Showing papers in "Protein and Peptide Letters in 2010"
TL;DR: This paper presents a bioinformatics classifier to predict cyclins based on Chou's pseudo amino acid composition, and demonstrates that the method can provide useful information for predicting cyclins.
Abstract: There are different types of cyclins, which are active during the cell cycle and enable cyclin-dependent kinases to phosphorylate different substrates. Since there is not much similarity between amino acid sequences of cyclins, predicting these proteins is an important job. This paper presents a bioinformatics classifier to predict cyclins based on Chou's pseudo amino acid composition. Analysis of the results by StAR, which is a program for the analysis of ROC curves, showed that accuracy of the approach was 83.53% (AUC=89.44%). The present work demonstrates that the method can provide useful information for predicting cyclins.
202 citations
TL;DR: Given the complex nature of PPIs, the performance of the proposed sequence-based method is promising, and it can be a helpful supplement for PPIs prediction.
Abstract: With a huge amount of protein sequence data, the computational method for protein – protein interaction (PPI) prediction using only the protein sequences information have drawn increasing interest. In this article, we propose a sequence- based method based on a novel representation of local protein sequence descriptors. Local descriptors account for the interactions between residues in both continuous and discontinuous regions of a protein sequence, so this method enables us to extract more PPI information from the sequence. A series of elaborate experiments are performed to optimize the prediction model by varying the parameter k and the distance measuring function of the k-nearest neighbors learning system and the ways of coding a protein pair. When performed on the PPI data of Saccharomyces cerevisiae, the method achieved 86.15% prediction accuracy with 81.03% sensitivity at the precision of 90.24%. An independent data set of 986 Escherichia coli PPIs was used to evaluate this prediction model and the prediction accuracy is 73.02%. Given the complex nature of PPIs, the performance of our method is promising, and it can be a helpful supplement for PPIs prediction.
143 citations
TL;DR: Experimental results show that the proposed sequence-based multiple classifier system, i.e., rotation forest, outperforms those previously published in literature, which demonstrates the effectiveness of the proposed method.
Abstract: We propose a sequence-based multiple classifier system, i.e., rotation forest, to infer protein-protein interactions (PPIs). Moreover, Moran autocorrelation descriptor is used to code an interaction protein pair. Experimental results on Saccharomyces cerevisiae and Helicobacter pylori datasets show that our approach outperforms those previously published in literature, which demonstrates the effectiveness of the proposed method.
137 citations
TL;DR: Adaboost classifier is adopted as the prediction engine and approximate entropy and hydrophobicity patterns are used to predict G-protein-coupled receptors to show encouraging results.
Abstract: We use approximate entropy and hydrophobicity patterns to predict G-protein-coupled receptors. Adaboost classifier is adopted as the prediction engine. A low homology dataset is used to validate the proposed method. Compared with the results reported, the successful rate is encouraging. The source code is written by Matlab.
134 citations
TL;DR: Structural peculiarities of viral proteins are focused on and the role of intrinsic disorder in their functions is focused on.
Abstract: Many proteins or their regions are disordered in their native, biologically active states. Bioinformatics has revealed that these proteins/regions are highly abundant in different proteomes and carry out mostly regulatory functions related to molecular recognition, signal transduction, protein-protein, and protein-nucleic acid interactions. Viruses, these "organisms at the edge of life", have uniquely evolved to be highly adaptive for fast change in their biological and physical environment. To sustain these fast environmental changes, viral proteins elaborated multiple measures, from relatively low van der Waals contact densities, to inclusion of a large fraction of residues that are not arranged in well-defined secondary structural elements, to heavy use of short disordered regions, and to high resistance to mutations. On the other hand, viral proteins are rich in intrinsic disorder. Some of the intrinsically disordered regions are heavily used in the functioning of viral proteins. Others likely have evolved to help viruses accommodate to their hostile habitats. Still others evolved to help viruses in managing their economic usage of genetic material via alternative splicing, overlapping genes, and anti-sense transcription. In this review, we focus on structural peculiarities of viral proteins and on the role of intrinsic disorder in their functions.
126 citations
TL;DR: This paper presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually cataloging and cataloging the components of a molecule.
