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.

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.

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.

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.

Nanoplasmonic mid-infrared biosensor for in vitro protein secondary structure detection

Plasmonic nanoantennas offer new applications in mid-infrared(mid-IR)absorption spectroscopy with ultrasensitive detection of structural signatures of biomolecules,such as proteins,due to their strong resonant near-fields.The amide I fingerprint of a protein contains conformational information that is greatly important for understanding its function in health and disease.Here,we introduce a non-invasive,label-free mid-IR nanoantenna-array sensor for secondary structure identification of nanometer-thin protein layers in aqueous solution by resolving the content of plasmonically enhanced amide I signatures.We successfully detect random coil to crossβ-sheet conformational changes associated withα-synuclein protein aggregation,a detrimental process in many neurodegenerative disorders.Notably,our experimental results demonstrate high conformational sensitivity by differentiating subtle secondary-structural variations in a nativeβ-sheet protein monolayer from those of crossβ-sheets,which are characteristic of pathological aggregates.Our nanoplasmonic biosensor is a highly promising and versatile tool for in vitro structural analysis of thin protein layers.

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.

A GASVM Algorithm for Predicting Protein Structure Classes

The research methods of protein structure prediction mainly focus on finding effective features of protein sequences and developing suitable machine learning algorithms. But few people consider the importance of weights of features in classification. We propose the GASVM algorithm (classification accuracy of support vector machine is regarded as the fitness value of genetic algorithm) to optimize the coefficients of these 16 features (5 features are proposed first time) in the classification, and further develop a new feature vector. Finally, based on the new feature vector, this paper uses support vector machine and 10-fold cross-validation to classify the protein structure of 3 low similarity datasets (25PDB, 1189, FC699). Experimental results show that the overall classification accuracy of the new method is better than other methods.

Functional structures and folding dynamics of two peptides

The folding dynamics and structural characteristics of peptides RTKAWNRQLYPEW （P1） and RTKQLYPEW （P2） are investigated by using all-atomic simulation procedure CHARMM in this work. The results show that P1, a segment of an antigen, has a folding motif of α-helix, whereas P2, which is derived by deleting four residues AWNR from peptide P1, prevents the formation of helix and presents a β-strand. And peptlde P1 experiences a more rugged energy landscape than peptide P2. From our results, it is inferred that the antibody CD8 cytolytic T lymphocyte prefers an antigen with a β-folding structure to that with an α-helical one.

Physical and chemical changes of rapeseed meal proteins during toasting and their effects on in vitro digestibility

Background： Toasting during the production of rapeseed meal（RSM） decreases ileal crude protein（CP） and amino acid（AA） digestibility. The mechanisms that determine the decrease in digestibility have not been fully elucidated. A high protein quality, low-denatured, RSM was produced and toasted up to 120 min, with samples taken every 20 min. The aim of this study was to characterize secondary structure and chemical changes of proteins and glucosinolates occurring during toasting of RSM and the effects on its in vitro CP digestibility.Results： The decrease in protein solubility and the increase of intermolecular β-sheets with increasing toasting time were indications of protein aggregation. The contents of NDF and ADIN increased with increasing toasting time.Contents of arginine, lysine and O-methylisourea reactive lysine（OMIU-RL） linearly decreased with increasing toasting time, with a larger decrease of OMIU-RL than lysine. First-order reactions calculated from the measured parameters show that glucosinolates were degraded faster than lysine, OMIU-RL and arginine and that physical changes to proteins seem to occur before chemical changes during toasting. Despite the drastic physical and chemical changes noticed on the proteins, the coefficient of in vitro CP digestibility ranged from 0.776 to 0.750 and there were no effects on the extent of protein hydrolysis after 120 min. In contrast, the rate of protein hydrolysis linearly decreased with increasing toasting time, which was largely correlated to the decrease in protein solubility, lysine and OMIU-RL observed. Rate of protein hydrolysis was more than 2-fold higher for the untoasted RSM compared to the 120 min toasted material.Conclusions： Increasing the toasting time for the production of RSM causes physical and chemical changes to the proteins that decrease the rate of protein hydrolysis. The observed decrease in the rate of protein hydrolysis could impact protein digestion and utilization.