Assessing protein-RNA interactions using microfluidic capillary mobility shift assays

The dynamic range of plasma protein abundance, ranging from milligrams to picograms per milliliter, makes characterization of this proteome nearly impossible with current analytical methods. Plasma preprocessing by high-abundance protein depletion may concomitantly remove important diagnostic information. This article describes an original chromatographic procedure to isolate proteins bound to human serum albumin (HSA). Using HSA as an “affinity agent”, we significantly improved the detection and identification of HSA ligands by two-dimensional liquid chromatography tandem mass spectrometry (2D LC–MS/MS). Some of the characterized species were not previously reported in published blood databases. Albumin-binding proteins may be classified as belonging to several putative functional categories and span a wide variety of predicted physiological functions.     

Computational methods for prediction of protein-RNA interactions

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

Cation-pi interactions at non-redundant protein-RNA interfaces

Cation-pi interactions have proved to be important in proteins and protein-ligand complexes. Here, cation-pi interactions are analyzed for 282 non-redundant protein-RNA interfaces. The statistical results show that this kind of interactions exists in 65% of the interfaces. The four RNA bases are ranked as Gua > Ade > Ura > Cyt according to their propensity to participate in cation-pi interactions. The corresponding ranking for the involved amino acid residues is: Arg > Lys > Asn > Gln. The same trends are obtained based on the empirical energy calculation. The Arg-Gua pairs have the greatest stability and are also most frequently observed. The number of cation-pi pairs involving unpaired bases is 2.5 times as many as those involving paired bases. Hence, cation-pi interactions show sequence and structural specificities. For the bicyclic bases, Gua and Ade, their 5-atom rings participate in cation-pi interactions somewhat more than the 6-atom rings, with percentages of 54 and 46%, respectively, which is due to the higher cation-pi participation proportion (63%) of 5-atom rings in the paired bases. These results give a general view of cation-pi interactions at protein-RNA interfaces and are helpful in understanding the specific recognition between protein and RNA.     

Structure-based evaluation of in silico predictions of protein-protein interactions using Comparative Docking

MOTIVATION: Due to the limitations in experimental methods for determining binary interactions and structure determination of protein complexes, the need exists for computational models to fill the increasing gap between genome sequence information and protein annotation. Here we describe a novel method that uses structural models to reduce a large number of in silico predictions to a high confidence subset that is amenable to experimental validation. RESULTS: A two-stage evaluation procedure was developed, first, a sequence-based method assessed the conservation of protein interface patches used in the original in silico prediction method, both in terms of position within the primary sequence, and in terms of sequence conservation. When applying the most stringent conditions it was found that 20.5% of the data set being assessed passed this test. Secondly, a high-throughput structure-based docking evaluation procedure assessed the soundness of three dimensional models produced for the putative interactions. Of the data set being assessed, 8264 interactions or over 70% could be modelled in this way, and 27% of these can be considered 'valid' by the applied criteria. In all, 6.9% of the interactions passed both the tests and can be considered to be a high confidence set of predicted interactions, several of which are described. AVAILABILITY: http://bioinformatics.leeds.ac.uk/~bmb4sjc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Global analysis of protein-RNA interactions in SARS-CoV-2-infected cells reveals key regulators of infection

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Investigation of protein-RNA interactions by mass spectrometry--Techniques and applications

Protein-RNA complexes play many important roles in diverse cellular functions. They are involved in a wide variety of different processes in growth and differentiation at the various stages of the cell cycle. As their function and catalytic activity are directly coupled to the structural arrangement of their components--proteins and ribonucleic acids--the investigation of protein-RNA interactions is of great functional and structural importance. Here we discuss the most prominent examples of protein-RNA complexes and describe some frequently used purification strategies. We present various techniques and applications of mass spectrometry to study protein-RNA complexes. We discuss the analysis of intact complexes as well as proteomics-based and crosslinking-based approaches in which proteins are cleaved into smaller peptides. This article is part of a Special Section entitled: Understanding genome regulation and genetic diversity by mass spectrometry.     

