Effects of β-TCP Ceramics on Intracellular Ca^(2+) Concentration,Mineralization of Osteoblast and Protein Structure

β-TCP, as one of calcium phosphates ceramics, exerts perfect biocompatibility and osteoconductivity, and is clinically used as a bone graft substitute for decades. Consequently, the effects of β-TCP ceramics on intracellular Ca2＋ concentration, mineralization of osteoblast and BSA protein structure were studied. Results showed that β-TCP could increase the intracelluar Ca2＋ concentration and mineralization of osteoblast, indicating that β-TCP ceramics could take part in the organic metabolism and the degradation product had no detrimental effect on osteoblast in vitro. Furthermore, β-TCP ceramics could increase the content of α-helix and β-pleated sheet and change BSA into more ordering structure, those changes might be favorable for the biomineralization after β-TCP ceramics implanted.

The Evolutionary Computation Techniques for Protein Structure Prediction:A Survey

In this paper, the applications of evolutionary algorithm in prediction of protein secondary structure and tertiary structures are introduced, and recent studies on solving protein structure prediction problems using evolutionary algorithms are reviewed, and the challenges and prospects of EAs applied to protein structure modeling are analyzed and discussed.

Heuristic Quasi-physical Algorithm for Protein Structure Prediction

A three-dimensional off-lattice protein model with two species of monomers, hydrophobic and hydrophilic, is studied. Enligh- tened by the law of reciprocity among things in the physical world, a heuristic quasi-physical algorithm for protein structure prediction problem is put forward. First, by elaborately simulating the movement of the smooth elastic balls in the physical world, the algorithm finds low energy configurations for a given monomer chain. An ＂off-trap＂ strategy is then proposed to get out of local minima. Experimental results show promising performance. For all chains with lengths 13≤n ≤55, the proposed algorithm finds states with lower energy than the putative ground states reported in literatures. Furthermore, for chain lengths n = 21, 34, and 55, the algorithm finds new low energy configurations different from those given in literatures.

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.

Changes of structure and functional properties of rice protein in the fresh edible rice during the seed development

It has been reported that fresh edible rice has more bioactive compounds and its protein is easier to digest and has lower hypoallergenic than mature rice. In this paper, the changes in structure and functional properties of proteins at five different stages, including early milky stage(EMS), middle milky stage(MMS), late milky stage(LMS), waxy ripe stage(WS)and ripening stage(RS), during the seed development were investigated. It was found that with the seed developing, the molecular weight of fresh rice protein gradually become larger while the secondary structure changed from the highest content of disordered structure at MMS to the highest content of ordered structure at RS, which affect the surface hydrophobicity and then the functional properties of proteins, including foaming properties, emulsifying properties and oil holding capacity. Fresh rice protein at MMS has the strongest surface hydrophobicity while fresh edible rice protein at RS has the strongest oil holding capability. The results of our study can provide a theoretical basis for the application of fresh rice protein in the food industry and help to develop new fresh edible rice food.

Protein Structure Prediction:Challenges,Advances,and the Shift of Research Paradigms

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer science.These researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure.These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic modeling.Recently,we have also witnessed the great success of deep learning in protein structure prediction.In this review,we present a survey of the efforts for protein structure prediction.We compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep learning.In short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

Characterization of physicochemical and immunogenic properties of allergenic proteins altered by food processing:a review

Food allergens are mainly naturally-occurring proteins with immunoglobulin E(IgE)-binding epitopes.Understanding the structural and immunogenic characteristics of allergenic proteins is essential in assessing whether and how food processing techniques reduce allergenicity.We here discuss the impacts of food processing technologies on the modification of physicochemical,structural,and immunogenic properties of allergenic proteins.Detection techniques for characterizing changes in these properties of food allergens are summarized.Food processing helps to reduce allergenicity by aggregating or denaturing proteins,which masks,modifies,or destroys antigenic epitopes,whereas,it cannot eliminate allergenicity completely,and sometimes even improves allergenicity by exposing new epitopes.Moreover,most food processing techniques have been tested on purified food allergens rather than food products due to potential interference of other food components.We provide guidance for further development of processing operations that can decrease the allergenicity of allergenic food proteins without negatively impacting the nutritional profile.

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.

Proteins:From sequence to structure

Protein sequences as special heterogeneous sequences are rare in the amino acid sequence space. The specific sequen- tial order of amino acids of a protein is essential to its 3D structure. On the whole, the correlation between sequence and structure of a protein is not so strong. How well would a protein sequence contain its structural information？ How does a sequence determine its native structure？ Keeping the globular proteins in mind, we discuss several problems from sequence to structure.

Can We Determine a Protein Structure Quickly?

