Domain organization and function of Salmonella FliK, a flagellar hook-length control protein

Salmonella hook-length control protein FliK, which consists of 405 amino acid residues, switches substrate specificity of the type III flagellar protein export apparatus from rod/ hook-type to filament-type by causing a conformational change in the cytoplasmic domain of FlhB (FlhB(C)) upon completion of the hook assembly. An N-terminal region of FliK contains an export signal, and a highly conserved C-terminal region consisting of amino acid residues 265-405 (FliK((265-405))) is directly involved in the switching of FlhB(C). Here, we have investigated the structural properties of FliK. Gel filtration chromatography, multi-angle light scattering and analytical ultracentrifugation showed that FliK is monomeric in solution and has an elongated shape. Limited proteolysis showed that FliK consists of two domains, the N-terminal (FliK(N)) and C-terminal domains (FliK(C)), and that the first 203 and the last 35 amino acid residues are partially unfolded and subjected to proteolysis. Both FliK(N) and FliK(C) are more globular than full-length FliK, suggesting that these domains are connected in tandem. Overproduced His-FliK((199-405)) failed to switch export specificity of the export apparatus. Affinity blotting revealed that FlhB(C) binds to FliK and FliK((1-147)), but not to FliK((265-405)). Based on these results, we propose that FliK(N) within the central channel of the hook-basal body during the export of FliK is the sensor and transmitter of hook completion information and that the binding interaction of FliK(C) to FlhB(C) is structurally regulated by FliK(N) so as to occur only when the hook has reached a preset length. The conformational flexibility of FliK(C) may play a role in interfering with switching at an inappropriate point of flagellar assembly.     

Novel hybrid method for the evaluation of parameters contributing in determination of protein structural classes

Due to the increasing gap between structure-determined and sequenced proteins, prediction of protein structural classes has been an important problem. It is very important to use efficient sequential parameters for developing class predictors because of the close sequence-structure relationship. The multinomial logistic regression model was used for the first time to evaluate the contribution of sequence parameters in determining the protein structural class. An in-house program generated parameters including single amino acid and all dipeptide composition frequencies. Then, the most effective parameters were selected by a multinomial logistic regression. Selected variables in the multinomial logistic model were Valine among single amino acid composition frequencies and Ala-Gly, Cys-Arg, Asp-Cys, Glu-Tyr, Gly-Glu, His-Tyr, Lys-Lys, Leu-Asp, Leu-Arg, Pro-Cys, Gln-Met, Gln-Thr, Ser-Trp, Val-Asn and Trp-Asn among dipeptide composition frequencies. Also a neural network model was constructed and fed by the parameters selected by multinomial logistic regression to build a hybrid predictor. In this study, self-consistency and jackknife tests on a database constructed by Zhou [1998. An intriguing controversy over protein structural class prediction. J. Protein Chem. 17(8), 729-738] containing 498 proteins are used to verify the performance of this hybrid method, and are compared with some of prior works. The results showed that our two-stage hybrid model approach is very promising and may play a complementary role to the existing powerful approaches.     

Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network

It is a critical challenge to develop automated methods for fast and accurately determining the structures of proteins because of the increasingly widening gap between the number of sequence-known proteins and that of structure-known proteins in the post-genomic age. The knowledge of protein structural class can provide useful information towards the determination of protein structure. Thus, it is highly desirable to develop computational methods for identifying the structural classes of newly found proteins based on their primary sequence. In this study, according to the concept of Chou's pseudo amino acid composition (PseAA), eight PseAA vectors are used to represent protein samples. Each of the PseAA vectors is a 40-D (dimensional) vector, which is constructed by the conventional amino acid composition (AA) and a series of sequence-order correlation factors as original introduced by Chou. The difference among the eight PseAA representations is that different physicochemical properties are used to incorporate the sequence-order effects for the protein samples. Based on such a framework, a dual-layer fuzzy support vector machine (FSVM) network is proposed to predict protein structural classes. In the first layer of the FSVM network, eight FSVM classifiers trained by different PseAA vectors are established. The 2nd layer FSVM classifier is applied to reclassify the outputs of the first layer. The results thus obtained are quite promising, indicating that the new method may become a useful tool for predicting not only the structural classification of proteins but also their other attributes.     