Abstract: Bin Xue,† David Blocquel, Johnny Habchi, Alexey V. Uversky, Lukasz Kurgan, Vladimir N. Uversky,*,‡,∇ and Sonia Longhi* †Department of Cell Biology, Microbiology and Molecular Biology, College of Fine Arts and Sciences, and ‡Department of Molecular Medicine and USF Health Byrd Alzheimer’s Research Institute, Morsani College of Medicine, University of South Florida, Tampa, Florida 33620, United States Architecture et Fonction des Macromolecules Biologiques (AFMB) UMR 7257, Aix-Marseille Universite, 13288 Marseille, France AFMB UMR 7257, CNRS, 13288 Marseille, France Center for Data Analytics and Biomedical Informatics, Department of Computer and Information Sciences, College of Science and Technology, Temple University, Philadelphia, Pennsylvania 19122, United States Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2R3 Institute for Biological Instrumentation, Russian Academy of Sciences, 142290 Pushchino, Russia
114 citations
TL;DR: A new method that coupled discrete wavelet transform with support vector machine based on the amino acid hydrophobicity to predict enzyme family is developed, indicating the current method could be an effective and promising highthroughput method in the enzyme research.
Abstract: The early determination of family for a newly found enzyme molecule becomes important because it is directly related to the detail information about which specific target it acts on, as well as to its catalytic process and biological function. Unfortunately, it is still a hard work to distinguish enzyme classes by experiments. With an enormous amount of protein sequences uncovered in the genome research, it is both challenging and indispensable to develop an automatic method for fast and reliably classifying the enzyme family. Using the concept of Chous pseudo amino acid composition, we developed a new method that coupled discrete wavelet transform with support vector machine based on the amino acid hydrophobicity to predict enzyme family. The overall success rate obtained by the 10-cross-validation for the identification of the six enzyme families was 91.9%, indicating the current method could be an effective and promising highthroughput method in the enzyme research.
104 citations
TL;DR: A novel hybrid Genetic Algorithm (GA)/Support Vector Machine (SVM) approach to predict apoptotic protein sequences using 119 sequence derived properties like frequency of amino acid groups, secondary structure, and physicochemical properties is reported.
Abstract: Apoptosis is an essential process for controlling tissue homeostasis by regulating a physiological balance between cell proliferation and cell death. The subcellular locations of proteins performing the cell death are determined by mostly independent cellular mechanisms. The regular bioinformatics tools to predict the subcellular locations of such apoptotic proteins do often fail. This work proposes a model for the sorting of proteins that are involved in apoptosis, allowing us to both the prediction of their subcellular locations as well as the molecular properties that contributed to it. We report a novel hybrid Genetic Algorithm (GA)/Support Vector Machine (SVM) approach to predict apoptotic protein sequences using 119 sequence derived properties like frequency of amino acid groups, secondary structure, and physicochemical properties. GA is used for selecting a near-optimal subset of informative features that is most relevant for the classification. Jackknife cross-validation is applied to test the predictive capability of the proposed method on 317 apoptosis proteins. Our method achieved 85.80% accuracy using all 119 features and 89.91% accuracy for 25 features selected by GA. Our models were examined by a test dataset of 98 apoptosis proteins and obtained an overall accuracy of 90.34%. The results show that the proposed approach is promising; it is able to select small subsets of features and still improves the classification accuracy. Our model can contribute to the understanding of programmed cell death and drug discovery. The software and dataset are available at http://www.inb.uni-luebeck.de/tools-demos/apoptosis/GASVM
71 citations
TL;DR: In this paper, a laccase has been purified from oyster mushroom (Pleurotus ostreatus) to homogeneity by DEAE Affi-gel blue gel, CM-Sephadex G-50 and CM-SDS-PAGE.
