Prediction of RNA binding sites in proteins from amino acid sequence

The synthesis and application of a diazirine-modified uridine analogue for investigating RNA–protein interactions

A uridine analogue equipped with a photoactive diazirine unit was generated and incorporated into RNA either syntheticallyviaphosphoramidite chemistry or by enzymatic polymerization. The new analogue was developed to identify and investigate RNA–protein interactions.

A novel protocol for three-dimensional structure prediction of RNA-protein complexes

Three-dimensional structures of RNA-protein complexes are crucial for understanding their diverse functions. However, the number of the RNA-protein complex structures solved by experiments is still limited at present. To solve this problem, some computational protocols have been proposed to predict three-dimensional RNA-protein complex structures. But the prediction accuracies of these protocols are lower. The reason may be that these protocols don't fully incorporate the features of RNA-protein interfaces. Here we propose a novel computational protocol for three-dimensional RNA-protein complex structure prediction, 3dRPC, which applies new schemes to the discreteness of molecule and charge in docking algorithm and the construction of the reference state in scoring function in order to take account of the features of RNA-protein interfaces. This protocol achieves a high accuracy comparable to the well-developed algorithms for three-dimensional structure prediction of protein-protein complexes when tested on a RNA-protein docking benchmark.

Predicting protein-binding RNA nucleotides using the feature-based removal of data redundancy and the interaction propensity of nucleotide triplets

Several learning approaches have been used to predict RNA-binding amino acids in a protein sequence, but there has been little attempt to predict protein-binding nucleotides in an RNA sequence. One of the reasons is that the differences between nucleotides in their interaction propensity are much smaller than those between amino acids. Another reason is that RNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding RNA nucleotides is much harder than predicting RNA-binding amino acids. We developed a new method that removes data redundancy in a training set of sequences based on their features. The new method constructs a larger and more informative training set than the standard redundancy removal method based on sequence similarity, and the constructed dataset is guaranteed to be redundancy-free. We computed the interaction propensity (IP) of nucleotide triplets by applying a new definition of IP to an extensive dataset of protein-RNA complexes, and developed a support vector machine (SVM) model to predict protein binding sites in RNA sequences. In a 5-fold cross-validation with 812 RNA sequences, the SVM model predicted protein-binding nucleotides with an accuracy of 86.4%, an F-measure of 84.8%, and a Matthews correlation coefficient of 0.66. With an independent dataset of 56 RNA sequences that were not used in training, the resulting accuracy was 68.1% with an F-measure of 71.7% and a Matthews correlation coefficient of 0.35. To the best of our knowledge, this is the first attempt to predict protein-binding RNA nucleotides in a given RNA sequence from the sequence data alone. The SVM model and datasets are freely available for academics at http://bclab.inha.ac.kr/primer.

Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information

BACKGROUND

The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure.

RESULTS

In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL). It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i) vitamin interacting residues (VIRs), (ii) vitamin-A interacting residues (VAIRs), (iii) vitamin-B interacting residues (VBIRs) and (iv) pyridoxal-5-phosphate (vitamin B6) interacting residues (PLPIRs) have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM) features of protein sequences. Finally, we selected best performing SVM modules and obtained highest MCC of 0.53, 0.48, 0.61, 0.81 for VIRs, VAIRs, VBIRs, PLPIRs respectively, using PSSM-based evolutionary information. All the modules developed in this study have been trained and tested on non-redundant datasets and evaluated using five-fold cross-validation technique. The performances were also evaluated on the balanced and different independent datasets.

CONCLUSIONS

This study demonstrates that it is possible to predict VIRs, VAIRs, VBIRs and PLPIRs from evolutionary information of protein sequence. In order to provide service to the scientific community, we have developed web-server and standalone software VitaPred (http://crdd.osdd.net/raghava/vitapred/).

Two distinct sites are essential for virulent infection and support of variant satellite RNA replication in spontaneous beet black scorch virus variants

Spontaneous point mutations of virus genomes are important in RNA virus evolution and often result in modifications of their biological properties. Spontaneous variants of beet black scorch virus (BBSV) and its satellite (sat) RNA were generated from cDNA clones by serial propagation in Chenopodium amaranticolor and Nicotiana benthamiana. Inoculation with recombinant RNAs synthesized in vitro revealed BBSV variants with divergent infectious phenotypes that affected either symptom expression or replication of satRNA variants. Sequence alignments showed a correlation between the phenotypes and distinct BBSV genomic loci in the 3'UTR or in the domain encoding the viral replicase. Comparative analysis between a virulent variant, BBSV-m294, and the wild-type (wt) BBSV by site-directed mutagenesis indicated that a single-nucleotide substitution of a uridine to a guanine at nt 3477 in the 3'UTR was responsible for significant increases in viral pathogenicity. Gain-of-function analyses demonstrated that the ability of the BBSV variants to support replication of variant satRNAs was mainly determined by aa 516 in the P82 replicase. In this case, an arginine substitution for a glutamine residue was essential for high levels of replication, and alterations of other residues surrounding position 516 in the wtBBSV isolate led to only minor phenotypic effects. These results provide evidence that divergence of virus functions affecting pathogenicity and supporting parasitic replication can be determined by a single genetic site, either a nucleotide or an amino acid. The results suggest that complex interactions occur between virus and associated satRNAs during virus evolution.

Inhibition of the interaction between NS3 protease and HCV IRES with a small peptide: a novel therapeutic strategy

Recently, we have demonstrated that the protease domain of NS3 alone can bind specifically to hepatitis C virus (HCV) internal ribosome entry site (IRES) near the initiator AUG, dislodges human La protein and inhibits translation in favor of viral RNA replication. Here, by using a computational approach, the contact points of the protease on the HCV IRES were putatively mapped. A 30-mer NS3 peptide was designed from the predicted RNA-binding region that retained RNA-binding ability and also inhibited IRES-mediated translation. This peptide was truncated to 15 mer and this also demonstrated ability to inhibit HCV RNA-directed translation as well as replication. More importantly, its activity was tested in an in vivo mouse model by encapsulating the peptide in Sendai virus virosomes followed by intravenous delivery. The study demonstrates for the first time that the HCV NS3-IRES RNA interaction can be selectively inhibited using a small peptide and reports a strategy to deliver the peptide into the liver.

Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure

RNA-protein interactions play important roles in various biological processes. The precise detection of RNA-protein interaction sites is very important for understanding essential biological processes and annotating the function of the proteins. In this study, based on various features from amino acid sequence and structure, including evolutionary information, solvent accessible surface area and torsion angles (φ, ψ) in the backbone structure of the polypeptide chain, a computational method for predicting RNA-binding sites in proteins is proposed. When the method is applied to predict RNA-binding sites in three datasets: RBP86 containing 86 protein chains, RBP107 containing 107 proteins chains and RBP109 containing 109 proteins chains, better sensitivities and specificities are obtained compared to previously published methods in five-fold cross-validation tests. In order to make further examination for the efficiency of our method, the RBP107 dataset is used as training set, RBP86 and RBP109 datasets are used as the independent test sets. In addition, as examples of our prediction, RNA-binding sites in a few proteins are presented. The annotated results are consistent with the PDB annotation. These results show that our method is useful for annotating RNA binding sites of novel proteins.

A novel rhodanese is required to maintain chloroplast translation in Chlamydomonas

Rhodanese-domain proteins (RDPs) are widespread in plants and other organisms, but their biological roles are mostly unknown. Here we report on a novel RDP from Chlamydomonas that has a single rhodanese domain, and a predicted chloroplast transit peptide. The protein was produced in Escherichia coli with a His-tag, but lacking most of the N-terminal transit peptide, and after purification was found to have rhodanese activity in vitro. It was also used to elicit antibodies for western blot analysis, which showed that the native Chlamydomonas protein migrated slower on SDS gels (apparent M(r) =34 kDa) than its predicted size (27 kDa), and co-fractionated with chloroplasts. To assess function in vivo, the tandem-RNAi approach was used to generate Chlamydomonas strains that had reductions of 30-70% for the mRNA and ~20-40% for the 34-kDa protein. These strains showed reduced growth under all trophic conditions, and were sensitive to even moderate light; properties reminiscent of chloroplast translation mutants. Pulse-labeling in the presence of cycloheximide indicated that chloroplast protein synthesis was broadly reduced in the RNAi strains, and transcript analysis (by RT-PCR and northern blotting) indicated the effect was mainly translational. These results identify a novel rhodanese-like protein that we have named CRLT, because it is required to maintain chloroplast translation.

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

Background

RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition ‘code’ that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction.

Results

We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naïve Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM)-based representation (PSSMSeq) outperform those that use an amino acid identity based representation (IDSeq) or a smoothed PSSM (SmoPSSMSeq); (ii) Structure-based classifiers that use smoothed PSSM representation (SmoPSSMStr) outperform those that use PSSM (PSSMStr) as well as sequence identity based representation (IDStr). PSSMSeq classifiers, when tested on an independent test set of 44 proteins, achieve performance that is comparable to that of three state-of-the-art structure-based predictors (including those that exploit geometric features) in terms of Matthews Correlation Coefficient (MCC), although the structure-based methods achieve substantially higher Specificity (albeit at the expense of Sensitivity) compared to sequence-based methods. We also find that the expected performance of the classifiers on a residue level can be markedly different from that on a protein level. Our experiments show that the classifiers trained on three different non-redundant protein-RNA interface datasets achieve comparable cross-validation performance. However, we find that the results are significantly affected by differences in the distance threshold used to define interface residues.

Conclusions

Our results demonstrate that protein-RNA interface residue predictors that use a PSSM-based encoding of sequence windows outperform classifiers that use other encodings of sequence windows. While structure-based methods that exploit geometric features can yield significant increases in the Specificity of protein-RNA interface residue predictions, such increases are offset by decreases in Sensitivity. These results underscore the importance of comparing alternative methods using rigorous statistical procedures, multiple performance measures, and datasets that are constructed based on several alternative definitions of interface residues and redundancy cutoffs as well as including evaluations on independent test sets into the comparisons.

Intellectual disabilities, neuronal posttranscriptional RNA metabolism, and RNA-binding proteins: three actors for a complex scenario

Intellectual disability (ID) is the most frequent cause of serious handicap in children and young adults and interests 2-3% of worldwide population, representing a serious problem from the medical, social, and economic points of view. The causes are very heterogeneous. Genes involved in ID have various functions altering different pathways important in neuronal function. Regulation of mRNA metabolism is particularly important in neurons for synaptic structure and function. Here, we review ID due to alteration of mRNA metabolism. Functional absence of some RNA-binding proteins--namely, FMRP, FMR2P, PQBP1, UFP3B, VCX-A--causes different forms of ID. These proteins are involved in different steps of RNA metabolism and, even if a detailed analysis of their RNA targets has been performed so far only for FMRP, it appears clear that they modulate some aspects (translation, stability, transport, and sublocalization) of a subset of RNAs coding for proteins, whose function must be relevant for neurons. Two other proteins, DYRK1A and CDKL5, involved in Down syndrome and Rett syndrome, respectively, have been shown to have an impact on splicing efficiency of specific mRNAs. Both proteins are kinases and their effect is indirect. Interestingly, both are localized in nuclear speckles, the nuclear domains where splicing factors are assembled, stocked, and recycled and influence their biogenesis and/or their organization.

Predicting RNA-protein interactions using only sequence information

Background

RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.

Results

We propose RPISeq, a family of classifiers for predicting RNA-protein interactions using only sequence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens.

Conclusions

Our experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. RPISeq is freely available as a web-based server at http://pridb.gdcb.iastate.edu/RPISeq/.

