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Sample records for peptides prediction methods

  1. Machine Learning Methods for Predicting HLA–Peptide Binding Activity

    PubMed Central

    Luo, Heng; Ye, Hao; Ng, Hui Wen; Shi, Leming; Tong, Weida; Mendrick, Donna L.; Hong, Huixiao

    2015-01-01

    As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA–peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA–peptide binding prediction. We also summarize the descriptors based on which the HLA–peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA–peptide binding prediction method based on network analysis. PMID:26512199

  2. Method for predicting peptide detection in mass spectrometry

    DOEpatents

    Kangas, Lars [West Richland, WA; Smith, Richard D [Richland, WA; Petritis, Konstantinos [Richland, WA

    2010-07-13

    A method of predicting whether a peptide present in a biological sample will be detected by analysis with a mass spectrometer. The method uses at least one mass spectrometer to perform repeated analysis of a sample containing peptides from proteins with known amino acids. The method then generates a data set of peptides identified as contained within the sample by the repeated analysis. The method then calculates the probability that a specific peptide in the data set was detected in the repeated analysis. The method then creates a plurality of vectors, where each vector has a plurality of dimensions, and each dimension represents a property of one or more of the amino acids present in each peptide and adjacent peptides in the data set. Using these vectors, the method then generates an algorithm from the plurality of vectors and the calculated probabilities that specific peptides in the data set were detected in the repeated analysis. The algorithm is thus capable of calculating the probability that a hypothetical peptide represented as a vector will be detected by a mass spectrometry based proteomic platform, given that the peptide is present in a sample introduced into a mass spectrometer.

  3. Method for enhanced accuracy in predicting peptides using liquid separations or chromatography

    DOEpatents

    Kangas, Lars J.; Auberry, Kenneth J.; Anderson, Gordon A.; Smith, Richard D.

    2006-11-14

    A method for predicting the elution time of a peptide in chromatographic and electrophoretic separations by first providing a data set of known elution times of known peptides, then creating a plurality of vectors, each vector having a plurality of dimensions, and each dimension representing the elution time of amino acids present in each of these known peptides from the data set. The elution time of any protein is then be predicted by first creating a vector by assigning dimensional values for the elution time of amino acids of at least one hypothetical peptide and then calculating a predicted elution time for the vector by performing a multivariate regression of the dimensional values of the hypothetical peptide using the dimensional values of the known peptides. Preferably, the multivariate regression is accomplished by the use of an artificial neural network and the elution times are first normalized using a transfer function.

  4. [A plain method of prediction of visibility of peptides in mass spectrometry with electrospray ionization].

    PubMed

    Rybina, A V; Skvortsov, V S; Kopylov, A T; Zgoda, V G

    2014-01-01

    A new method for screening of essential peptides for protein detection and quantification analysis in the direct positive electrospray mass spectrometry has been proposed. Our method is based on the prediction of the normalized abundance of the mass spectrometric peaks using a linear regression model. This method has the following limitations: (i) selected peptides should be taken so that at pH 2.5 the tested peptides must be presented mainly as the 2+ and 3+ ions; (ii) only peptides having C-terminal lysine or arginine residues are considered. The amino acid composition of the peptide, the peptide concentration, the ratio of the polar surface of peptide to common surface and ratio of the polar volume to common volume are used as independent variables in equation. Several combinations of variables were considered and the best linear regression model had a determination coefficient in leave-one-out validation procedure equal 0.54. This model confidently discriminates peptides with high response ability and peptides with low response ability, and therefore it allows to select only the most promising peptides. This screening method, a plain and fast, can be successfully applied to reduce the list of observed peptides.

  5. MHC2MIL: a novel multiple instance learning based method for MHC-II peptide binding prediction by considering peptide flanking region and residue positions

    PubMed Central

    2014-01-01

    Background Computational prediction of major histocompatibility complex class II (MHC-II) binding peptides can assist researchers in understanding the mechanism of immune systems and developing peptide based vaccines. Although many computational methods have been proposed, the performance of these methods are far from satisfactory. The difficulty of MHC-II peptide binding prediction comes mainly from the large length variation of binding peptides. Methods We develop a novel multiple instance learning based method called MHC2MIL, in order to predict MHC-II binding peptides. We deem each peptide in MHC2MIL as a bag, and some substrings of the peptide as the instances in the bag. Unlike previous multiple instance learning based methods that consider only instances of fixed length 9 (9 amino acids), MHC2MIL is able to deal with instances of both lengths of 9 and 11 (11 amino acids), simultaneously. As such, MHC2MIL incorporates important information in the peptide flanking region. For measuring the distances between different instances, furthermore, MHC2MIL explicitly highlights the amino acids in some important positions. Results Experimental results on a benchmark dataset have shown that, the performance of MHC2MIL is significantly improved by considering the instances of both 9 and 11 amino acids, as well as by emphasizing amino acids at key positions in the instance. The results are consistent with those reported in the literature on MHC-II peptide binding. In addition to five important positions (1, 4, 6, 7 and 9) for HLA(human leukocyte antigen, the name of MHC in Humans) DR peptide binding, we also find that position 2 may play some roles in the binding process. By using 5-fold cross validation on the benchmark dataset, MHC2MIL outperforms two state-of-the-art methods of MHC2SK and NN-align with being statistically significant, on 12 HLA DP and DQ molecules. In addition, it achieves comparable performance with MHC2SK and NN-align on 14 HLA DR molecules. MHC2MIL

  6. In Vitro and In Vivo Activities of Antimicrobial Peptides Developed Using an Amino Acid-Based Activity Prediction Method

    PubMed Central

    Wu, Xiaozhe; Wang, Zhenling; Li, Xiaolu; Fan, Yingzi; He, Gu; Wan, Yang; Yu, Chaoheng; Tang, Jianying; Li, Meng; Zhang, Xian; Zhang, Hailong; Xiang, Rong; Pan, Ying; Liu, Yan; Lu, Lian

    2014-01-01

    To design and discover new antimicrobial peptides (AMPs) with high levels of antimicrobial activity, a number of machine-learning methods and prediction methods have been developed. Here, we present a new prediction method that can identify novel AMPs that are highly similar in sequence to known peptides but offer improved antimicrobial activity along with lower host cytotoxicity. Using previously generated AMP amino acid substitution data, we developed an amino acid activity contribution matrix that contained an activity contribution value for each amino acid in each position of the model peptide. A series of AMPs were designed with this method. After evaluating the antimicrobial activities of these novel AMPs against both Gram-positive and Gram-negative bacterial strains, DP7 was chosen for further analysis. Compared to the parent peptide HH2, this novel AMP showed broad-spectrum, improved antimicrobial activity, and in a cytotoxicity assay it showed lower toxicity against human cells. The in vivo antimicrobial activity of DP7 was tested in a Staphylococcus aureus infection murine model. When inoculated and treated via intraperitoneal injection, DP7 reduced the bacterial load in the peritoneal lavage solution. Electron microscope imaging and the results indicated disruption of the S. aureus outer membrane by DP7. Our new prediction method can therefore be employed to identify AMPs possessing minor amino acid differences with improved antimicrobial activities, potentially increasing the therapeutic agents available to combat multidrug-resistant infections. PMID:24982064

  7. In vitro and in vivo activities of antimicrobial peptides developed using an amino acid-based activity prediction method.

    PubMed

    Wu, Xiaozhe; Wang, Zhenling; Li, Xiaolu; Fan, Yingzi; He, Gu; Wan, Yang; Yu, Chaoheng; Tang, Jianying; Li, Meng; Zhang, Xian; Zhang, Hailong; Xiang, Rong; Pan, Ying; Liu, Yan; Lu, Lian; Yang, Li

    2014-09-01

    To design and discover new antimicrobial peptides (AMPs) with high levels of antimicrobial activity, a number of machine-learning methods and prediction methods have been developed. Here, we present a new prediction method that can identify novel AMPs that are highly similar in sequence to known peptides but offer improved antimicrobial activity along with lower host cytotoxicity. Using previously generated AMP amino acid substitution data, we developed an amino acid activity contribution matrix that contained an activity contribution value for each amino acid in each position of the model peptide. A series of AMPs were designed with this method. After evaluating the antimicrobial activities of these novel AMPs against both Gram-positive and Gram-negative bacterial strains, DP7 was chosen for further analysis. Compared to the parent peptide HH2, this novel AMP showed broad-spectrum, improved antimicrobial activity, and in a cytotoxicity assay it showed lower toxicity against human cells. The in vivo antimicrobial activity of DP7 was tested in a Staphylococcus aureus infection murine model. When inoculated and treated via intraperitoneal injection, DP7 reduced the bacterial load in the peritoneal lavage solution. Electron microscope imaging and the results indicated disruption of the S. aureus outer membrane by DP7. Our new prediction method can therefore be employed to identify AMPs possessing minor amino acid differences with improved antimicrobial activities, potentially increasing the therapeutic agents available to combat multidrug-resistant infections.

  8. Predicting protein-peptide interactions from scratch

    NASA Astrophysics Data System (ADS)

    Yan, Chengfei; Xu, Xianjin; Zou, Xiaoqin; Zou lab Team

    Protein-peptide interactions play an important role in many cellular processes. The ability to predict protein-peptide complex structures is valuable for mechanistic investigation and therapeutic development. Due to the high flexibility of peptides and lack of templates for homologous modeling, predicting protein-peptide complex structures is extremely challenging. Recently, we have developed a novel docking framework for protein-peptide structure prediction. Specifically, given the sequence of a peptide and a 3D structure of the protein, initial conformations of the peptide are built through protein threading. Then, the peptide is globally and flexibly docked onto the protein using a novel iterative approach. Finally, the sampled modes are scored and ranked by a statistical potential-based energy scoring function that was derived for protein-peptide interactions from statistical mechanics principles. Our docking methodology has been tested on the Peptidb database and compared with other protein-peptide docking methods. Systematic analysis shows significantly improved results compared to the performances of the existing methods. Our method is computationally efficient and suitable for large-scale applications. Nsf CAREER Award 0953839 (XZ) NIH R01GM109980 (XZ).

  9. Toward structure prediction of cyclic peptides.

    PubMed

    Yu, Hongtao; Lin, Yu-Shan

    2015-02-14

    Cyclic peptides are a promising class of molecules that can be used to target specific protein-protein interactions. A computational method to accurately predict their structures would substantially advance the development of cyclic peptides as modulators of protein-protein interactions. Here, we develop a computational method that integrates bias-exchange metadynamics simulations, a Boltzmann reweighting scheme, dihedral principal component analysis and a modified density peak-based cluster analysis to provide a converged structural description for cyclic peptides. Using this method, we evaluate the performance of a number of popular protein force fields on a model cyclic peptide. All the tested force fields seem to over-stabilize the α-helix and PPII/β regions in the Ramachandran plot, commonly populated by linear peptides and proteins. Our findings suggest that re-parameterization of a force field that well describes the full Ramachandran plot is necessary to accurately model cyclic peptides.

  10. [A new peptide retention time prediction method for mass spectrometry based proteomic analysis by a serial and parallel support vector machine model].

    PubMed

    Zhang, Jiyang; Zhang, Daibing; Zhang, Wei; Xie, Hongwei

    2012-09-01

    The online reversed-phase liquid chromatography (RPLC) contributes a lot for the large scale mass spectrometry based protein identification in proteomics. Retention time (RT) as an important evidence can be used to distinguish the false positive/true positive peptide identifications. Because of the nonlinear concentration curve of organic phase in the whole range of run time and the interactions among peptides, the sequence based RT prediction of peptides has low accuracy and is difficult to generalize in practice, and thus is less effective in the validation of peptide identifications. A serial and parallel support vector machine (SP-SVM) method was proposed to characterize the nonlinear effect of organic phase concentration and the interactions among peptides. The SP-SVM contains a support vector regression (SVR) only for model training (named as p-SVR) and 4 SVM models (named as C-SVM, 1-SVR, s-SVR and n-SVR) for the RT prediction. After distinguishing the peptide chromatographic behavior by C-SVM, 1-SVR and s-SVR were used to predict the peptide RT specifically to improve the accuracy. Then the peptide RT was normalized by n-SVR to characterize the peptide interactions. The prediction accuracy was improved significantly by applying this method to the processing of the complex sample dataset. The coefficient of the determination between predictive and experimental RTs reaches 0. 95, the prediction error range was less than 20% of the total LC run time for more than 95% cases, and less than 10% of the total LC run time for more than 70% cases. The performance of this model reaches the best of known so far. More important, the SP-SVM method provides a framework to take into account the interactions among peptides in chromatographic separation, and its performance can be improved further by introducing new data processing and experiment strategy.

  11. Chapter 8. Methods for in silico prediction of microbial polyketide and nonribosomal peptide biosynthetic pathways from DNA sequence data.

    PubMed

    Bachmann, Brian O; Ravel, Jacques

    2009-01-01

    Fore-knowledge of the secondary metabolic potential of cultivated and previously uncultivated microorganisms can potentially facilitate the process of natural product discovery. By combining sequence-based knowledge with biochemical precedent, translated gene sequence data can be used to rapidly derive structural elements encoded by secondary metabolic gene clusters from microorganisms. These structural elements provide an estimate of the secondary metabolic potential of a given organism and a starting point for identification of potential lead compounds in isolation/structure elucidation campaigns. The accuracy of these predictions for a given translated gene sequence depends on the biochemistry of the metabolite class, similarity to known metabolite gene clusters, and depth of knowledge concerning its biosynthetic machinery. This chapter introduces methods for prediction of structural elements for two well-studied classes: modular polyketides and nonribosomally encoded peptides. A bioinformatics tool is presented for rapid preliminary analysis of these modular systems, and prototypical methods for converting these analyses into substructural elements are described.

  12. Additive method for the prediction of protein-peptide binding affinity. Application to the MHC class I molecule HLA-A*0201.

    PubMed

    Doytchinova, Irini A; Blythe, Martin J; Flower, Darren R

    2002-01-01

    A method has been developed for prediction of binding affinities between proteins and peptides. We exemplify the method through its application to binding predictions of peptides with affinity to major histocompatibility complex class I molecule HLA-A*0201. The method is named "additive" because it is based on the assumption that the binding affinity of a peptide could be presented as a sum of the contributions of the amino acids at each position and the interactions between them. The amino acid contributions and the contributions of the interactions between adjacent side chains and every second side chain were derived using a partial least squares (PLS) statistical methodology using a training set of 420 experimental IC50 values. The predictive power of the method was assessed using rigorous cross-validation and using an independent test set of 89 peptides. The mean value of the residuals between the experimental and predicted pIC50 values was 0.508 for this test set. The additive method was implemented in a program for rapid T-cell epitope search. It is universal and can be applied to any peptide-protein interaction where binding data is known.

  13. [High performance liquid chromatography of peptide bioregulators, their fragments and derivatives. III. Regularities of sorption, prediction of retention and analysis of peptides by a reversed phase HPLC method].

    PubMed

    Grigor'eva, V D; Shatts, V D

    1989-08-01

    Parameters of statistical models of fully or partially protected peptides' retention on Zorbax ODS and Silasorb C18 have been compared. The proposed model can be used for non-protected linear and cyclic peptides. Special increments have to be introduced in calculation of hydrophobicity of these peptides.