Abstract: There is no protective vaccine or effective drug against hepatitis C virus (HCV) Sustained virological response to INF/ribavirin treatment regimen has an efficiency of about 50% Many patients worldwide have used traditional medicines and herbal medicine in particular A laccase has been purified from oyster mushroom (Pleurotus ostreatus) to homogeneity by DEAE Affi-gel blue gel, CM-Sephadex G-50 and Sephadex G-100 The molecular weight of the laccase was about 58 kDa in SDS-PAGE The optimum pH and temperature of the laccase activity were pH 40 and 60 degrees C, respectively The activity of the enzyme increased steadily from 20 to 40 degrees C, then very slowly from 40 degrees to 60 degrees C, while the enzyme activity decreased to 9% at 90 degrees C The activity of the laccase changed gradually over the pH range 20-40 However, the enzyme activity was totally abrogated at the pH 8 and above Incubation of peripheral blood cells PBCs and hepatoma HepG2 cells with laccase which were then infected with HCV did not protect the cells from HCV attack and entry, while direct interaction between HCV and the laccase at the concentrations of 20 and 25 mg/ml led to a complete inhibition of virus entry after seven days of incubation Meantime, the laccase at the concentrations of 10 and 15 mg/ml did not display any blocking activity The potential activity of the laccase on intracellular HCV replication in infected HepG2 cells has been examined The laccase was capable of inhibiting HCV replication at the concentrations of 125 and 15 mg/ml after first dose of treatment for four days and at the concentrations of 075, 10, 125 and 15 mg/ml after the second dose of treatment for another four days
64 citations
TL;DR: Two support vector machine (SVM)-based methods for the imbalance problem, AdaBoost algorithm with RBFSVM and SVM with arithmetic mean (AM) offset (AM-SVM) in enzyme subfamily classification are compared and the improvement in the predictive performance suggests the AM-S VM might play a complementary role to the existing function annotation methods.
Abstract: Predicting enzyme subfamily class is an imbalance multi-class classification problem due to the fact that the number of proteins in each subfamily makes a great difference. In this paper, we focus on developing the computational methods specially designed for the imbalance multi-class classification problem to predict enzyme subfamily class. We compare two support vector machine (SVM)-based methods for the imbalance problem, AdaBoost algorithm with RBFSVM (SVM with RBF kernel) and SVM with arithmetic mean (AM) offset (AM-SVM) in enzyme subfamily classification. As input features for our predictive model, we use the conjoint triad feature (CTF). We validate two methods on an enzyme benchmark dataset, which contains six enzyme main families with a total of thirty-four subfamily classes, and those proteins have less than 40% sequence identity to any other in a same functional class. In predicting oxidoreductases subfamilies, AM-SVM obtains the over 0.92 Matthew's correlation coefficient (MCC) and over 93% accuracy, and in predicting lyases, isomerases and ligases subfamilies, it obtains over 0.73 MCC and over 82% accuracy. The improvement in the predictive performance suggests the AM-SVM might play a complementary role to the existing function annotation methods.
63 citations
TL;DR: The interaction between cyclophosphamide monohydrate with human serum albumin (HSA) and human serum transferrin (hTf) was studied with UV absorption, fluorescence and circular dichroism (CD) spectroscopies as well as molecular modeling to confirm the existence of static quenching for both proteins in the presence of cycloph phosphate monohydrate.
Abstract: The interaction between cyclophosphamide monohydrate with human serum albumin (HSA) and human serum transferrin (hTf) was studied with UV absorption, fluorescence and circular dichroism (CD) spectroscopies as well as molecular modeling. Based on the fluorescence quenching results, it was determined that HSA and hTf had two classes of apparent binding constants and binding sites at physiological conditions. The K(SV1), K(SV2), n(1) and n(2) values for HSA were found to be 8.6 x 10(8) Lmol(-1), 6.34 x 10(8) Lmol(-1), 0.7 and 0.8, respectively, and the corresponding results for hTf were 6.08 x 10(7) Lmol(-1), 4.65 x 10(7) Lmol(-1), 1.3 and 2.6, respectively. However, the binding affinity of cyclophosphamide monohydrate to HSA was more significant than to hTf. Circular dichroism results demonstrated that the binding of cyclophosphamide to HSA and hTf induced secondary changes in the structure and that the a-helix content became altered into b-sheet, turn and random coil forms. The participation of tyrosyl and tryptophan residues of proteins was also estimated in the drug-HSA and hTf complexes by synchronous fluorescence. The micro-environment of the HSA and hTf fluorophores was transferred to hydrophobic and hydrophilic conditions, respectively. The distance r between donor and acceptor was obtained by the Forster energy according to fluorescence resonance energy transfer (FRET) and found to be 1.84 nm and 1.73 nm for HSA and hTf, respectively. This confirmed the existence of static quenching for both proteins in the presence of cyclophosphamide monohydrate. Site marker competitive displacement experiments demonstrated that cyclophosphamide bound with high affinity to Site II, sub-domain IIIA of HSA, and for hTf, the C-lobe constituted the binding site. Furthermore, a study of molecular modeling showed that cyclophosphamide situated in domain II in HSA was bound through hydrogen bonding with Arg 257 and Ser 287, and that cyclophosphamide was situated in the C-lobe in hTf, presenting hydrogen bonding with Asp 625 and Arg 453. The modeling data thus confirmed the experimental results.
