Human serum albumin(HSA)is the most abundant protein in plasma and plays an essential physiological role in the human body.Ethanol precipitation is the most widely used way to obtain HSA,and pH and ethanol are crucial...Human serum albumin(HSA)is the most abundant protein in plasma and plays an essential physiological role in the human body.Ethanol precipitation is the most widely used way to obtain HSA,and pH and ethanol are crucial factors affecting the process.In this study,infrared(IR)spectroscopy and near-infrared(NIR)spectroscopy in combination with chemometrics were used to investigate the changes in the secondary structure and hydration of HSA at acidic pH(5.6-3.2)and isoelectric pH when ethanol concentration was varied from 0%to 40%as a perturbation.IR spectroscopy combined with the two-dimensional correlation spectroscopy(2DCOS)analysis for acid pH system proved that the secondary structure of HSA changed significantly when pH was around 4.5.What's more,the IR spectroscopy and 2DCOS analysis showed different secondary structure forms under different ethanol concentrations at the isoelectric pH.For the hydration effect analysis,NIR spectroscopy combined with the McCabe-Fisher method and aquaphotomics showed that the free hydrogen-bonded water fluctuates dynamically,with ethanol at 0-20%enhancing the hydrogen-bonded water clusters,while weak hydrogen-bonded water clusters were formed when the ethanol concentration increased continuously from 20%to 30%.These measurements provide new insights into the structural changes and changes in the hydration behavior of HSA,revealing the dynamic process of protein purification,and providing a theoretical basis for the selection of HSA alcoholic precipitation process parameters,as well as for further studies of complex biological systems.展开更多
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 p...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.展开更多
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 ch...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.展开更多
A novel method for the prediction of RNA secondary structure was proposed based on the particle swarm optimization(PSO). PSO is known to be effective in solving many different types of optimization problems and know...A novel method for the prediction of RNA secondary structure was proposed based on the particle swarm optimization(PSO). PSO is known to be effective in solving many different types of optimization problems and known for being able to approximate the global optimal results in the solution space. We designed an efficient objective function according to the minimum free energy, the number of selected stems and the average length of selected stems. We calculated how many legal stems there were in the sequence, and selected some of them to obtain an optimal result using PSO in the right of the objective function. A method based on the improved particle swarm optimization(IPSO) was proposed to predict RNA secondary structure, which consisted of three stages. The first stage was applied to encoding the source sequences, and to exploring all the legal stems. Then, a set of encoded stems were created in order to prepare input data for the second stage. In the second stage, IPSO was responsible for structure selection. At last, the optimal result was obtained from the secondary structures selected via IPSO. Nine sequences from the comparative RNA website were selected for the evaluation of the proposed method. Compared with other six methods, the proposed method decreased the complexity and enhanced the sensitivity and specificity on the basis of the experiment results.展开更多
The attenuated vaccine strains of CSFV have a 12-nucleotides (nt) insertion in the 3'-UTR of genome as compared to that of CSFV virulent strains. In this study, we found a distinct heterogeneity in the 3'-UTR of a...The attenuated vaccine strains of CSFV have a 12-nucleotides (nt) insertion in the 3'-UTR of genome as compared to that of CSFV virulent strains. In this study, we found a distinct heterogeneity in the 3'-UTR of attenuated Thiverval and HCLV strains. The longest 3'-UTR of Thiverval strain was 259 base pairs (bp) with a 32-nt insertion, the shortest 3'-UTR had only 233 bp with a 6-nt insertion. The longest 3'-UTR of HCLV strain was 244 bp with a 17-nt insertion and the shortest 3' UTR was 235 bp with a 8-nt insertion. Compared with the published sequences of 3'-UTR of vaccine and virulent strains, the 3'-UTR of CSFV vaccine strains have two variable regions where insertion among the different vaccine strains were frequently found. The first is located between the second conservative TALk codon and the start of T-rich region where we found the variable length insertion in the same vaccine strain Thiveral or HCLV and the second is located between the end of T-rich region and the front of GAA eodon, however, a 4-nt deletion was found in this region in the virulent Shimen strain. These two regions may represent the "hot spot" for mutation. Modeling the secondary structures of the 3'-UTR suggests that the T-rich insertion could result in the change of structure and free energy, thus affecting the stability of the 3'-UTR structure. These findings will help to understand the mechanism of attenuated vaccines and improve vaccine safety, stability, and efficacy.展开更多