  14. Empirical prediction of peptide octanol-water partition coefficients

    PubMed Central

    Hattotuwagama, Channa K; Flower, Darren R

    2006-01-01

    Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient P (commonly expressed in logarithm form: logP), is useful for screening out unsuitable molecules and also for the development of predictive Quantitative Structure-Activity Relationships (QSARs). In this paper we develop a new approach to the prediction of LogP values for peptides based on an empirical relationship between global molecular properties and measured physical properties. Our method was successful in terms of peptide prediction (total r2 = 0.641). The final model consisted of 5 physicochemical descriptors (molecular weight, number of single bonds, 2D-VDW volume, 2D-VSA hydrophobic and 2D-VSA polar). The approach is peptide specific and its predictive accuracy was high. Overall, 67% of the peptides were able to be predicted within +/-0.5 log units from the experimental values. Our method thus represents a novel prediction method with proven predictive ability. PMID:17597903

  15. A new fingerprint to predict nonribosomal peptides activity

    NASA Astrophysics Data System (ADS)

    Abdo, Ammar; Caboche, Ségolène; Leclère, Valérie; Jacques, Philippe; Pupin, Maude

    2012-10-01

    Bacteria and fungi use a set of enzymes called nonribosomal peptide synthetases to provide a wide range of natural peptides displaying structural and biological diversity. So, nonribosomal peptides (NRPs) are the basis for some efficient drugs. While discovering new NRPs is very desirable, the process of identifying their biological activity to be used as drugs is a challenge. In this paper, we present a novel peptide fingerprint based on monomer composition (MCFP) of NRPs. MCFP is a novel method for obtaining a representative description of NRP structures from their monomer composition in fingerprint form. Experiments with Norine NRPs database and MCFP show high prediction accuracy (>93 %). Also a high recall rate (>82 %) is obtained when MCFP is used for screening NRPs database. From this study it appears that our fingerprint, built from monomer composition, allows an effective screening and prediction of biological activities of NRPs database.

  16. Peptides and methods against diabetes

    DOEpatents

    Albertini, Richard J.; Falta, Michael T.

    2000-01-01

    This invention relates to methods of preventing or reducing the severity of diabetes. In one embodiment, the method involves administering to the individual a peptide having substantially the sequence of a on-conserved region sequence of a T cell receptor present on the surface of T cells mediating diabetes or a fragment thereof, wherein the peptide or fragment is capable of causing an effect on the immune system to regulate the T cells. In particular, the T cell receptor has the V.beta. regional V.beta.6 or V.beta.14. In another embodiment, the method involves gene therapy. The invention also relates to methods of diagnosing diabetes by determining the presence of diabetes predominant T cell receptors.

  17. Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information

    SciTech Connect

    Petritis, Konstantinos; Kangas, Lars J.; Yan, Bo; Monroe, Matthew E.; Strittmatter, Eric F.; Qian, Weijun; Adkins, Joshua N.; Moore, Ronald J.; Xu, Ying; Lipton, Mary S.; Camp, David G.; Smith, Richard D.

    2006-07-15

    We describe an improved artificial neural network (ANN)-based method for predicting peptide retention times in reversed phase liquid chromatography. In addition to the peptide amino acid composition, this study investigated several other peptide descriptors to improve the predictive capability, such as peptide length, sequence, hydrophobicity and hydrophobic moment, and nearest neighbor amino acid, as well as peptide predicted structural configurations (i.e., helix, sheet, coil). An ANN architecture that consisted of 1052 input nodes, 24 hidden nodes, and 1 output node was used to fully consider the amino acid residue sequence in each peptide. The network was trained using {approx}345,000 non-redundant peptides identified from a total of 12,059 LC-MS/MS analyses of more than 20 different organisms, and the predictive capability of the model was tested using 1303 confidently identified peptides that were not included in the training set. The model demonstrated an average elution time precision of {approx}1.5% and was able to distinguish among isomeric peptides based upon the inclusion of peptide sequence information. The prediction power represents a significant improvement over our earlier report (Petritis et al., Anal. Chem. 2003, 75, 1039-1048) and other previously reported models.

  18. Methods for peptide identification by spectral comparison

    PubMed Central

    Liu, Jian; Bell, Alexander W; Bergeron, John JM; Yanofsky, Corey M; Carrillo, Brian; Beaudrie, Christian EH; Kearney, Robert E

    2007-01-01

    Background Tandem mass spectrometry followed by database search is currently the predominant technology for peptide sequencing in shotgun proteomics experiments. Most methods compare experimentally observed spectra to the theoretical spectra predicted from the sequences in protein databases. There is a growing interest, however, in comparing unknown experimental spectra to a library of previously identified spectra. This approach has the advantage of taking into account instrument-dependent factors and peptide-specific differences in fragmentation probabilities. It is also computationally more efficient for high-throughput proteomics studies. Results This paper investigates computational issues related to this spectral comparison approach. Different methods have been empirically evaluated over several large sets of spectra. First, we illustrate that the peak intensities follow a Poisson distribution. This implies that applying a square root transform will optimally stabilize the peak intensity variance. Our results show that the square root did indeed outperform other transforms, resulting in improved accuracy of spectral matching. Second, different measures of spectral similarity were compared, and the results illustrated that the correlation coefficient was most robust. Finally, we examine how to assemble multiple spectra associated with the same peptide to generate a synthetic reference spectrum. Ensemble averaging is shown to provide the best combination of accuracy and efficiency. Conclusion Our results demonstrate that when combined, these methods can boost the sensitivity and specificity of spectral comparison. Therefore they are capable of enhancing and complementing existing tools for consistent and accurate peptide identification. PMID:17227583

  19. Automated benchmarking of peptide-MHC class I binding predictions

    PubMed Central

    Trolle, Thomas; Metushi, Imir G.; Greenbaum, Jason A.; Kim, Yohan; Sidney, John; Lund, Ole; Sette, Alessandro; Peters, Bjoern; Nielsen, Morten

    2015-01-01

    Motivation: Numerous in silico methods predicting peptide binding to major histocompatibility complex (MHC) class I molecules have been developed over the last decades. However, the multitude of available prediction tools makes it non-trivial for the end-user to select which tool to use for a given task. To provide a solid basis on which to compare different prediction tools, we here describe a framework for the automated benchmarking of peptide-MHC class I binding prediction tools. The framework runs weekly benchmarks on data that are newly entered into the Immune Epitope Database (IEDB), giving the public access to frequent, up-to-date performance evaluations of all participating tools. To overcome potential selection bias in the data included in the IEDB, a strategy was implemented that suggests a set of peptides for which different prediction methods give divergent predictions as to their binding capability. Upon experimental binding validation, these peptides entered the benchmark study. Results: The benchmark has run for 15 weeks and includes evaluation of 44 datasets covering 17 MHC alleles and more than 4000 peptide-MHC binding measurements. Inspection of the results allows the end-user to make educated selections between participating tools. Of the four participating servers, NetMHCpan performed the best, followed by ANN, SMM and finally ARB. Availability and implementation: Up-to-date performance evaluations of each server can be found online at http://tools.iedb.org/auto_bench/mhci/weekly. All prediction tool developers are invited to participate in the benchmark. Sign-up instructions are available at http://tools.iedb.org/auto_bench/mhci/join. Contact: mniel@cbs.dtu.dk or bpeters@liai.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25717196

  20. PQuad: Visualization of Predicted Peptides and Proteins

    SciTech Connect

    Havre, Susan L.; Singhal, Mudita; Payne, Deborah A.; Webb-Robertson, Bobbie-Jo M.

    2004-10-10

    New high-throughput proteomic techniques generate data faster than biologist and bioinformaticists can analyze it. Yet, hidden within this massive and complex data are answers to basic questions about how cells function to support life or respond to disease. Now biologists can take a global or systems approach studying not one or two proteins at a time but whole proteomes comprising all the proteins in a cell. However, the tremendous size and complexity of the high-throughput experiment data make it difficult to process and interpret. Visualization provides powerful analysis capabilities for such enormous and complex data. In this paper, we introduce a novel interactive visualization, PQuad (Peptide Permutation and Protein Prediction), designed for the visual analysis of peptides (protein fragments) identified from high-throughput data. PQuad depicts the experiment peptides in the context of their parent protein and DNA, thereby integrating proteomic and genomic information. A wrapped line metaphor is applied across key resolutions of the data, from a compressed view of an entire chromosome to the actual nucleotide sequence. PQuad provides a difference visualization for comparing peptides from different experimental conditions. We describe the requirements for such a visual analysis tool, the design decisions, and the novel aspects of PQuad.

  1. Peptide mass fingerprinting peak intensity prediction: extracting knowledge from spectra.

    PubMed

    Gay, Steven; Binz, Pierre-Alain; Hochstrasser, Denis F; Appel, Ron D

    2002-10-01

    Matrix-assisted laser desorption/ionization-time of flight mass spectrometry has become a valuable tool in proteomics. With the increasing acquisition rate of mass spectrometers, one of the major issues is the development of accurate, efficient and automatic peptide mass fingerprinting (PMF) identification tools. Current tools are mostly based on counting the number of experimental peptide masses matching with theoretical masses. Almost all of them use additional criteria such as isoelectric point, molecular weight, PTMs, taxonomy or enzymatic cleavage rules to enhance prediction performance. However, these identification tools seldom use peak intensities as parameter as there is currently no model predicting the intensities based on the physicochemical properties of peptides. In this work, we used standard datamining methods such as classification and regression methods to find correlations between peak intensities and the properties of the peptides composing a PMF spectrum. These methods were applied on a dataset comprising a series of PMF experiments involving 157 proteins. We found that the C4.5 method gave the more informative results for the classification task (prediction of the presence or absence of a peptide in a spectra) and M5' for the regression methods (prediction of the normalized intensity of a peptide peak). The C4.5 result correctly classified 88% of the theoretical peaks; whereas the M5' peak intensities had a correlation coefficient of 0.6743 with the experimental peak intensities. These methods enabled us to obtain decision and model trees that can be directly used for prediction and identification of PMF results. The work performed permitted to lay the foundations of a method to analyze factors influencing the peak intensity of PMF spectra. A simple extension of this analysis could lead to improve the accuracy of the results by using a larger dataset. Additional peptide characteristics or even PMF experimental parameters can also be taken into

  2. Prediction of bioactive peptides using artificial neural networks.

    PubMed

    Andreu, David; Torrent, Marc

    2015-01-01

    Peptides are molecules of varying complexity, with different functions in the organism and with remarkable therapeutic interest. Predicting peptide activity by computational means can help us to understand their mechanism of action and deliver powerful drug-screening methodologies. In this chapter, we describe how to apply artificial neural networks to predict antimicrobial peptide activity.

  3. Predicting and Improving the Membrane Permeability of Peptidic Small Molecules

    PubMed Central

    Rafi, Salma B.; Hearn, Brian R.; Vedantham, Punitha; Jacobson, Matthew P.; Renslo, Adam R.

    2012-01-01

    We evaluate experimentally and computationally the membrane permeability of matched sets of peptidic small molecules bearing natural or bioisosteric unnatural amino acids. We find that the intentional introduction of hydrogen bond acceptor-donor pairs in such molecules can improve membrane permeability while retaining or improving other favorable drug-like properties. We employ an all-atom force-field based method to calculate changes in free energy associated with the transfer of the peptidic molecules from water to membrane. This computational method correctly predicts rank-order experimental permeability trends within congeneric series and is much more predictive than calculations (e.g. clogP) that do not consider three-dimensional conformation. PMID:22394492

  4. A regularized method for peptide quantification.

    PubMed

    Yang, Chao; Yang, Can; Yu, Weichuan

    2010-05-07

    Peptide abundance estimation is generally the first step in protein quantification. In peptide abundance estimation, peptide overlapping and peak intensity variation are two challenges. The main objective of this paper is to estimate peptide abundance by taking advantage of peptide isotopic distribution and smoothness of peptide elution profile. Our method proposes to solve the peptide overlapping problem and provides a way to control the variance of estimation. We compare our method with a commonly used method on simulated data sets and two real data sets of standard protein mixtures. The results show that our method achieves more accurate estimation of peptide abundance on different samples. In our method, there is a variance-related parameter. Considering the well-known trade-off between the variance and the bias of estimation, we should not only focus on reducing the variance in real applications. A suggestion about parameter selection is given based on the discussion of variance and bias. Matlab source codes and detailed experimental results are available at http://bioinformatics.ust.hk/PeptideQuant/peptidequant.htm.

  5. Advantages of combined transmembrane topology and signal peptide prediction--the Phobius web server.

    PubMed

    Käll, Lukas; Krogh, Anders; Sonnhammer, Erik L L

    2007-07-01

    When using conventional transmembrane topology and signal peptide predictors, such as TMHMM and SignalP, there is a substantial overlap between these two types of predictions. Applying these methods to five complete proteomes, we found that 30-65% of all predicted signal peptides and 25-35% of all predicted transmembrane topologies overlap. This impairs predictions of 5-10% of the proteome, hence this is an important issue in protein annotation. To address this problem, we previously designed a hidden Markov model, Phobius, that combines transmembrane topology and signal peptide predictions. The method makes an optimal choice between transmembrane segments and signal peptides, and also allows constrained and homology-enriched predictions. We here present a web interface (http://phobius.cgb.ki.se and http://phobius.binf.ku.dk) to access Phobius.

  6. Towards the improved discovery and design of functional peptides: common features of diverse classes permit generalized prediction of bioactivity.

    PubMed

    Mooney, Catherine; Haslam, Niall J; Pollastri, Gianluca; Shields, Denis C

    2012-01-01

    The conventional wisdom is that certain classes of bioactive peptides have specific structural features that endow their particular functions. Accordingly, predictions of bioactivity have focused on particular subgroups, such as antimicrobial peptides. We hypothesized that bioactive peptides may share more general features, and assessed this by contrasting the predictive power of existing antimicrobial predictors as well as a novel general predictor, PeptideRanker, across different classes of peptides.We observed that existing antimicrobial predictors had reasonable predictive power to identify peptides of certain other classes i.e. toxin and venom peptides. We trained two general predictors of peptide bioactivity, one focused on short peptides (4-20 amino acids) and one focused on long peptides (> 20 amino acids). These general predictors had performance that was typically as good as, or better than, that of specific predictors. We noted some striking differences in the features of short peptide and long peptide predictions, in particular, high scoring short peptides favour phenylalanine. This is consistent with the hypothesis that short and long peptides have different functional constraints, perhaps reflecting the difficulty for typical short peptides in supporting independent tertiary structure.We conclude that there are general shared features of bioactive peptides across different functional classes, indicating that computational prediction may accelerate the discovery of novel bioactive peptides and aid in the improved design of existing peptides, across many functional classes. An implementation of the predictive method, PeptideRanker, may be used to identify among a set of peptides those that may be more likely to be bioactive.

  7. Discovering new in silico tools for antimicrobial peptide prediction.

    PubMed

    Torrent, Marc; Nogués, M Victòria; Boix, Ester

    2012-08-01

    Antimicrobial peptides (AMPs) are important effectors of the innate immune system and play a vital role in the prevention of infections. Due to the increased emergence of new antibiotic-resistant bacteria, new drugs are constantly under investigation. AMPs in particular are recognized as promising candidates because of their modularity and wide antimicrobial spectrum. However, the mechanisms of action of AMPs, as well as their structure-activity relationships, are not completely understood. AMPs display no conserved three-dimensional structure and poor sequence conservation, which hinders rational design. Several bioinformatics tools have been developed to generate new templates with appealing antimicrobial properties with the aim of finding highly active peptide compounds with low cytotoxicity. The current tools reviewed here allow for the prediction and design of new active peptides with reasonable accuracy. However, a reliable method to assess the antimicrobial activity of AMPs has not yet been developed. The standardization of procedures to experimentally evaluate the antimicrobial activity of AMPs, together with the constant growth of current well-established databases, may allow for the future development of new bioinformatics tools to accurately predict antimicrobial activity.

