Protein-RNA interactions: a structural analysis

A detailed computational analysis of 32 protein-RNA complexes is presented. A number of physical and chemical properties of the intermolecular interfaces are calculated and compared with those observed in protein-double-stranded DNA and protein-single-stranded DNA complexes. The interface properties of the protein-RNA complexes reveal the diverse nature of the binding sites. van der Waals contacts played a more prevalent role than hydrogen bond contacts, and preferential binding to guanine and uracil was observed. The positively charged residue, arginine, and the single aromatic residues, phenylalanine and tyrosine, all played key roles in the RNA binding sites. A comparison between protein-RNA and protein-DNA complexes showed that whilst base and backbone contacts (both hydrogen bonding and van der Waals) were observed with equal frequency in the protein-RNA complexes, backbone contacts were more dominant in the protein-DNA complexes. Although similar modes of secondary structure interactions have been observed in RNA and DNA binding proteins, the current analysis emphasises the differences that exist between the two types of nucleic acid binding protein at the atomic contact level

Molecular principles underlying dual RNA specificity in the Drosophila SNF protein

The first RNA recognition motif of the Drosophila SNF protein is an example of an RNA binding protein with multi-specificity. It binds different RNA hairpin loops in spliceosomal U1 or U2 small nuclear RNAs, and only in the latter case requires the auxiliary U2A′ protein. Here we investigate its functions by crystal structures of SNF alone and bound to U1 stem-loop II, U2A′ or U2 stem-loop IV and U2A′, SNF dynamics from NMR spectroscopy, and structure-guided mutagenesis in binding studies. We find that different loop-closing base pairs and a nucleotide exchange at the tips of the loops contribute to differential SNF affinity for the RNAs. U2A′ immobilizes SNF and RNA residues to restore U2 stem-loop IV binding affinity, while U1 stem-loop II binding does not require such adjustments. Our findings show how U2A′ can modulate RNA specificity of SNF without changing SNF conformation or relying on direct RNA contacts

Recognition of two distinct elements in the RNA substrate by the RNA-binding domain of the T. thermophilus DEAD box helicase Hera

DEAD box helicases catalyze the ATP-dependent destabilization of RNA duplexes. Whereas duplex separation is mediated by the helicase core shared by all members of the family, flanking domains often contribute to binding of the RNA substrate. The Thermus thermophilus DEAD-box helicase Hera (for “heat-resistant RNA-binding ATPase”) contains a C-terminal RNA-binding domain (RBD). We have analyzed RNA binding to the Hera RBD by a combination of mutational analyses, nuclear magnetic resonance and X-ray crystallography, and identify residues on helix α1 and the C-terminus as the main determinants for high-affinity RNA binding. A crystal structure of the RBD in complex with a single-stranded RNA resolves the RNA–protein interactions in the RBD core region around helix α1. Differences in RNA binding to the Hera RBD and to the structurally similar RBD of the Bacillus subtilis DEAD box helicase YxiN illustrate the versatility of RNA recognition motifs as RNA-binding platforms. Comparison of chemical shift perturbation patterns elicited by different RNAs, and the effect of sequence changes in the RNA on binding and unwinding show that the RBD binds a single-stranded RNA region at the core and simultaneously contacts double-stranded RNA through its C-terminal tail. The helicase core then unwinds an adjacent RNA duplex. Overall, the mode of RNA binding by Hera is consistent with a possible function as a general RNA chaperone

Identifying Interaction Sites in "Recalcitrant" Proteins: Predicted Protein and Rna Binding Sites in Rev Proteins of Hiv-1 and Eiav Agree with Experimental Data

Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein participate in its interactions with other proteins and/or nucleic acids, using only the protein sequence as input. In this paper, we describe an application of classifiers trained on datasets of well-characterized protein-protein and protein-RNA complexes for which experimental structures are available. We apply these classifiers to the problem of predicting protein and RNA binding sites in the sequence of a clinically important protein for which the structure is not known: the regulatory protein Rev, essential for the replication of HIV-1 and other lentiviruses. We compare our predictions with published biochemical, genetic and partial structural information for HIV-1 and EIAV Rev and with our own published experimental mapping of RNA binding sites in EIAV Rev. The predicted and experimentally determined binding sites are in very good agreement. The ability to predict reliably the residues of a protein that directly contribute to specific binding events - without the requirement for structural information regarding either the protein or complexes in which it participates - can potentially generate new disease intervention strategies.Comment: Pacific Symposium on Biocomputing, Hawaii, In press, Accepted, 200

RNA-binding protein CPEB1 remodels host and viral RNA landscapes.