Dissecting the protein-RNA interface: the role of protein surface shapes and RNA secondary structures in protein-RNA recognition

Protein-RNA interactions are essential for many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. Here, we analyzed the protein surface shape (dented, intermediate or protruded) and the RNA base pairing properties (paired or unpaired nucleotides) at the interfaces of 91 protein-RNA complexes derived from the Protein Data Bank. Dented protein surfaces prefer unpaired nucleotides to paired ones at the interface, and hydrogen bonds frequently occur between the protein backbone and RNA bases. In contrast, protruded protein surfaces do not show such a preference, rather, electrostatic interactions initiate the formation of hydrogen bonds between positively charged amino acids and RNA phosphate groups. Interestingly, in many protein-RNA complexes that interact via an RNA loop, an aspartic acid is favored at the interface. Moreover, in most of these complexes, nucleotide bases in the RNA loop are flipped out and form hydrogen bonds with the protein, which suggests that aspartic acid is important for RNA loop recognition through a base-flipping process. This study provides fundamental insights into the role of the shape of the protein surface and RNA secondary structures in mediating protein-RNA interactions.

Prediction of RNA-binding amino acids from protein and RNA sequences

Background

Many learning approaches to predicting RNA-binding residues in a protein sequence construct a non-redundant training dataset based on the sequence similarity. The sequence similarity-based method either takes a whole sequence or discards it for a training dataset. However, similar sequences or even identical sequences can have different interaction sites depending on their interaction partners, and this information is lost when the sequences are removed. Furthermore, a training dataset constructed by the sequence similarity-based method may contain redundant data when the remaining sequence contains similar subsequences within the sequence. In addition to the problem with the training dataset, most approaches do not consider the interacting partner (i.e., RNA) of a protein when they predict RNA-binding amino acids. Thus, they always predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNA molecules.

Results

We developed a feature vector-based method that removes data redundancy for a non-redundant training dataset. The feature vector-based method constructed a larger training dataset than the standard sequence similarity-based method, yet the dataset contained no redundant data. We identified effective features of protein and RNA (the interaction propensity of amino acid triplets, global features of the protein sequence, and RNA feature) for predicting RNA-binding residues. Using the method and features, we built a support vector machine (SVM) model that predicted RNA-binding residues in a protein sequence. Our SVM model showed an accuracy of 84.2%, an F-measure of 76.1%, and a correlation coefficient of 0.41 with 5-fold cross validation on a non-redundant dataset from 3,149 protein-RNA interacting pairs. In an independent test dataset that does not include the 3,149 pairs and were not used in training the SVM model, it achieved an accuracy of 90.3%, an F-measure of 72.8%, and a correlation coefficient of 0.24. Comparison with other methods on the same datasets demonstrated that our model was better than the others.

Conclusions

The feature vector-based redundancy reduction method is powerful for constructing a non-redundant training dataset for a learning model since it generates a larger dataset with non-redundant data than the standard sequence similarity-based method. Including the features of both RNA and protein sequences in a feature vector results in better performance than using the protein features only when predicting the RNA-binding residues in a protein sequence.

Incorporating evolutionary information and functional domains for identifying RNA splicing factors in humans

Regulation of pre-mRNA splicing is achieved through the interaction of RNA sequence elements and a variety of RNA-splicing related proteins (splicing factors). The splicing machinery in humans is not yet fully elucidated, partly because splicing factors in humans have not been exhaustively identified. Furthermore, experimental methods for splicing factor identification are time-consuming and lab-intensive. Although many computational methods have been proposed for the identification of RNA-binding proteins, there exists no development that focuses on the identification of RNA-splicing related proteins so far. Therefore, we are motivated to design a method that focuses on the identification of human splicing factors using experimentally verified splicing factors. The investigation of amino acid composition reveals that there are remarkable differences between splicing factors and non-splicing proteins. A support vector machine (SVM) is utilized to construct a predictive model, and the five-fold cross-validation evaluation indicates that the SVM model trained with amino acid composition could provide a promising accuracy (80.22%). Another basic feature, amino acid dipeptide composition, is also examined to yield a similar predictive performance to amino acid composition. In addition, this work presents that the incorporation of evolutionary information and domain information could improve the predictive performance. The constructed models have been demonstrated to effectively classify (73.65% accuracy) an independent data set of human splicing factors. The result of independent testing indicates that in silico identification could be a feasible means of conducting preliminary analyses of splicing factors and significantly reducing the number of potential targets that require further in vivo or in vitro confirmation.

Highly accurate and high-resolution function prediction of RNA binding proteins by fold recognition and binding affinity prediction

A full understanding of the mechanism of post- transcriptional regulation requires more than simple two- state prediction (binding or not binding) for RNA binding proteins. Here we report a sequence-based technique dedicated for predicting complex structures of protein and RNA by combining fold recognition with binding affinity prediction. The method not only provides a highly accurate complex structure prediction (77% of residues are within 4°A RMSD from native in average for the independent test set) but also achieves the best performing two-state binding or non-binding prediction with an accuracy of 98%, precision of 84%, and Mathews correlation coefficient (MCC) of 0.62. Moreover, it predicts binding residues with an accuracy of 84%, precision of 66% and MCC value of 0.51. In addition, it has a success rate of 77% in predicting RNA binding types (mRNA, tRNA or rRNA). We further demonstrate that it makes more than 10% improvement either in precision or sensitivity than PSI- BLAST, HHPRED and our previously developed structure- based technique. This method expects to be useful for highly accurate genome-scale, high-resolution prediction of RNA-binding proteins and their complex structures. A web server (SPOT) is freely available for academic users at http://sparks.informatics.iupui.edu.

Identification of protein-RNA interaction sites using the information of spatial adjacent residues

BACKGROUND

Protein-RNA interactions play an important role in numbers of fundamental cellular processes such as RNA splicing, transport and translation, protein synthesis and certain RNA-mediated enzymatic processes. The more knowledge of Protein-RNA recognition can not only help to understand the regulatory mechanism, the site-directed mutagenesis and regulation of RNA-protein complexes in biological systems, but also have a vitally effecting for rational drug design.

