Secondary Structural Elements of the HCV X-region Involved in Viral Replication

Background and Aims:The noncoding regions in the 3'-untranslated region (UTR) of the hepatitis C virus (HCV)genome contain secondary structures that are important for replication.The aim of this study was to identify detailed conformational elements of the X-region involved in HCV replication.Methods:Ribonucleic acid (RNA) structural analogs X94,X12,and X12c were constructed to have identical conformation but 94%,12%,and 0% sequence identity,respectively,to the X region of HCV genotype 2a.Effects of structural analogs on replication of HCV genotypes 1b and 2a HCV RNA were studied by quantitative reverse transcriptase polymerase chain reaction.Results:In replicon BB7 cells,a constitutive replication model,HCV RNA levels decreased to 55%,52%,53%,and 54% after transfection with expression plasmids generating RNA structural analogs 5B-46,X-94,X-12,and X-12c,respectively (p<0.001 for all).In an HCV genotype 2a infection model,RNA analogs 5B-46,X-94,and X-12 in hepatic cells inhibited replication to 11%,9%,and 12%,respectively.Because the X-12 analog was only 12% identical to the corresponding sequence of HCV genotype 2a,the sequence per se,or antisense effects were unlikely to be involved.Conclusions:The data suggest that conformation of secondary structures in 3'-UTR of HCV RNA genome is required for HCV replication.Stable expression of RNA analogs predicted to have identical stem-loop structures might inhibit HCV infection of hepatocytes in liver and may represent a novel approach to design anti-HCV agents.

Study on the secondary structure and hydration effect of human serum albumin under acidic pH and ethanol perturbation with IR/NIR spectroscopy

Human serum albumin(HSA)is the most abundant protein in plasma and plays an essential physiological role in the human body.Ethanol precipitation is the most widely used way to obtain HSA,and pH and ethanol are crucial factors affecting the process.In this study,infrared(IR)spectroscopy and near-infrared(NIR)spectroscopy in combination with chemometrics were used to investigate the changes in the secondary structure and hydration of HSA at acidic pH(5.6-3.2)and isoelectric pH when ethanol concentration was varied from 0%to 40%as a perturbation.IR spectroscopy combined with the two-dimensional correlation spectroscopy(2DCOS)analysis for acid pH system proved that the secondary structure of HSA changed significantly when pH was around 4.5.What's more,the IR spectroscopy and 2DCOS analysis showed different secondary structure forms under different ethanol concentrations at the isoelectric pH.For the hydration effect analysis,NIR spectroscopy combined with the McCabe-Fisher method and aquaphotomics showed that the free hydrogen-bonded water fluctuates dynamically,with ethanol at 0-20%enhancing the hydrogen-bonded water clusters,while weak hydrogen-bonded water clusters were formed when the ethanol concentration increased continuously from 20%to 30%.These measurements provide new insights into the structural changes and changes in the hydration behavior of HSA,revealing the dynamic process of protein purification,and providing a theoretical basis for the selection of HSA alcoholic precipitation process parameters,as well as for further studies of complex biological systems.

FTIR Analysis of Protein Secondary Structure in Cheddar Cheese during Ripening

Proteolysis is one of the most important biochemical reactions during cheese ripening.Studies on the secondary structure of proteins during ripening would be helpful for characterizing protein changes for assessing cheese quality.Fourier transform infrared spectroscopy(FTIR),with self-deconvolution,second derivative analysis and band curve-fitting,was used to characterize the secondary structure of proteins in Cheddar cheese during ripening.The spectra of the amide I region showed great similarity,while the relative contents of the secondary structures underwent a series of changes.As ripening progressed,the α-helix content decreased and the β-sheet content increased.This structural shift was attributed to the strengthening of hydrogen bonds that resulted from hydrolysis of caseins.In summary,FTIR could provide the basis for rapid characterization of cheese that is undergoing ripening.

