A comparative study of sequence- and structure-based features of small RNAs and other RNAs of bacteria.

Small RNAs (sRNAs) in bacteria have emerged as key players in transcriptional and post-transcriptional regulation of gene expression. Here, we present a statistical analysis of different sequence- and structure-related features of bacterial sRNAs to identify the descriptors that could discriminate sRNAs from other bacterial RNAs. We investigated a comprehensive and heterogeneous collection of 816 sRNAs, identified by northern blotting across 33 bacterial species and compared their various features with other classes of bacterial RNAs, such as tRNAs, rRNAs and mRNAs. We observed that sRNAs differed significantly from the rest with respect to G+C composition, normalized minimum free energy of folding, motif frequency and several RNA-folding parameters like base-pairing propensity, Shannon entropy and base-pair distance. Based on the selected features, we developed a predictive model using Random Forests (RF) method to classify the above four classes of RNAs. Our model displayed an overall predictive accuracy of 89.5%. These findings would help to differentiate bacterial sRNAs from other RNAs and further promote prediction of novel sRNAs in different bacterial species.

Probing binding hot spots at protein-RNA recognition sites.

We use evolutionary conservation derived from structure alignment of polypeptide sequences along with structural and physicochemical attributes of protein-RNA interfaces to probe the binding hot spots at protein-RNA recognition sites. We find that the degree of conservation varies across the RNA binding proteins; some evolve rapidly compared to others. Additionally, irrespective of the structural class of the complexes, residues at the RNA binding sites are evolutionary better conserved than those at the solvent exposed surfaces. For recognitions involving duplex RNA, residues interacting with the major groove are better conserved than those interacting with the minor groove. We identify multi-interface residues participating simultaneously in protein-protein and protein-RNA interfaces in complexes where more than one polypeptide is involved in RNA recognition, and show that they are better conserved compared to any other RNA binding residues. We find that the residues at water preservation site are better conserved than those at hydrated or at dehydrated sites. Finally, we develop a Random Forests model using structural and physicochemical attributes for predicting binding hot spots. The model accurately predicts 80% of the instances of experimental ΔΔG values in a particular class, and provides a stepping-stone towards the engineering of protein-RNA recognition sites with desired affinity.

Hydration of protein-RNA recognition sites.

We investigate the role of water molecules in 89 protein-RNA complexes taken from the Protein Data Bank. Those with tRNA and single-stranded RNA are less hydrated than with duplex or ribosomal proteins. Protein-RNA interfaces are hydrated less than protein-DNA interfaces, but more than protein-protein interfaces. Majority of the waters at protein-RNA interfaces makes multiple H-bonds; however, a fraction do not make any. Those making H-bonds have preferences for the polar groups of RNA than its partner protein. The spatial distribution of waters makes interfaces with ribosomal proteins and single-stranded RNA relatively 'dry' than interfaces with tRNA and duplex RNA. In contrast to protein-DNA interfaces, mainly due to the presence of the 2'OH, the ribose in protein-RNA interfaces is hydrated more than the phosphate or the bases. The minor groove in protein-RNA interfaces is hydrated more than the major groove, while in protein-DNA interfaces it is reverse. The strands make the highest number of water-mediated H-bonds per unit interface area followed by the helices and the non-regular structures. The preserved waters at protein-RNA interfaces make higher number of H-bonds than the other waters. Preserved waters contribute toward the affinity in protein-RNA recognition and should be carefully treated while engineering protein-RNA interfaces.

A repressor activator protein1 homologue from an oleaginous strain of Candida tropicalis increases storage lipid production in Saccharomyces cerevisiae.