The morphology, bending property and secondary structure estimation of dog hair were investigated by scanning electron microscope( SEM),fiber compression bending analyzer,fiber frictional coefficient tester and Fourie...The morphology, bending property and secondary structure estimation of dog hair were investigated by scanning electron microscope( SEM),fiber compression bending analyzer,fiber frictional coefficient tester and Fourier transform infrared spectroscopy( FTIR). The SEM micrograph of hair indicated guard hair( GH),intermediate hair( IH) and underhair( UH) from dog hair fibers displayed considerable differences in the diameter,length,scale shape and medulla. In addition,the bending property of fibers were related to the diameter of fibers and the percentage and structure of medulla. The UH had the greatest frictional coefficient,while the guard hair had the largest bending rigidity in three kinds of hairs. The analysis of amide I region implied that there was an apparent variety in the secondary structure of hairs,mainly the percentage of α-helix and β-pleated sheet and β-turn structure. The X-ray diffraction results showed that the crystallinity of the UH was the lowest in the three kinds of fibers. The tensile behaviors of dog hair also indicated that the increase of β-pleated and β-turn structure caused the increase of the breaking strength.展开更多
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...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.展开更多
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 peanu...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%.展开更多
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 us...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.展开更多
A new method for simulating the folding pathway of RNA secondary structure using the modified ant colony algorithmis proposed.For a given RNA sequence,the set of all possible stems is obtained and the energy of each s...A new method for simulating the folding pathway of RNA secondary structure using the modified ant colony algorithmis proposed.For a given RNA sequence,the set of all possible stems is obtained and the energy of each stem iscalculated and stored at the initial stage.Furthermore,a more realistic formula is used to compute the energy ofmulti-branch loop in the following iteration.Then a folding pathway is simulated,including such processes as constructionof the heuristic information,the rule of initializing the pheromone,the mechanism of choosing the initial andnext stem and the strategy of updating the pheromone between two different stems.Finally by testing RNA sequences withknown secondary structures from the public databases,we analyze the experimental data to select appropriate values forparameters.The measure indexes show that our procedure is more consistent with phylogenetically proven structures thansoftware RNAstructure sometimes and more effective than the standard Genetic Algorithm.展开更多
Secondary structure of [Val. Ala]sCT- an analog of salmon calcitonin (sCT) not containing an N-terminal disultide bridge. was investigated by circular dichroism (CD) and Fourier-transform intrared spectroscopy (FTIR) ...Secondary structure of [Val. Ala]sCT- an analog of salmon calcitonin (sCT) not containing an N-terminal disultide bridge. was investigated by circular dichroism (CD) and Fourier-transform intrared spectroscopy (FTIR) methods. Both CD and FTIR results show that the main contbrmational structure of [Val Ala']sCT in aqueous solution is random coil structure. while in trifluorethanol (TFE) it displays a strong α-helical structure. The relationship between the biological activity and the conformational structure of [Val, Ala] sCT is als0 discussed.展开更多
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 ...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.展开更多
Secondary structures of RNAs are the basis of understanding their tertiary structures and functions and so their predictions are widely needed due to increasing discovery of noncoding RNAs.In the last decades,a lot o...Secondary structures of RNAs are the basis of understanding their tertiary structures and functions and so their predictions are widely needed due to increasing discovery of noncoding RNAs.In the last decades,a lot of methods have been proposed to predict RNA secondary structures but their accuracies encountered bottleneck.Here we present a method for RNA secondary structure prediction using direct coupling analysis and a remove-and-expand algorithm that shows better performance than four existing popular multiple-sequence methods.We further show that the results can also be used to improve the prediction accuracy of the single-sequence methods.展开更多