RESULTS

Based on the information of spatial adjacent residues, novel feature extraction methods were proposed to predict protein-RNA interaction sites with SVM-KNN classifier. The total accuracies of spatial adjacent residue profile feature and spatial adjacent residues weighted accessibility solvent area feature are 78%, 67.07% respectively in 5-fold cross-validation test, which are 1.4%, 3.79% higher than that of sequence neighbour residue profile feature and sequence neighbour residue accessibility solvent area feature.

CONCLUSIONS

The results indicate that the performance of feature extraction method using the spatial adjacent information is superior to the sequence neighbour information approach. The performance of SVM-KNN classifier is little better than that of SVM. The feature extraction method of spatial adjacent information with SVM-KNN is very effective for identifying protein-RNA interaction sites and may at least play a complimentary role to the existing methods.

Epidemiology and mutational analysis of global strains of Crimean-Congo haemorrhagic fever virus

Crimean-Congo hemorrhagic fever (CCHF) is a severe illness with high fatality. Cases are reported in several countries in Africa, Europe, the Middle East, and Asia. Phylogenetic analyses based on the virus S (nucleocapsid), M (glycoprotein), and L (polymerase) genome segments sequences indicate distinct geographic lineages exist but their specific genetic characteristics require elucidation. In this work we collected all full length S segment sequences and generated a phylogenetic tree based on the alignment of these 62 samples. We then analyzed the alignment using entries from AAIndex, the Amino Acid Index database, to identify amino acid mutations that performed significant changes in charge, pka, hydropathy and side chain volume. Finally, we mapped these changes back to the tree and alignment to identify correlated mutations or sites that characterized a specific lineage. Based on this analysis we are able to propose a number of sites that appear to be important for virus function and which would be good candidates for experimental mutational analysis studies.

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.

Prediction of RNA-binding proteins by voting systems

It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS) is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy) value of 79.72% and MCC (Matthew's correlation coefficient) value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance) strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier's vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment.

From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces

Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins. Applying the method on experimentally solved three-dimensional structures of proteins we successfully classify double-stranded DNA (dsDNA) from single-stranded RNA (ssRNA) binding proteins, with 83% accuracy. We show that the method is insensitive to conformational changes that occur upon binding and can be applicable for de novo protein-function prediction. Remarkably, when concentrating on the zinc finger motif, we distinguish successfully between RNA and DNA binding interfaces possessing the same binding motif even within the same protein, as demonstrated for the RNA polymerase transcription-factor, TFIIIA. In conclusion, we present a novel methodology to characterize protein surfaces, which can accurately tell apart dsDNA from an ssRNA binding interfaces. The strength of our method in recognizing fine-tuned differences on NA binding interfaces make it applicable for many other molecular recognition problems, with potential implications for drug design.

Prediction of transporter targets using efficient RBF networks with PSSM profiles and biochemical properties

SUMMARY

Transporters are proteins that are involved in the movement of ions or molecules across biological membranes. Currently, our knowledge about the functions of transporters is limited due to the paucity of their 3D structures. Hence, computational techniques are necessary to annotate the functions of transporters. In this work, we focused on an important functional aspect of transporters, namely annotation of targets for transport proteins. We have systematically analyzed four major classes of transporters with different transporter targets: (i) electron, (ii) protein/mRNA, (iii) ion and (iv) others, using amino acid properties. We have developed a radial basis function network-based method for predicting transport targets with amino acid properties and position specific scoring matrix profiles. Our method showed a 10-fold cross-validation accuracy of 90.1, 80.1, 70.3 and 82.3% for electron transporters, protein/mRNA transporters, ion transporters and others, respectively, in a dataset of 543 transporters. We have also evaluated the performance of the method with an independent dataset of 108 proteins and we obtained similar accuracy. We suggest that our method could be an effective tool for functional annotation of transport proteins.

AVAILABILITY

http://rbf.bioinfo.tw/~sachen/ttrbf.html

Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence

Sequence-derived structural and physicochemical features have been extensively used for analyzing and predicting structural, functional, expression and interaction profiles of proteins and peptides. PROFEAT has been developed as a web server for computing commonly used features of proteins and peptides from amino acid sequence. To facilitate more extensive studies of protein and peptides, numerous improvements and updates have been made to PROFEAT. We added new functions for computing descriptors of protein–protein and protein–small molecule interactions, segment descriptors for local properties of protein sequences, topological descriptors for peptide sequences and small molecule structures. We also added new feature groups for proteins and peptides (pseudo-amino acid composition, amphiphilic pseudo-amino acid composition, total amino acid properties and atomic-level topological descriptors) as well as for small molecules (atomic-level topological descriptors). Overall, PROFEAT computes 11 feature groups of descriptors for proteins and peptides, and a feature group of more than 400 descriptors for small molecules plus the derived features for protein–protein and protein–small molecule interactions. Our computational algorithms have been extensively tested and used in a number of published works for predicting proteins of specific structural or functional classes, protein–protein interactions, peptides of specific functions and quantitative structure activity relationships of small molecules. PROFEAT is accessible free of charge at http://bidd.cz3.nus.edu.sg/cgi-bin/prof/protein/profnew.cgi.

The role of RNA sequence and structure in RNA--protein interactions

We investigate the sequence and structural properties of RNA--protein interaction sites in 211 RNA--protein chain pairs, the largest set of RNA--protein complexes analyzed to date. Statistical analysis confirms and extends earlier analyses made on smaller data sets. There are 24.6% of hydrogen bonds between RNA and protein that are nucleobase specific, indicating the importance of both nucleobase-specific and -nonspecific interactions. While there is no significant difference between RNA base frequencies in protein-binding and non-binding regions, distinct preferences for RNA bases, RNA structural states, protein residues, and protein secondary structure emerge when nucleobase-specific and -nonspecific interactions are considered separately. Guanine nucleobase and unpaired RNA structural states are significantly preferred in nucleobase-specific interactions; however, nonspecific interactions disfavor guanine, while still favoring unpaired RNA structural states. The opposite preferences of nucleobase-specific and -nonspecific interactions for guanine may explain discrepancies between earlier studies with regard to base preferences in RNA--protein interaction regions. Preferences for amino acid residues differ significantly between nucleobase-specific and -nonspecific interactions, with nonspecific interactions showing the expected bias towards positively charged residues. Irregular protein structures are strongly favored in interactions with the protein backbone, whereas there is little preference for specific protein secondary structure in either nucleobase-specific interaction or -nonspecific interaction. Overall, this study shows strong preferences for both RNA bases and RNA structural states in protein--RNA interactions, indicating their mutual importance in protein recognition.