Can we determine a high resolution protein structure quickly, say, in a week？ I will show this is possible by the current technologies together with new computational tools discussed in this article. We have three potential paths to explore： X-ray crystallography. While this method has produced the most protein structures in the PDB （Protein Data Bank）, the nasty trial-and-error crystallization step remains to be an inhibitive obstacle.NMR （Nuclear Magnetic Resonance） spectroscopy. While the NMR experiments are relatively easy to do, the interpretation of the NMR data for structure calculation takes several months on average.In silico protein structure prediction. Can we actually predict high resolution structures consistently？ If the predicted models remain to be labeled as “predicted”, and these structures still need to be experimentally verified by the wet lab methods, then this method at best can serve only as a screening tool. I investigate the question of “quick protein structure determination” from a computer scientist point of view and actually answer the more relevant question “what can a computer scientist effectively contribute to this goal”.

A Case Study of 3D Protein Structure Prediction with Genetic Algorithm and Tabu Search

This paper describes a case study of 3D protein structure prediction of six sequences from protein data bank （PDB） by genetic algorithm and tabu search （GATS）, where off-lattice AB model is considered as a simplified model of protein structure. The lowest-energy values required for forming the native conformation of proteins are searched by GATS, and then the coarse structures （i.e., simplified structure） of the proteins are obtained according to the multiple angle parameters corresponding to the lowest energies. All the coarse structures form single hydrophobic cores surrounded by hydrophilic residues, which stay on the right side of the actual characteristic of protein structure. It demonstrates that this approach can predict the 3D protein structure effectively.

Comparative Analysis of Different Evaluation Functions for Protein Structure Prediction Under the HP Model

The HP model for protein structure prediction abstracts the fact that hydrophobicity is a dominant force in the protein folding process. This challenging combinatorial optimization problem has been widely addressed through metaheuristics. The evaluation function is a key component for the success of metaheuristics; the poor discrimination of the conventional evaluation function of the HP model has motivated the proposal of alternative formulations for this component. This comparative analysis inquires into the effectiveness of seven different evaluation functions for the HP model. The degree of discrimination provided by each of the studied functions, their capability to preserve a rank ordering among potential solutions which is consistent with the original objective of the HP model, as well as their effect on the performance of local search methods are analyzed. The obtained results indicate that studying alternative evaluation schemes for the HP model represents a highly valuable direction which merits more attention.

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.

CORE:Common Region Extension Based Multiple Protein Structure Alignment for Producing Multiple Solution

Over the past several decades, biologists have conducted numerous studies examining both general and specific functions of proteins. Generally, if similarities in either the structure or sequence of amino acids exist for two proteins, then a common biological function is expected. Protein function is determined primarily based on the structure rather than the sequence of amino acids. The algorithm for protein structure alignment is an essential tool for the research. The quality of the algorithm depends on the quality of the similarity measure that is used, and the similarity measure is an objective function used to determine the best alignment because of their individual strength and weakness However, none of existing similarity measures became golden standard They require excessive filtering to find a single alignment. In this paper, we introduce a new strategy that finds not a single alignment, but multiple alignments with different lengths. This method has obvious benefits of high quality alignment. However, this novel method leads to a new problem that the running time for this method is considerably longer than that for methods that find only a single alignment. To address this problem~ we propose algorithms that can locate a common region （CORE） of multiple alignment candidates, and can then extend the CORE into multiple alignments. Because the CORE can be defined from a final alignment, we introduce CORE＊ that is similar to CORE and propose an algorithm to identify the CORE＊. By adopting CORE＊ and dynamic programming, our proposed method produces multiple alignments of various lengths with higher accuracy than previous methods. In the experiments, the alignments identified by our algorithm are longer than those obtained by TM-align by 17% and 15.48%, on average, when the comparison is conducted at the level of super-family and fold, respectively.

A Novel Design Method for Protein-Like Molecules from the Perspective of Sheaf Theory

Proteins perform a variety of functions in living organisms and their functions are largely determined by their shape. In this paper, we propose a novel mathematical method for designing protein-like molecules of a given shape. In the mathematical model, molecules are represented as loops of n-simplices (2-simplices are triangles and 3-simplices are tetrahedra). We design a new molecule of a given shape by patching together a set of smaller molecules that cover the shape. The covering set of small molecules is defined using a binary relation between sets of molecules. A new molecule is then obtained as a sum of the smaller molecules, where addition of molecules is defined using transformations acting on a set of (n + 1)-dimensional cones. Due to page limitations, only the two-dimensional case (i.e., loops of triangles) is considered. No prior knowledge of Sheaf Theory, Category Theory, or Protein Science is required. The author hopes that this paper will encourage further collaboration between Mathematics and Protein Science.

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.

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 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.