  8. Predicting sequences and structures of MHC-binding peptides: a computational combinatorial approach

    NASA Astrophysics Data System (ADS)

    Zeng, Jun; Treutlein, Herbert R.; Rudy, George B.

    2001-06-01

    Peptides bound to MHC molecules on the surface of cells convey critical information about the cellular milieu to immune system T cells. Predicting which peptides can bind an MHC molecule, and understanding their modes of binding, are important in order to design better diagnostic and therapeutic agents for infectious and autoimmune diseases. Due to the difficulty of obtaining sufficient experimental binding data for each human MHC molecule, computational modeling of MHC peptide-binding properties is necessary. This paper describes a computational combinatorial design approach to the prediction of peptides that bind an MHC molecule of known X-ray crystallographic or NMR-determined structure. The procedure uses chemical fragments as models for amino acid residues and produces a set of sequences for peptides predicted to bind in the MHC peptide-binding groove. The probabilities for specific amino acids occurring at each position of the peptide are calculated based on these sequences, and these probabilities show a good agreement with amino acid distributions derived from a MHC-binding peptide database. The method also enables prediction of the three-dimensional structure of MHC-peptide complexes. Docking, linking, and optimization procedures were performed with the XPLOR program [1].

  9. Fully Blind Docking at the Atomic Level for Protein-Peptide Complex Structure Prediction.

    PubMed

    Yan, Chengfei; Xu, Xianjin; Zou, Xiaoqin

    2016-10-04

    Protein-peptide interactions play an important role in many cellular processes. In silico prediction of protein-peptide complex structure is highly desirable for mechanistic investigation of these processes and for therapeutic design. However, predicting all-atom structures of protein-peptide complexes without any knowledge about the peptide binding site and the bound peptide conformation remains a big challenge. Here, we present a docking-based method for predicting protein-peptide complex structures, referred to as MDockPeP, which starts with the peptide sequence and globally docks the all-atom, flexible peptide onto the protein structure. MDockPeP was tested on the peptiDB benchmarking database using both bound and unbound protein structures. The results show that MDockPeP successfully generated near-native peptide binding modes in 95.0% of the bound docking cases and in 92.2% of the unbound docking cases. The performance is significantly better than other existing docking methods. MDockPeP is computationally efficient and suitable for large-scale applications.

  10. Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test.

    PubMed

    Hajisharifi, Zohre; Piryaiee, Moien; Mohammad Beigi, Majid; Behbahani, Mandana; Mohabatkar, Hassan

    2014-01-21

    Cancer is an important reason of death worldwide. Traditional cytotoxic therapies, such as radiation and chemotherapy, are expensive and cause severe side effects. Currently, design of anticancer peptides is a more effective way for cancer treatment. So there is a need to develop a computational method for predicting the anticancer peptides. In the present study, two methods have been developed to predict these peptides using support vector machine (SVM) as a powerful machine learning algorithm. Classifiers have been applied based on the concept of Chou's pseudo-amino acid composition (PseAAC) and local alignment kernel. Since a number of HIV-1 proteins have cytotoxic effect, therefore we predicted the anticancer effect of HIV-1 p24 protein with these methods. After the prediction, mutagenicity of 2 anticancer peptides and 2 non-anticancer peptides was investigated by Ames test. Our results show that, the accuracy and the specificity of local alignment kernel based method are 89.7% and 92.68%, respectively. The accuracy and specificity of PseAAC-based method are 83.82% and 85.36%, respectively. By computational analysis, out of 22 peptides of p24 protein, 4 peptides are anticancer and 18 are non-anticancer. In the Ames test results, it is clear that anticancer peptides (ARP788.8 and ARP788.21) are not mutagenic. Therefore the results demonstrate that the described computation methods are useful to identify potential anticancer peptides, which are worthy of further experimental validation and 2 peptides (ARP788.8 and ARP788.21) of HIV-1 p24 protein can be used as new anticancer candidates without mutagenicity.

  11. Improved nonlinear prediction method

    NASA Astrophysics Data System (ADS)

    Adenan, Nur Hamiza; Md Noorani, Mohd Salmi

    2014-06-01

    The analysis and prediction of time series data have been addressed by researchers. Many techniques have been developed to be applied in various areas, such as weather forecasting, financial markets and hydrological phenomena involving data that are contaminated by noise. Therefore, various techniques to improve the method have been introduced to analyze and predict time series data. In respect of the importance of analysis and the accuracy of the prediction result, a study was undertaken to test the effectiveness of the improved nonlinear prediction method for data that contain noise. The improved nonlinear prediction method involves the formation of composite serial data based on the successive differences of the time series. Then, the phase space reconstruction was performed on the composite data (one-dimensional) to reconstruct a number of space dimensions. Finally the local linear approximation method was employed to make a prediction based on the phase space. This improved method was tested with data series Logistics that contain 0%, 5%, 10%, 20% and 30% of noise. The results show that by using the improved method, the predictions were found to be in close agreement with the observed ones. The correlation coefficient was close to one when the improved method was applied on data with up to 10% noise. Thus, an improvement to analyze data with noise without involving any noise reduction method was introduced to predict the time series data.

  12. Prediction and interpretation of the lipophilicity of small peptides

    NASA Astrophysics Data System (ADS)

    Visconti, Alessia; Ermondi, Giuseppe; Caron, Giulia; Esposito, Roberto

    2015-04-01

    Peptide-based drug discovery has considerably expanded and solid in silico tools for the prediction of physico-chemical properties of peptides are urgently needed. In this work we tested some combinations of descriptors/algorithms to find the best model to predict log D_{ {oct}} of a series of peptides. To do that we evaluate the models statistical performances but also their skills in providing a reliable deconvolution of the balance of intermolecular forces governing the partitioning phenomenon. Results prove that a PLS model based on VolSurf+ descriptors is the best tool to predict log D_{ {oct}} of neutral and ionised peptides. The mechanistic interpretation also reveals that the inclusion in the chemical structure of a HBD group is more efficient in decreasing lipophilicity than the inclusion of a HBA group.

  13. Models to predict intestinal absorption of therapeutic peptides and proteins.

    PubMed

    Antunes, Filipa; Andrade, Fernanda; Ferreira, Domingos; Nielsen, Hanne Morck; Sarmento, Bruno

    2013-01-01

    Prediction of human intestinal absorption is a major goal in the design, optimization, and selection of drugs intended for oral delivery, in particular proteins, which possess intrinsic poor transport across intestinal epithelium. There are various techniques currently employed to evaluate the extension of protein absorption in the different phases of drug discovery and development. Screening protocols to evaluate protein absorption include a range of preclinical methodologies like in silico, in vitro, in situ, ex vivo and in vivo. It is the careful and critical use of these techniques that can help to identify drug candidates, which most probably will be well absorbed from the human intestinal tract. It is well recognized that the human intestinal permeability cannot be accurately predicted based on a single preclinical method. However, the present social and scientific concerns about the animal well care as well as the pharmaceutical industries need for rapid, cheap and reliable models predicting bioavailability give reasons for using methods providing an appropriate correlation between results of in vivo and in vitro drug absorption. The aim of this review is to describe and compare in silico, in vitro, in situ, ex vivo and in vivo methods used to predict human intestinal absorption, giving a special attention to the intestinal absorption of therapeutic peptides and proteins.

  14. Epitope prediction based on random peptide library screening: benchmark dataset and prediction tools evaluation.

    PubMed

    Sun, Pingping; Chen, Wenhan; Huang, Yanxin; Wang, Hongyan; Ma, Zhiqiang; Lv, Yinghua

    2011-06-16

    Epitope prediction based on random peptide library screening has become a focus as a promising method in immunoinformatics research. Some novel software and web-based servers have been proposed in recent years and have succeeded in given test cases. However, since the number of available mimotopes with the relevant structure of template-target complex is limited, a systematic evaluation of these methods is still absent. In this study, a new benchmark dataset was defined. Using this benchmark dataset and a representative dataset, five examples of the most popular epitope prediction software products which are based on random peptide library screening have been evaluated. Using the benchmark dataset, in no method did performance exceed a 0.42 precision and 0.37 sensitivity, and the MCC scores suggest that the epitope prediction results of these software programs are greater than random prediction about 0.09-0.13; while using the representative dataset, most of the values of these performance measures are slightly improved, but the overall performance is still not satisfactory. Many test cases in the benchmark dataset cannot be applied to these pieces of software due to software limitations. Moreover chances are that these software products are overfitted to the small dataset and will fail in other cases. Therefore finding the correlation between mimotopes and genuine epitope residues is still far from resolved and much larger dataset for mimotope-based epitope prediction is desirable.

  15. Prediction and verification of novel peptide targets of protein tyrosine phosphatase 1B.

    PubMed

    Li, Xun; Köhn, Maja

    2016-08-01

    Phosphotyrosine peptides are useful starting points for inhibitor design and for the search for protein tyrosine phosphatase (PTP) phosphoprotein substrates. To identify novel phosphopeptide substrates of PTP1B, we developed a computational prediction protocol based on a virtual library of protein sequences with known phosphotyrosine sites. To these we applied sequence-based methods, biologically meaningful filters and molecular docking. Five peptides were selected for biochemical testing of their potential as PTP1B substrates. All five peptides were equally good substrates for PTP1B compared to a known peptide substrate whereas appropriate control peptides were not recognized, showing that our protocol can be used to identify novel peptide substrates of PTP1B.

  16. Structural prediction and analysis of VIH-related peptides from selected crustacean species.

    PubMed

    Nagaraju, Ganji Purna Chandra; Kumari, Nunna Siva; Prasad, Ganji Lakshmi Vara; Rajitha, Balney; Meenu, Madan; Rao, Manam Sreenivasa; Naik, Bannoth Reddya

    2009-08-17

    The tentative elucidation of the 3D-structure of vitellogenesis inhibiting hormone (VIH) peptides is conversely underprivileged by difficulties in gaining enough peptide or protein, diffracting crystals, and numerous extra technical aspects. As a result, no structural information is available for VIH peptide sequences registered in the Genbank. In this situation, it is not surprising that predictive methods have achieved great interest. Here, in this study the molt-inhibiting hormone (MIH) of the kuruma prawn (Marsupenaeus japonicus) is used, to predict the structure of four VIHrelated peptides in the crustacean species. The high similarity of the 3D-structures and the calculated physiochemical characteristics of these peptides suggest a common fold for the entire family.

  17. Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation.

    PubMed

    Ishikawa, Takeshi

    2016-10-01

    Binding between major histocompatibility complex (MHC) class I molecules and immunogenic epitopes is one of the most important processes for cell-mediated immunity. Consequently, computational prediction of amino acid sequences of MHC class I binding peptides from a given sequence may lead to important biomedical advances. In this study, an efficient structure-based method for predicting peptide binding to MHC class I molecules was developed, in which the binding free energy of the peptide was evaluated by two individual docking simulations. An original penalty function and restriction of degrees of freedom were determined by analysis of 361 published X-ray structures of the complex and were then introduced into the docking simulations. To validate the method, calculations using a 50-amino acid sequence as a prediction target were performed. In 27 calculations, the binding free energy of the known peptide was within the top 5 of 166 peptides generated from the 50-amino acid sequence. Finally, demonstrative calculations using a whole sequence of a protein as a prediction target were performed. These data clearly demonstrate high potential of this method for predicting peptide binding to MHC class I molecules.

  18. Method of identity analyte-binding peptides

    DOEpatents

    Kauvar, L.M.

    1990-10-16

    A method for affinity chromatography or adsorption of a designated analyte utilizes a paralog as the affinity partner. The immobilized paralog can be used in purification or analysis of the analyte; the paralog can also be used as a substitute for antibody in an immunoassay. The paralog is identified by screening candidate peptide sequences of 4--20 amino acids for specific affinity to the analyte. 5 figs.

  19. Method of identity analyte-binding peptides

    DOEpatents

    Kauvar, Lawrence M.

    1990-01-01

    A method for affinity chromatography or adsorption of a designated analyte utilizes a paralog as the affinity partner. The immobilized paralog can be used in purification or analysis of the analyte; the paralog can also be used as a substitute for antibody in an immunoassay. The paralog is identified by screening candidate peptide sequences of 4-20 amino acids for specific affinity to the analyte.

  20. Development of Antimicrobial Peptide Prediction Tool for Aquaculture Industries.

    PubMed

    Gautam, Aditi; Sharma, Asuda; Jaiswal, Sarika; Fatma, Samar; Arora, Vasu; Iquebal, M A; Nandi, S; Sundaray, J K; Jayasankar, P; Rai, Anil; Kumar, Dinesh

    2016-09-01

    Microbial diseases in fish, plant, animal and human are rising constantly; thus, discovery of their antidote is imperative. The use of antibiotic in aquaculture further compounds the problem by development of resistance and consequent consumer health risk by bio-magnification. Antimicrobial peptides (AMPs) have been highly promising as natural alternative to chemical antibiotics. Though AMPs are molecules of innate immune defense of all advance eukaryotic organisms, fish being heavily dependent on their innate immune defense has been a good source of AMPs with much wider applicability. Machine learning-based prediction method using wet laboratory-validated fish AMP can accelerate the AMP discovery using available fish genomic and proteomic data. Earlier AMP prediction servers are based on multi-phyla/species data, and we report here the world's first AMP prediction server in fishes. It is freely accessible at http://webapp.cabgrid.res.in/fishamp/ . A total of 151 AMPs related to fish collected from various databases and published literature were taken for this study. For model development and prediction, N-terminus residues, C-terminus residues and full sequences were considered. Best models were with kernels polynomial-2, linear and radial basis function with accuracy of 97, 99 and 97 %, respectively. We found that performance of support vector machine-based models is superior to artificial neural network. This in silico approach can drastically reduce the time and cost of AMP discovery. This accelerated discovery of lead AMP molecules having potential wider applications in diverse area like fish and human health as substitute of antibiotics, immunomodulator, antitumor, vaccine adjuvant and inactivator, and also for packaged food can be of much importance for industries.

  1. Extending the coverage of spectral libraries: a neighbor-based approach to predicting intensities of peptide fragmentation spectra.

    PubMed

    Ji, Chao; Arnold, Randy J; Sokoloski, Kevin J; Hardy, Richard W; Tang, Haixu; Radivojac, Predrag

    2013-03-01

    Searching spectral libraries in MS/MS is an important new approach to improving the quality of peptide and protein identification. The idea relies on the observation that ion intensities in an MS/MS spectrum of a given peptide are generally reproducible across experiments, and thus, matching between spectra from an experiment and the spectra of previously identified peptides stored in a spectral library can lead to better peptide identification compared to the traditional database search. However, the use of libraries is greatly limited by their coverage of peptide sequences: even for well-studied organisms a large fraction of peptides have not been previously identified. To address this issue, we propose to expand spectral libraries by predicting the MS/MS spectra of peptides based on the spectra of peptides with similar sequences. We first demonstrate that the intensity patterns of dominant fragment ions between similar peptides tend to be similar. In accordance with this observation, we develop a neighbor-based approach that first selects peptides that are likely to have spectra similar to the target peptide and then combines their spectra using a weighted K-nearest neighbor method to accurately predict fragment ion intensities corresponding to the target peptide. This approach has the potential to predict spectra for every peptide in the proteome. When rigorous quality criteria are applied, we estimate that the method increases the coverage of spectral libraries available from the National Institute of Standards and Technology by 20-60%, although the values vary with peptide length and charge state. We find that the overall best search performance is achieved when spectral libraries are supplemented by the high quality predicted spectra.

