Geophysical Significance of the Senegalo-Mali Discontinuity: Evidence from Secondary Structures, Kédougou-Kéniéba Inlier, Western Mali

The present study focuses on the analysis and description of lineaments interpreted as secondary structures to describe the nature of Senegalo Malian Discontinuity. These lineaments cross-cut the large north-south oriented transcurrent lithospheric structure known as the Senegalo Malian Discontinuity (SMD). Two lineaments were selected oriented NNE (N15˚ to N25˚), one at Dialafara and one at Sadiola. Four profiles on each lineament of these 2 zones, so that there were 2 on each side of the SMD. The ground data collected were processed using proper parameter and software. Some filters were applied to enhance the signal level. These ground data were later compared to the existing airborne magnetic data for consistency and accuracy using the upward continuation filter. The results show that the quality of ground data is good. In addition, the ground magnetic data show the presence of certain local anomalies that are not visible in the regional data. The analytical signal was also used to determine domain boundaries or possible contact zones. The contact zone can be highlighted on certain profiles such as L300 and L600. The study showed that the west and east sides of the SMD are not the same. Secondary structures become wide when approaching the SMD on both sides. They are also duplicated to the east of the SMD when we move progressively away. In the Dialafara area, the ground magnetic data intersect an interpreted fold. The results of this work confirm the presence of the secondary structures and their evolution in relation to the SMD. The relationships between the secondary structures in the Dailafara and Sadiola zones and their relations with the SMD are highlighted. The technique used in this study, is an important approach to better description and interpreting of regional structures using the secondary structures and proposing a structural model.

Prediction of Secondary Structure and B Cell Epitope of GH Protein from Acipenser sinensis

[ Objective] The aim was to predict the secondary structure and B cell epitope of growth hormone （GH） protein from Acipenser sinensis. [Method] With the amino acid sequence of GH protein from A. sinensis as the base, the secondary structure of GH protein from A. sinensis was predicted by the method of Gamier-Robson, Chou-Fasman and Karpius-Schulz, and its cell epitope was predicted by the method of Kyte- Doolittle, Emini and Jameson-Wolf. [Result] The sections of 18 -23, 55 -55, 67 -73, 83 -86,112 -114,151 -157 and 209 -211 in the N terminal of GH protein molecule had softer structure and these sections could sway or fold to produce more complex tertiary structure. The sections of 19 -23, 51 -71,84 -95, 128 -139, 164 -176 and 189 -195 in the N terminal of GH protein could be the epitope of B cell and there were flexible regions in these sections or near these sections of GH protein molecule. So the dominant regions could be in these sections or near these sections. [ Conclusion] The research provided the basis for the preparation of monoctonal antibody of GH protein from A. sinensis and provided the reference for the discussion for the molecular regulation mechanism of A. sinensis.

DISTRIBUTIONS OF TRIPLET CODONS IN MESSENGER RNA SECONDARY STRUCTURES

Analysis of the secondary structures of mRNAs which encode mature peptides shows that the location of each codon in mRNA secondary structure has a trend, which appears to be in agreement with the conformational property of the corresponding amino acid to some extent. Most of the codons that encode hydrophobic amino acids are located in stable stem regions of mRNA secondary structures, and vice versa, most of the codons that encode hydrophilic amino acids are located in flexible loop regions. This result supports the recent conclusion that there may be the information transfer between the three dimensional structures of mRNA and the encoded protein.

Grammar Model Based on Lexical Substring Extraction for RNA Secondary Structure Prediction

[Objective] To examine the grammar model based on lexical substring exac- tion for RNA secondary structure prediction. [Method] By introducing cloud model into stochastic grammar model, a machine learning algorithm suitable for the lexicalized stochastic grammar model was proposed. The word grid mode was used to extract and divide RNA sequence to acquire lexical substring, and the cloud classifier was used to search the maximum probability of each lemma which was marked as a certain sec- ondary structure type. Then, the lemma information was introduced into the training stochastic grammar process as prior information, realizing the prediction on the sec- ondary structure of RNA, and the method was tested by experiment. [Result] The experimental results showed that the prediction accuracy and searching speed of stochastic grammar cloud model were significantly improved from the prediction with simple stochastic grammar. [Conclusion] This study laid the foundation for the wide application of stochastic grammar model for RNA secondary structure prediction.

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.