In silico characterization and prediction of global protein-mRNA interactions in yeast

Post-transcriptional gene regulation is mediated through complex networks of protein-RNA interactions. The targets of only a few RNA binding proteins (RBPs) are known, even in the well-characterized budding yeast. In silico prediction of protein-RNA interactions is therefore useful to guide experiments and to provide insight into regulatory networks. Computational approaches have identified RBP targets based on sequence binding preferences. We investigate here to what extent RBP-RNA interactions can be predicted based on RBP and mRNA features other than sequence motifs. We analyze global relationships between gene and protein properties in general and between selected RBPs and known mRNA targets in particular. Highly translated RBPs tend to bind to shorter transcripts, and transcripts bound by the same RBP show high expression correlation across different biological conditions. Surprisingly, a given RBP preferentially binds to mRNAs that encode interaction partners for this RBP, suggesting coordinated post-transcriptional auto-regulation of protein complexes. We apply a machine-learning approach to predict specific RBP targets in yeast. Although this approach performs well for RBPs with known targets, predictions for uncharacterized RBPs remain challenging due to limiting experimental data. We also predict targets of fission yeast RBPs, indicating that the suggested framework could be applied to other species once more experimental data are available.

Identification of amino acids in auxiliary replicase protein p27 critical for its RNA-binding activity and the assembly of the replicase complex in Red clover necrotic mosaic virus

The specific recognition of genomic RNAs by viral replicase proteins is a key regulatory step during the early replication process in positive-strand RNA viruses. In this study, we characterized the RNA-binding activity of the auxiliary replicase protein p27 of Red clover necrotic mosaic virus (RCNMV), which has a bipartite genome consisting of RNA1 and RNA2. Aptamer pull-down assays identified the amino acid residues of p27 involved in its specific interaction with RNA2. The RNA-binding activity of p27 correlated with its activity in recruiting RNA2 to membranes. We also identified the amino acids required for the formation of the 480-kDa replicase complex, a key player of RCNMV RNA replication. These amino acids are not involved in the functions of p27 that bind viral RNA or replicase proteins, suggesting an additional role for p27 in the assembly of the replicase complex. Our results demonstrate that p27 has multiple functions in RCNMV replication.

Bioinformatics for RNomics

Rapid improvements in high-throughput experimental technologies make it nowadays possible to study the expression, as well as changes in expression, of whole transcriptomes under different environmental conditions in a detailed view. We describe current approaches to identify genome-wide functional RNA transcripts (experimentally as well as computationally), and focus on computational methods that may be utilized to disclose their function. While genome databases offer a wealth of information about known and putative functions for protein-coding genes, functional information for novel non-coding RNA genes is almost nonexistent. This is mainly explained by the lack of established software tools to efficiently reveal the function and evolutionary origin of non-coding RNA genes. Here, we describe in detail computational approaches one may follow to annotate and classify an RNA transcript.

Amino acid changes within the Bunyamwera virus nucleocapsid protein differentially affect the mRNA transcription and RNA replication activities of assembled ribonucleoprotein templates

The genome of Bunyamwera virus (BUNV) comprises three RNA segments that are encapsidated by the virus-encoded nucleocapsid (N) protein to form ribonucleoprotein (RNP) complexes. These RNPs are the functional templates for RNA synthesis by the virus-encoded RNA-dependent RNA polymerase (RdRp). We investigated the roles of conserved positively charged N-protein amino acids in RNA binding, in oligomerization to form model RNPs and in generating RNP templates active for both RNA replication and mRNA transcription. We identified several residues that performed important roles in RNA binding, and furthermore showed that a single amino acid change can differentially affect the ability of the resulting RNP templates to regulate the transcription and replication activities of the RdRp. These results indicate that the BUNV N protein possesses functions outside of its primary role of RNA encapsidation.

Structure-based prediction of RNA-binding domains and RNA-binding sites and application to structural genomics targets

Mechanistic understanding of many key cellular processes often involves identification of RNA binding proteins (RBPs) and RNA binding sites in two separate steps. Here, they are predicted simultaneously by structural alignment to known protein-RNA complex structures followed by binding assessment with a DFIRE-based statistical energy function. This method achieves 98% accuracy and 91% precision for predicting RBPs and 93% accuracy and 78% precision for predicting RNA-binding amino-acid residues for a large benchmark of 212 RNA binding and 6761 non-RNA binding domains (leave-one-out cross-validation). Additional tests revealed that the method makes no false positive prediction from 311 DNA binding domains but correctly detects six domains binding with both DNA and RNA. In addition, it correctly identified 31 of 75 unbound RNA-binding domains with 92% accuracy and 65% precision for predicted binding residues and achieved 86% success rate in its application to SCOP RNA binding domain superfamily (Structural Classification Of Proteins). It further predicts 25 targets as RBPs in 2076 structural genomics targets: 20 of 25 predicted ones (80%) are putatively RNA binding. The superior performance over existing methods indicates the importance of dividing structures into domains, using a Z-score to measure relative structural similarity, and a statistical energy function to measure protein-RNA binding affinity.