Host and virus interactions occurring at the post-transcriptional level are critical for infection but remain poorly understood. Here, we performed comprehensive transcriptome-wide analyses revealing that human cytomegalovirus (HCMV) infection results in widespread alternative splicing (AS), shortening of 3' untranslated regions (3' UTRs) and lengthening of poly(A)-tails in host gene transcripts. We found that the host RNA-binding protein CPEB1 was highly induced after infection, and ectopic expression of CPEB1 in noninfected cells recapitulated infection-related post-transcriptional changes. CPEB1 was also required for poly(A)-tail lengthening of viral RNAs important for productive infection. Strikingly, depletion of CPEB1 reversed infection-related cytopathology and post-transcriptional changes, and decreased productive HCMV titers. Host RNA processing was also altered in herpes simplex virus-2 (HSV-2)-infected cells, thereby indicating that this phenomenon might be a common occurrence during herpesvirus infections. We anticipate that our work may serve as a starting point for therapeutic targeting of host RNA-binding proteins in herpesvirus infections

Crystal structure of Schmallenberg orthobunyavirus nucleoprotein-RNA complex reveals a novel RNA sequestration mechanism

Schmallenberg virus (SBV) is a newly emerged orthobunyavirus (family Bunyaviridae) that has caused severe disease in the offspring of farm animals across Europe. Like all orthobunyaviruses, SBV contains a tripartite negative-sense RNA genome that is encapsidated by the viral nucleocapsid (N) protein in the form of a ribonucleoprotein complex (RNP). We recently reported the three-dimensional structure of SBV N that revealed a novel fold. Here we report the crystal structure of the SBV N protein in complex with a 42-nt-long RNA to 2.16 Å resolution. The complex comprises a tetramer of N that encapsidates the RNA as a cross-shape inside the protein ring structure, with each protomer bound to 11 ribonucleotides. Eight bases are bound in the positively charged cleft between the N- and C-terminal domains of N, and three bases are shielded by the extended N-terminal arm. SBV N appears to sequester RNA using a different mechanism compared with the nucleoproteins of other negative-sense RNA viruses. Furthermore, the structure suggests that RNA binding results in conformational changes of some residues in the RNA-binding cleft and the N- and C-terminal arms. Our results provide new insights into the novel mechanism of RNA encapsidation by orthobunyaviruses

Interplay between single-stranded binding proteins on RNA secondary structure

RNA protein interactions control the fate of cellular RNAs and play an important role in gene regulation. An interdependency between such interactions allows for the implementation of logic functions in gene regulation. We investigate the interplay between RNA binding partners in the context of the statistical physics of RNA secondary structure, and define a linear correlation function between the two partners as a measurement of the interdependency of their binding events. We demonstrate the emergence of a long-range power-law behavior of this linear correlation function. This suggests RNA secondary structure driven interdependency between binding sites as a general mechanism for combinatorial post-transcriptional gene regulation.Comment: 26 pages, 17 figure

Nucleic acid - protein fingerprints. Novel protein classification based on nucleic acid - protein recognition

Protein chemistry uses protein description and classification based on molecular mass and isoelectric point as general features. Enzymes are also compared by enzymatic reaction constants, namely Km and kcat values. Proteins are also studied by binding to different oligonucleotides. Here we suggest a simple experimental method for such a comparison of DNA binding proteins, which we call "nucleic acid-protein fingerprints". The experimental design of the method is based on an use of short oligonucleotides immobilized inside microarray of hydrogel cells - biochip. As a first stage, we solved a simple experimental task: what is the shortest single strand oligonucleotide to be recognized by protein? We tested binding of oligonucleotides from 2 to 12 bases, and we have obtained unexpected result that tetranucleotide one is long enough for specific protein binding. This 4-mer can contain two universal bases - 5-nitroindole nucleoside analogue (Ni) and only two meaningful bases, like A, G, T and C. The result obtained opens a way for constructing the simplest protein binding microarray. This microarray consists of 16 meaningful dinucleotides, like AA, AG, CT, GG etc. Physical sequences of all the nucleotides were NiNiAA, etc, where Ni is bound to gel through the amino linker. We prepared such an array and tested it for specific binding of several DNA/RNA binding proteins, labeled with fluorescent dyes like Texas Red of Bodipy. We tested RNase A and Binase for binding on the simplest microarray. It contains only 16 units, and there is a significant difference in the binding patterns. The microarray based on 3-mers must contains 64 units and must have much more specificity. The new principle of protein classification based on nucleic acid-protein recognition has been proposed and experimentally proved. Such an experimental approach must lead to a universal classification of specific DNA/RNA binding proteins

The long noncoding RNA neuroLNC regulates presynaptic activity by interacting with the neurodegeneration-associated protein TDP-43

The cellular and the molecular mechanisms by which long noncoding RNAs (lncRNAs) may regulate presynaptic function and neuronal activity are largely unexplored. Here, we established an integrated screening strategy to discover lncRNAs implicated in neurotransmitter and synaptic vesicle release. With this approach, we identified neuroLNC, a neuron-specific nuclear lncRNA conserved from rodents to humans. NeuroLNC is tuned by synaptic activity and influences several other essential aspects of neuronal development including calcium influx, neuritogenesis, and neuronal migration in vivo. We defined the molecular interactors of neuroLNC in detail using chromatin isolation by RNA purification, RNA interactome analysis, and protein mass spectrometry. We found that the effects of neuroLNC on synaptic vesicle release require interaction with the RNA-binding protein TDP-43 (TAR DNA binding protein-43) and the selective stabilization of mRNAs encoding for presynaptic proteins. These results provide the first proof of an lncRNA that orchestrates neuronal excitability by influencing presynaptic function