Secondary Structural Features Research Articles

1H, 13C and 15N resonance assignment of the SARS-CoV-2 full-length nsp1 protein and its mutants reveals its unique secondary structure features in solution.

Structural characterization of the SARS-CoV-2 full length nsp1 protein will be an essential tool for developing new target-directed antiviral drugs against SARS-CoV-2 and for further understanding of intra- and intermolecular interactions of this protein. As a first step in the NMR studies of the protein, we report the 1H, 13C and 15N resonance backbone assignment as well as the Cβ of the apo form of the full-lengthSARS-CoV-2 nsp1 including the folded domain together with the flaking N- and C- terminal intrinsically disordered fragments. The 19.8 kD protein was characterized by high-resolution NMR. Validation of assignment have been done by using two different mutants, H81P and K129E/D48E as well as by amino acid specific experiments. According to the obtained assignment, the secondary structure of the folded domain in solution was almost identical to its previously published X-ray structure as well as another published secondary structure obtained by NMR, but some discrepancies have been detected. In the solution SARS-CoV-2 nsp1 exhibited disordered, flexible N- and C-termini with different dynamic characteristics. The short peptide in the beginning of the disordered C-terminal domain adopted two different conformations distinguishable on the NMR time scale. We propose that the disordered and folded nsp1 domains are not fully independent units but are rather involved in intramolecular interactions. Studies of the structure and dynamics of the SARS-CoV-2 mutant in solution are on-going and will provide important insights into the molecular mechanisms underlying these interactions.

Species and Fatty Acid Diversity of Desmodesmus (Chlorophyta) in a Local Japanese Area and Identification of New Docosahexaenoic Acid-Producing Species

Desmodesmus is a green microalgal genus that is frequently found in aquatic environments. Its high biomass productivity and potential as a source of lipids have attracted considerable attention. Although Desmodesmus is ubiquitous, it is difficult to identify; even within a small area, the diversity of the species and the fatty acids they produce are unknown. In this study, we performed scanning electron microscopy (SEM) and analyzed the genetic diversity of the internal transcribed spacer (ITS) region to accurately identify Desmodesmus in a local area of Japan (Saga City, Saga Pref.) and to assess its existence as a biological resource. In addition, we analyzed the fatty acid composition and content of the newly established strains. In total, 10 new strains were established, and 9 previously described species were identified. The presence of a cosmopolitan species indicated the global distribution of Desmodesmus. However, only regional species were found. One strain, dSgDes-b, did not form a clear clade with any described species in the phylogenetic analysis and had a characteristic ITS2 secondary structure. The cell wall of this strain exhibited a distinctive microstructure, and it produced docosahexaenoic acid (DHA); hence, the strain was described as a new species, Desmodesmus dohacommunis Demura sp. nov. Thus, useful information regarding the use of Desmodesmus as a bioresource was provided.

Unconventional conservation reveals structure-function relationships in the synaptonemal complex.

Functional requirements constrain protein evolution, commonly manifesting in a conserved amino acid sequence. Here, we extend this idea to secondary structural features by tracking their conservation in essential meiotic proteins with highly diverged sequences. The synaptonemal complex (SC) is a ~100-nm-wide ladder-like meiotic structure present in all eukaryotic clades, where it aligns parental chromosomes and regulates exchanges between them. Despite the conserved ultrastructure and functions of the SC, SC proteins are highly divergent within Caenorhabditis. However, SC proteins have highly conserved length and coiled-coil domain structure. We found the same unconventional conservation signature in Drosophila and mammals, and used it to identify a novel SC protein in Pristionchus pacificus, Ppa-SYP-1. Our work suggests that coiled-coils play wide-ranging roles in the structure and function of the SC, and more broadly, that expanding sequence analysis beyond measures of per-site similarity can enhance our understanding of protein evolution and function.

Unusual Pseudopeptides: Syntheses and Structural Analyses of Ethylenediprolyl Peptides and Their Metal Complexes with Cu(II) Ion.