  2. Peptide motif analysis predicts alphaviruses as triggers for rheumatoid arthritis.

    PubMed

    Hogeboom, Charissa

    2015-12-01

    Rheumatoid arthritis (RA) develops in response to both genetic and environmental factors. The strongest genetic determinant is HLA-DR, where polymorphisms within the P4 and P6 binding pockets confer elevated risk. However, low disease concordance across monozygotic twin pairs underscores the importance of an environmental factor, probably infectious. The goal of this investigation was to predict the microorganism most likely to interact with HLA-DR to trigger RA under the molecular mimicry hypothesis. A set of 185 structural proteins from viruses or intracellular bacteria was scanned for regions of sequence homology with a collagen peptide that binds preferentially to DR4; candidates were then evaluated against a motif required for T cell cross-reactivity. The plausibility of the predicted agent was evaluated by comparison of microbial prevalence patterns to epidemiological characteristics of RA. Peptides from alphavirus capsid proteins provided the closest fit. Variations in the P6 position suggest that the HLA binding preference may vary by species, with Ross River virus, Chikungunya virus, and Mayaro virus peptides binding preferentially to DR4, and peptides from Sindbis/Ockelbo virus showing stronger affinity to DR1. The predicted HLA preference is supported by epidemiological studies of post-infection chronic arthralgia. Parallels between the cytokine profiles of RA and chronic alphavirus infection are discussed.

  3. Prediction of Surface and pH-Specific Binding of Peptides to Metal and Oxide Nanoparticles

    NASA Astrophysics Data System (ADS)

    Heinz, Hendrik; Lin, Tzu-Jen; Emami, Fateme Sadat; Ramezani-Dakhel, Hadi; Naik, Rajesh; Knecht, Marc; Perry, Carole C.; Huang, Yu

    2015-03-01

    The mechanism of specific peptide adsorption onto metallic and oxidic nanostructures has been elucidated in atomic resolution using novel force fields and surface models in comparison to measurements. As an example, variations in peptide adsorption on Pd and Pt nanoparticles depending on shape, size, and location of peptides on specific bounding facets are explained. Accurate computational predictions of reaction rates in C-C coupling reactions using particle models derived from HE-XRD and PDF data illustrate the utility of computational methods for the rational design of new catalysts. On oxidic nanoparticles such as silica and apatites, it is revealed how changes in pH lead to similarity scores of attracted peptides lower than 20%, supported by appropriate model surfaces and data from adsorption isotherms. The results demonstrate how new computational methods can support the design of nanoparticle carriers for drug release and the understanding of calcification mechanisms in the human body.

  4. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    SciTech Connect

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; Rose, Kristie L.; Tabb, David L.

    2013-03-07

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.

  5. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    DOE PAGES

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; ...

    2013-03-07

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of chargedmore » peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.« less

  6. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    PubMed Central

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel C.; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; Rose, Kristie L.; Tabb, David L.

    2013-01-01

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification. PMID:23499924

  7. Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices.

    PubMed

    Liu, Yong; Munteanu, Cristian R; Fernández Blanco, Enrique; Tan, Zhiliang; Santos Del Riego, Antonino; Pazos, Alejandro

    2015-11-01

    The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this biological function is an important research topic. The current study proposes a mixed methodology to obtain the first classification model that is able to predict new nucleotide binding peptides, using only the amino acid sequence. Thus, the methodology uses a Star graph molecular descriptor of the peptide sequences and the Machine Learning technique for the best classifier. The best model represents a Random Forest classifier based on two features of the embedded and non-embedded graphs. The performance of the model is excellent, considering similar models in the field, with an Area Under the Receiver Operating Characteristic Curve (AUROC) value of 0.938 and true positive rate (TPR) of 0.886 (test subset). The prediction of new nucleotide binding peptides with this model could be useful for drug target studies in drug development.

  8. Generalized Pattern Search Algorithm for Peptide Structure Prediction

    PubMed Central

    Nicosia, Giuseppe; Stracquadanio, Giovanni

    2008-01-01

    Finding the near-native structure of a protein is one of the most important open problems in structural biology and biological physics. The problem becomes dramatically more difficult when a given protein has no regular secondary structure or it does not show a fold similar to structures already known. This situation occurs frequently when we need to predict the tertiary structure of small molecules, called peptides. In this research work, we propose a new ab initio algorithm, the generalized pattern search algorithm, based on the well-known class of Search-and-Poll algorithms. We performed an extensive set of simulations over a well-known set of 44 peptides to investigate the robustness and reliability of the proposed algorithm, and we compared the peptide conformation with a state-of-the-art algorithm for peptide structure prediction known as PEPstr. In particular, we tested the algorithm on the instances proposed by the originators of PEPstr, to validate the proposed algorithm; the experimental results confirm that the generalized pattern search algorithm outperforms PEPstr by 21.17% in terms of average root mean-square deviation, RMSD Cα. PMID:18487293

  9. A novel fuzzy Fisher classifier for signal peptide prediction.

    PubMed

    Gao, Cui-Fang; Qiu, Zi-Xue; Wu, Xiao-Jun; Tian, Feng-Wei; Zhang, Hao; Chen, Wei

    2011-08-01

    Signal peptides recognition by bioinformatics approaches is particularly important for the efficient secretion and production of specific proteins. We concentrate on developing an integrated fuzzy Fisher clustering (IFFC) and designing a novel classifier based on IFFC for predicting secretory proteins. IFFC provides a powerful optimal discriminant vector calculated by fuzzy intra-cluster scatter matrix and fuzzy inter-cluster scatter matrix. Because the training samples and test samples are processed together in IFFC, it is convenient for users to employ their own specific samples of high reliability as training data if necessary. The cross-validation results on some existing datasets indicate that the fuzzy Fisher classifier is quite promising for signal peptide prediction.

  10. Motor degradation prediction methods

    SciTech Connect

    Arnold, J.R.; Kelly, J.F.; Delzingaro, M.J.

    1996-12-01

    Motor Operated Valve (MOV) squirrel cage AC motor rotors are susceptible to degradation under certain conditions. Premature failure can result due to high humidity/temperature environments, high running load conditions, extended periods at locked rotor conditions (i.e. > 15 seconds) or exceeding the motor`s duty cycle by frequent starts or multiple valve stroking. Exposure to high heat and moisture due to packing leaks, pressure seal ring leakage or other causes can significantly accelerate the degradation. ComEd and Liberty Technologies have worked together to provide and validate a non-intrusive method using motor power diagnostics to evaluate MOV rotor condition and predict failure. These techniques have provided a quick, low radiation dose method to evaluate inaccessible motors, identify degradation and allow scheduled replacement of motors prior to catastrophic failures.

  11. Prediction of Functional Class of Proteins and Peptides Irrespective of Sequence Homology by Support Vector Machines

    PubMed Central

    Tang, Zhi Qun; Lin, Hong Huang; Zhang, Hai Lei; Han, Lian Yi; Chen, Xin; Chen, Yu Zong

    2007-01-01

    Various computational methods have been used for the prediction of protein and peptide function based on their sequences. A particular challenge is to derive functional properties from sequences that show low or no homology to proteins of known function. Recently, a machine learning method, support vector machines (SVM), have been explored for predicting functional class of proteins and peptides from amino acid sequence derived properties independent of sequence similarity, which have shown promising potential for a wide spectrum of protein and peptide classes including some of the low- and non-homologous proteins. This method can thus be explored as a potential tool to complement alignment-based, clustering-based, and structure-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using SVM for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented. PMID:20066123

  12. Prediction of Peptide and Protein Propensity for Amyloid Formation

    PubMed Central

    Família, Carlos; Dennison, Sarah R.; Quintas, Alexandre; Phoenix, David A.

    2015-01-01

    Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔG° values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation. PMID:26241652

  13. Prediction of Peptide and Protein Propensity for Amyloid Formation.

    PubMed

    Família, Carlos; Dennison, Sarah R; Quintas, Alexandre; Phoenix, David A

    2015-01-01

    Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔG° values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation.

  14. Clinical predictive circulating peptides in rectal cancer patients treated with neoadjuvant chemoradiotherapy.

    PubMed

    Crotti, Sara; Enzo, Maria Vittoria; Bedin, Chiara; Pucciarelli, Salvatore; Maretto, Isacco; Del Bianco, Paola; Traldi, Pietro; Tasciotti, Ennio; Ferrari, Mauro; Rizzolio, Flavio; Toffoli, Giuseppe; Giordano, Antonio; Nitti, Donato; Agostini, Marco

    2015-08-01

    Preoperative chemoradiotherapy is worldwide accepted as a standard treatment for locally advanced rectal cancer. Current standard of treatment includes administration of ionizing radiation for 45-50.4 Gy in 25-28 fractions associated with 5-fluorouracil administration during radiation therapy. Unfortunately, 40% of patients have a poor or absent response and novel predictive biomarkers are demanding. For the first time, we apply a novel peptidomic methodology and analysis in rectal cancer patients treated with preoperative chemoradiotherapy. Circulating peptides (Molecular Weight <3 kDa) have been harvested from patients' plasma (n = 33) using nanoporous silica chip and analyzed by Matrix-Assisted Laser Desorption/Ionization-Time of Flight mass spectrometer. Peptides fingerprint has been compared between responders and non-responders. Random Forest classification selected three peptides at m/z 1082.552, 1098.537, and 1104.538 that were able to correctly discriminate between responders (n = 16) and non-responders (n = 17) before therapy (T0) providing an overall accuracy of 86% and an area under the receiver operating characteristic (ROC) curve of 0.92. In conclusion, the nanoporous silica chip coupled to mass spectrometry method was found to be a realistic method for plasma-based peptide analysis and we provide the first list of predictive circulating biomarker peptides in rectal cancer patients underwent preoperative chemoradiotherapy.

  15. sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

    PubMed Central

    Luo, Heng; Ye, Hao; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L.; Hong, Huixiao

    2016-01-01

    Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system. PMID:27558848

  16. MetaMHCpan, A Meta Approach for Pan-Specific MHC Peptide Binding Prediction.

    PubMed

    Xu, Yichang; Luo, Cheng; Mamitsuka, Hiroshi; Zhu, Shanfeng

    2016-01-01

    Recent computational approaches in bioinformatics can achieve high performance, by which they can be a powerful support for performing real biological experiments, making biologists pay more attention to bioinformatics than before. In immunology, predicting peptides which can bind to MHC alleles is an important task, being tackled by many computational approaches. However, this situation causes a serious problem for immunologists to select the appropriate method to be used in bioinformatics. To overcome this problem, we develop an ensemble prediction-based Web server, which we call MetaMHCpan, consisting of two parts: MetaMHCIpan and MetaMHCIIpan, for predicting peptides which can bind MHC-I and MHC-II, respectively. MetaMHCIpan and MetaMHCIIpan use two (MHC2SKpan and LApan) and four (TEPITOPEpan, MHC2SKpan, LApan, and MHC2MIL) existing predictors, respectively. MetaMHCpan is available at http://datamining-iip.fudan.edu.cn/MetaMHCpan/index.php/pages/view/info .

  17. Prediction method abstracts

    SciTech Connect

    1994-12-31

    This conference was held December 4--8, 1994 in Asilomar, California. The purpose of this meeting was to provide a forum for exchange of state-of-the-art information concerning the prediction of protein structure. Attention if focused on the following: comparative modeling; sequence to fold assignment; and ab initio folding.

  18. Method for synthesizing peptides with saccharide linked enzyme polymer conjugates

    DOEpatents

    Callstrom, Matthew R.; Bednarski, Mark D.; Gruber, Patrick R.

    1997-01-01

    A method is disclosed for synthesizing peptides using water soluble enzyme polymer conjugates. The method comprises catalyzing the peptide synthesis with enzyme which has been covalently bonded to a polymer through at least three linkers which linkers have three or more hydroxyl groups. The enzyme is conjugated at lysines or arginines.

  19. Method for synthesizing peptides with saccharide linked enzyme polymer conjugates

    DOEpatents

    Callstrom, M.R.; Bednarski, M.D.; Gruber, P.R.

    1997-06-17

    A method is disclosed for synthesizing peptides using water soluble enzyme polymer conjugates. The method comprises catalyzing the peptide synthesis with enzyme which has been covalently bonded to a polymer through at least three linkers which linkers have three or more hydroxyl groups. The enzyme is conjugated at lysines or arginines. 19 figs.

  20. Detection of selective antibacterial peptides by the Polarity Profile method.

    PubMed

    Polanco, Carlos; Buhse, Thomas; Samaniego, José Lino; Castañón-González, Jorge Alberto

    2013-01-01

    Antimicrobial peptides occupy a prominent place in the production of pharmaceuticals, because of their effective contribution to the protection of the immune system against almost all types of pathogens. These peptides are thoroughly studied by computational methods designed to shed light on their main functions. In this paper, we propose a computational approach, named the Polarity Profile method that represents an improvement to the former Polarity Index method. The Polarity Profile method is very effective in detecting the subgroup of antibacterial peptides called selective cationic amphipathic antibacterial peptides (SCAAP) that show high toxicity towards bacterial membranes and exhibit almost zero toxicity towards mammalian cells. Our study was restricted to the peptides listed in the antimicrobial peptides database (APD2) of December 19, 2012. Performance of the Polarity Profile method is demonstrated through a comparison to the former Polarity Index method by using the same sets of peptides. The efficiency of the Polarity Profile method exceeds 85% taking into account the false positive and/or false negative peptides.

  1. Brain natriuretic peptide predicts mortality in the elderly.

    PubMed Central

    Wallén, T.; Landahl, S.; Hedner, T.; Nakao, K.; Saito, Y.

    1997-01-01

    OBJECTIVE: To study whether prospective measurements of circulating concentrations of brain natriuretic peptide (BNP) could predict mortality in the general elderly population. DESIGN AND SETTING: Circulating BNP was measured in a cohort of 85 year olds from the general population who were followed up prospectively for five years as part of a longitudinal population study, "70 year old people in Gothenburg, Sweden". PATIENTS: 541 subjects from the 85 year old population in Gothenburg. All subjects were investigated for the presence or absence of cardiovascular disorder such as congestive heart failure, ischaemic heart disease, hypertension, and atrial fibrillation. Venous plasma samples were obtained for BNP analysis. MAIN OUTCOME MEASURE: Overall mortality during the prospective follow up period. RESULTS: Circulating concentrations of BNP predicted five-year mortality in the total population (P < 0.001). In subjects with a known cardiovascular disorder, five-year mortality was correlated with increased BNP concentrations (P < 0.01). Increased BNP concentrations predicted five-year mortality in subjects without a defined cardiovascular disorder (P < 0.05). CONCLUSIONS: In an elderly population, measurements of BNP may add valuable prognostic information and may be used to predict mortality in the total population as well as in patients with known cardiovascular disorders. In subjects without any known cardiovascular disorder, BNP was a strong and independent predictor of total mortality. PMID:9093047

  2. Prediction of binding modes between protein L-isoaspartyl (D-aspartyl) O-methyltransferase and peptide substrates including isomerized aspartic acid residues using in silico analytic methods for the substrate screening.