Predicting RNA-binding residues from evolutionary information and sequence conservation

Abstract

PRIDB: a Protein-RNA interface database

The Protein–RNA Interface Database (PRIDB) is a comprehensive database of protein–RNA interfaces extracted from complexes in the Protein Data Bank (PDB). It is designed to facilitate detailed analyses of individual protein–RNA complexes and their interfaces, in addition to automated generation of user-defined data sets of protein–RNA interfaces for statistical analyses and machine learning applications. For any chosen PDB complex or list of complexes, PRIDB rapidly displays interfacial amino acids and ribonucleotides within the primary sequences of the interacting protein and RNA chains. PRIDB also identifies ProSite motifs in protein chains and FR3D motifs in RNA chains and provides links to these external databases, as well as to structure files in the PDB. An integrated JMol applet is provided for visualization of interacting atoms and residues in the context of the 3D complex structures. The current version of PRIDB contains structural information regarding 926 protein–RNA complexes available in the PDB (as of 10 October 2010). Atomic- and residue-level contact information for the entire data set can be downloaded in a simple machine-readable format. Also, several non-redundant benchmark data sets of protein–RNA complexes are provided. The PRIDB database is freely available online at http://bindr.gdcb.iastate.edu/PRIDB.

Recent advances in B-cell epitope prediction methods

Identification of epitopes that invoke strong responses from B-cells is one of the key steps in designing effective vaccines against pathogens. Because experimental determination of epitopes is expensive in terms of cost, time, and effort involved, there is an urgent need for computational methods for reliable identification of B-cell epitopes. Although several computational tools for predicting B-cell epitopes have become available in recent years, the predictive performance of existing tools remains far from ideal. We review recent advances in computational methods for B-cell epitope prediction, identify some gaps in the current state of the art, and outline some promising directions for improving the reliability of such methods.

Predicting success of oligomerized pool engineering (OPEN) for zinc finger target site sequences

BACKGROUND

Precise and efficient methods for gene targeting are critical for detailed functional analysis of genomes and regulatory networks and for potentially improving the efficacy and safety of gene therapies. Oligomerized Pool ENgineering (OPEN) is a recently developed method for engineering C2H2 zinc finger proteins (ZFPs) designed to bind specific DNA sequences with high affinity and specificity in vivo. Because generation of ZFPs using OPEN requires considerable effort, a computational method for identifying the sites in any given gene that are most likely to be successfully targeted by this method is desirable.

RESULTS

Analysis of the base composition of experimentally validated ZFP target sites identified important constraints on the DNA sequence space that can be effectively targeted using OPEN. Using alternate encodings to represent ZFP target sites, we implemented Naïve Bayes and Support Vector Machine classifiers capable of distinguishing "active" targets, i.e., ZFP binding sites that can be targeted with a high rate of success, from those that are "inactive" or poor targets for ZFPs generated using current OPEN technologies. When evaluated using leave-one-out cross-validation on a dataset of 135 experimentally validated ZFP target sites, the best Naïve Bayes classifier, designated ZiFOpT, achieved overall accuracy of 87% and specificity+ of 90%, with an ROC AUC of 0.89. When challenged with a completely independent test set of 140 newly validated ZFP target sites, ZiFOpT performance was comparable in terms of overall accuracy (88%) and specificity+ (92%), but with reduced ROC AUC (0.77). Users can rank potentially active ZFP target sites using a confidence score derived from the posterior probability returned by ZiFOpT.

CONCLUSION

ZiFOpT, a machine learning classifier trained to identify DNA sequences amenable for targeting by OPEN-generated zinc finger arrays, can guide users to target sites that are most likely to function successfully in vivo, substantially reducing the experimental effort required. ZiFOpT is freely available and incorporated in the Zinc Finger Targeter web server (http://bindr.gdcb.iastate.edu/ZiFiT).

Identification and characterization of RNA-binding activity in the ORF1-encoded replicase protein of Pelargonium flower break virus

Pelargonium flower break virus (PFBV) belongs to the genus Carmovirus (family Tombusviridae) and, as with the remaining members of the group, possesses a monopartite genome of single-stranded, positive-sense RNA that contains five ORFs. The two 5'-proximal ORFs (ORFs 1 and 2) encode two polypeptides of 27 and 86 kDa (p27 and p86), respectively, that show homology with replication proteins. The p27 does not present any motif to explain its presumed involvement in replication, while p86 has the motifs conserved in RNA-dependent RNA polymerases. In this work, we have confirmed the necessity of p27 and p86 for PFBV replication. To gain insights into the function(s) of p27, we have expressed and purified the protein from Escherichia coli and tested its ability to bind RNA in vitro. The results have shown that p27 is able to bind ssRNA with high affinity and in a cooperative fashion and that it is also capable of binding other types of nucleic acids, though to a lesser extent. Additionally, competition experiments suggest that p27 has a preference for PFBV-derived ssRNAs. Using truncated forms of p27, it can be concluded that several regions of the protein contribute to its RNA-binding properties and that this contribution is additive. This study is the first to show nucleic acid-binding ability of the ORF1 product of a carmovirus and the data obtained suggest that this product plays an essential role in selection and recruitment of viral RNA replication templates.

Identification of the 3C-protease-mediated 2A/2B and 2B/2C cleavage sites in the nonstructural polyprotein precursor of a Dicistrovirus lacking the NPGP motif

Dicistroviruses have motifs for picornavirus 2C, 3C, and 3D proteins in their nonstructural polyprotein C-terminal region. The proteins from the nonstructural, N-terminal region of the polyprotein remain to be characterized. We have identified 3C-mediated cleavage sites in the N-terminal region of the nonstructural polyprotein of the dicistrovirus Plautia stali intestine virus (PSIV). The 2B/2C cleavage site mapped to amino acids (aa) 408-409 (QD). 2B/2C cleavage sites were suggested to be conserved in dicistroviruses. The most N-terminal PSIV cleavage site was aa 286-287 (QS). Including previous results, the polyprotein contains nine proteins arranged as follows: 2A, 2B, 2C, 3A, 3B1, 3B2, 3B3, 3C, and 3D.