A simple stepwise folding process has been developed to simulate RNA secondary structure formation.Modifications for the energy parameters of various loops were included in the program.Five possible types of pseudokno...A simple stepwise folding process has been developed to simulate RNA secondary structure formation.Modifications for the energy parameters of various loops were included in the program.Five possible types of pseudoknots including the well known H-type pseudoknot were permitted to occur if reasonable.We have applied this approach to e number of RNA sequences.The prediction accuracies we obtained were higher than those in published papers.展开更多
ERK is involved in multiple cell signaling pathways through its interacting proteins. By </span><i><span style="font-size:12px;font-family:Verdana;">in</span></i> <i><s...ERK is involved in multiple cell signaling pathways through its interacting proteins. By </span><i><span style="font-size:12px;font-family:Verdana;">in</span></i> <i><span style="font-size:12px;font-family:Verdana;">silico</span></i><span style="font-size:12px;font-family:Verdana;"> 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</span></span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">(MAPK1), interleukin-17 receptor D precursor (IL17RD), WD repeat domain containing 83 (WDR83), </span><span style="font-size:12px;font-family:Verdana;">tescalcin (Tesc), mitogen-activated protein kinase kinase kinase 4 (MAPP3K4),</span><span style="font-size:12px;font-family:Verdana;"> 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</span></span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:12px;font-family:Verdana;">kDa (KININS220), membrane-associated guanylate kinase (MAGI3), phosphoprotein enriched in astrocytes 15</span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:12px;font-family:Verdana;">(PEA15), typtophenyl-tRNA synthetase, cytoplasmic (WARS), dual specificity phosphatase 9 (DUSP9), mitogen-activated protein kinase kinase kinase 1</span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:12px;font-family:Verdana;">(MAP3K1), UL16 binding protein 3 (ULBP3), SLAM family member 7 isoform a precursor (SLAMMF7) and mitogen activated protein kinase kinase kinase 11 (MAP3K11) (</span><span "="" style="font-size:10pt;"><a href="file:///E:/%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2021/0225-wqs-%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89(1)/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89/7-1390595.docx#T1"><b><span color:#943634;"="" style="font-size: 12px;font-family: Verdana;">Table 1</span></b></a></span><span "="" style="font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">). However, prediction of secondary structure and domain/motif present in aforementioned ERK interacting proteins is not studied. In this paper, </span><i><span style="font-size:12px;font-family:Verdana;">in</span></i></span><i><span style="font-size:10.0pt;font-family:;" "=""> </span><span style="font-size:12px;font-family:Verdana;" "="">silico</span></i><span "="" style="font-size:12px;font-family:Verdana;"> 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 (</span><span "="" style="font-size:10pt;"><a href="file:///E:/%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2021/0225-wqs-%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89(1)/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89/7-1390595.docx#T2"><b><span color:#943634;"="" style="font-size: 12px;font-family: Verdana;">Table 2</span></b></a></span><span "="" style="font-size:12px;font-family:Verdana;">)</span><span "="" style="font-size:12px;font-family:Verdana;"> and</span><span "="" style="font-size:12px;font-family:Verdana;"> 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 (</span><span "="" style="font-size:10pt;"><a href="file:///E:/%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2021/0225-wqs-%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89(1)/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89/7-1390595.docx#T3"><b><span color:#943634;"="" style="font-size: 12px;font-family: Verdana;">Table 3</span></b></a></span><span "="" style="font-size:12px;font-family:Verdana;">). Such study may be helpful to design engineered molecules for regulating ERK dependent pathways in disease condition.展开更多
The Shine-Dalgarno (SD) sequence, when present, is known to promote translation initiation in a bacterial cell. However, the thermodynamic stability of the messenger RNA (mRNA) through its secondary structures has an ...The Shine-Dalgarno (SD) sequence, when present, is known to promote translation initiation in a bacterial cell. However, the thermodynamic stability of the messenger RNA (mRNA) through its secondary structures has an inhibitory effect on the efficiency of translation. This poses the question of whether bacterial mRNAs with SD have low secondary structure formation or not. About 3500 protein-coding genes in <i>Rhodobacter sphaeroides</i> were analyzed and a sliding window analysis of the last 100 nucleotides of the 5’ UTR and the first 100 nucleotides of ORFs was performed using <i>RNAfold</i>, a software for RNA secondary structure analysis. It was shown that mRNAs with SD are less stable than those without SD for genes located on the primary chromosome, but not for the plasmid encoded genes. Furthermore, mRNA stability is similar for genes within each chromosome except those encoded by the accessory chromosome (second chromosome). Results highlight the possible contribution of other factors like replicon-specific nucleotide composition (GC content), codon bias, and protein stability in determining the efficiency of translation initiation in both SD-dependent and SD-independent translation systems.展开更多