The repressor activator protein1 (Rap1) has been studied over the years as a multifunctional regulator in Saccharomyces cerevisiae. However, its role in storage lipid accumulation has not been investigated. This report documents the identification and isolation of a putative transcription factor CtRap1 gene from an oleaginous strain of Candida tropicalis, and establishes the direct effect of its expression on the storage lipid accumulation in S. cerevisiae, usually a non-oleaginous yeast. In silico analysis revealed that the CtRap1 polypeptide binds relatively more strongly to the promoter of fatty acid synthase1 (FAS1) gene of S. cerevisiae than ScRap1. The expression level of CtRap1 transcript in vivo was found to correlate directly with the amount of lipid produced in oleaginous native host C. tropicalis. Heterologous expression of the CtRap1 gene resulted in ∼ 4-fold enhancement of storage lipid content (57.3%) in S. cerevisiae. We also showed that the functionally active CtRap1 upregulates the endogenous ScFAS1 and ScDGAT genes of S. cerevisiae, and this, in turn, might be responsible for the increased lipid production in the transformed yeast. Our findings pave the way for the possible utility of the CtRap1 gene in suitable microorganisms to increase their storage lipid content through transcription factor engineering.

PRince: a web server for structural and physicochemical analysis of protein-RNA interface.

We have developed a web server, PRince, which analyzes the structural features and physicochemical properties of the protein-RNA interface. Users need to submit a PDB file containing the atomic coordinates of both the protein and the RNA molecules in complex form (in '.pdb' format). They should also mention the chain identifiers of interacting protein and RNA molecules. The size of the protein-RNA interface is estimated by measuring the solvent accessible surface area buried in contact. For a given protein-RNA complex, PRince calculates structural, physicochemical and hydration properties of the interacting surfaces. All these parameters generated by the server are presented in a tabular format. The interacting surfaces can also be visualized with software plug-in like Jmol. In addition, the output files containing the list of the atomic coordinates of the interacting protein, RNA and interface water molecules can be downloaded. The parameters generated by PRince are novel, and users can correlate them with the experimentally determined biophysical and biochemical parameters for better understanding the specificity of the protein-RNA recognition process. This server will be continuously upgraded to include more parameters. PRince is publicly accessible and free for use. Available at http://www.facweb.iitkgp.ernet.in/~rbahadur/prince/home.html.

Computational insights into intrinsically disordered regions in protein-nucleic acid complexes.

We study transitions in intrinsically disordered regions (IDRs) upon complex formation, utilizing X-ray-solved structural dataset of protein-DNA and protein-RNA complexes, along with their available unbound protein forms. The identified IDRs are categorized into three classes: Disordered-to-Ordered (D-O), Disordered-to-Partial Ordered (D-PO) and Disordered-to-Disordered (D-D) after comparing them in unbound and complex forms. In the D-O class, IDRs form secondary structures like coils, helices, and strands upon binding to nucleic acids. Though a majority of these IDRs are present at the surface of the complexes, a significant number of IDRs are also observed at the interfaces and are involved in polar interactions. The hydrogen bonds made by the interface IDRs (B_IDRs) with phosphates and bases of nucleic acids are comparatively more than those formed with sugars. B_IDRs form more H-bonds with the ribose in protein-RNA than with the deoxyribose in protein-DNA. Among the B_IDRs, Arg and Lys prefer to interact with the major and minor grooves of DNA and RNA, respectively. Ser, however, prefers the minor groove in both the nucleic acids. Interestingly, we report 61 and 48 IDRs in 31 protein-DNA and 22 protein-RNA complexes, respectively, suggesting nucleic acid binding to proteins may also result in ordered-to-disordered transitions.

A protein-RNA docking benchmark (I): nonredundant cases.

We have developed a nonredundant protein-RNA docking benchmark dataset, which is derived from the available bound and unbound structures in the Protein Data Bank involving polypeptide and nucleic acid chains. It consists of nine unbound-unbound cases where both the protein and the RNA are available in the free form. The other 36 cases are of unbound-bound type where only the protein is available in the free form. The conformational change upon complex formation is calculated by a distance matrix alignment method, and based on that, complexes are classified into rigid, semi-flexible, and full flexible. Although in the rigid body category, no significant conformational change accompanies complex formation, the fully flexible test cases show large domain movements, RNA base flips, etc. The benchmark covers four major groups of RNA, namely, t-RNA, ribosomal RNA, duplex RNA, and single-stranded RNA. We find that RNA is generally more flexible than the protein in the complexes, and the interface region is as flexible as the molecule as a whole. The structural diversity of the complexes in the benchmark set should provide a common ground for the development and comparison of the protein-RNA docking methods. The benchmark can be freely downloaded from the internet.

Disordered Function Conjunction: On the in-silico function annotation of intrinsically disordered regions.