TL;DR: A novel sequence representation is proposed that incorporates the evolution information represented in the position-specific score matrices by the auto covariance transformation and the support vector machine classifier is adopted to predict subcellular location of apoptosis proteins.
Abstract: Knowledge of apoptosis proteins plays an important role in understanding the mechanism of programmed cell death. Thus, annotating the function of apoptosis proteins is of significant value. Since the function of apoptosis proteins correlates with their subcellular location, the information about their subcellular location can be very useful in understanding their role in the process of programmed cell death. In the present study, we propose a novel sequence representation that incorporates the evolution information represented in the position-specific score matrices by the auto covariance transformation. Then the support vector machine classifier is adopted to predict subcellular location of apoptosis proteins. To verify the performance of this method, jackknife cross-validation tests are performed on three widely used benchmark datasets and results show that our approach achieves relatively high prediction accuracies over some classical methods.
TL;DR: The results in this study indicated that flavonoids, as mixed-type inhibitors, quenched the intrinsic fluorescence of YAGH by a mixed fluorescence quenching mechanism, supply a basis for understanding the mechanisms of multiple biological functions of flavonoid molecules.
Abstract: Flavonoids, also called vitamin P, are widely distributed in plants fulfilling many functions. Yeast α-glucosidase (YAGH; EC 3.2.1.20), as extensively used target protein for screening bioactive compounds from medicine plants, was selected to explore the possible mechanisms of multiple biological function of flavonoids. The results in this study indicated that flavonoids, as mixed-type inhibitors, quenched the intrinsic fluorescence of YAGH by a mixed fluorescence quenching mechanism. The interaction information between flavonoids and YAGH was analyzed using a flexible docking method (AutoDock) and showed that 3', 4' dihydroxyl groups of B ring and 3-OH of C ring played a more important role in the inhibition activity than other hydroxyl groups, because the 3', 4' dihydroxyl groups of B ring directly interacted with the active-site residues of YAGH to inhibit enzyme activity and 3-OH of C ring seemed to be necessary to maintain the proper binding orientation of flavonoid molecules, thereby making the hydroxyl groups of B ring interact with active-site residues tightly in the hydrophobic pocket of YAGH. The results supply a basis for understanding the mechanisms of multiple biological functions of flavonoids.
TL;DR: The results indicated that Goutherman model can predict the predominant conformations of hemoglobin.
Abstract: Band assignment for oxy, deoxy and methemoglobin using orbital promotion is crucial to understanding inter-relation of electronic transitions. Spectral changes may be correlated with conformational alterations. Conformational changes of hemoglobin were interpreted using four-orbital model of Gouterman. Our results indicated that Goutherman model can predict the predominant conformations of hemoglobin.
TL;DR: Simulation studies have deepened the understanding of the interactions between AMPs and biological membranes, and will help to design new synthetic peptides with enhanced biomedical potential.
Abstract: Antimicrobial peptides (AMPs) are short, cationic, membrane-interacting proteins that exhibit broad-spectrum antimicrobial activity, and are hence of significant biomedical interest. They exert their activity by selectively binding to and lysing target cell membranes, but the precise molecular details of their mechanism are not known. This is further complicated by the fact that their structural characteristics are dependent upon the local lipid environment. As a result, molecular dynamics (MD) simulations have been applied to understand the conformation and mechanism of AMPs, as well as related viral and cell-penetrating peptides. In particular, atomically detailed MD simulation studies on the timescale of tens to hundreds of nanoseconds have successfully helped to: (i) model or refine the conformation of AMPs and their aggregates in the presence of membrane-mimicking solvent mixtures, detergent micelles, and lipid bilayers; (ii) follow the process of adsorption of individual AMPs to membrane surfaces; and (iii) observe the spontaneous assembly of multiple peptides and subsequent cooperative membrane lysis. More recently, coarse-grained (CG) models have been developed to extend the time and length scales accessible to simulations of membrane/peptide systems. CG simulations on the order of microseconds have provided insight into AMP lytic mechanisms, and how they depend upon such factors as peptide concentration, lipid composition, and bilayer curvature. These studies have been supplemented by combined atomistic/CG and integrated multiscale models. Together, simulations have deepened our understanding of the interactions between AMPs and biological membranes, and will help to design new synthetic peptides with enhanced biomedical potential.