Cross-validation of protein structural class prediction using statistical clustering and neural networks.

We present an approach to predicting protein structural class that uses amino acid composition and hydrophobic pattern frequency information as input to two types of neural networks: (1) a three-layer back-propagation network and (2) a learning vector quantization network. The results of these methods are compared to those obtained from a modified Euclidean statistical clustering algorithm. The protein sequence data used to drive these algorithms consist of the normalized frequency of up to 20 amino acid types and six hydrophobic amino acid patterns. From these frequency values the structural class predictions for each protein (all-alpha, all-beta, or alpha-beta classes) are derived. Examples consisting of 64 previously classified proteins were randomly divided into multiple training (56 proteins) and test (8 proteins) sets. The best performing algorithm on the test sets was the learning vector quantization network using 17 inputs, obtaining a prediction accuracy of 80.2%. The Matthews correlation coefficients are statistically significant for all algorithms and all structural classes. The differences between algorithms are in general not statistically significant. These results show that information exists in protein primary sequences that is easily obtainable and useful for the prediction of protein structural class by neural networks as well as by standard statistical clustering algorithms.     

Study of the coordination of amino acids with metals using [trans-en2Os(eta2-H2)L]2+ as a 1H NMR probe

This is a study of the interaction of 23 kinds of amino acids, peptides and their analogues with Os((II)) at different pD values. Experiments show that in acidic conditions, the carboxyl group in amino acids can coordinate with Os((II)), and there exists H-D coupling of the dihydrogen of the probe with D2O in strongly acidic conditions, N does not coordinate with Os((II)); In alkaline conditions, the carboxyl group can coordinate with Os, and the coordinating species have trans and cis isomers, and the trans isomer can convert to cis with time; N of -NH2- in alpha-amino group can coordinate with Os((II)) while that in gamma-amino-n-butyric acid cannot do that. Since the target of some anti tumor agents are nucleic acids and proteins, we demonstrate a competitive mode to study how the anti tumor complex Me2SnCl2 binds to amino acid Ala, and the minimum binding amount and formation constant of the metal anti tumor metal complexes binding with amino acid are also obtained.     

Structural properties of carnation mottle virus p7 movement protein and its RNA-binding domain

Plant viral movement proteins (MPs) participate actively in the intra- and intercellular movement of RNA plant viruses to such an extent that MP dysfunction impairs viral infection. However, the molecular mechanism(s) of their interaction with cognate nucleic acids are not well understood, partly due to the lack of structural information. In this work, a protein dissection approach was used to gain information on the structural and RNA-binding properties of this class of proteins, as exemplified by the 61-amino acid residue p7 MP from carnation mottle virus (CarMV). Circular dichroism spectroscopy showed that CarMV p7 is an alpha/beta RNA-binding soluble protein. Using synthetic peptides derived from the p7 sequence, we have identified three distinct putative domains within the protein. EMSA showed that the central region, from residue 17 to 35 (represented by peptide p7(17-35)), is responsible for the RNA binding properties of CarMV p7. This binding peptide populates a nascent alpha-helix in water solution that is further stabilized in the presence of either secondary structure inducers, such as trifluoroethanol and monomeric SDS, or RNA (which also changes its conformation upon binding to the peptide). Thus, the RNA recognition appears to occur via an "adaptive binding" mechanism. Interestingly, the amino acid sequence and structural properties of the RNA-binding domain of p7 seem to be conserved among carmoviruses and some other RNA-binding proteins and peptides. The low conserved N terminus of p7 (peptide p7(1-16)) is unstructured in solution. In contrast, the highly conserved C terminus motif (peptide p7(40-61)) adopts a beta-sheet conformation in aqueous solution. Alanine scanning mutagenesis of the RNA-binding motif showed how selected positive charged amino acids are more relevant than others in the RNA binding process and how hydrophobic amino acid side chains would participate in the stabilization of the protein-RNA complex.     