Impact of hot alkali modification conditions on secondary structure of peanut protein and embedding rate of curcumin

This study aimed to modify isolated and extracted peanut protein with hot alkali to study the impact of pH,heating temperature,processing time and other alkali liquor conditions on the molecular structure of the peanut.Curcumin was loaded in modified peanut protein.The results of the study are as follows:Within the alkaline range of 8<pH<12,the percentage of amino acid residue(AAR)and-turns first increased and then decreased with the increasing pH,and the percentage of AAR reached a maximum 5.21±0.33%when the pH was 11(p<0.01).The percentage of˛-helices andβ-sheets gradually decreased with increasing pH,while that of random coils gradually increased with increasing pH,reaching a maximum 11.24±0.87%when the pH was 11(p<0.05).Within the range of the heating temperature 75℃<T<95℃,the percentage of random coils andβ-sheets gradually increased with increasing heating temperature,while that of-helices and AAR gradually decreased with increasing heating temperature;they remained unchanged when the heating temperature was 90℃,and then decreased to(10.41±1.18%;p<0.01)and(4.02±2.12%;p<0.01),respectively.Within the range of 5 min<t<20 min,the percentage of random coils and AAR gradually increased with increasing heating time,while the percentage ofα-helices decreased from 11.83±1.04%to 10.75±2.34%with increased heating time(p<0.01).The optimum conditions for hot alkali modification of peanut protein as followed:heating temperature of 90℃,heating time of 20 min and a pH of alkali liquor of 11.Under these optimum conditions,the embedding rate of curcumin by the modified protein can reach 88.32±1.29%.

Prediction of RNA Secondary Structure Based on Particle Swarm Optimization

A novel method for the prediction of RNA secondary structure was proposed based on the particle swarm optimization（PSO）. PSO is known to be effective in solving many different types of optimization problems and known for being able to approximate the global optimal results in the solution space. We designed an efficient objective function according to the minimum free energy, the number of selected stems and the average length of selected stems. We calculated how many legal stems there were in the sequence, and selected some of them to obtain an optimal result using PSO in the right of the objective function. A method based on the improved particle swarm optimization（IPSO） was proposed to predict RNA secondary structure, which consisted of three stages. The first stage was applied to encoding the source sequences, and to exploring all the legal stems. Then, a set of encoded stems were created in order to prepare input data for the second stage. In the second stage, IPSO was responsible for structure selection. At last, the optimal result was obtained from the secondary structures selected via IPSO. Nine sequences from the comparative RNA website were selected for the evaluation of the proposed method. Compared with other six methods, the proposed method decreased the complexity and enhanced the sensitivity and specificity on the basis of the experiment results.

Heterogeneity and Secondary Structure Analysis of 3' Untranslated Region in Classical Swine Fever Viruses

The attenuated vaccine strains of CSFV have a 12-nucleotides （nt） insertion in the 3＇-UTR of genome as compared to that of CSFV virulent strains. In this study, we found a distinct heterogeneity in the 3＇-UTR of attenuated Thiverval and HCLV strains. The longest 3＇-UTR of Thiverval strain was 259 base pairs （bp） with a 32-nt insertion, the shortest 3＇-UTR had only 233 bp with a 6-nt insertion. The longest 3＇-UTR of HCLV strain was 244 bp with a 17-nt insertion and the shortest 3＇ UTR was 235 bp with a 8-nt insertion. Compared with the published sequences of 3＇-UTR of vaccine and virulent strains, the 3＇-UTR of CSFV vaccine strains have two variable regions where insertion among the different vaccine strains were frequently found. The first is located between the second conservative TALk codon and the start of T-rich region where we found the variable length insertion in the same vaccine strain Thiveral or HCLV and the second is located between the end of T-rich region and the front of GAA eodon, however, a 4-nt deletion was found in this region in the virulent Shimen strain. These two regions may represent the ＂hot spot＂ for mutation. Modeling the secondary structures of the 3＇-UTR suggests that the T-rich insertion could result in the change of structure and free energy, thus affecting the stability of the 3＇-UTR structure. These findings will help to understand the mechanism of attenuated vaccines and improve vaccine safety, stability, and efficacy.

Morphology,Bending Property and Secondary Structure Estimation of Dog Hair

The morphology, bending property and secondary structure estimation of dog hair were investigated by scanning electron microscope( SEM),fiber compression bending analyzer,fiber frictional coefficient tester and Fourier transform infrared spectroscopy( FTIR). The SEM micrograph of hair indicated guard hair( GH),intermediate hair( IH) and underhair( UH) from dog hair fibers displayed considerable differences in the diameter,length,scale shape and medulla. In addition,the bending property of fibers were related to the diameter of fibers and the percentage and structure of medulla. The UH had the greatest frictional coefficient,while the guard hair had the largest bending rigidity in three kinds of hairs. The analysis of amide I region implied that there was an apparent variety in the secondary structure of hairs,mainly the percentage of α-helix and β-pleated sheet and β-turn structure. The X-ray diffraction results showed that the crystallinity of the UH was the lowest in the three kinds of fibers. The tensile behaviors of dog hair also indicated that the increase of β-pleated and β-turn structure caused the increase of the breaking strength.