TL;DR: Inhibition of AGE formation may be a novel therapeutic target for improving insulin resistance in diabetes with obesity by decreasing fasting insulin levels and improving insulin sensitivity in KK-Ay mice of 10 weeks old, although it did not affect fasting blood glucose levels.
Abstract: There is a growing body of evidence that the formation and accumulation of advanced glycation end products (AGE) have been known to progress under diabetic conditions, thereby being involved in diabetic vascular complications. Further, we, along with others, have recently found AGE could disturb insulin actions in cultured adipocytes and skeletal muscles. However, the pathological role of AGE in insulin resistance in vivo is not fully understood. Therefore, in this study, we examined whether pyridoxamine, an inhibitor of AGE formation could ameliorate insulin resistance in KK-Ay mice, a model animal of obese, type 2 diabetes. Fasting blood glucose, serum levels of insulin and AGE in KK-Ay mice were elevated as the mice got older (from 5 weeks old to 15 weeks old). Serum levels of AGE were positively correlated with insulin (R2=0.3956, P=0.002) in KK-Ay mice. Administration of pyridoxamine dose-dependently decreased fasting insulin levels and improved insulin sensitivity in KK-Ay mice of 10 weeks old, although it did not affect fasting blood glucose levels. Our present study suggests the involvement of AGE in insulin resistance in KK-Ay mice. Inhibition of AGE formation may be a novel therapeutic target for improving insulin resistance in diabetes with obesity.
TL;DR: Data is presented on the kinetics of insertion of melittin, a peptide from bee venom, into lipid membranes of different composition and how this affects the action of PLA₂ and how the composition of the membrane is altered.
Abstract: Here we present data on the kinetics of insertion of melittin, a peptide from bee venom, into lipid membranes of different composition. Another component of bee venom is the enzyme phospholipase A2 (PLA₂). We have examined the interaction of melittin and PLA₂ with liposomes both separately and combined and demonstrate that they work synergistically to disrupt the membranes. A dramatic difference in the action of melittin and PLA₂ is observed when the composition of the membrane is altered. Temperature also has a large effect on the kinetics of insertion and membrane disruption. We use a combination of techniques to measure liposome size (dynamic light scattering), peptide secondary structure (circular dichroism spectroscopy), peptide orientation relative to the membrane (linear dichroism spectroscopy) and enzymatic digestion of the lipids (mass spectrometry).
TL;DR: It is shown that the NS5A-D2 domain of the hepatitis C virus forms an anchoring point for the host cell cyclophilin prolyl cis/trans isomerase, providing a molecular basis for the use of cyclophILin inhibitors in an antiviral strategy.
Abstract: We present here our current understanding of the NS5A-D2 domain of the hepatitis C virus. Whereas this protein domain is globally unstructured as assessed by macroscopic techniques such as size exclusion chromatography, circular dichroism and homonuclear NMR spectroscopy, high resolution triple resonance spectroscopy allows the identification of a small region of residual structure. This region corresponds moreover to the most conserved sequence over the different genotypes of the virus, underscoring its functional importance. We show that it forms an anchoring point for the host cell cyclophilin prolyl cis/trans isomerase, providing a molecular basis for the use of cyclophilin inhibitors in an antiviral strategy.
TL;DR: TNF-alpha and ghrelin seem to have opposite effects regarding the hypothalamic regulation of eating behavior, modulation of the immune response and the state of mental health.
Abstract: Tumor necrosis factor-alpha (TNF-alpha) is a glycoprotein hormone with important functions in inflammation and apoptosis. It plays a significant role as a pro-inflammatory cytokine in the defense against viral, bacterial and parasitic infections and autoimmune disorders. Furthermore, it influences energy homeostasis and has an anorexigenic effect on the hypothalamus. TNF-alpha has also been shown to be involved in the pathogenesis of psychiatric disorders such as depression or narcolepsy. Ghrelin is a peptide hormone which primarily regulates eating behavior through modulation of expression of orexigenic peptides in the hypothalamus. Ghrelin administration increases food intake and body weight, while weight loss in turn increases ghrelin levels. Secondly, it posesses anti-inflammatory properties. It also seems to have an impact on mental health as it is has been suggested to have antidepressant and anxiolytic properties. Therefore, TNF-alpha and ghrelin seem to have opposite effects regarding the hypothalamic regulation of eating behavior, modulation of the immune response and the state of mental health.
