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.

Application of ACO algorithm in protein structure prediction

The hydrophobic-polar (HP) lattice model is an important simplified model for studying protein folding. In this paper, we present an improved ACO algorithm for the protein structure prediction. In the algorithm, the "lone"ethod is applied to deal with the infeasible structures, and the "oint mutation and reconstruction"ethod is applied in local search phase. The empirical results show that the presented method is feasible and effective to solve the problem of protein structure prediction, and notable improvements in CPU time are obtained.

PHYLETIC RELATIONSHIP OF PROTEIN STRUCTURES BASED ON SPATIAL PREFERENCE OF RESIDUES

A distance measure that infers to indicate the evolutionary relationship of protein structures has been developed based on spatial preference factors of residues. The spatial preference factor is a reflection of the environment of residues in tertiary structure. Compared with the phyletic relationships derived from sequence homologies and three-dimensional structures, we find that the two lines of evolution are similar in general. This approach is applied to a group of glins here.

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.

Broadening environmental research in the era of accurate protein structure determination and predictions

The deep-learning protein structure prediction method AlphaFold2 has garnered enormous attention beyond the realm of structural biology,for its groundbreaking contribution to solving the"protein foiding problem"In this perspective,we explore the connection between protein structure studies and environmental research,delving into the potential for addressing specific environmental challenges.Proteins are promising for environmental applications because of the functional diversity endowed by their structural complexity.However,structural studies on proteins with environmental significance remain scarce.Here,we present the opportunity to study proteins by advancing experimental determination and deep-learning prediction methods.Specifically,the latest progress in environmental research via cryogenic electron microscopy is highlighted.It allows us to determine the structure of protein complexes in their native state within cells at molecular resolution,revealing environmentally-associated structural dynamics.With the remarkable advancements in computational power and experimental resolution,the study of protein structure and dynamics has reached unprecedented depth and accuracy.These advancements will undoubtedly accelerate the establishment of comprehensive environmental protein structural and functional databases.Tremendous opportunities for protein engineering exist to enable innovative solutions for environmental applications,such as the degradation of persistent contaminants,and the recovery of valuable metals as well as rare earth elements.

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.

Prediction of Protein OmpH in Structure of C47-8 Pasteurella multocida

[Objective] This study aimed to predict the structure of protein OmpH from Pasteurella multocida C47-8 （PmC47-8） strain of yak. [Method] Online BLAST, signal peptide prediction, secondary structure prediction and protein characteristics of sequencing result of gene OmpH from PmC47-8 strain were analyzed. [Result] The similarities of gene OmpH from PmC47-8 with the published 81 OmpH genes were between 84% and 99%; a signal peptide was found with the cleavage sites between 20 and 21 in the polypeptide; secondary structure prediction showed that folding structure accounted for 49.8% and loop structure for 50.2%; it predicted that there were 7 O-glycosylation sites in OmpH protein with the amino acid residual sites of 2, 45, 48, 330, 716, 721, 723, respectively, and 2 N-glycosylation sites with the amino acid residual sites of 15 and 35. [Conclusion] This study lays the foundation for the study on the immunity of OmpH gene from yak.

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.

Plant Cell Wall Proteomics： Mass Spectrometry Data, a Trove for Research on Protein Structure/Function Relationships

Proteomics allows the large-scale study of protein expression either in whole organisms or in purified organelles. In particular, mass spectrometry （MS） analysis of gel-separated proteins produces data not only for protein identification, but for protein structure, location, and processing as well. An in-depth analysis was performed on MS data from etiolated hypocotyl cell wall proteomics ofArabidopsis thaliana. These analyses show that highly homologous members of multigene families can be differentiated. Two lectins presenting 93% amino acid identity were identified using peptide mass fingerprinting. Although the identification of structural proteins such as extensins or hydroxyproline/proline-rich proteins （H/PRPs） is arduous, different types of MS spectra were exploited to identify and characterize an H/PRP. Maturation events in a couple of cell wall proteins （CWPs） were analyzed using site mapping. N-glycosylation of CWPs as well as the hydroxylation or oxidation of amino acids were also explored, adding information to improve our understanding of CWP structure/function relationships. A bioinformatic tool was developed to locate by means of MS the N-terminus of mature secreted proteins and N-glycosylation.

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.

MINIMUM HAUSDORFF DISTANCE UNDER RIGID MOTIONS AND COMPARISON OF PROTEIN STRUCTURES

Hausdorff distance between two compact sets, defined as the maximum distance from a point of one set to another set, has many application in computer science. It is a good measure for the similarity of two sets. This paper proves that the shape distance between two compact sets in R^n defined by nfinimum Hausdorff distance under rigid motions is a distance. The authors introduce similarity comparison problems in protein science, and propose that this measure may have good application to comparison of protein structure as well. For calculation of this distance, the authors give one dimensional formulas for problems （2, n）, （3, 3）, and （3, 4）. These formulas can reduce time needed for solving these problems. The authors did some data, this formula can reduce time needed to one As n increases, it would save more time. numerical experiments for （2, n）. On these sets of fifteenth of the best algorithms known on average.

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.