HMM in Predicting Protein Secondary Structure

We introduced a new method---duration Hidden Markov Model (dHMM) to predicate the secondary structure of Protein. In our study, we divide the basic second structure of protein into three parts: H (a-Helix), E (B-sheet) and O (others, include coil and turn). HMM is a kind of probabilistic model which more thinking of the interaction between adjacent amino acids (these interaction were represented by transmit probability), and we use genetic algorithm to determine the model parameters. After improving on the model and fixed on the parameters of the model, we write a program HMMPS. Our example shows that HMM is a nice method for protein secondary structure prediction.

Protein Secondary Structure Prediction with Dynamic Self-Adaptation Combination Strategy Based on Entropy

The algorithm based on combination learning usually is superior to a singleclassification algorithm on the task of protein secondary structure prediction. However,the assignment of the weight of the base classifier usually lacks decision-makingevidence. In this paper, we propose a protein secondary structure prediction method withdynamic self-adaptation combination strategy based on entropy, where the weights areassigned according to the entropy of posterior probabilities outputted by base classifiers.The higher entropy value means a lower weight for the base classifier. The final structureprediction is decided by the weighted combination of posterior probabilities. Extensiveexperiments on CB513 dataset demonstrates that the proposed method outperforms theexisting methods, which can effectively improve the prediction performance.

Protein structural codes and nucleation sites for protein folding

One of the long-standing controversial arguments in protein folding is Levinthal＇s paradox. We have recently proposed a new nucleation hypothesis and shown that the nucleation residues are the most conserved sequences in protein. To avoid the complicated effect of tertiary interactions, we limit our search for structural codes to the nucleation residues. Starting with the hypotheses of secondary structure nucleation and conservation of residues important for folding, we have analysed 762 folds classified as unique by SCOP. Segments of 17 residues around the top 20% conserved amino acids are analysed, resulting in approximately 100 clusters each for the main secondary structure classes of helix, sheet and coil. Helical clusters have the longest correlation range, coils the shortest （four residues）. Strong specific sequence-structure correlation is observed for coil but not for helix and sheet, suggesting a mapping relationship between the sequence and the structure for coil. We propose that the central sequences in these clusters form ＇structural codes＇, a useful basis set for identifying nucleation sites, protein fragments stable in isolation, and secondary structural patterns in proteins （particularly turns and loops）.

A Deep Learning Approach for Prediction of Protein Secondary Structure

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary structures.For this reason,it is important to design methods for accurate protein secondary structure prediction.Most of the existing computational techniques for protein structural and functional prediction are based onmachine learning with shallowframeworks.Different deep learning architectures have already been applied to tackle protein secondary structure prediction problem.In this study,deep learning based models,i.e.,convolutional neural network and long short-term memory for protein secondary structure prediction were proposed.The input to proposed models is amino acid sequences which were derived from CulledPDB dataset.Hyperparameter tuning with cross validation was employed to attain best parameters for the proposed models.The proposed models enables effective processing of amino acids and attain approximately 87.05%and 87.47%Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models,respectively.

Simulating the Folding Pathway of RNA Secondary Structure Using the Modified Ant Colony Algorithm

A new method for simulating the folding pathway of RNA secondary structure using the modified ant colony algorithmis proposed.For a given RNA sequence,the set of all possible stems is obtained and the energy of each stem iscalculated and stored at the initial stage.Furthermore,a more realistic formula is used to compute the energy ofmulti-branch loop in the following iteration.Then a folding pathway is simulated,including such processes as constructionof the heuristic information,the rule of initializing the pheromone,the mechanism of choosing the initial andnext stem and the strategy of updating the pheromone between two different stems.Finally by testing RNA sequences withknown secondary structures from the public databases,we analyze the experimental data to select appropriate values forparameters.The measure indexes show that our procedure is more consistent with phylogenetically proven structures thansoftware RNAstructure sometimes and more effective than the standard Genetic Algorithm.

Secondary Structure in Solution of an Analog of Salmon Calcitonin:[Val^1, Ala^7]sCT

Secondary structure of [Val. Ala]sCT- an analog of salmon calcitonin (sCT) not containing an N-terminal disultide bridge. was investigated by circular dichroism (CD) and Fourier-transform intrared spectroscopy (FTIR) methods. Both CD and FTIR results show that the main contbrmational structure of [Val Ala']sCT in aqueous solution is random coil structure. while in trifluorethanol (TFE) it displays a strong α-helical structure. The relationship between the biological activity and the conformational structure of [Val, Ala] sCT is als0 discussed.