    PubMed

    Oda, Akifumi; Noji, Ikuhiko; Fukuyoshi, Shuichi; Takahashi, Ohgi

    2015-12-10

    Because the aspartic acid (Asp) residues in proteins are occasionally isomerized in the human body, not only l-α-Asp but also l-β-Asp, D-α-Asp and D-β-Asp are found in human proteins. In these isomerized aspartic acids, the proportion of D-β-Asp is the largest and the proportions of l-β-Asp and D-α-Asp found in human proteins are comparatively small. To explain the proportions of aspartic acid isomers, the possibility of an enzyme able to repair l-β-Asp and D-α-Asp is frequently considered. The protein L-isoaspartyl (D-aspartyl) O-methyltransferase (PIMT) is considered one of the possible repair enzymes for l-β-Asp and D-α-Asp. Human PIMT is an enzyme that recognizes both l-β-Asp and D-α-Asp, and catalyzes the methylation of their side chains. In this study, the binding modes between PIMT and peptide substrates containing l-β-Asp or D-α-Asp residues were investigated using computational protein-ligand docking and molecular dynamics simulations. The results indicate that carboxyl groups of both l-β-Asp and D-α-Asp were recognized in similar modes by PIMT and that the C-terminal regions of substrate peptides were located in similar positions on PIMT for both the l-β-Asp and D-α-Asp peptides. In contrast, for peptides containing l-α-Asp or D-β-Asp residues, which are not substrates of PIMT, the computationally constructed binding modes between PIMT and peptides greatly differed from those between PIMT and substrates. In the nonsubstrate peptides, not inter- but intra-molecular hydrogen bonds were observed, and the conformations of peptides were more rigid than those of substrates. Thus, the in silico analytical methods were able to distinguish substrates from nonsubstrates and the computational methods are expected to complement experimental analytical methods.

  3. Seizure Prediction: Methods

    PubMed Central

    Carney, Paul R.; Myers, Stephen; Geyer, James D.

    2011-01-01

    Epilepsy, one of the most common neurological diseases, affects over 50 million people worldwide. Epilepsy can have a broad spectrum of debilitating medical and social consequences. Although antiepileptic drugs have helped treat millions of patients, roughly a third of all patients have seizures that are refractory to pharmacological intervention. The evolution of our understanding of this dynamic disease leads to new treatment possibilities. There is great interest in the development of devices that incorporate algorithms capable of detecting early onset of seizures or even predicting them hours before they occur. The lead time provided by these new technologies will allow for new types of interventional treatment. In the near future, seizures may be detected and aborted before physical manifestations begin. In this chapter we discuss the algorithms that make these devices possible and how they have been implemented to date. We also compare and contrast these measures, and review their individual strengths and weaknesses. Finally, we illustrate how these techniques can be combined in a closed-loop seizure prevention system. PMID:22078526

  4. Prediction of binding free energy for adsorption of antimicrobial peptide lactoferricin B on a POPC membrane

    NASA Astrophysics Data System (ADS)

    Vivcharuk, Victor; Tomberli, Bruno; Tolokh, Igor S.; Gray, C. G.

    2008-03-01

    Molecular dynamics (MD) simulations are used to study the interaction of a zwitterionic palmitoyl-oleoyl-phosphatidylcholine (POPC) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with POPC is used as a model system for studying the details of membrane-peptide interactions, with the peptide selected because of its antimicrobial nature. Seventy-two 3 ns MD simulations, with six orientations of LFCinB at 12 different distances from a POPC membrane, are carried out to determine the potential of mean force (PMF) or free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the PMF for this relatively large system a new variant of constrained MD and thermodynamic integration is developed. A simplified method for relating the PMF to the LFCinB-membrane binding free energy is described and used to predict a free energy of adsorption (or binding) of -1.05±0.39kcal/mol , and corresponding maximum binding force of about 20 pN, for LFCinB-POPC. The contributions of the ions-LFCinB and the water-LFCinB interactions to the PMF are discussed. The method developed will be a useful starting point for future work simulating peptides interacting with charged membranes and interactions involved in the penetration of membranes, features necessary to understand in order to rationally design peptides as potential alternatives to traditional antibiotics.

  5. Narrow-range peptide isoelectric focusing as peptide prefractionation method prior to tandem mass spectrometry analysis.

    PubMed

    Pernemalm, Maria

    2013-01-01

    High sample complexity is one of the major challenges in mass spectrometry-based proteomics today. Despite massive improvement in instrumentation, sample prefractionation is still needed to reduce sample complexity and improve proteome coverage. Isoelectric focusing (IEF) has been traditionally used as a first-dimension protein separation technique in two-dimensional gel electrophoresis-based proteomics. Recently, peptide IEF has emerged as appealing alternative for anion exchange chromatography in multidimensional LC-MS/MS workflows. The rationale behind using narrow-range peptide isoelectric focusing as a prefractionation method prior to ms/ms is to reduce the complexity induced by tryptic digestion. This is done by selectively analyzing a sub-fraction of peptides with an acidic pI. The pI range is chosen as it has previously been shown that 96 % of human proteins have at least one tryptic peptide between pH 3.4 and 4.9. This ensures high proteome coverage while reducing the number of peptides with 2/3. In addition the focusing precision is optimal in this range. Therefore, by analyzing this sub-fraction of peptides the complexity of the sample can be reduced without significant loss of proteome coverage. As the theoretical pI of peptides can be calculated, the pI of the identified peptides can be used to validate the peptide sequence (identified peptides with pI outside the pH range 3.4-4.9 are more likely to be false positives). In addition, this approach is compatible with iTRAQ labelling as the different iTRAQ labels migrate similarly in IEF.

  6. Analysis of proteins and peptides by electromigration methods in microchips.

    PubMed

    Štěpánová, Sille; Kašička, Václav

    2017-01-01

    This review presents the developments and applications of microchip electromigration methods in the separation and analysis of peptides and proteins in the period 2011-mid-2016. The developments in sample preparation and preconcentration, microchannel material, and surface treatment are described. Separations by various microchip electromigration methods (zone electrophoresis in free and sieving media, affinity electrophoresis, isotachophoresis, isoelectric focusing, electrokinetic chromatography, and electrochromatography) are demonstrated. Advances in detection methods are reported and novel applications in the areas of proteomics and peptidomics, quality control of peptide and protein pharmaceuticals, analysis of proteins and peptides in biomatrices, and determination of physicochemical parameters are shown.

  7. Enhancing in silico protein-based vaccine discovery for eukaryotic pathogens using predicted peptide-MHC binding and peptide conservation scores.

    PubMed

    Goodswen, Stephen J; Kennedy, Paul J; Ellis, John T

    2014-01-01

    Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC) molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein's potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response-one using random forest (a machine learning algorithm) with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico vaccine discovery

  8. Enhancing In Silico Protein-Based Vaccine Discovery for Eukaryotic Pathogens Using Predicted Peptide-MHC Binding and Peptide Conservation Scores

    PubMed Central

    Goodswen, Stephen J.; Kennedy, Paul J.; Ellis, John T.

    2014-01-01

    Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC) molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein’s potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response–one using random forest (a machine learning algorithm) with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico vaccine

  9. Peptide reactivity assay using spectrophotometric method for high-throughput screening of skin sensitization potential of chemical haptens.

    PubMed

    Jeong, Yun Hyeok; An, Susun; Shin, Kyeho; Lee, Tae Ryong

    2013-02-01

    Haptens must react with cellular proteins to be recognized by antigen presenting cells. Therefore, monitoring reactivity of chemicals with peptide/protein has been considered an in vitro skin sensitization testing method. The reactivity of peptides with chemicals (peptide reactivity) has usually been monitored by chromatographic methods like HPLC or LC/MS, which are robust tools for monitoring common chemical reactions but are rather expensive and time consuming. Here, we examined the possibility of using spectrophotometric methods to monitor peptide reactivity. Two synthetic peptides, Ac-RWAACAA and Ac-RWAAKAA, were reacted with 48 chemicals (34 sensitizers and 14 non-sensitizers). Peptide reactivity was measured by monitoring unreacted peptides with UV-Vis spectrophotometer using 5,5'-dithiobis-2-nitrobenzoic acid as a detection reagent for the free thiol group of cysteine-containing peptide or fluorometer using fluorescamine™ as a detection reagent for the free amine group of lysine-containing peptide. Chemicals were categorized as sensitizers when they induced more than 10% depletion of cysteine-containing peptide or 20% depletion of lysine-containing peptide. The sensitivity, specificity, and accuracy of this method were 82.4%, 85.7%, and 83.3%, respectively. These results demonstrate that spectrophotometric methods can be easy, fast, and high-throughput screening tools for the prediction of the skin sensitization potential of chemical haptens.

  10. Computational analysis and structure predictions of CHH-related peptides from Litopenaeus vannamei.

    PubMed

    Nagaraju, G Purna Chandra; Kumari, N Siva; Prasad, G L V; Naik, B Reddya; Borst, D W

    2011-03-01

    The crustaceans produce several related peptides that belong to the crustacean hyperglycemic hormone (CHH) family. While these peptides have similar amino acid sequences, they have diverse biological functions that must arise, in part, from differences in the 3D shape of these peptides. However, it is generally accepted that peptides with a high degree of sequence similarity also have a similar 3-D structure. We used the solution structure of one peptide in the crustacean hyperglycemic hormone family, the molt-inhibiting hormone of the kuruma prawn (Marsupenaeus japonicus), to predict the shape of the five known peptides related to CHH in the Pacific white shrimp, Litopenaeus vannamei. The high similarity of the 3-D structures of these peptides suggests a common fold for the entire family. Nevertheless, minor differences in the shape of these peptides were observed, which may be the basis for their different biological properties.

  11. Improved Methods for the Enrichment and Analysis of Glycated Peptides

    SciTech Connect

    Zhang, Qibin; Schepmoes, Athena A; Brock, Jonathan W; Wu, Si; Moore, Ronald J; Purvine, Samuel O; Baynes, John; Smith, Richard D; Metz, Thomas O

    2008-12-15

    Non-enzymatic glycation of tissue proteins has important implications in the development of complications of diabetes mellitus. Herein we report improved methods for the enrichment and analysis of glycated peptides using boronate affinity chromatography and electron transfer dissociation mass spectrometry, respectively. The enrichment of glycated peptides was improved by replacing an off-line desalting step with an on-line wash of column-bound glycated peptides using 50 mM ammonium acetate. The analysis of glycated peptides by MS/MS was improved by considering only higher charged (≥3) precursor-ions during data-dependent acquisition, which increased the number of glycated peptide identifications. Similarly, the use of supplemental collisional activation after electron transfer (ETcaD) resulted in more glycated peptide identifications when the MS survey scan was acquired with enhanced resolution. In general, acquiring ETD-MS/MS data at a normal MS survey scan rate, in conjunction with the rejection of both 1+ and 2+ precursor-ions, increased the number of identified glycated peptides relative to ETcaD or the enhanced MS survey scan rate. Finally, an evaluation of trypsin, Arg-C, and Lys-C showed that tryptic digestion of glycated proteins was comparable to digestion with Lys-C and that both were better than Arg-C in terms of the number glycated peptides identified by LC-MS/MS.

  12. Improved PEP-FOLD Approach for Peptide and Miniprotein Structure Prediction.

    PubMed

    Shen, Yimin; Maupetit, Julien; Derreumaux, Philippe; Tufféry, Pierre

    2014-10-14

    Peptides and mini proteins have many biological and biomedical implications, which motivates the development of accurate methods, suitable for large-scale experiments, to predict their experimental or native conformations solely from sequences. In this study, we report PEP-FOLD2, an improved coarse grained approach for peptide de novo structure prediction and compare it with PEP-FOLD1 and the state-of-the-art Rosetta program. Using a benchmark of 56 structurally diverse peptides with 25-52 amino acids and a total of 600 simulations for each system, PEP-FOLD2 generates higher quality models than PEP-FOLD1, and PEP-FOLD2 and Rosetta generate near-native or native models for 95% and 88% of the targets, respectively. In the situation where we do not have any experimental structures at hand, PEP-FOLD2 and Rosetta return a near-native or native conformation among the top five best scored models for 80% and 75% of the targets, respectively. While the PEP-FOLD2 prediction rate is better than the ROSETTA prediction rate by 5%, this improvement is non-negligible because PEP-FOLD2 explores a larger conformational space than ROSETTA and consists of a single coarse-grained phase. Our results indicate that if the coarse-grained PEP-FOLD2 method is approaching maturity, we are not at the end of the game of mini-protein structure prediction, but this opens new perspectives for large-scale in silico experiments.

  13. Prediction of Leymus arenarius (L.) antimicrobial peptides based on de novo transcriptome assembly.

    PubMed

    Slavokhotova, Anna A; Shelenkov, Andrey A; Odintsova, Tatyana I

    2015-10-01

    Leymus arenarius is a unique wild growing Poaceae plant exhibiting extreme tolerance to environmental conditions. In this study we for the first time performed whole-transcriptome sequencing of lymegrass seedlings using Illumina platform followed by de novo transcriptome assembly and functional annotation. Our goal was to identify transcripts encoding antimicrobial peptides (AMPs), one of the key components of plant innate immunity. Using the custom software developed for this study that predicted AMPs and classified them into families, we revealed more than 160 putative AMPs in lymegrass seedlings. We classified them into 7 families based on their cysteine motifs and sequence similarity. The families included defensins, thionins, hevein-like peptides, snakins, cyclotide, alfa-hairpinins and LTPs. This is the first communication about the presence of almost all known AMP families in trascriptomic data of a single plant species. Additionally, cysteine-rich peptides that potentially represent novel families of AMPs were revealed. We have confirmed by RT-PCR validation the presence of 30 transcripts encoding selected AMPs in lymegrass seedlings. In summary, the presented method of pAMP prediction developed by us can be applied for relatively fast and simple screening of novel components of plant immunity system and is well suited for whole-transcriptome or genome analysis of uncharacterized plants.