The role of G-quadruplex in RNA metabolism: involvement of FMRP and FMR2P

Regulation of post-transcriptional gene expression is a cellular process that is accomplished through the activity of multiple mRNP (messenger RiboNucleoProtein) complexes which are composed of mRNA-binding proteins and RNA molecules interacting with those proteins. The specificity of these interactions is mediated by the ability of the RNA-binding proteins to precisely recognize and bind RNA sequences or structures. Alterations of their function may have some dramatic consequences, resulting in different pathologies. An increasing body of data is emerging showing the impact of a G-quadruplex forming structure in the maturation and expression of some RNA molecules. We review here the role of the G-quadruplex RNA structure in the regulation of translation and splicing, when it interacts with two RNA-binding proteins: FMRP (Fragile X Mental Retardation Protein) and FMR2P (Fragile X Mental Retardation 2 protein). Impaired expression of these proteins causes two forms of intellectual disability: the Fragile X Mental Retardation syndrome (FXS) and the FRAXE-associated mental retardation (FRAXE), respectively. FMRP is involved in different steps of RNA metabolism and, in particular, in translational regulation. FMR2P has been initially described as a transcription factor and we recently showed also its role in regulation of alternative splicing. By the study of the functional significance of the interaction of both FMRP and FMR2P with a G-quadruplex forming RNA we were able to show an impact of this structure in translational regulation and also in splicing, behaving as an Exonic Splicing Enhancer.

Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information

Background

Guanosine triphosphate (GTP)-binding proteins play an important role in regulation of G-protein. Thus prediction of GTP interacting residues in a protein is one of the major challenges in the field of the computational biology. In this study, an attempt has been made to develop a computational method for predicting GTP interacting residues in a protein with high accuracy (Acc), precision (Prec) and recall (Rc).

Result

All the models developed in this study have been trained and tested on a non-redundant (40% similarity) dataset using five-fold cross-validation. Firstly, we have developed neural network based models using single sequence and PSSM profile and achieved maximum Matthews Correlation Coefficient (MCC) 0.24 (Acc 61.30%) and 0.39 (Acc 68.88%) respectively. Secondly, we have developed a support vector machine (SVM) based models using single sequence and PSSM profile and achieved maximum MCC 0.37 (Prec 0.73, Rc 0.57, Acc 67.98%) and 0.55 (Prec 0.80, Rc 0.73, Acc 77.17%) respectively. In this work, we have introduced a new concept of predicting GTP interacting dipeptide (two consecutive GTP interacting residues) and tripeptide (three consecutive GTP interacting residues) for the first time. We have developed SVM based model for predicting GTP interacting dipeptides using PSSM profile and achieved MCC 0.64 with precision 0.87, recall 0.74 and accuracy 81.37%. Similarly, SVM based model have been developed for predicting GTP interacting tripeptides using PSSM profile and achieved MCC 0.70 with precision 0.93, recall 0.73 and accuracy 83.98%.

Conclusion

These results show that PSSM based method performs better than single sequence based method. The prediction models based on dipeptides or tripeptides are more accurate than the traditional model based on single residue. A web server "GTPBinder" http://www.imtech.res.in/raghava/gtpbinder/ based on above models has been developed for predicting GTP interacting residues in a protein.

BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features

Background

Understanding how biomolecules interact is a major task of systems biology. To model protein-nucleic acid interactions, it is important to identify the DNA or RNA-binding residues in proteins. Protein sequence features, including the biochemical property of amino acids and evolutionary information in terms of position-specific scoring matrix (PSSM), have been used for DNA or RNA-binding site prediction. However, PSSM is rather designed for PSI-BLAST searches, and it may not contain all the evolutionary information for modelling DNA or RNA-binding sites in protein sequences.

Results

In the present study, several new descriptors of evolutionary information have been developed and evaluated for sequence-based prediction of DNA and RNA-binding residues using support vector machines (SVMs). The new descriptors were shown to improve classifier performance. Interestingly, the best classifiers were obtained by combining the new descriptors and PSSM, suggesting that they captured different aspects of evolutionary information for DNA and RNA-binding site prediction. The SVM classifiers achieved 77.3% sensitivity and 79.3% specificity for prediction of DNA-binding residues, and 71.6% sensitivity and 78.7% specificity for RNA-binding site prediction.

Conclusions

Predictions at this level of accuracy may provide useful information for modelling protein-nucleic acid interactions in systems biology studies. We have thus developed a web-based tool called BindN+ (http://bioinfo.ggc.org/bindn+/) to make the SVM classifiers accessible to the research community.

Prediction of protein-RNA binding sites by a random forest method with combined features

MOTIVATION

Protein-RNA interactions play a key role in a number of biological processes, such as protein synthesis, mRNA processing, mRNA assembly, ribosome function and eukaryotic spliceosomes. As a result, a reliable identification of RNA binding site of a protein is important for functional annotation and site-directed mutagenesis. Accumulated data of experimental protein-RNA interactions reveal that a RNA binding residue with different neighbor amino acids often exhibits different preferences for its RNA partners, which in turn can be assessed by the interacting interdependence of the amino acid fragment and RNA nucleotide.

RESULTS

In this work, we propose a novel classification method to identify the RNA binding sites in proteins by combining a new interacting feature (interaction propensity) with other sequence- and structure-based features. Specifically, the interaction propensity represents a binding specificity of a protein residue to the interacting RNA nucleotide by considering its two-side neighborhood in a protein residue triplet. The sequence as well as the structure-based features of the residues are combined together to discriminate the interaction propensity of amino acids with RNA. We predict RNA interacting residues in proteins by implementing a well-built random forest classifier. The experiments show that our method is able to detect the annotated protein-RNA interaction sites in a high accuracy. Our method achieves an accuracy of 84.5%, F-measure of 0.85 and AUC of 0.92 prediction of the RNA binding residues for a dataset containing 205 non-homologous RNA binding proteins, and also outperforms several existing RNA binding residue predictors, such as RNABindR, BindN, RNAProB and PPRint, and some alternative machine learning methods, such as support vector machine, naive Bayes and neural network in the comparison study. Furthermore, we provide some biological insights into the roles of sequences and structures in protein-RNA interactions by both evaluating the importance of features for their contributions in predictive accuracy and analyzing the binding patterns of interacting residues.