Ensemble Machine Learning to Enhance Q8 Protein Secondary Structure Prediction

Protein structure prediction is one of the most essential objectives practiced by theoretical chemistry and bioinformatics as it is of a vital importance in medicine,biotechnology and more.Protein secondary structure prediction(PSSP)has a significant role in the prediction of protein tertiary structure,as it bridges the gap between the protein primary sequences and tertiary structure prediction.Protein secondary structures are classified into two categories:3-state category and 8-state category.Predicting the 3 states and the 8 states of secondary structures from protein sequences are called the Q3 prediction and the Q8 prediction problems,respectively.The 8 classes of secondary structures reveal more precise structural information for a variety of applications than the 3 classes of secondary structures,however,Q8 prediction has been found to be very challenging,that is why all previous work done in PSSP have focused on Q3 prediction.In this paper,we develop an ensemble Machine Learning(ML)approach for Q8 PSSP to explore the performance of ensemble learning algorithms compared to that of individual ML algorithms in Q8 PSSP.The ensemble members considered for constructing the ensemble models are well known classifiers,namely SVM(Support Vector Machines),KNN(K-Nearest Neighbor),DT(Decision Tree),RF(Random Forest),and NB(Naïve Bayes),with two feature extraction techniques,namely LDA(Linear Discriminate Analysis)and PCA(Principal Component Analysis).Experiments have been conducted for evaluating the performance of single models and ensemble models,with PCA and LDA,in Q8 PSSP.The novelty of this paper lies in the introduction of ensemble learning in Q8 PSSP problem.The experimental results confirmed that ensemble ML models are more accurate than individual ML models.They also indicated that features extracted by LDA are more effective than those extracted by PCA.

Improving RNA secondary structure prediction using direct coupling analysis

Secondary structures of RNAs are the basis of understanding their tertiary structures and functions and so their predictions are widely needed due to increasing discovery of noncoding RNAs.In the last decades,a lot of methods have been proposed to predict RNA secondary structures but their accuracies encountered bottleneck.Here we present a method for RNA secondary structure prediction using direct coupling analysis and a remove-and-expand algorithm that shows better performance than four existing popular multiple-sequence methods.We further show that the results can also be used to improve the prediction accuracy of the single-sequence methods.

IMPROVED METHOD FOR RNA SECONDARY STRUCTURE PREDICTION'

A simple stepwise folding process has been developed to simulate RNA secondary structure formation.Modifications for the energy parameters of various loops were included in the program.Five possible types of pseudoknots including the well known H-type pseudoknot were permitted to occur if reasonable.We have applied this approach to e number of RNA sequences.The prediction accuracies we obtained were higher than those in published papers.

Expression,Purification and Structural Characterization of Mouse Talin-1 Head Δ139-168

Talin-1 head(hereinafter referred to as TH)is the head structure of talin protein,which contains a four-point-one-protein/ezrin/radixin/moesin(FERM)domain.Its F1 domain contains an unstructured loop of 30 amino acids(139-168),which does not interact with other domains.Because TH doesn’t get the crystal structure and whether the unstructured loop has obvious influence on the TH secondary structure,therefore,the truncated talin-1 headΔ139-168(hereinafter referred to as THΔ)was constructed and its structure and the impact of stability after truncation were analyzed.Molecular biology and structural biology methods were used to construct prokaryotic expression vectors of TH and THΔ,explore and optimize the expression conditions of recombinants,and they were purified by affinity chromatography and FPLC gel filtration chromatography.Finally,a large number of stable,high-purity protein samples were prepared successfully.The physicochemical properties and structural stability of the proteins were investigated by dynamic light scattering and circular dichroism.The results showed that the THΔsecondary structure of the truncated body did not change significantly,the structural stability was enhanced and the resistance to guanidine hydrochloride and high temperature was stronger.