Peptides of the liver stage antigen-1 (LSA-1) of Plasmodium falciparum bind to human hepatocytes

Synthetic peptides from the liver stage antigen-1 (LSA-1) antigen sequence were used in HepG2 cell and erythrocyte binding assays to identify regions that could be involved in parasite invasion. LSA-1 protein peptides 20630 ((21)INGKIIKNSEKDEIIKSNLRY(40)), 20637 ((157)KEKLQGQQSDSEQERRAY(173)), 20638 ((174)KEKLQEQQSDLEQERLAY(190)) and 20639 (191KEKLQEQQSDLEQERRAY(207)) had high binding activity in HepG2 assays. Were located in immunogenic regions; peptide cell binding was saturable. Peptide 20630 bound specifically to 48kDa HepG2 membrane surface protein. LSA-1 peptides 20630 ((21)INGKIIKNSEKDEIIKSNLRY(40)) and 20633 ((81)DKELTMSNVKNVSQTNFKSLY(100)) showed specific erythrocyte binding activity and inhibited merozoite invasion of erythrocytes in vitro. A monkey serum prepared against LSA-1 20630 peptide analog (CGINGKNIKNAEKPMIIKSNLRGC) inhibited merozoite invasion in vitro. The data suggest LSA-1 "High Activity Binding Peptides" could play a possible role in hepatic cell invasion as well as merozoite invasion of erythrocytes.     

Selenium-dependent glutathione peroxidase gene expression during gonad development and its response to LPS and H(2)O(2) challenge in Scylla paramamosain

A selenium-dependent glutathione peroxidase cDNA was obtained from green mud crab Scylla paramamosain (SpGPx) by homology PCR technique and rapid amplification of cDNA ends (RACE) methods. The 1135?bp full-length cDNA contains a 9?bp 5'-untranslated region (UTR), an open reading frame (ORF) of 564 bp encoded a deduced protein of 187 amino acids (aa), and a 562?bp 3'-UTR with a 100 bp conserved eukaryotic selenocysteine insertion sequence (SECIS). It involves a putative selenocysteine (Sec(40), or U(40)) residue which is encoded by an opal codon, (127)TGA(129), and forms an active site with residues Q(74) and W(142). Sequence characterization revealed that SpGPx contain a characteristic GPx signature motif 2 ((64)LAFPCNQF(71)), an active site motif ((152)WNFEKF(157)), a potential N-glycosylation site ((76)NTT(78)), and two residues (R(90) and R(168)) which contribute to the electrostatic architecture by directing the glutathione donor substrate. Multiple sequence alignment and phylogenetic analysis showed that SpGPx share a high level of identities and closer relationship with other selected invertebrate GPxs and vertebrate GPx1 and GPx2. Molecular modelling analysis results also supported these observations. Real time quantitative PCR analysis revealed that SpGPx was constitutively expressed in 10 selected tissues, and its expression level in gill and testis was higher than that in the other tissues (p?相似文献   

非同源蛋白质空间折叠的动力学参数研究

从蛋白质折叠成自由能最小的稳定结构类型为研究的出发点,为揭示蛋白质空间折叠的动力学本质,对非同源蛋白质数据库,以蛋白质序列的氮基酸频率和自协方差函数为特征矢量,求出表征特征矢量中各分量耦合作用与协同作用的协方差矩阵所对应的特征值．与Chou的方法相比,更全面地反映了蛋白质折叠密码的简并性、全局性和多意性,为定量表征折叠成不同结构类的蛋白质,提供了一种动力学参数分析方法．     

How good is prediction of protein structural class by the component-coupled method?

