More Like this

1. Producing Hfq/Sm Proteins and sRNAs for Structural and Biophysical Studies of Ribonucleoprotein Assembly

   https://doi.org/10.1007/978-1-4939-7634-8_16  

   Stanek, Kimberly A; Mura, C. (February 2018, Methods in molecular biology)

   Hfq is a bacterial RNA-binding protein that plays key roles in the post-transcriptional regulation of gene expression. Like other Sm proteins, Hfq assembles into toroidal discs that bind RNAs with varying affinities and degrees of sequence specificity. By simultaneously binding to a regulatory small RNA (sRNA) and an mRNA target, Hfq hexamers facilitate productive RNA∙∙∙RNA interactions; the generic nature of this chaperone-like functionality makes Hfq a hub in many sRNA-based regulatory networks. That Hfq is crucial in diverse cellular pathways—including stress response, quorum sensing, and biofilm formation—has motivated genetic and “RNAomic” studies of its function and physiology (in vivo), as well as biochemical and structural analyses of Hfq∙∙∙RNA interactions (in vitro). Indeed, crystallographic and biophysical studies first established Hfq as a member of the phylogenetically conserved Sm superfamily. Crystallography and other biophysical methodologies enable the RNA-binding properties of Hfq to be elucidated in atomic detail, but such approaches have stringent sample requirements, viz.: reconstituting and characterizing an Hfq·RNA complex requires ample quantities of well-behaved (sufficient purity, homogeneity) specimens of Hfq and RNA (sRNA, mRNA fragments, short oligoribonucleotides, or even single nucleotides). The production of such materials is covered in this chapter, with a particular focus on recombinant Hfq proteins for crystallization experiments. 

   more » « less
2. An RNA folding motif: GNRA tetraloop–receptor interactions

   https://doi.org/10.1017/S0033583513000048  

   Fiore, Julie L.; Nesbitt, David J. (August 2013, Quarterly Reviews of Biophysics)

   Abstract Nearly two decades after Westhof and Michel first proposed that RNA tetraloops may interact with distal helices, tetraloop–receptor interactions have been recognized as ubiquitous elements of RNA tertiary structure. The unique architecture of GNRA tetraloops ( N =any nucleotide, R =purine) enables interaction with a variety of receptors, e.g., helical minor grooves and asymmetric internal loops. The most common example of the latter is the GAAA tetraloop–11 nt tetraloop receptor motif. Biophysical characterization of this motif provided evidence for the modularity of RNA structure, with applications spanning improved crystallization methods to RNA tectonics. In this review, we identify and compare types of GNRA tetraloop–receptor interactions. Then we explore the abundance of structural, kinetic, and thermodynamic information on the frequently occurring and most widely studied GAAA tetraloop–11 nt receptor motif. Studies of this interaction have revealed powerful paradigms for structural assembly of RNA, as well as providing new insights into the roles of cations, transition states and protein chaperones in RNA folding pathways. However, further research will clearly be necessary to characterize other tetraloop–receptor and long-range tertiary binding interactions in detail – an important milestone in the quantitative prediction of free energy landscapes for RNA folding. 

   more » « less
3. Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features

   https://doi.org/10.1093/bib/bbaa076  

   Zhu, Yi-Heng; Hu, Jun; Ge, Fang; Li, Fuyi; Song, Jiangning; Zhang, Yang; Yu, Dong-Jun (May 2020, Briefings in Bioinformatics)