RNA secondary structure plays a critical role in gene regulation. Rice (Oryza sativa) is one of the most important food crops in the world. However, RNA structure in rice has scarcely been studied. Here, we have suc...RNA secondary structure plays a critical role in gene regulation. Rice (Oryza sativa) is one of the most important food crops in the world. However, RNA structure in rice has scarcely been studied. Here, we have successfully generated in vivo Structure-seq libraries in rice. We found that the structural flexibility of mRNAs might associate with the dynamics of biological function. Higher N6-methyladenosine (mSA) modification tends to have less RNA structure in 3' UTR, whereas GC content does not significantly affect in vivo mRNA structure to maintain efficient biological processes such as translation. Comparative analysis of RNA structurome between rice and Arabidopsis revealed that higher GC content does not lead to stronger structure and less RNA structural flexibility. Moreover, we found a weak correlation between sequence and structure conservation of the orthologs between rice and Arabidopsis. The conservation and divergence of both sequence and in vivo RNA structure corresponds to diverse and specific biological processes. Our results indicate that RNA secondary structure might offer a separate layer of selection to the sequence between monocot and dicot. Therefore, our study implies that RNA structure evolves differently in various biological processes to maintain robustness in development and adaptational flexibility during angiosperm evolution.展开更多
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-fi...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.展开更多
Peptide amphiphiles with well-organized secondary structure are an important family of molecules that are known to assemble into a variety of nanostructures.In this work,we present three guanidiniocarbonylpyrrole(GCP)...Peptide amphiphiles with well-organized secondary structure are an important family of molecules that are known to assemble into a variety of nanostructures.In this work,we present three guanidiniocarbonylpyrrole(GCP)containing peptide amphiphiles,which show versatile morphology and secondary structure changes as a result of different chain lengths and in different concentration regimes.The random coil conformation,α-helix,andβ-sheet are obtained for peptide 1,peptide 2,and peptide 3,respectively under neutral aqueous conditions.Furthermore,all peptide amphiphiles can aggregate to form nanoparticles at low concentrations.However,at high concentrations,peptide 1 selfassembles into left-ha nded twisted helical fibers,while longer bamboo-like mo rphology can be obse rved exclusively for peptide 2.For peptide 3,freshly prepared samples show uniform spherical morphology,whereas an obvious morphological transition from original nanoparticles to disordered fibers was realized after incubating for one week.These fascinating morphology changes were determined by the combination of circular dichroism,dynamic light scattering,transmission electron microscopy,atomic force microscopy,and theoretical calculations.展开更多
Calcium ions play an important role in the oxygen_evolving process of photosystem Ⅱ as demonstrated in many experiments. The changes of the secondary structures of PS Ⅱ induced by the depletion of Ca 2+ were reporte...Calcium ions play an important role in the oxygen_evolving process of photosystem Ⅱ as demonstrated in many experiments. The changes of the secondary structures of PS Ⅱ induced by the depletion of Ca 2+ were reported. The results indicated that the removal of Ca 2+ led to the transition of α helix to turns and sheet structures. While Ca 2+ was re_added to the media, only the structures changed to turns could be recovered. The protein conformational changes of PS Ⅱ during the donor side photoinhibition induced by the depletion of Ca 2+ were also studied. This showed that the protein conformational changes differed between the control and Ca 2+ _depleted samples in a short period of illumination (within 10 min). However, the changes became similar when the illumination time was increased.展开更多
基金support of the National Key Research and Development Program of China (Grant Numbers 2021YFB3201200 and 2021YFB3201202)the Shandong Province Natural Science Foundation (Grant Numbers ZR2021QB177 and ZR2022QB205).