Proteins of known structures are usually classified into four structural classes: all-alpha, all-beta, alpha+beta, and alpha/beta type of proteins. A number of methods to predicting the structural class of a protein based on its amino acid composition have been developed during the past few years. Recently, a component-coupled method was developed for predicting protein structural class according to amino acid composition. This method is based on the least Mahalanobis distance principle, and yields much better predicted results in comparison with the previous methods. However, the success rates reported for structural class prediction by different investigators are contradictory. The highest reported accuracies by this method are near 100%, but the lowest one is only about 60%. The goal of this study is to resolve this paradox and to determine the possible upper limit of prediction rate for structural classes. In this paper, based on the normality assumption and the Bayes decision rule for minimum error, a new method is proposed for predicting the structural class of a protein according to its amino acid composition. The detailed theoretical analysis indicates that if the four protein folding classes are governed by the normal distributions, the present method will yield the optimum predictive result in a statistical sense. A non-redundant data set of 1,189 protein domains is used to evaluate the performance of the new method. Our results demonstrate that 60% correctness is the upper limit for a 4-type class prediction from amino acid composition alone for an unknown query protein. The apparent relatively high accuracy level (more than 90%) attained in the previous studies was due to the preselection of test sets, which may not be adequately representative of all unrelated proteins.     

Plasmodium falciparum: red blood cell binding studies using peptides derived from rhoptry-associated protein 2 (RAP2)

Plasmodium falciparum rhoptry-associated proteins 1 (RAP1) and 2 (RAP2) are antigens presenting themselves as candidates for a subunit malaria vaccine. RAP2 protein, non-overlapping, consecutive peptides were synthesised and tested in red blood cell (RBC) binding assays to identify their receptor-ligand interaction in recognising RAP2 regions involved in the in vitro merozoite invasion process. Four high activity binding peptides (HABPs) were identified in the resulting 20 peptides. Peptides 26220 ((61)NHFSSADELIKYLEKTNINT(80)), 26225 ((161)IKKNPFLRVLNKASTTTHAT(180)) and 26229 ((241)RSVNNVISKNKTLGLRKRSS(260)) were located in the amino terminal and central part of the protein and HABP 26235 ((361)FLAEDFVELFDVTMDCYSRQ(380)) was located at the carboxy terminal. All these HABPs showed saturable binding and presented dissociation constants between 500 and 950 nM; the number of binding sites per RBC ranged from 48,000 to 160,000. High binding peptides' critical amino acids involved in RBC binding were determined by competition binding assays; their amino acids appear in bold in the sequences shown above. SDS-PAGE results showed that peptides 26220, 26225 and 26229 had at least two different sets of 62 and 42 kDa HABP receptors on RBCs and that peptide 26235 had at least two different sets of 77 and 62 kDa. HABPs inhibited in vitro merozoite invasion by between 54% and 94% at 200 microM, suggesting that these RAP2 peptides are involved in the in vitro P. falciparum invasion process.     

Structural analysis based on state-space modeling.

A new method has been developed to compute the probability that each amino acid in a protein sequence is in a particular secondary structural element. Each of these probabilities is computed using the entire sequence and a set of predefined structural class models. This set of structural classes is patterned after Jane Richardson''s taxonomy for the domains of globular proteins. For each structural class considered, a mathematical model is constructed to represent constraints on the pattern of secondary structural elements characteristic of that class. These are stochastic models having discrete state spaces (referred to as hidden Markov models by researchers in signal processing and automatic speech recognition). Each model is a mathematical generator of amino acid sequences; the sequence under consideration is modeled as having been generated by one model in the set of candidates. The probability that each model generated the given sequence is computed using a filtering algorithm. The protein is then classified as belonging to the structural class having the most probable model. The secondary structure of the sequence is then analyzed using a "smoothing" algorithm that is optimal for that structural class model. For each residue position in the sequence, the smoother computes the probability that the residue is contained within each of the defined secondary structural elements of the model. This method has two important advantages: (1) the probability of each residue being in each of the modeled secondary structural elements is computed using the totality of the amino acid sequence, and (2) these probabilities are consistent with prior knowledge of realizable domain folds as encoded in each model. As an example of the method''s utility, we present its application to flavodoxin, a prototypical alpha/beta protein having a central beta-sheet, and to thioredoxin, which belongs to a similar structural class but shares no significant sequence similarity.     