TL;DR: It is proposed that the main functional advantage of the abundance of disorder within viruses would reside in pleiotropy and genetic compaction, where a single gene would encode a single protein product able to establish multiple interactions via its disordered regions, and hence to exert multiple concomitant biological effects.
Abstract: In this review, we summarize the main experimental data showing the abundance of structural disorder within the measles virus (MeV) nucleoprotein (N) and phosphoprotein (P), and focus on the molecular mechanisms governing the disorder-to-order transition of the intrinsically disordered C-terminal domain of MeV N (N(TAIL)) upon binding to the C-terminal X domain of P (XD). The functional implications of structural disorder are discussed in light of the ability of disordered regions to establish a complex molecular partnership, thereby leading to a variety of biological effects, including tethering of the polymerase complex onto the nucleocapsid template, stimulation of viral transcription and replication, and virus assembly. We also discuss the ability of N(TAIL) to establish interactions with additional cellular co-factors, including the major inducible heat shock protein, which can modulate the strength of the N(TAIL)-XD interaction. Taking into account the promiscuity that typifies disordered regions, we propose that the main functional advantage of the abundance of disorder within viruses would reside in pleiotropy and genetic compaction, where a single gene would encode a single (regulatory) protein product able to establish multiple interactions via its disordered regions, and hence to exert multiple concomitant biological effects.
TL;DR: This work presents a SVMCRYS method which use support vector machine to classify protein sequence into 'amenable to crystallization' and 'resistant to crystallography' and suggests that SVM CRYS can be used to predict proteins which are amenable to crystalization and proteins which is difficult for crystallization.
Abstract: X-ray crystallography is the most widely used method for protein 3-dimensional structure determination. Selection of target protein that can yield high quality crystal for X-ray crystallography is a challenging task. Prediction of protein crystallization propensity from sequence information is useful for the selection of target protein for crystallization. Recently, support vector machines have been widely used to solve various biological problems. In this work, we present a SVMCRYS method which use support vector machine to classify protein sequence into ‘amenable to crystallization’ and ‘resistant to crystallization’. SVMCRYS was trained on a dataset containing 728 sequences that gave diffraction quality crystal and 728 sequences where work had been stopped before obtaining crystal. The performance of SVMCRYS method was compared with other sequence-based crystallization prediction methods such as SECRET, CRYSTALP, OB-Score, ParCrys and XtalPred using three different datasets. SVMCRYS achieved better prediction rate with higher sensitivity and specificity. Our analysis suggests that SVMCRYS can be used to predict proteins which are amenable to crystallization and proteins which are difficult for crystallization. The SVMCRYS software, dataset and feature set can be obtained from http://www3.ntu.edu.sg/home/EPNSugan/index_files/svmcrys.htm.
TL;DR: Intrinsic disorder explains the protein's capacity to interact with multiple partners and effect multiple biological functions; the large buried surface in the X-ray diffraction structure illustrates how a disordered protein can have a high affinity and high specificity for its partners and how disordered Tat assembles a protein complex to enhance transcription elongation.
Abstract: The type 1 Human Immunodeficiency Virus transcriptional regulator Tat is a small RNA-binding protein es- sential for viral gene expression and replication. The protein binds to a large number of proteins within infected cells and non-infected cells, and has been demonstrated to impact a wide variety of cellular activities. Early circular dichroism stud- ies showed a lack of regular secondary structure in the protein whereas proton NMR studies suggested several different conformations. Multinuclear NMR structure and dynamics analysis indicates that the reduced protein is intrinsically dis- ordered with a predominantly extended conformation at pH 4. Multiple resonances for several atoms suggest the existence of multiple local conformers in rapid equilibrium. An X-ray diffraction structure of equine Tat, in a complex with its cog- nate RNA and cyclin T1, supports this conclusion. Intrinsic disorder explains the protein's capacity to interact with multi- ple partners and effect multiple biological functions; the large buried surface in the X-ray diffraction structure illustrates how a disordered protein can have a high affinity and high specificity for its partners and how disordered Tat assembles a protein complex to enhance transcription elongation.