文摘Human serum albumin(HSA)is the most abundant protein in plasma and plays an essential physiological role in the human body.Ethanol precipitation is the most widely used way to obtain HSA,and pH and ethanol are crucial factors affecting the process.In this study,infrared(IR)spectroscopy and near-infrared(NIR)spectroscopy in combination with chemometrics were used to investigate the changes in the secondary structure and hydration of HSA at acidic pH(5.6-3.2)and isoelectric pH when ethanol concentration was varied from 0%to 40%as a perturbation.IR spectroscopy combined with the two-dimensional correlation spectroscopy(2DCOS)analysis for acid pH system proved that the secondary structure of HSA changed significantly when pH was around 4.5.What's more,the IR spectroscopy and 2DCOS analysis showed different secondary structure forms under different ethanol concentrations at the isoelectric pH.For the hydration effect analysis,NIR spectroscopy combined with the McCabe-Fisher method and aquaphotomics showed that the free hydrogen-bonded water fluctuates dynamically,with ethanol at 0-20%enhancing the hydrogen-bonded water clusters,while weak hydrogen-bonded water clusters were formed when the ethanol concentration increased continuously from 20%to 30%.These measurements provide new insights into the structural changes and changes in the hydration behavior of HSA,revealing the dynamic process of protein purification,and providing a theoretical basis for the selection of HSA alcoholic precipitation process parameters,as well as for further studies of complex biological systems.
文摘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.
基金financially supported by Beijing Municipal Commission of Education Co-Constructed Programand Chinese Universities Scientific Fund(2009-4-25)
文摘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.
基金Supported by the National Natural Science Foundation of China(No60971089)
文摘A novel method for the prediction of RNA secondary structure was proposed based on the particle swarm optimization(PSO). PSO is known to be effective in solving many different types of optimization problems and known for being able to approximate the global optimal results in the solution space. We designed an efficient objective function according to the minimum free energy, the number of selected stems and the average length of selected stems. We calculated how many legal stems there were in the sequence, and selected some of them to obtain an optimal result using PSO in the right of the objective function. A method based on the improved particle swarm optimization(IPSO) was proposed to predict RNA secondary structure, which consisted of three stages. The first stage was applied to encoding the source sequences, and to exploring all the legal stems. Then, a set of encoded stems were created in order to prepare input data for the second stage. In the second stage, IPSO was responsible for structure selection. At last, the optimal result was obtained from the secondary structures selected via IPSO. Nine sequences from the comparative RNA website were selected for the evaluation of the proposed method. Compared with other six methods, the proposed method decreased the complexity and enhanced the sensitivity and specificity on the basis of the experiment results.
