Specificity of mRNA Folding and Its Association with Evolutionarily Adaptive mRNA Secondary Structures

The secondary structure is a fundamental feature of both non-coding RNAs(ncRNAs)and messenger RNAs(mRNAs).However,our understanding of the secondary structures of mRNAs,especially those of the coding regions,remains elusive,likely due to translation and the lack of RNA-binding proteins that sustain the consensus structure like those binding to ncRNAs.Indeed,mRNAs have recently been found to adopt diverse alternative structures,but the overall functional significance remains untested.We hereby approach this problem by estimating the folding specificity,i.e.,the probability that a fragment of an mRNA folds back to the same partner once refolded.We show that the folding specificity of mRNAs is lower than that of ncRNAs and exhibits moderate evolutionary conservation.Notably,we find that specific rather than alternative folding is likely evolutionarily adaptive since specific folding is frequently associated with functionally important genes or sites within a gene.Additional analysis in combination with ribosome density suggests the ability to modulate ribosome movement as one potential functional advantage provided by specific folding.Our findings reveal a novel facet of the RNA structurome with important functional and evolutionary implications and indicate a potential method for distinguishing the mRNA secondary structures maintained by natural selection from molecular noise.

IRIS:A method for predicting in vivo RNA secondary structures using PARIS data

Background:RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs,yet in vivo RNA secondary structures remain enigmatic.PARIS(Psoralen Analysis of RNA Interactions and Structures)is a recently developed high-throughput sequencing-based approach that enables direct capture of RNA duplex structures in vivo.However,the existence of incompatible,fuzzy pairing information obstructs the integration of PARIS data with the existing tools for reconstructing RNA secondary structure models at the single-base resolution.Methods:We introduce IRIS,a method for predicting RNA secondary structure ensembles based on PARIS data.IRIS generates a large set of candidate RNA secondary structure models under the guidance of redistributed PARIS reads and then uses a Bayesian model to identify the optimal ensemble,according to both thermodynamic principles and PARIS data.Results:The predicted RNA structure ensembles by IRIS have been verified based on evolutionary conservation information and consistency with other experimental RNA structural data.HIS is implemented in Python and freely available at http://iris.zhanglab.net.Conclusion:IRIS capitalizes upon PARIS data to improve the prediction of in vivo RNA secondary structure ensembles.We expect that IRIS will enhance the application of the PARIS technology and shed more insight on in vivo RNA secondary structures.

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.

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.

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%.

Secondary Instability of Large Scale Structure in Free Turbulent Shear Layer

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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.

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.

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.

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.

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.

Dietary bamboo leaf flavonoids improve quality and microstructure of broiler meat by changing untargeted metabolome

Background Dietary bamboo leaf flavonoids(BLFs)are rarely used in poultry production,and it is unknown whether they influence meat texture profile,perceived color,or microstructure.Results A total of 720 one-day-old Arbor Acres broilers were supplemented with a basal diet with 20 mg bacitracin/kg,50 mg BLFs/kg,or 250 mg BLFs/kg or without additions.Data showed that the dietary BLFs significantly(P<0.05)changed growth performance and the texture profile.In particular,BLFs increased birds’average daily gain and average daily feed intake,decreased the feed:gain ratio and mortality rate,improved elasticity of breast meat,enhanced the gumminess of breast and leg meat,and decreased the hardness of breast meat.Moreover,a significant(P<0.05)increase in redness(a*)and chroma(c*)of breast meat and c*and water-holding capacity of leg meat was found in BLF-supplemented broilers compared with control broilers.In addition,BLFs supplementation significantly decreased(P<0.05)theβ-sheet ratio and serum malondialdehyde and increased theβ-turn ratio of protein secondary structure,superoxide dismutase,and glutathione peroxidase of breast meat and total antioxidant capacity and catalase of serum.Based on the analysis of untargeted metabolome,BLFs treatment considerably altered 14 metabolites of the breast meat,including flavonoids,amino acids,and organic acids,as well as phenolic and aromatic compounds.Conclusions Dietary BLFs supplementation could play a beneficial role in improving meat quality and sensory color in the poultry industry by changing protein secondary structures and modulating metabolites.

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.

Phylogeny and Geographical Derivation of Genus Hildenbrandia Based on Morphological and Molecular Evidence

The marine and freshwater Hildenbrandia samples were collected from diverse locations of China and Japan,and mor-phological characterization and molecular phylogenetic analysis were conducted on these samples.Morphological measurements of freshwater specimens were consistent with the results of H.jigongshanensis,whereas cell dimensions of marine specimens were slightly larger than those of widely distributed H.rubra and H.crouanii.Phylogenetic trees based on rbcL,psbA and UPA sequences were consistent.Freshwater specimens collected in this study formed an independent clade with H.jigongshanensis supported by high values.Species attribution was confirmed further by the similar ITS2 secondary structures among the samples of H.jigongshan-ensis.It was found that the intraspecific divergence of rbcL gene in H.jigongshanensis was smaller than other two freshwater species H.rivularis and H.angolensis.Phylogenetic trees showed that marine specimens in this study grouped together with H.rubra samples of North America,and were different with the H.rubra samples of Europe.Combining the comparison results of CBC numbers in ITS2 secondary structure,we propose the marine samples collected in this study to be a new species Hildenbrandia qingdaoensis sp.nov.The separation of the marine and freshwater specimens was supported by the rbcL,UPA and psbA phylogeny as well as ITS2 se-condary structure inference.Biogeographical reconstruction showed the ancestor of freshwater Hildenbrandia might derive from South America and dispersed from North America to Europe and then to Asian countries,which needs to be verified further with more sam-ples and molecular evidences from South America.