TL;DR: The present knowledge about the structure of Flaviviridae core proteins is summarized and the importance of flexible, intrinsically unstructured protein regions in viral assembly and hub formation in the virus-host protein-protein interaction network (infection network) is discussed.
Abstract: Hepatitis C virus and related viruses in the Flaviviridae family (such as dengue virus, yellow fever virus or West Nile virus) are amongst the most important human pathogens, causing substantial morbidity and mortality world-wide. The production of viral progeny in Flaviviridae is orchestrated by the small, multifunctional core protein, which coats and condenses the viral genomic RNA during Nucleocapsid formation. In addition to their structural role, mounting experimental evidence links core proteins to viral persistence and pathogenesis, by virtue of their promiscuous interactions with host cell factors. In this review, we summarize the present knowledge about the structure of Flaviviridae core proteins and discuss the importance of flexible, intrinsically unstructured protein regions in viral assembly and hub formation in the virus-host protein-protein interaction network (infection network).
TL;DR: This study yielded information about the potentially exploitable activities of P. cornucopiae laccase, which inhibited proliferation of murine leukemia cell line L1210 and human hepatoma cell line HepG2, and reduced the activity of HIV-1 reverse transcriptase with an IC50 of 22 microM.
Abstract: A 66-kDa laccase, with an N-terminal sequence different from those of other mushroom laccases, was purified from fresh fruiting bodies of the edible mushroom Pleurotus cornucopiae by using affinity chromatography on Affi-gel blue gel, ion exchange chromatography on Mono Q and gel filtration on Superdex 75. The procedure resulted in a 16-fold purification and a specific enzyme activity of 17.3 U mg(-1). The optimum pH and temperature for the purified laccase were pH 4 and 40 degrees C, respectively. This laccase inhibited proliferation of murine leukemia cell line L1210 and human hepatoma cell line HepG2, and reduced the activity of HIV-1 reverse transcriptase with an IC50 of 22 microM. There was neither mitogenic activity toward mouse splenocytes, nor hemagglutinating/hemolytic activity toward rabbit erythrocytes. This study yielded information about the potentially exploitable activities of P. cornucopiae laccase.
TL;DR: The peptide fraction containing the LTP inhibited the growth of the fungi, Fusarium oxysporum, Colletotrium lindemunthianum, the yeasts, Saccharomyces cerevisiae and Pichia membranifaciens.
Abstract: The aims of this study were to isolate and characterize peptides present in chilli pepper seeds and evaluate their antifungal activities. An isolated peptide closer to 9 kDa showed high sequence homology to the antimicrobial peptide lipid transfer protein. The peptide fraction containing the LTP inhibited the growth of the fungi, Fusarium oxysporum, Colletotrium lindemunthianum, the yeasts, Saccharomyces cerevisiae, Pichia membranifaciens, Candida tropicalis, Candida albicans, inhibited glucose-stimulated acidification of the medium by yeast cells of S. cerevisiae and caused several morphological changes in P. membranifaciens.
TL;DR: Triphala is an Ayurvedic herbal formulation consisting of equal parts of three myrobalans: Terminalia chebula, Terminalia bellerica and Emblica officinalis and studies indicate that GA is a selective inhibitor of COX-2.
Abstract: Triphala is an Ayurvedic herbal formulation consisting of equal parts of three myrobalans: Terminalia chebula, Terminalia bellerica and Emblica officinalis. We recently reported that chebulagic acid (CA) isolated from Terminalia chebula is a potent COX-2/5-LOX dual inhibitor. In this study, compounds isolated from Terminalia bellerica were tested for inhibition against COX and 5-LOX. One of the fractionated compounds showed potent inhibition against COX enzymes with no inhibition against 5-LOX. It was identified as gallic acid (GA) by LC-MS, NMR and IR analyses. We report here the inhibitory effects of GA, with an IC50 value of 74 nM against COX-2 and 1500 nM for COX-1, showing ~20 fold preference towards COX-2. Further docking studies revealed that GA binds in the active site of COX-2 at the nonsteroidal anti-inflammatory drug (NSAID) binding site. The carboxylate moiety of GA interacts with Arg120 and Glu524. Based on substrate dependent kinetics, GA was found to be a competitive inhibitor of both COX-1 and COX-2, with more affinity towards COX-2. Taken together, our studies indicate that GA is a selective inhibitor of COX-2. Being a small natural product with selective and reversible inhibition of COX-2, GA would form a lead molecule for developing potent anti-inflammatory drug candidates.
