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.

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis

Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain injury.This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function.However,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional connectivity.Given that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is critical.In the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury.In this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy controls.Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis.In the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network.More than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 patients.Furthermore,better cognitive function was associated with more efficient networks.Finally,our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury.These findings highlight the importance of integrating structural and functional connectivity in unde rstanding the occurrence and evolution of mild traumatic brain injury.Additionally,exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.

Structure and function of the contactin-associated protein family in myelinated axons and their relationship with nerve diseases

The contactin-associated protein （Caspr） family participates in nerve excitation and conduction, and neurotransmitter release in myelinated axons. We analyzed the structures and functions of the Caspr family- CNTNAP1 （Casprl）, CNTNAP2 （Caspr2）, CNTNAP3 （Caspr3）, CNTNAP4 （Caspr4） and CNTNAP5 （Caspr5）, Casprl-5 is not only involved in the formation of myelinated axons, but also participates in maintaining the stability of adjacent connections. Casprl participates in the formation, differentiation, and proliferation of neurons and astrocytes, and in motor control and cognitive function. We also analyzed the relationship between the Caspr family and neurodegenerative diseases, multiple sclerosis, and autoimmune encephalitis. However, the effects of Caspr on disease course and prognosis remain poorly understood. The effects of Caspr on disease diagnosis and treatment need further investigation.

Structure and Function Analysis of Canine ZP3 Protein

To further study the biological function and mechanism of ZP3 in domestic canine （ CaMs lupu familiaris） , the coding sequence （CDS） of ZP3 gene was searched from NCBI database using bioinformatics method, and further transformed into protein sequence via MAGE software. The primary, secondary and terti- ary structure of protein was predicted with ExPASy, BLAST and DNA Star bioinformatics online software and program; the evolution and selected sites of ZP3 pro- tein extracted from 11 species were analyzed using PAML software; the conservation of ZP3 protein gene was analyzed with Predictprotein and Weblogo program; the tertiary structure of protein was edited by Python and PyMOL. The results showed that canine ZP3 gene encoded 426 amino acids, and the encoding product was a hydrophilic transmembrane protein with signal peptide; the 1 -23ra amino acids were signal peptide areas, and transmembrane domain distributed in the （x-helix area of the 386th -408th amino acids; four loci were affected by phosphorylation, and these phosphorylation sites might be associated with signal transduction ; there were nine protein binding sites on ZP domain; a high variation region was found in 325 -385 section of ZP3, and most of phosphorylation and selected amino acid sites were distributed in this area. This indicated that the area had experienced rapid evolution, suggesting that ZP domain and the high variation area might be in- volved in interaction of sperm and egg.

Structure elucidation and in vitro rat intestinal fermentation properties of a novel sulfated glucogalactan from Porphyra haitanensis

This study was to investigate the structure and rat fecal microbial fermentation properties of a polysaccharide fraction(PHP2)isolated from the red marine alga Porphyra haitanensis.PHP2 was characterized as a sulfated glucogalactan,with a hypothetical backbone structure of→4)Gα(1→6)G4 Sβ(1→4)Glc(1→and a side chain of Man(1→6)Glc.PHP2 had an irregular spherical chain conformation.The 16 S r RNA sequence analysis revealed that PHP2 modulated the rat fecal micro-flora composition,with a similar effect to inulin,changing the dominant genus(Lactobacillus and Escherichia-Shigella)and promoting the growth of organisms that degrade sulfur-containing polysaccharides,such as Desulfovibrio,Ruminococcaceae_UCG-005,and Ruminococcus_2.PHP2 can promote production of acetic,propionic and butyric acid by rat fecal micro-flora.Prediction of metabolic function suggested that PHP2 could modulate cholesterol metabolism.The sulfated glucogalactan fermentation behavior may be associated with its monosaccharide composition,chain branching and chain conformation.PHP2 appeared to have considerable potential as functional food,and was associated with sulfur-containing polysaccharides in general.