A comparison of two phage coat protein-RNA interactions.

The interaction between the coat protein of the group I bacteriophage fr with its translational operator site is compared with the previously studied R17 interaction. The sequence of the two RNA binding sites differ by 2 of 20 nucleotides and two coat proteins by 17 of 129 amino acids. An analysis of the binding of fr coat protein to 24 operator variants revealed that the two proteins recognize operator sequences in virtually the same way. However, fr coat protein binds to nearly every RNA 6 to 14-fold tighter than R17 coat protein. Since the fr operator is a weaker binding variant and the fr coat protein shows a different temperature dependence of binding, it is unlikely that the two systems have different Kas in vivo. RNA fragments containing the operator sequences can initiate the capsid assembly with both fr and R17 coat protein. Surprisingly, the two coat proteins can form a mixed capsid in vitro.     

Nonspecific effect of m7GMP on protein-RNA interactions.

Cap analogs m7GMP and m7GDP inhibit binding of eukaryotic initiation factors to reovirus capped mRNA but also inhibit complex formation involving uncapped mRNA or 18 S rRNA. Furthermore, Escherichia coli DNA-dependent RNA polymerase binds 18 S rRNA and this interaction is also blocked by m7GMP. These results indicate that inhibition by cap analogs is not a stringent test for putative cap-specific binding between proteins and mRNA.     

High-throughput assays probing protein-RNA interactions of eukaryotic translation initiation factors

Protein-RNA interactions are involved in all facets of RNA biology. The identification of small molecules that selectively block such bimolecular interactions could provide insight into previously unexplored steps of gene regulation. Such is the case for regulation of eukaryotic protein synthesis where interactions between messenger RNA (mRNA) and several eukaryotic initiation factors govern the recruitment of 40S ribosomes (and associated factors) to mRNA templates during the initiation phase. We have designed simple fluorescence polarization-based high-throughput screening assays that query the binding of several translation factors to RNA and found that the mixed inhibitor p-chloromercuribenzoate interferes with poly(A) binding protein-RNA interaction.     

Probing FinO-FinP RNA interactions by site-directed protein-RNA crosslinking and gelFRET

The conjugative transfer of F-plasmids is repressed by a two-component system, which consists of the antisense RNA FinP and the protein FinO. FinO binds FinP, protecting it from endonucleolytic degradation and facilitating duplex formation between FinP and its complementary RNA. Here we present the results of site-specific protein-RNA cross-linking and gel-based fluorescence resonance energy transfer (gelFRET) experiments used to probe the structure of a complex of FinO bound to an RNA target consisting of a duplex with 5' and 3' single-stranded tails. The crosslinking experiments reveal that an extensive, largely positively charged surface on FinO contacts RNA. The gelFRET measurements indicate that the 5' single-stranded tail of the RNA is in closer contact with much of the protein than the distal, blunt end of the RNA duplex. These data suggest that significant conformational adjustments in the protein and/or the RNA accompany complex formation.     

TDP-43: gumming up neurons through protein-protein and protein-RNA interactions

Since the discovery that 43 kDa TAR DNA binding protein (TDP-43) is involved in neurodegeneration, studies of this protein have focused on the global effects of TDP-43 expression modulation on cell metabolism and survival. The major difficulty with these global searches, which can yield hundreds to thousands of variations in gene expression level and/or mRNA isoforms, is our limited ability to separate specific TDP-43 effects from secondary dysregulations occurring at the gene expression and various mRNA processing steps. In this review, we focus on two biochemical properties of TDP-43: its ability to bind RNA and its protein-protein interactions. In particular, we overview how these two properties may affect potentially very important processes for the pathology, from the autoregulation of TDP-43 to aggregation in the cytoplasmic/nuclear compartments.