  14. A Peptide-Based Method for 13C Metabolic Flux Analysis in Microbial Communities

    PubMed Central

    Ghosh, Amit; Nilmeier, Jerome; Weaver, Daniel; Adams, Paul D.; Keasling, Jay D.; Mukhopadhyay, Aindrila; Petzold, Christopher J.; Martín, Héctor García

    2014-01-01

    The study of intracellular metabolic fluxes and inter-species metabolite exchange for microbial communities is of crucial importance to understand and predict their behaviour. The most authoritative method of measuring intracellular fluxes, 13C Metabolic Flux Analysis (13C MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during 13C labeling experiments to derive intracellular fluxes. However, these metabolite labeling patterns cannot easily be obtained for each of the members of the community. Here we propose a new type of 13C MFA that infers fluxes based on peptide labeling, instead of amino acid labeling. The advantage of this method resides in the fact that the peptide sequence can be used to identify the microbial species it originates from and, simultaneously, the peptide labeling can be used to infer intracellular metabolic fluxes. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques. We show that, using this method, it is theoretically possible to recover intracellular metabolic fluxes in the same way as through the standard amino acid based 13C MFA, and quantify the amount of information lost as a consequence of using peptides instead of amino acids. We show that by using a relatively small number of peptides we can counter this information loss. We computationally tested this method with a well-characterized simple microbial community consisting of two species. PMID:25188426

  15. Abundance-based Classifier for the Prediction of Mass Spectrometric Peptide Detectability Upon Enrichment (PPA)*

    PubMed Central

    Muntel, Jan; Boswell, Sarah A.; Tang, Shaojun; Ahmed, Saima; Wapinski, Ilan; Foley, Greg; Steen, Hanno; Springer, Michael

    2015-01-01

    The function of a large percentage of proteins is modulated by post-translational modifications (PTMs). Currently, mass spectrometry (MS) is the only proteome-wide technology that can identify PTMs. Unfortunately, the inability to detect a PTM by MS is not proof that the modification is not present. The detectability of peptides varies significantly making MS potentially blind to a large fraction of peptides. Learning from published algorithms that generally focus on predicting the most detectable peptides we developed a tool that incorporates protein abundance into the peptide prediction algorithm with the aim to determine the detectability of every peptide within a protein. We tested our tool, “Peptide Prediction with Abundance” (PPA), on in-house acquired as well as published data sets from other groups acquired on different instrument platforms. Incorporation of protein abundance into the prediction allows us to assess not only the detectability of all peptides but also whether a peptide of interest is likely to become detectable upon enrichment. We validated the ability of our tool to predict changes in protein detectability with a dilution series of 31 purified proteins at several different concentrations. PPA predicted the concentration dependent peptide detectability in 78% of the cases correctly, demonstrating its utility for predicting the protein enrichment needed to observe a peptide of interest in targeted experiments. This is especially important in the analysis of PTMs. PPA is available as a web-based or executable package that can work with generally applicable defaults or retrained from a pilot MS data set. PMID:25473088

  16. Enrichment of phosphorylated peptides and proteins by selective precipitation methods.

    PubMed

    Rainer, Matthias; Bonn, Günther K

    2015-01-01

    Protein phosphorylation is one of the most prominent post-translational modifications involved in the regulation of cellular processes. Fundamental understanding of biological processes requires appropriate bioanalytical methods for selectively enriching phosphorylated peptides and proteins. Most of the commonly applied enrichment approaches include chromatographic materials including Fe(3+)-immobilized metal-ion affinity chromatography or metal oxides. In the last years, the introduction of several non-chromatographic isolation technologies has increasingly attracted the interest of many scientists. Such approaches are based on the selective precipitation of phosphorylated peptides and proteins by applying various metal cations. The excellent performance of precipitation-based enrichment methods can be explained by the absence of any stationary phase, resin or sorbent, which usually leads to unspecific binding. This review provides an overview of recently published methods for the selective precipitation of phosphorylated peptides and proteins.

  17. C-Peptide Levels Predict the Effectiveness of Dipeptidyl Peptidase-4 Inhibitor Therapy

    PubMed Central

    Demir, Sevin; Sargin, Mehmet

    2016-01-01

    Background. Our aim was to define the conditions that affect therapeutic success when dipeptidyl peptidase-4 (DPP-4) inhibitor is added to metformin monotherapy. Materials and Methods. We reviewed the medical records of 56 patients who had received DPP-4 inhibitor as an add-on to metformin monotherapy and evaluated their response in the first year of therapy. Fasting blood glucose (FBG), HbA1c, C-peptide, and weight of the patients were recorded at 3-month intervals during the first year of treatment. Results. Patients who added DPP-4 inhibitor to metformin monotherapy had significant weight loss (P = 0.004) and FBG and HbA1c levels were significantly lowered during the first 6 months (both P < 0.001). Baseline levels of C-peptide were predictive for success of the treatment (P = 0.02), even after correction for confounding factors, for example, age, gender, or BMI (P = 0.03). Duration of diabetes was not a predictor of response to treatment (P = 0.60). Conclusion. Our study demonstrates that in patients having inadequate glycemic control, the addition of a DPP-4 inhibitor as a second oral agent to metformin monotherapy provides better glycemic control, protects β-cell reserves, and does not cause weight gain. These effects depend on baseline C-peptide levels. PMID:27882332

  18. Fetal urinary peptides to predict postnatal outcome of renal disease in fetuses with posterior urethral valves (PUV).

    PubMed

    Klein, Julie; Lacroix, Chrystelle; Caubet, Cécile; Siwy, Justyna; Zürbig, Petra; Dakna, Mohammed; Muller, Françoise; Breuil, Benjamin; Stalmach, Angelique; Mullen, William; Mischak, Harald; Bandin, Flavio; Monsarrat, Bernard; Bascands, Jean-Loup; Decramer, Stéphane; Schanstra, Joost P

    2013-08-14

    Bilateral congenital abnormalities of the kidney and urinary tract (CAKUT), although are individually rare diseases, remain the main cause of chronic kidney disease in infants worldwide. Bilateral CAKUT display a wide spectrum of pre- and postnatal outcomes ranging from death in utero to normal postnatal renal function. Methods to predict these outcomes in utero are controversial and, in several cases, lead to unjustified termination of pregnancy. Using capillary electrophoresis coupled with mass spectrometry, we have analyzed the urinary proteome of fetuses with posterior urethral valves (PUV), the prototypic bilateral CAKUT, for the presence of biomarkers predicting postnatal renal function. Among more than 4000 fetal urinary peptide candidates, 26 peptides were identified that were specifically associated with PUV in 13 patients with early end-stage renal disease (ESRD) compared to 15 patients with absence of ESRD before the age of 2. A classifier based on these peptides correctly predicted postnatal renal function with 88% sensitivity and 95% specificity in an independent blinded validation cohort of 38 PUV patients, outperforming classical methods, including fetal urine biochemistry and fetal ultrasound. This study demonstrates that fetal urine is an important pool of peptides that can predict postnatal renal function and thus be used to make clinical decisions regarding pregnancy.

  19. MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction

    PubMed Central

    2013-01-01

    Background Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consider two important characteristics of the problem: (1) the length of binding peptides is highly flexible; and (2) MHC molecules are extremely polymorphic and for the vast majority of them there are no sufficient training data. Methods We develop a novel string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among peptides with variable lengths. By considering the distinct features of MHC-II peptide binding prediction problem, MHC2SK differs significantly from the recently developed kernel based method, GS (Generic String) kernel, in the way of computing similarities. Furthermore, we extend MHC2SK to MHC2SKpan for pan-specific MHC-II peptide binding prediction by leveraging the binding data of various MHC molecules. Results MHC2SK outperformed GS in allele specific prediction using a benchmark dataset, which demonstrates the effectiveness of MHC2SK. Furthermore, we evaluated the performance of MHC2SKpan using various benckmark data sets from several different perspectives: Leave-one-allele-out (LOO), 5-fold cross validation as well as independent data testing. MHC2SKpan has achieved comparable performance with NetMHCIIpan-2.0 and outperformed NetMHCIIpan-1.0, TEPITOPEpan and MultiRTA, being statistically significant. MHC2SKpan can be freely accessed at http://datamining-iip.fudan.edu.cn/service/MHC2SKpan/index.html. PMID:24564280

  20. Peptide Suboptimal Conformation Sampling for the Prediction of Protein-Peptide Interactions.

    PubMed

    Lamiable, Alexis; Thévenet, Pierre; Eustache, Stephanie; Saladin, Adrien; Moroy, Gautier; Tuffery, Pierre

    2017-01-01

    The blind identification of candidate patches of interaction on the protein surface is a difficult task that can hardly be accomplished without a heuristic or the use of simplified representations to speed up the search. The PEP-SiteFinder protocol performs a systematic blind search on the protein surface using a rigid docking procedure applied to a limited set of peptide suboptimal conformations expected to approximate satisfactorily the conformation of the peptide in interaction. All steps rely on a coarse-grained representation of the protein and the peptide. While simple, such a protocol can help to infer useful information, assuming a critical analysis of the results. Moreover, such a protocol can be extended to a semi-flexible protocol where the suboptimal conformations are directly folded in the vicinity of the receptor.

  1. Prediction of Scylla olivacea (Crustacea; Brachyura) peptide hormones using publicly accessible transcriptome shotgun assembly (TSA) sequences.

    PubMed

    Christie, Andrew E

    2016-05-01

    The aquaculture of crabs from the genus Scylla is of increasing economic importance for many Southeast Asian countries. Expansion of Scylla farming has led to increased efforts to understand the physiology and behavior of these crabs, and as such, there are growing molecular resources for them. Here, publicly accessible Scylla olivacea transcriptomic data were mined for putative peptide-encoding transcripts; the proteins deduced from the identified sequences were then used to predict the structures of mature peptide hormones. Forty-nine pre/preprohormone-encoding transcripts were identified, allowing for the prediction of 187 distinct mature peptides. The identified peptides included isoforms of adipokinetic hormone-corazonin-like peptide, allatostatin A, allatostatin B, allatostatin C, bursicon β, CCHamide, corazonin, crustacean cardioactive peptide, crustacean hyperglycemic hormone/molt-inhibiting hormone, diuretic hormone 31, eclosion hormone, FMRFamide-like peptide, HIGSLYRamide, insulin-like peptide, intocin, leucokinin, myosuppressin, neuroparsin, neuropeptide F, orcokinin, pigment dispersing hormone, pyrokinin, red pigment concentrating hormone, RYamide, short neuropeptide F, SIFamide and tachykinin-related peptide, all well-known neuropeptide families. Surprisingly, the tissue used to generate the transcriptome mined here is reported to be testis. Whether or not the testis samples had neural contamination is unknown. However, if the peptides are truly produced by this reproductive organ, it could have far reaching consequences for the study of crustacean endocrinology, particularly in the area of reproductive control. Regardless, this peptidome is the largest thus far predicted for any brachyuran (true crab) species, and will serve as a foundation for future studies of peptidergic control in members of the commercially important genus Scylla.

  2. Simultaneous prediction of binding free energy and specificity for PDZ domain-peptide interactions

    NASA Astrophysics Data System (ADS)

    Crivelli, Joseph J.; Lemmon, Gordon; Kaufmann, Kristian W.; Meiler, Jens

    2013-12-01

    Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain-peptide interactions capable of predicting both affinity and specificity of binding based on X-ray crystal structures and comparative modeling with R osetta. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several R osetta protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain-peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain-peptide interactions.

  3. Machine learning techniques for the prediction of the peptide mobility in capillary zone electrophoresis.

    PubMed

    Yu, Ke; Cheng, Yiyu

    2007-02-15

    Three machine learning techniques including back propagation artificial neural network (BP-ANN), radial basis function artificial neural network (RBF-ANN) and support vector regression (SVR) were applied to predicting the peptide mobility in capillary zone electrophoresis through the development of quantitative structure-mobility relationship (QSMR) models. A data set containing 102 peptides with a large range of size, charge and hydrophobicity was used as a typical study. The optimal modeling parameters of the models were determined by grid-searching approach using 10-fold cross-validation. The predicted results were compared with that obtained by the multiple linear regression (MLR) method. The results showed that the relative standard errors (R.S.E.) of the developed models for the test set obtained by MLR, BP-ANN, RBF-ANN and SVR were 11.21%, 7.47%, 5.79% and 5.75%, respectively, while the R.S.E.s for the external validation set were 11.18%, 7.87%, 7.54% and 7.18%, respectively. The better generalization ability of the QSMR models developed by machine learning techniques over MLR was exactly presented. It was shown that the machine learning techniques were effective for developing the accurate and relaible QSMR models.

  4. Hydrogen exchange-mass spectrometry measures stapled peptide conformational dynamics and predicts pharmacokinetic properties.

    PubMed

    Shi, Xiangguo Eric; Wales, Thomas E; Elkin, Carl; Kawahata, Noriyuki; Engen, John R; Annis, D Allen

    2013-12-03

    Peptide drugs have traditionally suffered from poor pharmacokinetic properties due to their conformational flexibility and the interaction of proteases with backbone amide bonds. "Stapled Peptides" are cyclized using an all-hydrocarbon cross-linking strategy to reinforce their α-helical conformation, yielding improved protease resistance and drug-like properties. Here we demonstrate that hydrogen exchange-mass spectrometry (HX-MS) effectively probes the conformational dynamics of Stapled Peptides derived from the survivin-borealin protein-protein interface and predicts their susceptibility to proteolytic degradation. In Stapled Peptides, amide exchange was reduced by over five orders-of-magnitude versus the native peptide sequence depending on staple placement. Furthermore, deuteration kinetics correlated directly with rates of proteolysis to reveal the optimal staple placement for improved drug properties.

  5. Towards peptide vaccines against Zika virus: Immunoinformatics combined with molecular dynamics simulations to predict antigenic epitopes of Zika viral proteins

    PubMed Central

    Usman Mirza, Muhammad; Rafique, Shazia; Ali, Amjad; Munir, Mobeen; Ikram, Nazia; Manan, Abdul; Salo-Ahen, Outi M. H.; Idrees, Muhammad

    2016-01-01

    The recent outbreak of Zika virus (ZIKV) infection in Brazil has developed to a global health concern due to its likely association with birth defects (primary microcephaly) and neurological complications. Consequently, there is an urgent need to develop a vaccine to prevent or a medicine to treat the infection. In this study, immunoinformatics approach was employed to predict antigenic epitopes of Zika viral proteins to aid in development of a peptide vaccine against ZIKV. Both linear and conformational B-cell epitopes as well as cytotoxic T-lymphocyte (CTL) epitopes were predicted for ZIKV Envelope (E), NS3 and NS5 proteins. We further investigated the binding interactions of altogether 15 antigenic CTL epitopes with three class I major histocompatibility complex (MHC I) proteins after docking the peptides to the binding groove of the MHC I proteins. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlight the limits of rigid-body docking methods. Some of the antigenic epitopes predicted and analyzed in this work might present a preliminary set of peptides for future vaccine development against ZIKV. PMID:27934901

  6. New Methods for Labeling RGD Peptides with Bromine-76

    PubMed Central

    Lang, Lixin; Li, Weihua; Jia, Hong-Mei; Fang, De-Cai; Zhang, Shushu; Sun, Xilin; Zhu, Lei; Ma, Ying; Shen, Baozhong; Kiesewetter, Dale O.; Niu, Gang; Chen, Xiaoyuan

    2011-01-01

    Direct bromination of the tyrosine residues of peptides and antibodies with bromine-76, to create probes for PET imaging, has been reported. For peptides that do not contain tyrosine residues, however, a prosthetic group is required to achieve labeling via conjugation to other functional groups such as terminal α-amines or lysine ε-amines. The goal of this study was to develop new strategies for labeling small peptides with Br-76 using either a direct labeling method or a prosthetic group, depending on the available functional group on the peptides. A new labeling agent, N-succinimidyl-3-[76Br]bromo-2,6-dimethoxybenzoate ([76Br]SBDMB) was prepared for cyclic RGD peptide labeling. N-succinimidyl-2, 6-dimethoxybenzoate was also used to pre-attach a 2, 6-dimethoxybenzoyl (DMB) moiety to the peptide, which could then be labeled with Br-76. A competitive cell binding assay was performed to determine the binding affinity of the brominated peptides. PET imaging of U87MG human glioblastoma xenografted mice was performed using [76Br]-BrE[c(RGDyK)]2 and [76Br]-BrDMB-E[c(RGDyK)]2. An ex vivo biodistribution assay was performed to confirm PET quantification. The mechanisms of bromination reaction between DMB-c(RGDyK) and the brominating agent CH3COOBr were investigated with the SCRF-B3LYP/6-31G* method with the Gaussian 09 program package. The yield for direct labeling of c(RGDyK) and E[c(RGDyK)]2 using chloramine-T and peracetic acid at ambient temperature was greater than 50%. The yield for [76Br]SBDMB was over 60% using peracetic acid. The conjugation yields for labeling c(RGDfK) and c(RGDyK) were over 70% using the prosthetic group at room temperature. Labeling yield for pre-conjugated peptides was over 60%. SDMB conjugation and bromination did not affect the binding affinity of the peptides with integrin receptors. Both [76Br]Br-E[c(RGDyK)]2 and [76Br]BrDMB-E[c(RGDyK)]2 showed high tumor uptake in U87MG tumor bearing mice. The specificity of the imaging tracers was

  7. Binding Site Prediction of Proteins with Organic Compounds or Peptides Using GALAXY Web Servers.

    PubMed

    Heo, Lim; Lee, Hasup; Baek, Minkyung; Seok, Chaok

    2016-01-01

    We introduce two GALAXY web servers called GalaxySite and GalaxyPepDock that predict protein complex structures with small organic compounds and peptides, respectively. GalaxySite predicts ligands that may bind the input protein and generates complex structures of the protein with the predicted ligands from the protein structure given as input or predicted from the input sequence. GalaxyPepDock takes a protein structure and a peptide sequence as input and predicts structures for the protein-peptide complex. Both GalaxySite and GalaxyPepDock rely on available experimentally resolved structures of protein-ligand complexes evolutionarily related to the target. With the continuously increasing size of the protein structure database, the probability of finding related proteins in the database is increasing. The servers further relax the complex structures to refine the structural aspects that are missing in the available structures or that are not compatible with the given protein by optimizing physicochemical interactions. GalaxyPepDock allows conformational change of the protein receptor induced by peptide binding. The atomistic interactions with ligands predicted by the GALAXY servers may offer important clues for designing new molecules or proteins with desired binding properties.