Identification of Secondary Structure of Extracellular Signal Regulated Kinase (ERK) Interacting Proteins and Their Domain: An in Silico Study

ERK is involved in multiple cell signaling pathways through its interacting proteins. By in silico analysis, earlier we have identified 22 putative ERK interacting proteins namely;ephrin type-B receptor 2 isoform 2 precursor (EPHB2), mitogen-activated protein kinase 1 (MAPK1), interleukin-17 receptor D precursor (IL17RD), WD repeat domain containing 83 (WDR83), tescalcin (Tesc), mitogen-activated protein kinase kinase kinase 4 (MAPP3K4), kinase suppressor of Ras2 (KSR2), mitogen-activated protein kinase kinase 6 (MAP3K6), UL16 binding protein 2 (ULBP2), UL16 binding protein 1 (ULBP1), dual specificity phosphatase 14 (DUSP14), dual specificity phosphatase 6 (DUSP6), hyaluronan-mediated motility receptor (RHAMM), kinase D interacting substrate of 220 kDa (KININS220), membrane-associated guanylate kinase (MAGI3), phosphoprotein enriched in astrocytes 15 (PEA15), typtophenyl-tRNA synthetase, cytoplasmic (WARS), dual specificity phosphatase 9 (DUSP9), mitogen-activated protein kinase kinase kinase 1 (MAP3K1), UL16 binding protein 3 (ULBP3), SLAM family member 7 isoform a precursor (SLAMMF7) and mitogen activated protein kinase kinase kinase 11 (MAP3K11) (Table 1). However, prediction of secondary structure and domain/motif present in aforementioned ERK interacting proteins is not studied. In this paper, in silico prediction of secondary structure of ERK interacting proteins was done by SOPMA and motif/domain identification using motif search. Briefly, SOPMA predicted higher random coil and alpha helix percentage in these proteins (Table 2) and motif scan predicted serine/threonine kinases active site signature and protein kinase ATP binding region in majority of ERK interacting proteins. Moreover, few have commonly dual specificity protein phosphatase family and tyrosine specific protein phosphatase domains (Table 3). Such study may be helpful to design engineered molecules for regulating ERK dependent pathways in disease condition.

Minimal Secondary Structure Formation on mRNAs with a Shine-Dalgarno Sequence for Chromosomal Genes in <i>Rhodobacter sphaeroides</i>

The Shine-Dalgarno (SD) sequence, when present, is known to promote translation initiation in a bacterial cell. However, the thermodynamic stability of the messenger RNA (mRNA) through its secondary structures has an inhibitory effect on the efficiency of translation. This poses the question of whether bacterial mRNAs with SD have low secondary structure formation or not. About 3500 protein-coding genes in Rhodobacter sphaeroides were analyzed and a sliding window analysis of the last 100 nucleotides of the 5’ UTR and the first 100 nucleotides of ORFs was performed using RNAfold, a software for RNA secondary structure analysis. It was shown that mRNAs with SD are less stable than those without SD for genes located on the primary chromosome, but not for the plasmid encoded genes. Furthermore, mRNA stability is similar for genes within each chromosome except those encoded by the accessory chromosome (second chromosome). Results highlight the possible contribution of other factors like replicon-specific nucleotide composition (GC content), codon bias, and protein stability in determining the efficiency of translation initiation in both SD-dependent and SD-independent translation systems.

Diagnosis and characterization of the ribosomal DNA-ITS of potato rot nematode(Ditylenchus destructor) populations from Chinese medicinal herbs

The potato rot nematode(Ditylenchus destructor) is a very economically important nematode in agronomic and horticultural plants worldwide. In this study, 43 populations of D. destructor were collected from different hosts across China, including 37 populations from Chinese herbal medicine plants. Obtained sequences of ITS-rDNA and D2–D3of 28S-rDNA genes of D. destructor were compared and analyzed. Nine types of significant length variations in ITS sequences were observed among all populations. The differences in ITS1 length were mainly caused by the presence of repetitive elements with substantial base substitutions. Reconstructions of ITS1 secondary structures showed that the minisatellites formed a stem structure. Ten haplotypes were observed in all populations based on mutations and variations of helix H9. Among them, 3 known haplotypes(A–C) were found in 7 populations isolated from potato,sweet potato, and Codonopsis pilosula, and 7 unique haplotypes were found in other 36 populations collected from C. pilosula and Angelica sinensis compared with 7 haplotypes(A–G) according to Subbotin' system. These unique haplotypes were different from haplotypes A–G, and we named them as haplotypes H–N. The present results showed that a total of 14 haplotypes(A–N) of ITS-rDNA have been found in D. destructor. Phylogenetic analyses of ITSrDNA and D2–D3 showed that all populations of D. destructor were clustered into two major clades: one clade only containing haplotype A from sweet potato and the other containing haplotypes B–N from other plants. For further verification, PCR-ITS-RFLP profiles were conducted on 7 new haplotypes. Collectively, our study suggests that D. destructor populations on Chinese medicinal materials are very different from those on other hosts and this work provides a paradigm for relevant researches.