The synthetic unnatural amino acids and their peptides as peptidomimetics have shown remarkable structural and functional properties. In the repertoire of synthetic peptides, pseudopeptides have emerged as attractive small peptidomimetics that are capable of forming the characteristic secondary structures in the solid/solution phase, as in natural peptides. This report describes the synthesis and structural analyses of novel pseudopeptides as ethylenediprolyl (etpro) tetra/hexapeptides, comprising a chiral diaminedicarboxylate scaffold. Their NMR and CD spectral analyses strongly support the formation of the β-turn-type structures in organic solvents (ACN/MeOH). Further, the single-crystal X-ray studies of tetrapseudopeptide confirm the formation of a unique self-assembly structure as β-strand type in the solid state through hydrogen bonding. Importantly, their diamine moiety influences the formation of Cu-complexes with Cu(II) ions. A tetrapseudopeptide monocarboxylate-Cu(II) complex forms the single crystal that is studied by the single-crystal X-ray diffractometer. The crystal structure of the tetrapseudopeptide-Cu(II) complex confirms the formation of the distorted square planar geometry structure, almost like the amyloid β(Aβ)-peptide-Cu(II) complex structural geometry. Hence, these etpro-pseudopeptides are emerging peptidomimatics that form β-turn types of structures and metal complexes mainly with Cu(II) ions. These molecules could be considered for the development of peptide-based catalysts and peptide-based therapeutic drug candidates.

Extraction of mannoprotein from Saccharomyces cerevisiae and analysis of its chemical composition and molecular structure

The purpose of this study was to screen the method with the highest yield of mannoprotein (MP) without damaging their functional structure. The recovery rates of the extracts, protein, and mannose were determined along with the mannose/protein ratio, molecular weight and distribution, monosaccharide and amino acid compositions, and secondary structures of the three MP extracts to compare the extraction methods. The MP extracts recovery rate prepared by the thermal method was significantly higher (35.89%) than those obtained using fermentation (31.66%) and SDS treatment (19.77%). Three protein bands with similar molecular weights of 59, 47, and 34 kDa were detected in the MPs obtained via the different extraction methods. The thermally extracted MP has a broader molecular weight distribution. After purification, the proportion of mannose in the polysaccharide parts of the MPs increased from 6–7 to 90.4–91.3%. The essential amino acid content of the hot-extracted MP (170.07 mg/g) was the highest. The thermally extracted MP had similar secondary structural characteristics to that isolated at room temperature, and had a higher protein characteristic peak intensity. In general, the heating method to extract yeast mannoprotein is time-saving and efficient.

Improved recognition of splice sites in A. thaliana by incorporating secondary structure information into sequence-derived features: a computational study.

The online version contains supplementary material available at 10.1007/s13205-021-03036-8.

Universal and strain specific structure features of segment 8 genomic RNA of influenza A virus—application of 4-thiouridine photocrosslinking

RNA structure in the influenza A virus (IAV) has been the focus of several studies that have shown connections between conserved secondary structure motifs and their biological function in the virus replication cycle. Questions have arisen on how to best recognize and understand the pandemic properties of IAV strains from an RNA perspective, but determination of the RNA secondary structure has been challenging. Herein, we used chemical mapping to determine the secondary structure of segment 8 viral RNA (vRNA) of the pandemic A/California/04/2009 (H1N1) strain of IAV. Additionally, this long, naturally occurring RNA served as a model to evaluate RNA mapping with 4-thiouridine (4sU) crosslinking. We explored 4-thiouridine as a probe of nucleotides in close proximity, through its incorporation into newly transcribed RNA and subsequent photoactivation. RNA secondary structural features both universal to type A strains and unique to the A/California/04/2009 (H1N1) strain were recognized. 4sU mapping confirmed and facilitated RNA structure prediction, according to several rules: 4sU photocross-linking forms efficiently in the double-stranded region of RNA with some flexibility, in the ends of helices, and across bulges and loops when their structural mobility is permitted. This method highlighted three-dimensional properties of segment 8 vRNA secondary structure motifs and allowed to propose several long-range three-dimensional interactions. 4sU mapping combined with chemical mapping and bioinformatic analysis could be used to enhance the RNA structure determination as well as recognition of target regions for antisense strategies or viral RNA detection.

