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.

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 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.

Sensitive and Label-Free Detection of Protein Secondary Structure by Amide Ⅲ Spectral Signals using Surface-Enhanced Raman Spectroscopy

Proteins and peptides perform a vital role in living systems, however it remains a challenge for accurate description of proteins at the molecular level. Despite that surface-enhanced Raman spectroscopy (SERS) can provide the intrinsic fingerprint information of samples with ultrahigh sensitivity, it suffers from the poor reproducibility and reliability. Herein, we demonstrate that the silver nanorod array fabricated by an oblique angle deposition method is a powerful substrate for SERS to probe the protein secondary structures without exogenous labels. With this method, the SERS signals of two typical proteins (lysozyme and cytochrome c) are successfully obtained. Additionally, by analyzing the spectral signals of the amide Ⅲ of protein backbone, the influence of concentration on the folding status of proteins has been elucidated. With the concentration increasing, the components of α-helix and β-sheet structures of lysozyme increase while the secondary structures of cytochrome c almost keep constant. The SERS method in this work offers an effective optical marker to characterize the structures of proteins.

Inspections of Mobile Phone Microwaves Effects on Proteins Secondary Structure by Means of Fourier Transform Infrared Spectroscopy

In this study the effects of microwaves on the secondary structure of three typical proteins have been investigated. A set of samples of lysozyme, bovine serum albumin and myoglobin in D2O solutions were exposed for 8 hours to mobile phone microwaves at 900 MHz at a magnetic field intensity around 16 mA/m. The relative effects on the secondary structure of the proteins were studied by means of Fourier Transform Infrared Spectroscopy. An increase of the amide I band intensity in the secondary structure of the proteins was observed after the microwaves exposure. Furthermore, a weak shift of the amide I mode of bovine serum albumin and a heavier shift of the amide I of myoglobin occurred after the exposure. In addition, a clear increasing of the β-sheet components with respect to the α-helix content was observed in the spectra of bovine serum albumin and myoglobin after the exposure, suggesting the hypothesis of the formation of aggregates.

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.

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%.

Neural Network Based on GA-BP Algorithm and its Application in the Protein Secondary Structure Prediction

The advantages and disadvantages of genetic algorithm and BP algorithm are introduced. A neural network based on GA-BP algorithm is proposed and applied in the prediction of protein secondary structure, which combines the advantages of BP and GA. The prediction and training on the neural network are made respectively based on 4 structure classifications of protein so as to get higher rate of predication---the highest prediction rate 75.65%,the average prediction rate 65.04%.

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.

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.

Using Neural Networks to Predict Secondary Structure for Protein Folding

Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.

A Hybrid Ant Colony Optimization for the Prediction of Protein Secondary Structure

Based on the concept of ant colony optimization and the idea of population in genetic algorithm, a novel global optimization algorithm, called the hybrid ant colony optimization （HACO）, is proposed in this paper to tackle continuous-space optimization problems. It was compared with other well-known stochastic methods in the optimization of the benchmark functions and was also used to solve the problem of selecting appropriate dilation efficiently by optimizing the wavelet power spectrum of the hydrophobic sequence of protein, which is the key step on using continuous wavelet transform （CWT） to predict a-helices and connecting peptides.

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.

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.

A Novel Prediction Method of Protein Structural Classes Based on Protein Super-Secondary Structure

At present, the feature extraction of protein sequences is the most basic issue to predict protein structural classes and is also the key problem to decide the quality of prediction. In order to predict protein structural classes accurately, we construct a 14-dimensional feature vector based on protein secondary and super-secondary structure information to reflect the content and spatial ordering of the given protein sequences. Among the vector, seven features about -helix bundle, hairpin motifs, Rossman folds, -plaits and other super-secondary structure information are first proposed in our paper. Experiments show that our method improves overall accuracy of lower similarity datasets 1189 and 640 by 0.9% - 3.8% and 0.5% - 4.2% respectively compared with other methods and has a competitive advantage for predicting proteins in and classes.

Effects of water deficit and high N fertilization on wheat storage protein synthesis,gluten secondary structure,and breadmaking quality

The content and composition of wheat storage proteins are the major determinants of dough rheological properties and breadmaking quality and are influenced by cultivation conditions.This study aimed to investigate the effects of water deficit and high N-fertilizer application on wheat storage protein synthesis,gluten secondary structure,and breadmaking quality.Reverse-phase ultrahigh-performance liquid chromatography analysis showed that storage protein and gluten macropolymer accumulation was promoted under both independent applications and a combination of water-deficit and high N-fertilizer treatments.Fourier-transform infrared spectroscopy showed that water deficit and high N-fertilizer treatments generally improved protein secondary structure formation and lipid accumulation,and reduced flour moisture.In particular,high N-fertilizer application increasedβ-sheet content by 10.4%and the combination of water-deficit and high N-fertilizer treatments increased random coil content by 7.6%.These changes in gluten content and secondary structure led to improved dough rheological properties and breadmaking quality,including superior loaf internal structure,volume,and score.Our results demonstrate that moderately high N-fertilizer application under drought conditions can improve gluten accumulation,gluten secondary structure formation,and baking quality.

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.

Secondary Structure Changes and Thermal Stability of Plasma Membrane Proteins of Wheat Roots in Heat Stress

The wheat roots membrane separates the cell from the environment around it and encloses the cell contents. The pro-tein secondary structure and thermal stability of the plasma membrane of wheat root have been characterized in D2O buffer from 20°C to 90°C by Attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR). Quantitative analysis of the amide I band (1700 - 1600 cm–1) showed that the plasma membrane proteins contains 41% α-helix, 16% β-sheet, 18% turn, and 25% disorder structures at 20°C. At elevated temperatures from 25°C up to 90°C, the α-helix and the β-sheet structure unfold into turns and the disorder structure, with a major conformational transition occurring at 50°C. There is a rapid decline in H+-ATPase activity of plasma membrane from 35°C to 55°C and it remain very low level H+-ATPase activity of PM from 55°C to 90°C. Therefore the protein conformational transition was one of reasons of loses H+-ATPase activity of plasma membrane.

Solid-State NMR Spectroscopic Approaches to Investigate Dynamics, Secondary Structure and Topology of Membrane Proteins

Solid-state NMR spectroscopy is routinely used to determine the structural and dynamic properties of both membrane proteins and peptides in phospholipid bilayers [1-26]. From the perspective of the perpetuated lipids, 2H solid-state NMR spectroscopy can be used to probe the effect of embedded proteins on the order and dynamics of the acyl chains of phospholipid bilayers [8-13]. Moreover, 31P solid-state NMR spectroscopy can be used to investigate the interaction of peptides, proteins and drugs with phospholipid head groups [11-14]. The secondary structure of 13C = O site-specific isotopically labeled peptides or proteins inserted into lipid bilayers can be probed utilizing 13C CPMAS solid-state NMR spectroscopy [15-18]. Also, solid-state NMR spectroscopic studies can be utilized to ascertain pertinent informa- tion on the backbone and side-chain dynamics of 2H- and 15N-labeled proteins, respectively, in phospholipid bilayers [19-26]. Finally, specific 15N-labeled amide sites on a protein embedded inside oriented bilayers can be used to probe the alignment of the helices with respect to the bilayer normal [2]. A brief summary of all these solid-state NMR ap- proaches are provided in this minireview.

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.