Intrinsically disorder regions (IDRs) lack a stable structure, yet perform biological functions. The functions of IDRs include mediating interactions with other molecules, including proteins, DNA, or RNA and entropic functions, including domain linkers. Computational predictors provide residue-level indications of function for disordered proteins, which contrasts with the need to functionally annotate the thousands of experimentally and computationally discovered IDRs. In this work, we investigate the feasibility of using residue-level prediction methods for region-level function predictions. For an initial examination of the multiple function region-level prediction problem, we constructed a dataset of (likely) single function IDRs in proteins that are dissimilar to the training datasets of the residue-level function predictors. We find that available residue-level prediction methods are only modestly useful in predicting multiple region-level functions. Classification is enhanced by simultaneous use of multiple residue-level function predictions and is further improved by inclusion of amino acids content extracted from the protein sequence. We conclude that multifunction prediction for IDRs is feasible and benefits from the results produced by current residue-level function predictors, however, it has to accommodate inaccuracy in functional annotations.

DEPICTER: Intrinsic Disorder and Disorder Function Prediction Server.

Computational predictions of the intrinsic disorder and its functions are instrumental to facilitate annotation for the millions of unannotated proteins. However, access to these predictors is fragmented and requires substantial effort to find them and to collect and combine their results. The DEPICTER (DisorderEd PredictIon CenTER) server provides first-of-its-kind centralized access to 10 popular disorder and disorder function predictions that cover protein and nucleic acids binding, linkers, and moonlighting regions. It automates the prediction process, runs user-selected methods on the server side, visualizes the results, and outputs all predictions in a consistent and easy-to-parse format. DEPICTER also includes two accurate consensus predictors of disorder and disordered protein binding. Empirical tests on an independent (low similarity) benchmark dataset reveal that the computational tools included in DEPICTER generate accurate predictions that are significantly better than the results secured using sequence alignment. The DEPICTER server is freely available at http://biomine.cs.vcu.edu/servers/DEPICTER/.

Molecular architecture of protein-RNA recognition sites.

The molecular architecture of protein-RNA interfaces are analyzed using a non-redundant dataset of 152 protein-RNA complexes. We find that an average protein-RNA interface is smaller than an average protein-DNA interface but larger than an average protein-protein interface. Among the different classes of protein-RNA complexes, interfaces with tRNA are the largest, while the interfaces with the single-stranded RNA are the smallest. Significantly, RNA contributes more to the interface area than its partner protein. Moreover, unlike protein-protein interfaces where the side chain contributes less to the interface area compared to the main chain, the main chain and side chain contributions flipped in protein-RNA interfaces. We find that the protein surface in contact with the RNA in protein-RNA complexes is better packed than that in contact with the DNA in protein-DNA complexes, but loosely packed than that in contact with the protein in protein-protein complexes. Shape complementarity and electrostatic potential are the two major factors that determine the specificity of the protein-RNA interaction. We find that the H-bond density at the protein-RNA interfaces is similar with that of protein-DNA interfaces but higher than the protein-protein interfaces. Unlike protein-DNA interfaces where the deoxyribose has little role in intermolecular H-bonds, due to the presence of an oxygen atom at the 2' position, the ribose in RNA plays significant role in protein-RNA H-bonds. We find that besides H-bonds, salt bridges and stacking interactions also play significant role in stabilizing protein-nucleic acids interfaces; however, their contribution at the protein-protein interfaces is insignificant.

Identification of single exon genes and their encoded proteins in rice (Oryza sativa L.) genome: an in silico approach.

In this study, we predicted Single Exon Genes (SEGs) distributed in whole rice genome and their expressed proteins. Complete genome of rice was retrieved from TIGR. CDS annotation in the FEATURE (GenBank format) was used to predict SEGs sequences. Organelle gene sequences, pseudogenes, tRNA genes, rRNA genes and duplicated genes were eliminated through different bioinformatics tools. A sizeable number (8.1%) of SEGs in whole rice genome were detected. Predicted SEGs were further searched for their differential response under anoxia. Out of total detected SEGs, only 39.33% were anoxia responsive. Among the total detected anoxia-responsive SEG, only 23.48% encode the known proteins.