Singular value decomposition analysis of the secondary structure features contributing to the circular dichroism spectra of model proteins

Amyloid fibril formation occurs in restricted environment, such as the interface between intercellular fluids and bio-membranes. Conformational interconversion from α-helix to β-structure does not progress in fluids; however, it can occur after sedimentary aggregation during amyloid fibril formation induced by heat treatment of hen egg white lysozyme (HEWL). Secondary structures of various proteins and denatured proteins titrated with 2,2,2-trifluoroethanol (TFE) were examined using their CD spectra. Gaussian peak/trough and singular value decompositions (SVD) showed that the spectral pattern of the α-helix comprised a sharp trough at wavelength 207 nm and a broad trough at 220 nm. Conversely, we distinguished two patterns for β-sheet—a spread barrel type, corresponding to ConA, and a tightly weaved type, corresponding to the soybean trypsin inhibitor. Herein, we confirmed that the spectral/conformational interconversion of the heat-treated HEWL was not observed in the dissolved fluid.

Characterizing viral circRNAs and their application in identifying circRNAs in viruses.

Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism, which play an important role in a variety of biological activities. Viruses can encode circRNA, and viral circRNAs have been found in multiple single-stranded and double-stranded viruses. However, the characteristics and functions of viral circRNAs remain unknown. Sequence alignment showed that viral circRNAs are less conserved than circRNAs in animal, indicating that the viral circRNAs may evolve rapidly. Through the analysis of the sequence characteristics of viral circRNAs and circRNAs in animal, it was found that viral circRNAs and animals circRNAs are similar in nucleic acid composition, but have obvious differences in secondary structure and autocorrelation characteristics. Based on these characteristics of viral circRNAs, machine learning algorithms were employed to construct a prediction model to identify viral circRNA. Additionally, analysis of the interaction between viral circRNA and miRNAs showed that viral circRNA is expected to interact with 518 human miRNAs, and preliminary analysis of the role of viral circRNA. And it has been also found that viral circRNAs may be involved in many KEGG pathways related to nervous system and cancer. We curated an online server, and the data and code are available: http://server.malab.cn/viral-CircRNA/.

Evolution of wake structures behind oscillating hydrofoils with combined heaving and pitching motion

The wake of an oscillating teardrop hydrofoil with combined heaving and pitching motion was studied numerically at Reynolds number of 8000 and Strouhal numbers of $St=0.21{-}0.94$ . The lower Strouhal number exhibited high efficiency propulsion with small thrust generation. However, larger thrust generation at high $St$ required more power, which lowered the propulsive efficiency. Quantitative assessment of vortex evolution, along with qualitative investigation of the formation and interaction of primary structures, revealed the association with elliptic instability characteristics for both co-rotating and counter-rotating vortex structures in both wakes. With respect to advection of the leading-edge vortex, the pressure distribution further depicted evidence of spanwise instability with distinct temporal evolution along the suction and pressure surfaces of the oscillating foil. Three-dimensional assessment of wake structures located downstream of the trailing edge depicted the existence of dislocations associated with primary vortex ‘rollers’. At low $St$ , these were limited to fine spanwise corrugations (valleys and bulges) on weaker leading edge rollers, which enlarged as the rollers advected downstream. In contrast, at high $St$ , the wake exhibited conjoint hairpin-horseshoe vortex structures that led to stronger deformations on the coupled vortex rollers. The statistical characteristics of secondary structures resembled the long wavelength mode and mode A identified previously for purely pitching and heaving foils, respectively. They also mimicked mode B for stationary cylinders. Novel wake models are introduced based on a complete vivid three-dimensional depiction of coherent wake structures.

Mapping the glycosyltransferase fold landscape using interpretable deep learning

Glycosyltransferases (GTs) play fundamental roles in nearly all cellular processes through the biosynthesis of complex carbohydrates and glycosylation of diverse protein and small molecule substrates. The extensive structural and functional diversification of GTs presents a major challenge in mapping the relationships connecting sequence, structure, fold and function using traditional bioinformatics approaches. Here, we present a convolutional neural network with attention (CNN-attention) based deep learning model that leverages simple secondary structure representations generated from primary sequences to provide GT fold prediction with high accuracy. The model learns distinguishing secondary structure features free of primary sequence alignment constraints and is highly interpretable. It delineates sequence and structural features characteristic of individual fold types, while classifying them into distinct clusters that group evolutionarily divergent families based on shared secondary structural features. We further extend our model to classify GT families of unknown folds and variants of known folds. By identifying families that are likely to adopt novel folds such as GT91, GT96 and GT97, our studies expand the GT fold landscape and prioritize targets for future structural studies.