基金supported by the National Natural Science Foundation of China (30571377)the National High-Tech R&D Program of China (863 Program,2006AA10A204)
文摘The attenuated vaccine strains of CSFV have a 12-nucleotides (nt) insertion in the 3'-UTR of genome as compared to that of CSFV virulent strains. In this study, we found a distinct heterogeneity in the 3'-UTR of attenuated Thiverval and HCLV strains. The longest 3'-UTR of Thiverval strain was 259 base pairs (bp) with a 32-nt insertion, the shortest 3'-UTR had only 233 bp with a 6-nt insertion. The longest 3'-UTR of HCLV strain was 244 bp with a 17-nt insertion and the shortest 3' UTR was 235 bp with a 8-nt insertion. Compared with the published sequences of 3'-UTR of vaccine and virulent strains, the 3'-UTR of CSFV vaccine strains have two variable regions where insertion among the different vaccine strains were frequently found. The first is located between the second conservative TALk codon and the start of T-rich region where we found the variable length insertion in the same vaccine strain Thiveral or HCLV and the second is located between the end of T-rich region and the front of GAA eodon, however, a 4-nt deletion was found in this region in the virulent Shimen strain. These two regions may represent the "hot spot" for mutation. Modeling the secondary structures of the 3'-UTR suggests that the T-rich insertion could result in the change of structure and free energy, thus affecting the stability of the 3'-UTR structure. These findings will help to understand the mechanism of attenuated vaccines and improve vaccine safety, stability, and efficacy.
基金the Fundamental Research Fund for the Central Universities,China(No.2232013D3-02)
文摘The morphology, bending property and secondary structure estimation of dog hair were investigated by scanning electron microscope( SEM),fiber compression bending analyzer,fiber frictional coefficient tester and Fourier transform infrared spectroscopy( FTIR). The SEM micrograph of hair indicated guard hair( GH),intermediate hair( IH) and underhair( UH) from dog hair fibers displayed considerable differences in the diameter,length,scale shape and medulla. In addition,the bending property of fibers were related to the diameter of fibers and the percentage and structure of medulla. The UH had the greatest frictional coefficient,while the guard hair had the largest bending rigidity in three kinds of hairs. The analysis of amide I region implied that there was an apparent variety in the secondary structure of hairs,mainly the percentage of α-helix and β-pleated sheet and β-turn structure. The X-ray diffraction results showed that the crystallinity of the UH was the lowest in the three kinds of fibers. The tensile behaviors of dog hair also indicated that the increase of β-pleated and β-turn structure caused the increase of the breaking strength.
基金Supported by the National Natural Science Foundation of China(30170214)
文摘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.
基金This work was financially supported by The foundation for young scientists of hubei province(grant number 610112246)the foundation for Doctoral startup project of Hubei University of Technology(grant number 337/338).
文摘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%.
文摘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.
基金supported by the National Natural Science Foundation of China(Grant No.60971089)the Specialized Research Foundation for the Doctoral Program of Higher Education of China(Grant No.20070183057)
文摘A new method for simulating the folding pathway of RNA secondary structure using the modified ant colony algorithmis proposed.For a given RNA sequence,the set of all possible stems is obtained and the energy of each stem iscalculated and stored at the initial stage.Furthermore,a more realistic formula is used to compute the energy ofmulti-branch loop in the following iteration.Then a folding pathway is simulated,including such processes as constructionof the heuristic information,the rule of initializing the pheromone,the mechanism of choosing the initial andnext stem and the strategy of updating the pheromone between two different stems.Finally by testing RNA sequences withknown secondary structures from the public databases,we analyze the experimental data to select appropriate values forparameters.The measure indexes show that our procedure is more consistent with phylogenetically proven structures thansoftware RNAstructure sometimes and more effective than the standard Genetic Algorithm.
文摘Secondary structure of [Val. Ala]sCT- an analog of salmon calcitonin (sCT) not containing an N-terminal disultide bridge. was investigated by circular dichroism (CD) and Fourier-transform intrared spectroscopy (FTIR) methods. Both CD and FTIR results show that the main contbrmational structure of [Val Ala']sCT in aqueous solution is random coil structure. while in trifluorethanol (TFE) it displays a strong α-helical structure. The relationship between the biological activity and the conformational structure of [Val, Ala] sCT is als0 discussed.
文摘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.
基金Project supported by the National Natural Science Foundation of China(Grant No.31570722).