Identification,structure and function of a novel tetraspaninhoniologue from Spirometra erinaceieuropaei

Objective:To identify a full length cDNA sequence of a novel tetraspanin(TSP) homologue from Spirometra erinaceieuropaei and to predict the structure and function of its encoding protein using bioinformatics methods.Methods:Using the NCBI,EMBI,Expasy and other online sites, the open reading frame(ORF),conserved domain,physical and chemical parameters,signal peptide,transmembrane domain,epitope,topological structures of the protein sequences were predicted.And Vector NTI software was used for multiple sequence alignment and phylogenetic tree construction.Results:’Hie target sequence was 1 132 hp length with a 681 hp biggest ORF encoding 226 amino acids protein with typical TSP conserved domain.It was confirmed as full length cDNA of TSP16 from Spirometra erinaceieuropaei and named as SeTSP16 (GenBank accession number:JF728872).The predicted molecular weight and isoelectric point of the deduced protein were 24 750.5 Da and 7.88 Da,respectively.Compared with TSP16s from Schistosoma japonicum and Schistosoma mansoni.it showed similarity of 59%and 59%, respectively.SeTSP16 contained four transmembrane domains(TM 1-4),intracellular N and C-termini,one short small extracellular loop and one large extracellular loop.Four major epitopes that were significant different from the corresponding epitope regions of TSP16 from Schistosoma mansoni and Schistosoma japonicum were predicted.Conclusions:The full length cDNA sequences of SeTSP16 arc identified.It encodes a transmembrane protein which might be an ideal diagnosis antigen and target molecule for antiparasitic drugs.

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%.

Prediction of Crystal Structure and Density for Benzotrifuroxan(BTF) by AAPM

因苯并三氧化呋咱（ＢＴＦ）是具有广泛应用的很重要的化合物，且其晶体结构和性能的信息缺乏，故本文基于原子原子势方法（ＡＡＰＭ）预示了它的晶体结构和密度．分析了静电相互作用对ＢＴＦ晶体结构的影响．ＢＴＦ的非共面结构不能简单地不考虑电荷模型而加以预测．运用ａｂｉｎｉｔｉｏ计算求得的净原子电荷计算了它的结构参数和密度．结果发现，ＭＫＳ电荷能提供结构和密度预示与实测之间的最佳对应．由整体探索求得的分子堆积给出了合理成功的ＢＴＦ的结构和密度预测．密度预测的非精确度不超过０．０２ｇ·ｃｍ－３．

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.

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.

DFT Predictions on Structures and Stabilities of Eleven-vertex nido- and closo-Heteroboranes

Based on the octadecahedron of eleven-vertex closo-borane, the eleven-vertex closo-heteroborane was suggested with nonmetallic atoms instead of the different nonequivalent boron, and the stabilities were predicted at G96PW91/6-31＋G（3d,2p） level. The small heteroatoms, C, N, O, preferentially occupy vertex 2 with the absolutely lowest relative energy to form the high stabilization closo-heteroboranes. They cap four-membered rings to satisfy the geometrical demand of short B--Z bonds. The electron attractions from the vicinal boron atoms make the frameworks shrink. Differently, Si and Ge preferentially substitute for boron at vertex 1 with six tight B--Z bonds and form stabilized molecules. P, As, S, and Se tend to occupy vertex 4 and the optimized structures belong to the nido configura- tions. In contrast to high electronegative heteroatoms, S and Se transfer less negative charges to framework and the electropositive heteroatoms, Si and Ge transfer more negative charges to framework to form the delocalization structures. The HOMO-LUMO gaps show that most of predicted clusters possess chemical stabilities. The substitutions of heteroatoms for boron atoms in eleven-vertex closo-heteroboranes are consistent with the topological charge stabilization rule proposed by Gimarc.