AVAILABILITY

All the source data and code are available at http://www.aporc.org/doc/wiki/PRNA or http://www.sysbio.ac.cn/datatools.asp

CONTACT

lnchen@sibs.ac.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

NAPS: a residue-level nucleic acid-binding prediction server

Nucleic acid-binding proteins are involved in a great number of cellular processes. Understanding the mechanisms underlying these proteins first requires the identification of specific residues involved in nucleic acid binding. Prediction of NA-binding residues can provide practical assistance in the functional annotation of NA-binding proteins. Predictions can also be used to expedite mutagenesis experiments, guiding researchers to the correct binding residues in these proteins. Here, we present a method for the identification of amino acid residues involved in DNA- and RNA-binding using sequence-based attributes. The method used in this work combines the C4.5 algorithm with bootstrap aggregation and cost-sensitive learning. Our DNA-binding model achieved 79.1% accuracy, while the RNA-binding model reached an accuracy of 73.2%. The NAPS web server is freely available at http://proteomics.bioengr.uic.edu/NAPS.

Inferring the ancient history of the translation machinery and genetic code via recapitulation of ribosomal subunit assembly orders

Universally conserved positions in ribosomal proteins have significant biases in amino acid usage, likely indicating the expansion of the genetic code at the time leading up to the most recent common ancestor(s) (MRCA). Here, we apply this principle to the evolutionary history of the ribosome before the MRCA. It has been proposed that the experimentally determined order of assembly for ribosomal subunits recapitulates their evolutionary chronology. Given this model, we produce a probabilistic evolutionary ordering of the universally conserved small subunit (SSU) and large subunit (LSU) ribosomal proteins. Optimizing the relative ordering of SSU and LSU evolutionary chronologies with respect to minimizing differences in amino acid usage bias, we find strong compositional evidence for a more ancient origin for early LSU proteins. Furthermore, we find that this ordering produces several trends in specific amino acid usages compatible with models of genetic code evolution.

Rrp17p is a eukaryotic exonuclease required for 5' end processing of Pre-60S ribosomal RNA

Ribosomal processing requires a series of endo- and exonucleolytic steps for the production of mature ribosomes, of which most have been described. To ensure ribosome synthesis, 3′ end formation of rRNA uses multiple nucleases acting in parallel; however, a similar parallel mechanism had not been described for 5′ end maturation. Here, we identify Rrp17p as a previously unidentified 5′–3′ exonuclease essential for ribosome biogenesis, functioning with Rat1p in a parallel processing pathway analogous to that of 3′ end formation. Rrp17p is required for efficient exonuclease digestion of the mature 5′ ends of 5.8S_S and 25S rRNAs, contains a catalytic domain close to its N terminus, and is highly conserved among higher eukaryotes, being a member of a family of exonucleases. We show that Rrp17p binds late pre-60S ribosomes, accompanying them from the nucleolus to the nuclear periphery, and provide evidence for physical and functional links between late 60S subunit processing and export.

Coronavirus nucleocapsid protein facilitates template switching and is required for efficient transcription

Purified nucleocapsid protein (N protein) from transmissible gastroenteritis virus (TGEV) enhanced hammerhead ribozyme self-cleavage and favored nucleic acid annealing, properties that define RNA chaperones, as previously reported. Several TGEV N-protein deletion mutants were expressed in Escherichia coli and purified, and their RNA binding ability and RNA chaperone activity were evaluated. The smallest N-protein domain analyzed with RNA chaperone activity, facilitating DNA and RNA annealing, contained the central unstructured region (amino acids 117 to 268). Interestingly, N protein and its deletion mutants with RNA chaperone activity enhanced template switching in a retrovirus-derived heterologous system, reinforcing the concept that TGEV N protein is an RNA chaperone that could be involved in template switching. This result is in agreement with the observation that in vivo, N protein is not necessary for TGEV replication, but it is required for efficient transcription.

Exploiting structural and topological information to improve prediction of RNA-protein binding sites

Background

RNA-protein interactions are important for a wide range of biological processes. Current computational methods to predict interacting residues in RNA-protein interfaces predominately rely on sequence data. It is, however, known that interface residue propensity is closely correlated with structural properties. In this paper we systematically study information obtained from sequences and structures and compare their contributions in this prediction problem. Particularly, different geometrical and network topological properties of protein structures are evaluated to improve interface residue prediction accuracy.

Results

We have quantified the impact of structural information on the prediction accuracy in comparison to the purely sequence based approach using two machine learning techniques: Naïve Bayes classifiers and Support Vector Machines. The highest AUC of 0.83 was achieved by a Support Vector Machine, exploiting PSI-BLAST profile, accessible surface area, betweenness-centrality and retention coefficient as input features. Taking into account that our results are based on a larger non-redundant data set, the prediction accuracy is considerably higher than reported in previous, comparable studies. A protein-RNA interface predictor (PRIP) and the data set have been made available at .

Conclusion

Graph-theoretic properties of residue contact maps derived from protein structures such as betweenness-centrality can supplement sequence or structure features to improve the prediction accuracy for binding residues in RNA-protein interactions. While Support Vector Machines perform better on this task, Naïve Bayes classifiers also have been found to achieve good prediction accuracies but require much less training time and are an attractive choice for large scale predictions.

Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling

BACKGROUND

Identification of functionally important sites in biomolecular sequences has broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Experimental determination of such sites lags far behind the number of known biomolecular sequences. Hence, there is a need to develop reliable computational methods for identifying functionally important sites from biomolecular sequences.

RESULTS

We present a mixture of experts approach to biomolecular sequence labeling that takes into account the global similarity between biomolecular sequences. Our approach combines unsupervised and supervised learning techniques. Given a set of sequences and a similarity measure defined on pairs of sequences, we learn a mixture of experts model by using spectral clustering to learn the hierarchical structure of the model and by using bayesian techniques to combine the predictions of the experts. We evaluate our approach on two biomolecular sequence labeling problems: RNA-protein and DNA-protein interface prediction problems. The results of our experiments show that global sequence similarity can be exploited to improve the performance of classifiers trained to label biomolecular sequence data.

CONCLUSION

The mixture of experts model helps improve the performance of machine learning methods for identifying functionally important sites in biomolecular sequences.