  8. Computer-predicted peptides that mimic discontinuous epitopes on the A2 domain of factor VIII.

    PubMed

    Lebreton, A; Simon, N; Moreau, V; Demolombe, V; Cayzac, C; Nguyen, C; Schved, J F; Granier, C; Lavigne-Lissalde, G

    2015-05-01

    Development of antibodies (Abs) against factor VIII (FVIII) is a severe complication of haemophilia A treatment. Recent publications suggest that domain specificity of anti-FVIII antibodies, particularly during immune tolerance induction (ITI), might be related to the outcome of the treatment. Obtaining suitable tools for a fine mapping of discontinuous epitopes could thus be helpful. The aim of this study was to map discontinuous epitopes on FVIII A2 domain using a new epitope prediction functionality of the PEPOP bioinformatics tool and a peptide inhibition assay based on the Luminex technology. We predicted, selected and synthesized 40 peptides mimicking discontinuous epitopes on the A2 domain of FVIII. A new inhibition assays using Luminex technology was performed to identify peptides able to inhibit the binding of anti-A2 Abs to A2 domain. We identified two peptides (IFKKLYHVWTKEVG and LYSRRLPKGVKHFD) able to block the binding of anti-A2 allo-antibodies to this domain. The three-dimensional representation of these two peptides on the A2 domain revealed that they are localized on a limited region of A2. We also confirmed that residues 484-508 of the A2 domain define an antigenic site. We suggest that dissection of the antibody response during ITI using synthetic peptide epitopes could provide important information for the management of patients with inhibitors.

  9. Syndecan-4 as a biomarker to predict clinical outcome for glioblastoma multiforme treated with WT1 peptide vaccine

    PubMed Central

    Takashima, Satoshi; Oka, Yoshihiro; Fujiki, Fumihiro; Morimoto, Soyoko; Nakajima, Hiroko; Nakae, Yoshiki; Nakata, Jun; Nishida, Sumiyuki; Hosen, Naoki; Tatsumi, Naoya; Mizuguchi, Kenji; Hashimoto, Naoya; Oji, Yusuke; Tsuboi, Akihiro; Kumanogoh, Atsushi; Sugiyama, Haruo

    2016-01-01

    Aim: In cancer immunotherapy, biomarkers are important for identification of responsive patients. This study was aimed to find biomarkers that predict clinical outcome of WT1 peptide vaccination. Materials & methods: Candidate genes that were expressed differentially between long- and short-term survivors were identified by cDNA microarray analysis of peripheral blood mononuclear cells that were extracted from 30 glioblastoma patients (discovery set) prior to vaccination and validated by quantitative RT-PCR using discovery set and different 23 patients (validation set). Results: SDC-4 mRNA expression levels distinguished between the long- and short-term survivors: 1-year survival rates were 64.0 and 18.5% in SDC4-low and -high patients, respectively. Conclusion: SDC-4 is a novel predictive biomarker for the efficacy of WT1 peptide vaccine. PMID:28116121

  10. Predicting retention time shifts associated with variation of the gradient slope in peptide RP-HPLC.

    PubMed

    Spicer, Vic; Grigoryan, Marine; Gotfrid, Alexander; Standing, Kenneth G; Krokhin, Oleg V

    2010-12-01

    We have developed a sequence-specific model for predicting slopes (S) in the fundamental equation of linear solvent strength theory for the reversed-phase HPLC separation of tryptic peptides detected in a typical bottom-up-proteomics experiment. These slopes control the variation in the separation selectivity observed when the physical parameters of chromatographic separation, such as gradient slope, flow rate, and column size are altered. For example, with the use of an arbitrarily chosen set of tryptic peptides with a 4-times difference in the gradient slope between two experiments, the R(2)-value of correlation between the observed retention times of identical species decreases to ~0.993-0.996 (compared to a theoretical value of ~1.00). The observed retention time shifts associated with variations of the gradient slope can be predicted a priori using the approach described here. The proposed model is based on our findings for a set of synthetic species (Vu, H.; Spicer, V.; Gotfrid, A.; Krokhin, O. V. J. Chromatogr., A, 2010, 1217, 489-497), which postulate that slopes S can be predicted taking into account simultaneously peptide length, charge, and hydrophobicity. Here we extend this approach using an extensive set of real tryptic peptides. We developed the procedure to accurately measure S-values in nano-RP HPLC MS experiments and introduced sequence-specific corrections for a more accurate prediction of the slopes S. A correlation of ~0.95 R(2)-value between the predicted and experimental S-values was demonstrated. Predicting S-values and calculating the expected retention time shifts when the physical parameters of separation like gradient slope are altered will facilitate a more accurate application of peptide retention prediction protocols, aid in the transfer of scheduled MRM (SRM) procedures between LC systems, and increase the efficiency of interlaboratory data collection and comparison.

  11. Genome-Wide Prediction and Validation of Peptides That Bind Human Prosurvival Bcl-2 Proteins

    PubMed Central

    DeBartolo, Joe; Taipale, Mikko; Keating, Amy E.

    2014-01-01

    Programmed cell death is regulated by interactions between pro-apoptotic and prosurvival members of the Bcl-2 family. Pro-apoptotic family members contain a weakly conserved BH3 motif that can adopt an alpha-helical structure and bind to a groove on prosurvival partners Bcl-xL, Bcl-w, Bcl-2, Mcl-1 and Bfl-1. Peptides corresponding to roughly 13 reported BH3 motifs have been verified to bind in this manner. Due to their short lengths and low sequence conservation, BH3 motifs are not detected using standard sequence-based bioinformatics approaches. Thus, it is possible that many additional proteins harbor BH3-like sequences that can mediate interactions with the Bcl-2 family. In this work, we used structure-based and data-based Bcl-2 interaction models to find new BH3-like peptides in the human proteome. We used peptide SPOT arrays to test candidate peptides for interaction with one or more of the prosurvival proteins Bcl-xL, Bcl-w, Bcl-2, Mcl-1 and Bfl-1. For the 36 most promising array candidates, we quantified binding to all five human receptors using direct and competition binding assays in solution. All 36 peptides showed evidence of interaction with at least one prosurvival protein, and 22 peptides bound at least one prosurvival protein with a dissociation constant between 1 and 500 nM; many peptides had specificity profiles not previously observed. We also screened the full-length parent proteins of a subset of array-tested peptides for binding to Bcl-xL and Mcl-1. Finally, we used the peptide binding data, in conjunction with previously reported interactions, to assess the affinity and specificity prediction performance of different models. PMID:24967846

  12. PIPI: PTM-Invariant Peptide Identification Using Coding Method.

    PubMed

    Yu, Fengchao; Li, Ning; Yu, Weichuan

    2016-12-02

    In computational proteomics, the identification of peptides with an unlimited number of post-translational modification (PTM) types is a challenging task. The computational cost associated with database search increases exponentially with respect to the number of modified amino acids and linearly with respect to the number of potential PTM types at each amino acid. The problem becomes intractable very quickly if we want to enumerate all possible PTM patterns. To address this issue, one group of methods named restricted tools (including Mascot, Comet, and MS-GF+) only allow a small number of PTM types in database search process. Alternatively, the other group of methods named unrestricted tools (including MS-Alignment, ProteinProspector, and MODa) avoids enumerating PTM patterns with an alignment-based approach to localizing and characterizing modified amino acids. However, because of the large search space and PTM localization issue, the sensitivity of these unrestricted tools is low. This paper proposes a novel method named PIPI to achieve PTM-invariant peptide identification. PIPI belongs to the category of unrestricted tools. It first codes peptide sequences into Boolean vectors and codes experimental spectra into real-valued vectors. For each coded spectrum, it then searches the coded sequence database to find the top scored peptide sequences as candidates. After that, PIPI uses dynamic programming to localize and characterize modified amino acids in each candidate. We used simulation experiments and real data experiments to evaluate the performance in comparison with restricted tools (i.e., Mascot, Comet, and MS-GF+) and unrestricted tools (i.e., Mascot with error tolerant search, MS-Alignment, ProteinProspector, and MODa). Comparison with restricted tools shows that PIPI has a close sensitivity and running speed. Comparison with unrestricted tools shows that PIPI has the highest sensitivity except for Mascot with error tolerant search and Protein

  13. A Priori Intrinsic PTM Size Parameters for Predicting the Ion Mobilities of Modified Peptides

    NASA Astrophysics Data System (ADS)

    Kaszycki, Julia L.; Shvartsburg, Alexandre A.

    2017-02-01

    The rising profile of ion mobility spectrometry (IMS) in proteomics has driven the efforts to predict peptide cross-sections. In the simplest approach, these are derived by adding the contributions of all amino acid residues and post-translational modifications (PTMs) defined by their intrinsic size parameters (ISPs). We show that the ISPs for PTMs can be calculated from properties of constituent atoms, and introduce the "impact scores" that govern the shift of cross-sections from the central mass-dependent trend for unmodified peptides. The ISPs and scores tabulated for 100 more common PTMs enable predicting the domains for modified peptides in the IMS/MS space that would guide subproteome investigations.

  14. Prediction of Protein-Peptide Interactions: Application of the XPairIT to Anthrax Lethal Factor and Substrates

    DTIC Science & Technology

    2013-09-01

    Prediction of Protein-Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates by Margaret M. Hurley and...Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates Margaret M. Hurley and Michael S. Sellers Weapons and...Prediction of Protein-Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates 5a. CONTRACT NUMBER ORAUW911QX-04-C

  15. Prediction of Impending Type 1 Diabetes through Automated Dual-Label Measurement of Proinsulin:C-Peptide Ratio

    PubMed Central

    Balti, Eric V.; Keymeulen, Bart; Gillard, Pieter; Lapauw, Bruno; De Block, Christophe; Abrams, Pascale; Weber, Eric; Vermeulen, Ilse; De Pauw, Pieter; Pipeleers, Daniël; Weets, Ilse; Gorus, Frans K.

    2016-01-01

    Background The hyperglycemic clamp test, the gold standard of beta cell function, predicts impending type 1 diabetes in islet autoantibody-positive individuals, but the latter may benefit from less invasive function tests such as the proinsulin:C-peptide ratio (PI:C). The present study aims to optimize precision of PI:C measurements by automating a dual-label trefoil-type time-resolved fluorescence immunoassay (TT-TRFIA), and to compare its diagnostic performance for predicting type 1 diabetes with that of clamp-derived C-peptide release. Methods Between-day imprecision (n = 20) and split-sample analysis (n = 95) were used to compare TT-TRFIA (AutoDelfia, Perkin-Elmer) with separate methods for proinsulin (in-house TRFIA) and C-peptide (Elecsys, Roche). High-risk multiple autoantibody-positive first-degree relatives (n = 49; age 5–39) were tested for fasting PI:C, HOMA2-IR and hyperglycemic clamp and followed for 20–57 months (interquartile range). Results TT-TRFIA values for proinsulin, C-peptide and PI:C correlated significantly (r2 = 0.96–0.99; P<0.001) with results obtained with separate methods. TT-TRFIA achieved better between-day %CV for PI:C at three different levels (4.5–7.1 vs 6.7–9.5 for separate methods). In high-risk relatives fasting PI:C was significantly and inversely correlated (rs = -0.596; P<0.001) with first-phase C-peptide release during clamp (also with second phase release, only available for age 12–39 years; n = 31), but only after normalization for HOMA2-IR. In ROC- and Cox regression analysis, HOMA2-IR-corrected PI:C predicted 2-year progression to diabetes equally well as clamp-derived C-peptide release. Conclusions The reproducibility of PI:C benefits from the automated simultaneous determination of both hormones. HOMA2-IR-corrected PI:C may serve as a minimally invasive alternative to the more tedious hyperglycemic clamp test. PMID:27907006

  16. Dissociation of POMC Peptides after Self-Injury Predicts Responses To Centrally Acting Opiate Blockers.

    ERIC Educational Resources Information Center

    Sandman, Curt A.; Hetrick, William; Taylor, Derek V.; Chicz-DeMet, Aleksandra

    1997-01-01

    This study investigated whether blood plasma levels of pro-opiomelanocortin-derived (POMC) peptides, beta-endorphin-like activity, adrenocorticotrophic hormone, and adrenal cortisol immediately after self injurious behavior (SIB) episodes predicted subsequent response to an opiate blocker in 10 patients with mental retardation. Results suggest…

  17. Lipid Tail Protrusion in Simulations Predicts Fusogenic Activity of Influenza Fusion Peptide Mutants and Conformational Models

    PubMed Central

    Larsson, Per; Kasson, Peter M.

