Similarity of Protein-RNA Interfaces Based on Motif Analysis

An updated dataset and a structure-based prediction model for protein-RNA binding affinity

Understanding the process of protein-RNA interaction is essential for structural biology. The thermodynamic process is an important part to uncover the protein-RNA interaction mechanism. The regulatory networks between protein and RNA in organisms are dominated by the binding or dissociation in the cells. Therefore, determining the binding affinity for protein-RNA complexes can help us to understand the regulation mechanism of protein-RNA interaction. Since it is time-consuming and labor-intensive to determine the binding affinity for protein-RNA complexes by experimental methods, it is necessary and urgent to develop computational methods to predict that. To develop a binding affinity prediction model, first we update the dataset of protein-RNA binding affinity benchmark (PRBAB), which includes 145 complexes now. Second, we extract the structural features based on complex structure, and then we analyze and select the representative structural features to train the regression model. Third, we random select the subset from the PRBAB2.0 to fit the protein-RNA binding affinity determined by experiment. In the end, we tested our model on the nonredundant PDBbind dataset, and the results showed that Pearson correlation coefficient r = .57 and RMSE = 2.51 kcal/mol. The Pearson correlation coefficient achieves 0.7 while removing 5 complex structures with modified residues/nucleotides and metal ions. While testing on ProNAB, the results showed that 71.60% of the prediction achieves Pearson correlation coefficient r = .61 and RMSE = 1.56 kcal/mol with experiment values.

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.

Modeling of the water network at protein-RNA interfaces

Water plays an important role in the mediation of biomolecular interactions. Thus, accurate prediction and evaluation of water-mediated interactions is an important element in the computational design of interfaces involving proteins, RNA, and DNA. Here, we use an algorithm (WATGEN) to predict the locations of interfacial water molecules for a data set of 224 protein-RNA interfaces. The accuracy of the prediction is validated against water molecules present in the X-ray structures of 105 of these complexes. The complexity of the water networks is deconvoluted through definition of the characteristics of each water molecule based on its bridging properties between the protein and RNA and on its depth in the interface with respect to the bulk solvent. This approach has the potential for scoring the water network for incorporation into the computational design of protein-RNA complexes.

Systematic classification and analysis of themes in protein-DNA recognition

Protein-DNA recognition plays a central role in the regulation of gene expression. With the rapidly increasing number of protein-DNA complex structures available at atomic resolution in recent years, a systematic, complete, and intuitive framework to clarify the intrinsic relationship between the global binding modes of these complexes is needed. In this work, we modified, extended, and applied previously defined RNA-recognition themes to describe protein-DNA recognition and used a protocol that incorporates automatic methods into manual inspection to plant a comprehensive classification tree for currently available high-quality protein-DNA structures. Further, a nonredundant (representative) data set consisting of 200 thematically diverse complexes was extracted from the leaves of the classification tree by using a locally sensitive interface comparison algorithm. On the basis of the representative data set, various physical and chemical properties associated with protein-DNA interactions were analyzed using empirical or semiempirical methods. We also examined the individual energetic components involved in protein-DNA interactions and highlighted the importance of conformational entropy, which has been almost completely ignored in previous studies of protein-DNA binding energy.