文摘Secondary structures of RNAs are the basis of understanding their tertiary structures and functions and so their predictions are widely needed due to increasing discovery of noncoding RNAs.In the last decades,a lot of methods have been proposed to predict RNA secondary structures but their accuracies encountered bottleneck.Here we present a method for RNA secondary structure prediction using direct coupling analysis and a remove-and-expand algorithm that shows better performance than four existing popular multiple-sequence methods.We further show that the results can also be used to improve the prediction accuracy of the single-sequence methods.
文摘A simple stepwise folding process has been developed to simulate RNA secondary structure formation.Modifications for the energy parameters of various loops were included in the program.Five possible types of pseudoknots including the well known H-type pseudoknot were permitted to occur if reasonable.We have applied this approach to e number of RNA sequences.The prediction accuracies we obtained were higher than those in published papers.
文摘ERK is involved in multiple cell signaling pathways through its interacting proteins. By </span><i><span style="font-size:12px;font-family:Verdana;">in</span></i> <i><span style="font-size:12px;font-family:Verdana;">silico</span></i><span style="font-size:12px;font-family:Verdana;"> 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</span></span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">(MAPK1), interleukin-17 receptor D precursor (IL17RD), WD repeat domain containing 83 (WDR83), </span><span style="font-size:12px;font-family:Verdana;">tescalcin (Tesc), mitogen-activated protein kinase kinase kinase 4 (MAPP3K4),</span><span style="font-size:12px;font-family:Verdana;"> 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</span></span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:12px;font-family:Verdana;">kDa (KININS220), membrane-associated guanylate kinase (MAGI3), phosphoprotein enriched in astrocytes 15</span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:12px;font-family:Verdana;">(PEA15), typtophenyl-tRNA synthetase, cytoplasmic (WARS), dual specificity phosphatase 9 (DUSP9), mitogen-activated protein kinase kinase kinase 1</span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:12px;font-family:Verdana;">(MAP3K1), UL16 binding protein 3 (ULBP3), SLAM family member 7 isoform a precursor (SLAMMF7) and mitogen activated protein kinase kinase kinase 11 (MAP3K11) (</span><span "="" style="font-size:10pt;"><a href="file:///E:/%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2021/0225-wqs-%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89(1)/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89/7-1390595.docx#T1"><b><span color:#943634;"="" style="font-size: 12px;font-family: Verdana;">Table 1</span></b></a></span><span "="" style="font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">). However, prediction of secondary structure and domain/motif present in aforementioned ERK interacting proteins is not studied. In this paper, </span><i><span style="font-size:12px;font-family:Verdana;">in</span></i></span><i><span style="font-size:10.0pt;font-family:;" "=""> </span><span style="font-size:12px;font-family:Verdana;" "="">silico</span></i><span "="" style="font-size:12px;font-family:Verdana;"> 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 (</span><span "="" style="font-size:10pt;"><a href="file:///E:/%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2021/0225-wqs-%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89(1)/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89/7-1390595.docx#T2"><b><span color:#943634;"="" style="font-size: 12px;font-family: Verdana;">Table 2</span></b></a></span><span "="" style="font-size:12px;font-family:Verdana;">)</span><span "="" style="font-size:12px;font-family:Verdana;"> and</span><span "="" style="font-size:12px;font-family:Verdana;"> 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 (</span><span "="" style="font-size:10pt;"><a href="file:///E:/%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2021/0225-wqs-%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89(1)/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89/7-1390595.docx#T3"><b><span color:#943634;"="" style="font-size: 12px;font-family: Verdana;">Table 3</span></b></a></span><span "="" style="font-size:12px;font-family:Verdana;">). Such study may be helpful to design engineered molecules for regulating ERK dependent pathways in disease condition.