NcDLRES: a novel method for non-coding RNAs family prediction based on dynamic LSTM and ResNet

BackgroundStudies have proven that the same family of non-coding RNAs (ncRNAs) have similar functions, so predicting the ncRNAs family is helpful to the research of ncRNAs functions. The existing calculation methods mainly fall into two categories: the first type is to predict ncRNAs family by learning the features of sequence or secondary structure, and the other type is to predict ncRNAs family by the alignment among homologs sequences. In the first type, some methods predict ncRNAs family by learning predicted secondary structure features. The inaccuracy of predicted secondary structure may cause the low accuracy of those methods. Different from that, ncRFP directly learning the features of ncRNA sequences to predict ncRNAs family. Although ncRFP simplifies the prediction process and improves the performance, there is room for improvement in ncRFP performance due to the incomplete features of its input data. In the secondary type, the homologous sequence alignment method can achieve the highest performance at present. However, due to the need for consensus secondary structure annotation of ncRNA sequences, and the helplessness for modeling pseudoknots, the use of the method is limited.ResultsIn this paper, a novel method “ncDLRES”, which according to learning the sequence features, is proposed to predict the family of ncRNAs based on Dynamic LSTM (Long Short-term Memory) and ResNet (Residual Neural Network).ConclusionsncDLRES extracts the features of ncRNA sequences based on Dynamic LSTM and then classifies them by ResNet. Compared with the homologous sequence alignment method, ncDLRES reduces the data requirement and expands the application scope. By comparing with the first type of methods, the performance of ncDLRES is greatly improved.

Using a Coarse-Grained Modeling Framework to Identify Oligomeric Motifs with Tunable Secondary Structure.

Coarse-grained modeling can be used to explore general theories that are independent of specific chemical detail. In this paper, we present cg_openmm, a Python-based simulation framework for modeling coarse-grained hetero-oligomers and screening them for structural and thermodynamic characteristics of cooperative secondary structures. cg_openmm facilitates the building of coarse-grained topology and random starting configurations, setup of GPU-accelerated replica exchange molecular dynamics simulations with the OpenMM software package, and features a suite of postprocessing thermodynamic and structural analysis tools. In particular, native contact analysis, heat capacity calculations, and free energy of folding calculations are used to identify and characterize cooperative folding transitions and stable secondary structures. In this work, we demonstrate the capabilities of cg_openmm on a simple 1-1 Lennard-Jones coarse-grained model, in which each residue contains 1 backbone and 1 side-chain bead. By scanning both nonbonded and bonded force-field parameter spaces at the coarse-grained level, we identify and characterize sets of parameters which result in the formation of stable helices through cooperative folding transitions. Moreover, we show that the geometries and stabilities of these helices can be tuned by manipulating the force-field parameters.

Insights into Platinum Complex-HSA Bindings: Spectral and Molecular Dynamics Simulation Studies

Mutual interactions of human serum albumin (HSA) with the two binuclear platinum complexes, containing [(bhq)Pt(dppm)2(Cl)Pt(bhq)(Cl)] (1) and [(bhq)Pt(dppm)2(PhMe)Pt(bhq)(CO2CF3)] (2), in which bhq=benzo[h]quinoline, and dppm=bis(diphenylphosphino) methane, were thoroughly investigated using spectroscopic and molecular modeling techniques. In this respect, fluorescence, Ultraviolet-Visible (UV-Vis) and circular dichroism (CD) spectroscopies, along with the docking and molecular dynamics simulations (MD) were utilized. Analysis of spectroscopic and MD simulation results revealed the structural alterations of HSA, upon binding to the binuclear platinum complexes, while the hydrogen bonding and van der Waals forces were found to mainly contribute to the protein-ligand intermolecular interactions. Results of far-UV CD measurements showed the strong ability of platinum complexes, in reducing the α-helical content of HSA, while other secondary structural features were increased. Due to their different chemical natures, these complexes bind to HSA in different manners. Binding constants and thermodynamic binding parameters between these complexes and HSA were calculated using the Stern−Volmer and van’t Hoff equations. Calculated thermodynamic binding parameters indicated that the interaction is spontaneous and enthalpy driven, through the static and dynamic quenching mechanisms, for complexes 1 and 2, respectively.

