonredundant and nonribosomal protein-RNA interaction database. To test the validity of the potentials, we used them to select near-native structures from protein-RNA docking poses. The results show that considering secondary structure information, especially for RNAs, greatly improves the predictive power of pair potentials. The success rate is raised from 50.7 to 65.5% for the top 2000 structures, and the number of cases in which a near-native structure is ranked in top 50 is increased from 7 to 13 out of 17 cases. Furthermore, the exclusion of ribosomes from the database contributes 8.3% to the success rate. In addition, some very interesting findings follow: (i) the protein secondary structure element π-helix is strongly associated with RNA-binding sites; (ii) the nucleotide uracil occurs frequently in the most preferred pairs in which the unpaired and non-Watson-Crick paired uracils are predominant, which is probably significant in evolution. The new residue-nucleotide potentials can be helpful for the progress of protein-RNA docking methods, and for understanding the mechanisms of protein-RNA interactions.

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

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/

A coarse-grained force field for Protein–RNA docking

The awareness of important biological role played by functional, non coding (nc) RNA has grown tremendously in recent years. To perform their tasks, ncRNA molecules typically unite with protein partners, forming ribonucleoprotein complexes. Structural insight into their architectures can be greatly supplemented by computational docking techniques, as they provide means for the integration and refinement of experimental data that is often limited to fragments of larger assemblies or represents multiple levels of spatial resolution. Here, we present a coarse-grained force field for protein-RNA docking, implemented within the framework of the ATTRACT program. Complex structure prediction is based on energy minimization in rotational and translational degrees of freedom of binding partners, with possible extension to include structural flexibility. The coarse-grained representation allows for fast and efficient systematic docking search without any prior knowledge about complex geometry.

Docking-calculation-based method for predicting protein-RNA interactions.

Elucidating protein-RNA interactions (PRIs) is important for understanding many cellular systems. We developed a PRI prediction method by using a rigid-body protein-RNA docking calculation with tertiary structure data. We evaluated this method by using 78 protein-RNA complex structures from the Protein Data Bank. We predicted the interactions for pairs in 78×78 combinations. Of these, 78 original complexes were defined as positive pairs, and the other 6,006 complexes were defined as negative pairs; then an F-measure value of 0.465 was obtained with our prediction system.

Evaluating conformational changes in protein structures binding RNA

Many protein-RNA recognition events are known to exhibit conformational changes from qualitative observations of individual complexes. However, a quantitative estimation of conformational changes is required if protein-RNA docking and template-based methods for RNA binding site prediction are to be developed. This study presents the first quantitative evaluation of conformational changes that occur when proteins bind RNA. The analysis of twelve RNA-binding proteins in the bound and unbound states using error-scaled difference distance matrices is presented. The binding site residues are mapped to each structure, and the conformational changes that affect these residues are evaluated. Of the twelve proteins four exhibit greater movements in nonbinding site residues, and a further four show the greatest movements in binding site residues. The remaining four proteins display no significant conformational change. When interface residues are found to be in conformationally variable regions of the protein they are typically seen to move less than 2 A between the bound and unbound conformations. The current data indicate that conformational changes in the binding site residues of RNA binding proteins may not be as significant as previously suggested, but a larger data set is required before wider conclusions may be drawn. The implications of the observed conformational changes for protein function prediction are discussed.

Visualizing the dual space of biological molecules

An important part of protein structure characterization is the determination of excluded space such as fissures in contact interfaces, pores, inaccessible cavities, and catalytic pockets. We introduce a general tessellation method for visualizing the dual space around, within, and between biological molecules. Using Delaunay triangulation, a three-dimensional graph is constructed to provide a displayable discretization of the continuous volume. This graph structure is also used to compare the dual space of a system in two different states. Tessellator, a cross-platform implementation of the algorithm, is used to analyze the cavities within myoglobin, the protein-RNA docking interface between aspartyl-tRNA synthetase and tRNA Asp, and the ammonia channel in the hisH–hisF complex of imidazole glycerol phosphate synthase.