TL;DR: Nearest Neighbor Algorithm is used as a prediction model to predict the protein subcellular locations, and gains a correct prediction rate of 70.63%, evaluated by Jackknife cross-validation.
Abstract: In this paper, we propose a strategy to predict the subcellular locations of proteins by combining various feature selection methods. Firstly, proteins are coded by amino-acid composition and physicochemical properties, then these features are arranged by Minimum Redundancy Maximum Relevance method and further filtered by feature selection procedure. Nearest Neighbor Algorithm is used as a prediction model to predict the protein subcellular locations, and gains a correct prediction rate of 70.63%, evaluated by Jackknife cross-validation. Results of feature selection also enable us to identify the most important protein properties. The prediction software is available for public access on the website http://chemdata.shu.edu.cn/sub22/, which may play a important complementary role to a series of web-server predictors summarized recently in a review by Chou and Shen (Chou, K.C., Shen, H.B. Natural Science, 2009, 2, 63-92, http://www.scirp.org/journal/NS/).
TL;DR: Recent applications of the use of RDCs to quantitatively describe the level of local structure in intrinsically disordered proteins involved in replication and transcription in Sendai virus are described.
Abstract: Intrinsically disordered regions of significant length are present throughout eukaryotic genomes, and are particularly prevalent in viral proteins. Due to their inherent flexibility, these proteins inhabit a conformational landscape that is too complex to be described by classical structural biology. The elucidation of the role that conformational flexibility plays in molecular function will redefine our understanding of the molecular basis of biological function, and the development of appropriate technology to achieve this aim remains one of the major challenges for the future of structural biology. NMR is the technique of choice for studying intrinsically disordered proteins, providing information about structure, flexibility and interactions at atomic resolution even in completely disordered proteins. In particular residual dipolar couplings (RDCs) are sensitive and powerful tools for determining local and long-range structural behaviour in flexible proteins. Here we describe recent applications of the use of RDCs to quantitatively describe the level of local structure in intrinsically disordered proteins involved in replication and transcription in Sendai virus.
TL;DR: Results clearly demonstrated that interaction between HDAC6 and tubulin is independent of other proteins, andHDAC6 can independently catalyze deacetylation of both tubulin dimer and microtubule polymer.
Abstract: Histone deacetylase 6 (HDAC6) is a cytosolic enzyme that catalyzes deacetylation of several proteins. Acetylated tubulin has been recently identified as a physiological substrate of HDAC6. However in previous reports, all in vitro binding and enzymatic assays were accomplished with only partially purified protein samples. Therefore, it still remained unclear whether HDAC6 alone could interact with tubulin and catalyze deacetylation. In this study, both binding and enzymatic assays were conducted using recombinant-derived HDAC6 and purified alpha/beta tubulin to eliminate possible contamination. The results clearly demonstrated that interaction between HDAC6 and tubulin is independent of other proteins. In addition, HDAC6 can independently catalyze deacetylation of both tubulin dimer and microtubule polymer.
TL;DR: 2S albumins of dandelion seeds represent a novel example of storage proteins with defense functions and possess inhibitory activity against phytopathogenic fungi and the oomycete Phytophtora infestans at micromolar concentrations with various isoforms differing in their antifungal activity.
Abstract: In this work, we isolated and characterized novel antifungal proteins from seeds of dandelion (Taraxacum officinale Wigg.). We showed that they are represented by five isoforms, each consisting of two disulphide-bonded large and small subunits. One of them, To-A1 was studied in detail, including N-terminal amino acid sequencing of both subunits, and shown to display sequence homology with the sunflower 2S albumin. Using different assays we demonstrated that dandelion 2S albumins possess inhibitory activity against phytopathogenic fungi and the oomycete Phytophtora infestans at micromolar concentrations with various isoforms differing in their antifungal activity. Thus, 2S albumins of dandelion seeds represent a novel example of storage proteins with defense functions.