文摘The Shine-Dalgarno (SD) sequence, when present, is known to promote translation initiation in a bacterial cell. However, the thermodynamic stability of the messenger RNA (mRNA) through its secondary structures has an inhibitory effect on the efficiency of translation. This poses the question of whether bacterial mRNAs with SD have low secondary structure formation or not. About 3500 protein-coding genes in <i>Rhodobacter sphaeroides</i> were analyzed and a sliding window analysis of the last 100 nucleotides of the 5’ UTR and the first 100 nucleotides of ORFs was performed using <i>RNAfold</i>, a software for RNA secondary structure analysis. It was shown that mRNAs with SD are less stable than those without SD for genes located on the primary chromosome, but not for the plasmid encoded genes. Furthermore, mRNA stability is similar for genes within each chromosome except those encoded by the accessory chromosome (second chromosome). Results highlight the possible contribution of other factors like replicon-specific nucleotide composition (GC content), codon bias, and protein stability in determining the efficiency of translation initiation in both SD-dependent and SD-independent translation systems.
文摘RNA secondary structure plays a critical role in gene regulation. Rice (Oryza sativa) is one of the most important food crops in the world. However, RNA structure in rice has scarcely been studied. Here, we have successfully generated in vivo Structure-seq libraries in rice. We found that the structural flexibility of mRNAs might associate with the dynamics of biological function. Higher N6-methyladenosine (mSA) modification tends to have less RNA structure in 3' UTR, whereas GC content does not significantly affect in vivo mRNA structure to maintain efficient biological processes such as translation. Comparative analysis of RNA structurome between rice and Arabidopsis revealed that higher GC content does not lead to stronger structure and less RNA structural flexibility. Moreover, we found a weak correlation between sequence and structure conservation of the orthologs between rice and Arabidopsis. The conservation and divergence of both sequence and in vivo RNA structure corresponds to diverse and specific biological processes. Our results indicate that RNA secondary structure might offer a separate layer of selection to the sequence between monocot and dicot. Therefore, our study implies that RNA structure evolves differently in various biological processes to maintain robustness in development and adaptational flexibility during angiosperm evolution.
基金supported by the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(Grant No.682167)European Commission Horizon 2020(grant no.FETOPEN-737071)Swiss National Foundation for Science(Grant No.152958,SNF31003A_146680,P2ELP2_162116 and P300P2_171219).
文摘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.
基金supported by the National Natural Science Foundation of China(No.21572101)the Natural Science Foundation of Jiangsu Province(No.BK20180055)+1 种基金Qinghai Provincial Key Laboratory of Tibetan Medicine Research(No.2017-ZJ-Y11)funding by the Fonds der Chemischen Industrie(Liebig-Fellowship)。
文摘Peptide amphiphiles with well-organized secondary structure are an important family of molecules that are known to assemble into a variety of nanostructures.In this work,we present three guanidiniocarbonylpyrrole(GCP)containing peptide amphiphiles,which show versatile morphology and secondary structure changes as a result of different chain lengths and in different concentration regimes.The random coil conformation,α-helix,andβ-sheet are obtained for peptide 1,peptide 2,and peptide 3,respectively under neutral aqueous conditions.Furthermore,all peptide amphiphiles can aggregate to form nanoparticles at low concentrations.However,at high concentrations,peptide 1 selfassembles into left-ha nded twisted helical fibers,while longer bamboo-like mo rphology can be obse rved exclusively for peptide 2.For peptide 3,freshly prepared samples show uniform spherical morphology,whereas an obvious morphological transition from original nanoparticles to disordered fibers was realized after incubating for one week.These fascinating morphology changes were determined by the combination of circular dichroism,dynamic light scattering,transmission electron microscopy,atomic force microscopy,and theoretical calculations.
文摘Calcium ions play an important role in the oxygen_evolving process of photosystem Ⅱ as demonstrated in many experiments. The changes of the secondary structures of PS Ⅱ induced by the depletion of Ca 2+ were reported. The results indicated that the removal of Ca 2+ led to the transition of α helix to turns and sheet structures. While Ca 2+ was re_added to the media, only the structures changed to turns could be recovered. The protein conformational changes of PS Ⅱ during the donor side photoinhibition induced by the depletion of Ca 2+ were also studied. This showed that the protein conformational changes differed between the control and Ca 2+ _depleted samples in a short period of illumination (within 10 min). However, the changes became similar when the illumination time was increased.