Visualization of RNA secondary structure with pseudoknots

The function of pseudoknots cannot be ignored in the RNA secondary structure. Existing methods for analyzing RNA secondary structures with pseudoknots exhibit many shortcomings. This paper presents a novel RNA secondary structure visualization method in the case of a joint analysis of RNA primary structures and secondary structures. The way is based on the page number representation of the RNA secondary structure. It innovatively uses five vectors to represent bases, which are sequentially connected to outline the characteristics of the RNA secondary structure. The method covers almost all the constituent elements of the RNA secondary structure and extracts features completely. Experiments are based on the available techniques for large-scale annotation of RNA secondary structures, using a combination method of discrete wavelet transform and fractal dimension. The classification effect is compared with the previous RNA secondary structure representation methods. Experimental results show that the RNA secondary structure visualization method proposed in this paper has good application prospects in RNA secondary structure classification.

High Throughput miRNA Screening Identifies miR-574-3p Hyperproductive Effect in CHO Cells.

CHO is the cell line of choice for the manufacturing of many complex biotherapeutics. The constant upgrading of cell productivity is needed to meet the growing demand for these life-saving drugs. Manipulation of small non-coding RNAs—miRNAs—is a good alternative to a single gene knockdown approach due to their post-transcriptional regulation of entire cellular pathways without posing translational burden to the production cell. In this study, we performed a high-throughput screening of 2042-human miRNAs and identified several candidates able to increase cell-specific and overall production of Erythropoietin and Etanercept in CHO cells. Some of these human miRNAs have not been found in Chinese hamster cells and yet were still effective in them. We identified miR-574-3p as being able, when overexpressed in CHO cells, to improve overall productivity of Erythropoietin and Etanercept titers from 1.3 to up to 2-fold. In addition, we validated several targets of miR-574-3p and identified p300 as a main target of miR-574-3p in CHO cells. Furthermore, we demonstrated that stable CHO cell overexpressing miRNAs from endogenous CHO pri-miRNA sequences outperform the cells with human pri-miRNA sequences. Our findings highlight the importance of flanking genomic sequences, and their secondary structure features, on pri-miRNA processing offering a novel, cost-effective and fast strategy as a valuable tool for efficient miRNAs engineering in CHO cells.

Predicting Protein Therapeutic Candidates for Bovine Babesiosis Using Secondary Structure Properties and Machine Learning.

Bovine babesiosis causes significant annual global economic loss in the beef and dairy cattle industry. It is a disease instigated from infection of red blood cells by haemoprotozoan parasites of the genus Babesia in the phylum Apicomplexa. Principal species are Babesia bovis, Babesia bigemina, and Babesia divergens. There is no subunit vaccine. Potential therapeutic targets against babesiosis include members of the exportome. This study investigates the novel use of protein secondary structure characteristics and machine learning algorithms to predict exportome membership probabilities. The premise of the approach is to detect characteristic differences that can help classify one protein type from another. Structural properties such as a protein’s local conformational classification states, backbone torsion angles ϕ (phi) and ψ (psi), solvent-accessible surface area, contact number, and half-sphere exposure are explored here as potential distinguishing protein characteristics. The presented methods that exploit these structural properties via machine learning are shown to have the capacity to detect exportome from non-exportome Babesia bovis proteins with an 86–92% accuracy (based on 10-fold cross validation and independent testing). These methods are encapsulated in freely available Linux pipelines setup for automated, high-throughput processing. Furthermore, proposed therapeutic candidates for laboratory investigation are provided for B. bovis, B. bigemina, and two other haemoprotozoan species, Babesia canis, and Plasmodium falciparum.