Does mRNA structure contain genetic information for regulating co-translational protein folding？

Currently many facets of genetic information are illdefined. In particular, how protein folding is genetically regulated has been a long-standing issue for genetics and protein biology. And a generic mechanistic model with supports of genomic data is still lacking. Recent technological advances have enabled much needed genome-wide experiments. While putting the effect of codon optimality on debate, these studies have supplied mounting evidence suggesting a role of m RNA structure in the regulation of protein folding by modulating translational elongation rate. In conjunctions with previous theories, this mechanistic model of protein folding guided by m RNA structure shall expand our understandings of genetic information and offer new insights into various biomedical puzzles.

Structure Changes of Silk Fibroin(SF) by Blending with Poly(ε-caprolactone)(PCL):Characterization of SF and PCL Blended Electrospinning Films

The mechanical properties and water solubility of electrospinning SF films limit their use as biomaterials. In order to develop a tissue engineering biomaterial with both satisfying biological properties and sufficient biomechanical properties,blended films composed of silk fibroin( SF) and poly( ε-caprolactone)( PCL) were fabricated by electrospinning in this study. Scanning electron microscope( SEM), X-ray diffraction( XRD),thermal analysis,Fourier transform-infrared( FT-IR),Raman spectra,mechanical testing,and water solubility were used to characterize the morphological, structural and mechanical properties of the blended electrospinning films. Results showed that the diameter of the blended fiber was distributed between 600 and1000 nm,and the fiber diameter increased as the PCL content increased. There is no obvious phase separation due to the similarity and intermiscibility,as well as the interactions( mainly hydrogen bonds), between the two polymers. Meanwhile, the secondary structures of SF changed from random coils and Silk I to Silk II because of the interactions between SF and PCL. For this reason,the tensile strength and elongation at break of the electrospinning films improved significantly,and the water solubility decreased. In conclusion,the blended electrospinning films fabricated in this study showed satisfying mechanical properties and water insolubilities,and they may be promising biomaterials for applications in tissue engineering for blood vessels,nerve conduits,tendons,ligaments and other tissues.

Correlation of Gluten Molecular Conformation with Dough Viscoelastic Properties during Storage

Significant correlations exist for the total sample between the dough viscoelastic properties and quality, but little information about these links is available. This study investigates the relationship between the gluten molecular conformation and the dough viscoelastic properties of the Zhengmai 379 wheat variety with different relative humidity(RH) values during storage.The results showed that protein extractability, free sulfhydryl(SH) groups, and wet gluten contents decreased to a certain degree during storage, while the glutenin quantity significantly varied. Significant negative correlation coefficients were found between the dough viscosity at 50% RH and the β-turn contents at 40% RH(-0.918) or 60% RH(-0.949)(P < 0.01), the dough viscosity at50% RH and the β-turn contents at 50% RH(-0.912)(P < 0.05), and the dough viscosity at 50% RH and the α-helix/β-sheet ratio at 40% RH(-0.875), 50% RH(-0.869), or 60% RH(-0.843)(P < 0.05). Significant correlation coefficients were further observed between the dough viscosity at 50% RH and the β-sheet contents at 60% RH(0.927)(P < 0.01) and between the dough viscosity at50% RH and the β-sheet contents at 40% RH(0.910) or 50% RH(0.908)(P < 0.05). A decrease in the free SH groups of gluten and an increase in the low-molecular weight contents suggested that gliadin was incorporated via SS crosslinking to the glutenin-like protein, which did not dissolve in ethanol.

Dephosphorylated mutations affect the protein-protein interactions of ERF in Populus simonii x P.nigra

Phosphorylation is a common type of post-translational modification(PTM).It plays a vital role in many cellular processes.The reversible phosphorylation and dephosphorylation affect protein structures and protein-protein interactions.Previously,we obtained five proteins that interact with ethylene-responsive factor(ERF)from the cDNA library of Populus simonii x Populus nigra.To further investigate the effect of dephosphorylation of PsnERF on its protein binding ability,we generated different phosphorylation states of PsnERF and demonstrated their protein binding capacity by the yeast two-hybrid assay(Y2H).The secondary structures and 3D structures of PsnERF,ERFm,TrunERF,and psnerf^(197/198/202a) were predicted by homology modeling.The Y2H assay indicated that the deletion of serine-rich regions does not affect the interactions,while dephosphorylated mutations blocked the interactions.Homology modeling results suggested that the protein-binding activity was affected by dephosphorylation,and the S197/S198/S202 residues of PsnERF may be the key phosphorylation sites influencing its binding ability.