   Abstract X-ray crystallography is the major approach for determining atomic-level protein structures. Because not all proteins can be easily crystallized, accurate prediction of protein crystallization propensity provides critical help in guiding experimental design and improving the success rate of X-ray crystallography experiments. This study has developed a new machine-learning-based pipeline that uses a newly developed deep-cascade forest (DCF) model with multiple types of sequence-based features to predict protein crystallization propensity. Based on the developed pipeline, two new protein crystallization propensity predictors, denoted as DCFCrystal and MDCFCrystal, have been implemented. DCFCrystal is a multistage predictor that can estimate the success propensities of the three individual steps (production of protein material, purification and production of crystals) in the protein crystallization process. MDCFCrystal is a single-stage predictor that aims to estimate the probability that a protein will pass through the entire crystallization process. Moreover, DCFCrystal is designed for general proteins, whereas MDCFCrystal is specially designed for membrane proteins, which are notoriously difficult to crystalize. DCFCrystal and MDCFCrystal were separately tested on two benchmark datasets consisting of 12 289 and 950 proteins, respectively, with known crystallization results from various experimental records. The experimental results demonstrated that DCFCrystal and MDCFCrystal increased the value of Matthew’s correlation coefficient by 199.7% and 77.8%, respectively, compared to the best of other state-of-the-art protein crystallization propensity predictors. Detailed analyses show that the major advantages of DCFCrystal and MDCFCrystal lie in the efficiency of the DCF model and the sensitivity of the sequence-based features used, especially the newly designed pseudo-predicted hybrid solvent accessibility (PsePHSA) feature, which improves crystallization recognition by incorporating sequence-order information with solvent accessibility of residues. Meanwhile, the new crystal-dataset constructions help to train the models with more comprehensive crystallization knowledge. 

   more » « less
4. Crystal structures of bacterial small multidrug resistance transporter EmrE in complex with structurally diverse substrates

   https://doi.org/10.7554/eLife.76766  

   Kermani, Ali A; Burata, Olive E; Koff, B Ben; Koide, Akiko; Koide, Shohei; Stockbridge, Randy B (March 2022, eLife)

   Proteins from the bacterial small multidrug resistance (SMR) family are proton-coupled exporters of diverse antiseptics and antimicrobials, including polyaromatic cations and quaternary ammonium compounds. The transport mechanism of the Escherichia coli transporter, EmrE, has been studied extensively, but a lack of high-resolution structural information has impeded a structural description of its molecular mechanism. Here, we apply a novel approach, multipurpose crystallization chaperones, to solve several structures of EmrE, including a 2.9 Å structure at low pH without substrate. We report five additional structures in complex with structurally diverse transported substrates, including quaternary phosphonium, quaternary ammonium, and planar polyaromatic compounds. These structures show that binding site tryptophan and glutamate residues adopt different rotamers to conform to disparate structures without requiring major rearrangements of the backbone structure. Structural and functional comparison to Gdx-Clo, an SMR protein that transports a much narrower spectrum of substrates, suggests that in EmrE, a relatively sparse hydrogen bond network among binding site residues permits increased sidechain flexibility. 

   more » « less
5. Improving RNA secondary structure prediction via state inference with deep recurrent neural networks

   https://doi.org/10.1515/cmb-2020-0002  

   Willmott, Devin; Murrugarra, David; Ye, Qiang (March 2020, Computational and Mathematical Biophysics)

   Abstract The problem of determining which nucleotides of an RNA sequence are paired or unpaired in the secondary structure of an RNA, which we call RNA state inference, can be studied by different machine learning techniques. Successful state inference of RNA sequences can be used to generate auxiliary information for data-directed RNA secondary structure prediction. Typical tools for state inference, such as hidden Markov models, exhibit poor performance in RNA state inference, owing in part to their inability to recognize nonlocal dependencies. Bidirectional long short-term memory (LSTM) neural networks have emerged as a powerful tool that can model global nonlinear sequence dependencies and have achieved state-of-the-art performances on many different classification problems. This paper presents a practical approach to RNA secondary structure inference centered around a deep learning method for state inference. State predictions from a deep bidirectional LSTM are used to generate synthetic SHAPE data that can be incorporated into RNA secondary structure prediction via the Nearest Neighbor Thermodynamic Model (NNTM). This method produces predicted secondary structures for a diverse test set of 16S ribosomal RNA that are, on average, 25 percentage points more accurate than undirected MFE structures. Accuracy is highly dependent on the success of our state inference method, and investigating the global features of our state predictions reveals that accuracy of both our state inference and structure inference methods are highly dependent on the similarity of pairing patterns of the sequence to the training dataset. Availability of a large training dataset is critical to the success of this approach. Code available at https://github.com/dwillmott/rna-state-inf . 

   more » « less