Differences in biochemical properties and in biological function between human SP-A1 and SP-A2 variants, and the impact of ozone-induced oxidation

The human surfactant protein A (SP-A) locus consists of two functional genes, SP-A1 and SP-A2, with several alleles characterized for each gene. Functional variations between SP-A1 and SP-A2 variants either before or after ozone exposure have been observed. To understand the basis of these differences, we studied SP-A1 and SP-A2 variants by comparing coding sequences, oligomerization patterns under various conditions, composition of oligomers with regard to amino terminal sequence isoforms, biological activity (regulation of phosphatidylcholine (PC) secretion by alveolar type II cells), and the impact of ozone-induced oxidation. We found that (i) the SP-A1 (6A(4)) allele is the most divergent from all SP-A2 alleles, particularly from the SP-A2 (1A(1)). (ii) Differences exist in oligomerization among SP-A1, SP-A2, and coexpressed SP-A1/SP-A2, with higher order multimers (i.e., consisting of more subunits) observed for SP-A1 than for SP-A2 variants. Differences among SP-A1 or SP-A2 gene products are minimal. (iii) Amino acid variants in the amino terminal sequences are observed after signal peptide removal, including variants with an extra cysteine. (iv) Oxidation is observed after ozone exposure, involving several SP-A residues that include cysteine, methionine, and tryptophan. (v) The SP-A2 variant (1A(0)) and the coexpressed protein 1A(0)/6A(2) inhibit ATP-stimulated PC secretion from alveolar type II cells to a greater extent than SP-A1 (6A(2)), a biologic activity that was susceptible to ozone treatment.     

Inhibition of bacterial IF2 binding to fMet-tRNA((fMet)) by aminoglycosides

Screening for inhibitors of bacterial protein synthesis Initiation Factor 2 (IF2) binding to N-formyl-Methionyl-transfer RNA (fMet-tRNA((fMet))) identified a series of aminoglycosides, that included amikacin and kanamycin A1, as inhibitors of this interaction. Subsequent testing revealed that aminoglycosides displayed a wide range of inhibitory activity. However, the failure of these compounds to completely inhibit binding of IF2 to fMet-tRNA((fMet)), the known ability of aminoglycosides to bind RNA, and the ability of the aminoglycosides to displace PicoGreen bound to fMet-tRNA((fMet)) suggest these compounds act by binding fMet-tRNA((fMet)). This hypothesis is further supported by isothermal denaturation experiments that failed to show any interaction between the IF2 protein and the aminoglycosides.     

Nearest-neighbor classifier as a tool for classification of protein families

Knowledge about protein function is essential in understanding the biological processes. A specific class or family of protein shares common structural and chemical properties amongst its member sequences. The set of properties that display its unique characteristics for clearly classifying a protein sequence into its corresponding protein family needs to be studied. Our study of these important properties conducted on four major classes of proteins namely Globins, Homeoboxes, Heat Shock proteins (HSP) and Kinase have shown that frequency of twenty naturally occurring amino acids, hydrophobic content of protein, molecular weight of protein, isoelectric point of protein, secondary structure composition of amino acid residues as helices, coils and sheets and the composition of helices, coils and sheets in the secondary structure topology plays a significant role in correctly classifying the protein into its corresponding class or family as indicated by the overall efficiency of Nearest Neighbor Classifier as 84.92%.     