Predicting RBP Binding Sites of RNA With High-Order Encoding Features and CNN-BLSTM Hybrid Model.

RNA binding protein (RBP) is extensively involved in various cellular regulatory processes through the interaction with RNAs. Capturing the RBP binding preferences is fundamental for revealing the pathogenesis of complex diseases. Many experimental detection techniques are still time-consuming and labor-intensive, therefore, it is indispensable to develop a computational method with convincing accuracy. In this study, we proposed a CNN-BLSTM hybrid deep learning framework, named DeepDW, for predicting the RBP binding sites on RNAs with high-order encoding features of RNA sequence and secondary structure. The high-order encoding strategy was used to characterize the dependencies among adjacency nucleotides. For CNN-BLSTM hybrid model, DeepDW first employed two 1-D convolutional neural networks (CNNs) for learning the local features from high-order encoded matrices of RNA sequence and structure separately, and then applied two bidirectional long short-term memory networks (BLSTMs) to capture the global information in a higher level. Moreover, a series of experiments were carried out on 31 public datasets to evaluate our proposed framework, and DeepDW achieved superior performance than the state-of-the-art methods. The results indicated that the combination of high-order encoding method and CNN-BLSTM hybrid model had advantages in identifying RBP-RNA binding sites.

RNA Secondary Structure Representation Network for RNA-proteins Binding Prediction

RNA-binding proteins (RBPs) play a significant part in several biological processes in the living cell, such as gene regulation and mRNA localization. Several deep learning methods, especially the model based on convolutional neural network(CNN), have been used to predict the binding sites. However, previous methods fail to represent RNA secondary structure features. The traditional deep learning methods generally transform the RNA secondary structure to a regular matrix that cannot reveal the topological structure information of RNA. To effectively extract the structure features of RNA, we propose an RNA secondary structure representation network (RNASSR-Net) based on graph convolutional neural network (GCN) and convolution neural network (CNN) for RBP binding prediction. RNASSR-Net constructs the graph model derived from the RNA secondary structure to learn the topological properties of RNA. Then, it obtains the spatial importance of each base in RNA with CNN to guide the representation of the RNA secondary structure. Finally, RNASSR-Net combines the structure and sequence features to predict the binding sites. Experimental results demonstrate the proposed method outperforms a few state-of-the-art methods on the benchmark datasets and gets a higher improvement on the small-size data. Besides, the proposed RNASSR-Net is also used to detect the accurate motifs compared with the experimentally verified motifs, which reveals the binding region location and RNA structure interpretation for some biological guidance in the future.

In Vivo and In Vitro Genome-Wide Profiling of RNA Secondary Structures Reveals Key Regulatory Features in Plasmodium falciparum.

It is widely accepted that the structure of RNA plays important roles in a number of biological processes, such as polyadenylation, splicing, and catalytic functions. Dynamic changes in RNA structure are able to regulate the gene expression programme and can be used as a highly specific and subtle mechanism for governing cellular processes. However, the nature of most RNA secondary structures in Plasmodium falciparum has not been determined. To investigate the genome-wide RNA secondary structural features at single-nucleotide resolution in P. falciparum, we applied a novel high-throughput method utilizing the chemical modification of RNA structures to characterize these structures. Structural data from parasites are in close agreement with the known 18S ribosomal RNA secondary structures of P. falciparum and can help to predict the in vivo RNA secondary structure of a total of 3,396 transcripts in the ring-stage and trophozoite-stage developmental cycles. By parallel analysis of RNA structures in vivo and in vitro during the Plasmodium parasite ring-stage and trophozoite-stage intraerythrocytic developmental cycles, we identified some key regulatory features. Recent studies have established that the RNA structure is a ubiquitous and fundamental regulator of gene expression. Our study indicate that there is a critical connection between RNA secondary structure and mRNA abundance during the complex biological programme of P. falciparum. This work presents a useful framework and important results, which may facilitate further research investigating the interactions between RNA secondary structure and the complex biological programme in P. falciparum. The RNA secondary structure characterized in this study has potential applications and important implications regarding the identification of RNA structural elements, which are important for parasite infection and elucidating host-parasite interactions and parasites in the environment.