FTIR Characterization of the Secondary Structure of Insulin Encapsulated within Liposome

Aim To determine the secondary structure of insulin encapsulated withinliposome. Methods The secondary structure of native insulin, mixture of insulin with liposome(sample Ⅰ) and insulin encapsulated within liposome( sample Ⅱ) were determined by FTIR (FourierTransform Infrared) spectroscopy. Results The secondary structure of insulin encapsulated withinliposome(Ⅱ) are similar with the secondary structure of native insulin. The difference existed inthe amount of α-helices (from 36% of insulin to 31% of sample Ⅱ) and β-sheet(from 48% of insulinto 51% of sample Ⅱ). The content of α-helices and β-sheet of insulin in sample Ⅰ was found to bevery close to that of sample Ⅱ. The results revealed that the insulin encapsulated within liposomepossibly spread on the surface of liposome, without inserting into the liposome membrane.Conclusion The secondary structure of insulin encapsulated within liposome is similar with thenative insulin.

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.

RNA secondary structures located in the interchromosomal region of human ACAT1 chimeric mRNA are required to produce the 56-kDa isoform

We have previously reported that the human ACAT1 gene produces a chimeric mRNA through the interchromosomal processing of two discontinuous RNAs transcribed from chromosomes 1 and 7. The chimeric mRNA uses AUG1397-1399 and GGC1274-1276 as translation initiation codons to produce normal 50-kDa ACAT1 and a novel enzymatically active 56-kDa isoform, respectively, with the latter being authentically present in human cells, including human monocyte- derived macrophages. In this work, we report that RNA secondary structures located in the vicinity of the GGC1274-1276 codon are required for production of the 56-kDa isoform. The effects of the three predicted stem-loops （nt 1255-1268, 1286-1342 and 1355-1384） were tested individually by transfecting expression plasmids into cells that contained the wild-type, deleted or mutant stem-loop sequences linked to a partial ACAT1 AUG open reading frame （ORF） or to the ORFs of other genes. The expression patterns were monitored by western blot analyses. We found that the upstream stem-loop1255-1268 from chromosome 7 and downstream stem-loop1286-1342 from chromosome 1 were needed for production of the 56-kDa isoform, whereas the last stem-loop135s-1384 from chromosome 1 was dispensable. The results of experi- ments using both monocistronic and bicistronic vectors with a stable hairpin showed that translation initiation from the GGC1274-1276 codon was mediated by an internal ribosome entry site （IRES）. Further experiments revealed that translation initiation from the GGC1274-1276 codon requires the upstream AU-constituted RNA secondary structure and the downstream GC-rich structure. This mechanistic work provides further support for the biological significance of the chimeric nature of the human ACAT1 transcript.

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%.

Effect of microwave irradiation on secondary structure of α-amylase by circular dichroism

Based on the starch hydrolysis reaction accelerated by microwave irradiation with α-amylase, the circular dichroism （CD） and secondary structure changes of α-amylase under the condition of microwave irradiation and water bath were studied by circular dichroism spectra. The results showed that, both the peak heights （at 2=193 nm） of the CD spectra of the samples treated by microwave irradiation and water bath reduced. The reduced rate by microwave irradiation ranged from 140% to 220%, while the reduced rate by water bath ranged from 60% to 140%. The peak of the sample treated by microwave irradiation for 60 min disappeared at λ=193 nm, while the sample showed a wake peak by water bath. The peak position by microwave irradiation emerged a blue shift in the range of 5-8 nm at λ=204 nm and λ=220 nm, while it emerged in the range of 3-5 nm by water bath. With time going on, the microwave irradiation and water bath have prompted the secondary structure of α-helix, β-sheet, β-turn and the mutual transformations of random coil, but the trends were different.

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.

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.

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 Analysis of Native Cellulose by Molecular Dynamics Simulations with Coarse-Grained Model

The secondary structure of different Iβ cellulose was analyzed by a molecular dynamics sim- ulation with MARTINI coarse-grained force field, where each chain of the cellulose includes 40 D-glucoses units. Calculation gives a satisfied description about the secondary structure of the cellulose. As the chain number increasing, the cellulose becomes the form of a helix, with the diameter of screw growing and spiral rising. Interestingly, the celluloses with chain number N of 4, 6, 24 and 36 do show right-hand twisting. On the contrast, the celluloses with N of 8, 12, 16 chains are left-hand twisting. These simulations indicate that the cellulose with chain number larger than 36 will break down to two parts. Besides, the result indicates that 36-chains cellulose model is the most stable among all models. Furthermore, the Lennard-Jones potential determines the secondary structure. In addition, an equation was set up to analyze the twisting structure.

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.