An Optimal Structure-Discriminative Amino Acid Index for Protein Fold Recognition

Identifying the fold class of a protein sequence of unknown structure is a fundamental problem in modern biology. We apply a supervised learning algorithm to the classification of protein sequences with low sequence identity from a library of 174 structural classes created with the Combinatorial Extension structural alignment methodology. A class of rules is considered that assigns test sequences to structural classes based on the closest match of an amino acid index profile of the test sequence to a profile centroid for each class. A mathematical optimization procedure is applied to determine an amino acid index of maximal structural discriminatory power by maximizing the ratio of between-class to within-class profile variation. The optimal index is computed as the solution to a generalized eigenvalue problem, and its performance for fold classification is compared to that of other published indices. The optimal index has significantly more structural discriminatory power than all currently known indices, including average surrounding hydrophobicity, which it most closely resembles. It demonstrates >70% classification accuracy over all folds and nearly 100% accuracy on several folds with distinctive conserved structural features. Finally, there is a compelling universality to the optimal index in that it does not appear to depend strongly on the specific structural classes used in its computation.     

Structure-activity studies of alpha-melanotropin fragments on cAMP production in striatal slices

In this work, we characterized the active site in the alpha-melanotropin hormone (alpha-MSH) sequence responsible for the enhancement of cAMP production in incubated striatal slices by using different alpha-MSH fragments. We also analyzed the effects of the co-incubation of the SCH23390, a dopaminergic D(1) antagonist, with the MSH fragments, to study the involvement of the D(1) receptor on this induction. A rise was observed in the levels of cAMP after addition of the 6 microM fragments MSH((1-10)), and 0.6 and 6 microM MSH((5-13)); however, the values were lower than those induced by 6 microM alpha-MSH. On the contrary, the addition of MSH((9-13)), MSH((7-11)), or MSH((6-9)) did not affect the cAMP content. The presence of 10 microM SCH23390 blocked the effect of the fragments on cAMP production. We conclude that the biologic activity of alpha-MSH, as observed through the levels of cAMP, declines when the length of its polypeptide chain is shortened, and that the presence of glutamic acid in the molecule, as well as the core sequence, are of importance for fragments' activity.     

Synthetic peptides from Plasmodium falciparum apical membrane antigen 1 (AMA-1) specifically interacting with human hepatocytes

Plasmodium falciparum apical membrane antigen 1 (AMA-1) is expressed during both the sporozoite and merozoite stage of the parasite's life cycle. The role placed by AMA-1 during sporozoite invasion of hepatocytes has not been made sufficiently clear to date. Identifying the sequences involved in binding to hepatocytes is an important step towards understanding the structural basis for sporozoite-hepatocyte interaction. Binding assays between P. falciparum AMA-1 peptides and HepG2 cell were performed in this study to identify possible AMA-1 functional regions. Four AMA-1 high activity binding peptides (HABPs) bound specifically to hepatocytes: 4310 ((74)QHAYPIDHEGAEPAPQEQNL(93)), 4316 ((194)TLDEMRHFYKDNKYVKNLDE(213)), 4321 ((294)VVDNWEKVCPRKNLQNAKFGY(313)) and 4332 ((514)AEVTSNNEVVVKEEYKDEYA(533)). Their binding to these cells became saturable and resistant to treatment with neuraminidase. Most of these peptides were located in AMA-1 domains I and III, these being target regions for protective antibody responses. These peptides interacted with 36 and 58 kDa proteins on the erythrocyte surface. Some of the peptides were found in exposed regions of the AMA-1 protein, thereby facilitating their interaction with host cells. It is thus probable that AMA-1 regions defined by the four peptides mentioned above are involved in sporozoite-hepatocyte interaction.     

A hybrid genetic-neural model for predicting protein structural classes

A genetic algorithm (GA) for feature selection in conjunction with neural network was applied to predict protein structural classes based on single amino acid and all dipeptide composition frequencies. These sequence parameters were encoded as input features for a GA in feature selection procedure and classified with a three-layered neural network to predict protein structural classes. The system was established through optimization of the classification performance of neural network which was used as evaluation function. In this study, self-consistency and jackknife tests on a database containing 498 proteins were used to verify the performance of this hybrid method, and were compared with some of prior works. The adoption of a hybrid model, which encompasses genetic and neural technologies, demonstrated to be a promising approach in the task of protein structural class prediction.