Structural changes of proteins in liver cirrhosis and consequential changes in their function

The liver is the site of synthesis of the majority of circulating proteins.Besides initial polypeptide synthesis,sophisticated machinery is involved in the further processing of proteins by removing parts of them and/or adding functional groups and small molecules tailoring the final molecule to suit its physiological purpose.Posttranslational modifications(PTMs)design a network of molecules with the common protein ancestor but with slightly or considerably varying activity/localization/purpose.PTMs can change under pathological conditions,giving rise to aberrant or overmodified proteins.Undesired changes in the structure of proteins most often accompany undesired changes in their function,such as reduced activity or the appearance of new effects.Proper protein processing is essential for the reactions in living beings and crucial for the overall quality control.Modifications that occur on proteins synthesized in the liver whose PTMs are cirrhosis-related are oxidation,nitration,glycosylation,acetylation,and ubiquitination.Some of them predominantly affect proteins that remain in liver cells,whereas others predominantly occur on proteins that leave the liver or originate from other tissues and perform their function in the circulation.Altered PTMs of certain proteins are potential candidates as biomarkers of liver-related diseases,including cirrhosis.This review will focus on PTMs on proteins whose structural changes in cirrhosis exert or are suspected to exert the most serious functional consequences.

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.

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.

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Structural genomics (SG) is an international effort that aims at solving three-dimensional shapes of important biological macro-molecules with primary focus on proteins. One of the main bottlenecks in SG is the ability to produce dif-fraction quality crystals for X-ray crystallogra-phy based protein structure determination. SG pipelines allow for certain flexibility in target selection which motivates development of in- silico methods for sequence-based prediction/ assessment of the protein crystallization pro-pensity. We overview existing SG databanks that are used to derive these predictive models and we discuss analytical results concerning protein sequence properties that were discov-ered to correlate with the ability to form crystals. We also contrast and empirically compare mo- dern sequence-based predictors of crystalliza-tion propensity including OB-Score, ParCrys, XtalPred and CRYSTALP2. Our analysis shows that these methods provide useful and compli-mentary predictions. Although their average ac- curacy is similar at around 70%, we show that application of a simple majority-vote based en-semble improves accuracy to almost 74%. The best improvements are achieved by combining XtalPred with CRYSTALP2 while OB-Score and ParCrys methods overlap to a larger extend, although they still complement the other two predictors. We also demonstrate that 90% of the protein chains can be correctly predicted by at least one of these methods, which suggests that more accurate ensembles could be built in the future. We believe that current protein crystalli-zation propensity predictors could provide useful input for the target selection procedures utilized by the SG centers.

Response of fungal communities to afforestation and its indication for forest restoration

Soil fungi in forest ecosystems have great potential to enhance host plant growth and systemic ecological functions and services.Reforestation at Saihanba Mechanized Forest Farm,the world's largest artificial plantation,has been integral to global forest ecosystem preservation since the 1950s.To better assess the ecological effects of soil microbiology after afforestation,fungal diversity and community structure(using Illumina sequencing)from forests dominated by Larix gmelinii var.principis-rupprechtii,Pinus sylvestris var.mongolica and Picea asperata,and from grassland were surveyed.In total,4,540 operational taxonomic units(OTUs)were identified,with Mortierella and Solicoccozyma being the dominant genera of grassland soil and Inocybe,Cortinarius,Piloderma,Tomentella,Sebacina,Hygrophorus and Saitozyma dominating the plantation soil.Principle coordinate analysis(PCoA)and co-occurrence networks revealed differences in fungal structure after afforestation.Significantly,more symbiotroph guilds were dominated by ectomycorrhizal fungi in plantations under the prediction of FUNGuild.The community composition and diversity of soil fungi were significantly influenced by pH via redundancy analysis(RDA)and the Mantel test(p<0.01).This finding emphasizes that soil pH has a strong effect on the transition of fungal communities and functional taxa from grassland to plantation,providing a novel indicator for forest restoration.

Computational prediction of RNA tertiary structures using machine learning methods

RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.

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.

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.

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.