    2013-01-01

    Fusion peptides from influenza hemagglutinin act on membranes to promote membrane fusion, but the mechanism by which they do so remains unknown. Recent theoretical work has suggested that contact of protruding lipid tails may be an important feature of the transition state for membrane fusion. If this is so, then influenza fusion peptides would be expected to promote tail protrusion in proportion to the ability of the corresponding full-length hemagglutinin to drive lipid mixing in fusion assays. We have performed molecular dynamics simulations of influenza fusion peptides in lipid bilayers, comparing the X-31 influenza strain against a series of N-terminal mutants. As hypothesized, the probability of lipid tail protrusion correlates well with the lipid mixing rate induced by each mutant. This supports the conclusion that tail protrusion is important to the transition state for fusion. Furthermore, it suggests that tail protrusion can be used to examine how fusion peptides might interact with membranes to promote fusion. Previous models for native influenza fusion peptide structure in membranes include a kinked helix, a straight helix, and a helical hairpin. Our simulations visit each of these conformations. Thus, the free energy differences between each are likely low enough that specifics of the membrane environment and peptide construct may be sufficient to modulate the equilibrium between them. However, the kinked helix promotes lipid tail protrusion in our simulations much more strongly than the other two structures. We therefore predict that the kinked helix is the most fusogenic of these three conformations. PMID:23505359

  18. Prediction of ozone concentrations using nonlinear prediction method

    NASA Astrophysics Data System (ADS)

    Abd Hamid, Nor Zila; Md Noorani, Mohd Salmi; Juneng, Liew; Latif, Mohd Talib

    2013-04-01

    Prediction of ozone (O3) is very important because O3 gives effects on human health, human activities and more. Nonlinear prediction method, a method which was developed based on the idea comes from chaos theory is used to predict the concentrations of O3. There are two steps in the nonlinear prediction method. First is the reconstruction of the observed data from the form of a one-dimensional to multi-dimensional phase space. Second is the prediction of the reconstructed phase space through local linear approximation method. Hourly O3 concentrations observed in Shah Alam city located in the state of Selangor, Malaysia were studied. Predictions found in a close agreement with those observed ones. The value of the correlation coefficient obtained in this study is 0.9097. This demonstrates the suitability of the nonlinear prediction method to predict the hourly concentrations of O3. At the end of this paper, suggestions were made for better prediction in the future.

  19. PEP-FOLD: an online resource for de novo peptide structure prediction

    PubMed Central

    Maupetit, Julien; Derreumaux, Philippe; Tuffery, Pierre

    2009-01-01

    Rational peptide design and large-scale prediction of peptide structure from sequence remain a challenge for chemical biologists. We present PEP-FOLD, an online service, aimed at de novo modelling of 3D conformations for peptides between 9 and 25 amino acids in aqueous solution. Using a hidden Markov model-derived structural alphabet (SA) of 27 four-residue letters, PEP-FOLD first predicts the SA letter profiles from the amino acid sequence and then assembles the predicted fragments by a greedy procedure driven by a modified version of the OPEP coarse-grained force field. Starting from an amino acid sequence, PEP-FOLD performs series of 50 simulations and returns the most representative conformations identified in terms of energy and population. Using a benchmark of 25 peptides with 9–23 amino acids, and considering the reproducibility of the runs, we find that, on average, PEP-FOLD locates lowest energy conformations differing by 2.6 Å Cα root mean square deviation from the full NMR structures. PEP-FOLD can be accessed at http://bioserv.rpbs.univ-paris-diderot.fr/PEP-FOLD PMID:19433514

  20. PEP-FOLD: an online resource for de novo peptide structure prediction.

    PubMed

    Maupetit, Julien; Derreumaux, Philippe; Tuffery, Pierre

    2009-07-01

    Rational peptide design and large-scale prediction of peptide structure from sequence remain a challenge for chemical biologists. We present PEP-FOLD, an online service, aimed at de novo modelling of 3D conformations for peptides between 9 and 25 amino acids in aqueous solution. Using a hidden Markov model-derived structural alphabet (SA) of 27 four-residue letters, PEP-FOLD first predicts the SA letter profiles from the amino acid sequence and then assembles the predicted fragments by a greedy procedure driven by a modified version of the OPEP coarse-grained force field. Starting from an amino acid sequence, PEP-FOLD performs series of 50 simulations and returns the most representative conformations identified in terms of energy and population. Using a benchmark of 25 peptides with 9-23 amino acids, and considering the reproducibility of the runs, we find that, on average, PEP-FOLD locates lowest energy conformations differing by 2.6 A Calpha root mean square deviation from the full NMR structures. PEP-FOLD can be accessed at http://bioserv.rpbs.univ-paris-diderot.fr/PEP-FOLD.

  1. Predictive sensor method and apparatus

    NASA Technical Reports Server (NTRS)

    Nail, William L. (Inventor); Koger, Thomas L. (Inventor); Cambridge, Vivien (Inventor)

    1990-01-01

    A predictive algorithm is used to determine, in near real time, the steady state response of a slow responding sensor such as hydrogen gas sensor of the type which produces an output current proportional to the partial pressure of the hydrogen present. A microprocessor connected to the sensor samples the sensor output at small regular time intervals and predicts the steady state response of the sensor in response to a perturbation in the parameter being sensed, based on the beginning and end samples of the sensor output for the current sample time interval.

  2. Methods and kits for predicting a response to an erythropoietic agent

    DOEpatents

    Merchant, Michael L.; Klein, Jon B.; Brier, Michael E.; Gaweda, Adam E.

    2015-06-16

    Methods for predicting a response to an erythropoietic agent in a subject include providing a biological sample from the subject, and determining an amount in the sample of at least one peptide selected from the group consisting of SEQ ID NOS: 1-17. If there is a measurable difference in the amount of the at least one peptide in the sample, when compared to a control level of the same peptide, the subject is then predicted to have a good response or a poor response to the erythropoietic agent. Kits for predicting a response to an erythropoietic agent are further provided and include one or more antibodies, or fragments thereof, that specifically recognize a peptide of SEQ ID NOS: 1-17.

  3. Predicting the effects of amino acid replacements in peptide hormones on their binding affinities for class B GPCRs and application to the design of secretin receptor antagonists

    NASA Astrophysics Data System (ADS)

    Te, Jerez A.; Dong, Maoqing; Miller, Laurence J.; Bordner, Andrew J.

    2012-07-01

    Computational prediction of the effects of residue changes on peptide-protein binding affinities, followed by experimental testing of the top predicted binders, is an efficient strategy for the rational structure-based design of peptide inhibitors. In this study we apply this approach to the discovery of competitive antagonists for the secretin receptor, the prototypical member of class B G protein-coupled receptors (GPCRs). Proteins in this family are involved in peptide hormone-stimulated signaling and are implicated in several human diseases, making them potential therapeutic targets. We first validated our computational method by predicting changes in the binding affinities of several peptides to their cognate class B GPCRs due to alanine replacement and compared the results with previously published experimental values. Overall, the results showed a significant correlation between the predicted and experimental ΔΔG values. Next, we identified candidate inhibitors by applying this method to a homology model of the secretin receptor bound to an N-terminal truncated secretin peptide. Predictions were made for single residue replacements to each of the other nineteen naturally occurring amino acids at peptide residues within the segment binding the receptor N-terminal domain. Amino acid replacements predicted to most enhance receptor binding were then experimentally tested by competition-binding assays. We found two residue changes that improved binding affinities by almost one log unit. Furthermore, a peptide combining both of these favorable modifications resulted in an almost two log unit improvement in binding affinity, demonstrating the approximately additive effect of these changes on binding. In order to further investigate possible physical effects of these residue changes on receptor binding affinity, molecular dynamics simulations were performed on representatives of the successful peptide analogues (namely A17I, G25R, and A17I/G25R) in bound and

  4. Polyvalent display and packing of peptides and proteins on semiconductor quantum dots: predicted versus experimental results.

    PubMed

    Prasuhn, Duane E; Deschamps, Jeffrey R; Susumu, Kimihiro; Stewart, Michael H; Boeneman, Kelly; Blanco-Canosa, Juan B; Dawson, Philip E; Medintz, Igor L

    2010-02-22

    Quantum dots (QDs) are loaded with a series of peptides and proteins of increasing size, including a <20 residue peptide, myoglobin, mCherry, and maltose binding protein, which together cover a range of masses from <2.2 to approximately 44 kDa. Conjugation to the surface of dihydrolipoic acid-functionalized QDs is facilitated by polyhistidine metal affinity coordination. Increasing ratios of dye-labeled peptides and proteins are self-assembled to the QDs and then the bioconjugates are separated and analyzed using agarose gel electrophoresis. Fluorescent visualization of both conjugated and unbound species allows determination of an experimentally derived maximum loading number. Molecular modeling utilizing crystallographic coordinates or space-filling structures of the peptides and proteins also allow the predicted maximum loadings to the QDs to be estimated. Comparison of the two sets of results provides insight into the nature of the QD surface and reflects the important role played by the nanoparticle's hydrophilic solubilizing surface ligands. It is found that for the larger protein molecules steric hindrance is the major packing constraint. In contrast, for the smaller peptides, the number of available QD binding sites is the principal determinant. These results can contribute towards an overall understanding of how to engineer designer bioconjugates for both QDs and other nanoparticle materials.

  5. Quantitative evaluation of peptide-extraction methods by HPLC-triple-quad MS-MS.

    PubMed

    Du, Yan; Wu, Dapeng; Wu, Qian; Guan, Yafeng

    2015-02-01

    In this study, the efficiency of five peptide-extraction methods—acetonitrile (ACN) precipitation, ultrafiltration, C18 solid-phase extraction (SPE), dispersed SPE with mesoporous carbon CMK-3, and mesoporous silica MCM-41—was quantitatively investigated. With 28 tryptic peptides as target analytes, these methods were evaluated on the basis of recovery and reproducibility by using high-performance liquid chromatography-triple-quad tandem mass spectrometry in selected-reaction-monitoring mode. Because of the distinct extraction mechanisms of the methods, their preferences for extracting peptides of different properties were revealed to be quite different, usually depending on the pI values or hydrophobicity of peptides. When target peptides were spiked in bovine serum albumin (BSA) solution, the extraction efficiency of all the methods except ACN precipitation changed significantly. The binding of BSA with target peptides and nonspecific adsorption on adsorbents were believed to be the ways through which BSA affected the extraction behavior. When spiked in plasma, the performance of all five methods deteriorated substantially, with the number of peptides having recoveries exceeding 70% being 15 for ACN precipitation, and none for the other methods. Finally, the methods were evaluated in terms of the number of identified peptides for extraction of endogenous plasma peptides. Only ultrafiltration and CMK-3 dispersed SPE performed differently from the quantitative results with target peptides, and the wider distribution of the properties of endogenous peptides was believed to be the main reason.

  6. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

    PubMed Central

    Nielsen, Morten; Lundegaard, Claus; Lund, Ole

    2007-01-01

    Background Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles. Results The predictive performance of the SMM-align method was demonstrated to be superior to that of the Gibbs sampler, TEPITOPE, SVRMHC, and MHCpred methods. Cross validation between peptide data set obtained from different sources demonstrated that direct incorporation of peptide length potentially results in over-fitting of the binding prediction method. Focusing on amino terminal peptide flanking residues (PFR), we demonstrate a consistent gain in predictive performance by favoring binding registers with a minimum PFR length of two amino acids. Visualizing the binding motif as obtained by the SMM-align and TEPITOPE methods highlights a series of fundamental discrepancies between the two predicted motifs. For the DRB1*1302 allele for instance, the TEPITOPE method favors basic amino acids at most anchor positions, whereas the SMM-align method identifies a preference for hydrophobic or neutral amino acids at the anchors. Conclusion The SMM-align method was

  7. Exploiting heterogeneous features to improve in silico prediction of peptide status – amyloidogenic or non-amyloidogenic

    PubMed Central

    2011-01-01

    Background Prediction of short stretches in protein sequences capable of forming amyloid-like fibrils is important in understanding the underlying cause of amyloid illnesses thereby aiding in the discovery of sequence-targeted anti-aggregation pharmaceuticals. Due to the constraints of experimental molecular techniques in identifying such motif segments, it is highly desirable to develop computational methods to provide better and affordable in silico predictions. Results Accurate in silico prediction techniques of amyloidogenic peptide regions rely on the cooperation between informative features and classifier design. In this research article, we propose one such efficient fibril prediction implementation exploiting heterogeneous features based on bio-physio-chemical (BPC) properties, auto-correlation function of carefully selected amino acid indices and atomic composition within a protein fragment of amino acids in a window. In an attempt to get an optimal number of BPC features, an evolutionary Support Vector Machine (SVM) integrating a novel implementation of hybrid Genetic Algorithm termed Memetic Algorithm and SVM is utilized. Five prediction modules designed using Artificial Neural Network (ANN) models are trained with independent and integrated features in order to validate the fibril forming motifs. The results provide evidence that incorporating new feature namely auto-correlation function besides BPC, attempt to strengthen the sequence interaction effect in forming the feature vector thereby obtaining better prediction quality in terms of sensitivity, specificity, Mathews Correlation Coefficient and Area under the Receiver Operating Characteristics curve. Conclusion A significant improvement in performance is observed by introducing features like auto-correlation function that maintains sequence order effect, in addition to the conventional BPC properties selected through a novel optimization strategy to predict the peptide status – amyloidogenic or

  8. Interim prediction method for jet noise

    NASA Technical Reports Server (NTRS)

    Stone, J. R.

    1974-01-01

    A method is provided for predicting jet noise for a wide range of nozzle geometries and operating conditions of interest for aircraft engines. Jet noise theory, data and existing prediction methods was reviewed, and based on this information a interim method of jet noise prediction is proposed. Problem areas are idenified where further research is needed to improve the prediction method. This method predicts only the noise generated by the exhaust jets mixing with the surrounding air and does not include other noises emanating from the engine exhaust, such as combustion and machinery noise generated inside the engine (i.e., core noise). It does, however, include thrust reverser noise. Prediction relations are provided for conical nozzles, plug nozzles, coaxial nozzles and slot nozzles.

  9. Signal peptide prediction based on analysis of experimentally verified cleavage sites

    PubMed Central

    Zhang, Zemin; Henzel, William J.

    2004-01-01

    A number of computational tools are available for detecting signal peptides, but their abilities to locate the signal peptide cleavage sites vary significantly and are often less than satisfactory. We characterized a set of 270 secreted recombinant human proteins by automated Edman analysis and used the verified cleavage sites to evaluate the success rate of a number of computational prediction programs. An examination of the frequency of amino acid in the N-terminal region of the data set showed a preference of proline and glutamine but a bias against tyrosine. The data set was compared to the SWISS-PROT database and revealed a high percentage of discrepancies with cleavage site annotations that were computationally generated. The best program for predicting signal sequences was found to be SignalP 2.0-NN with an accuracy of 78.1% for cleavage site recognition. The new data set can be utilized for refining prediction algorithms, and we have built an improved version of profile hidden Markov model for signal peptides based on the new data. PMID:15340161

  10. Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity.

    PubMed

    Kleandrova, Valeria V; Ruso, Juan M; Speck-Planche, Alejandro; Dias Soeiro Cordeiro, M Natália

    2016-08-08

    Antimicrobial peptides (AMPs) represent promising alternatives to fight against bacterial pathogens. However, cellular toxicity remains one of the main concerns in the early development of peptide-based drugs. This work introduces the first multitasking (mtk) computational model focused on performing simultaneous predictions of antibacterial activities, and cytotoxicities of peptides. The model was created from a data set containing 3592 cases, and it displayed accuracy higher than 96% for classifying/predicting peptides in both training and prediction (test) sets. The technique known as alanine scanning was computationally applied to illustrate the calculation of the quantitative contributions of the amino acids (in their respective positions of the sequence) to the biological effects of a defined peptide. A small library formed by 10 peptides was generated, where peptides were designed by considering the interpretations of the different descriptors in the mtk-computational model. All the peptides were predicted to exhibit high antibacterial activities against multiple bacterial strains, and low cytotoxicity against various cell types. The present mtk-computational model can be considered a very useful tool to support high throughput research for the discovery of potent and safe AMPs.

  11. An improved method for utilization of peptide substrates for antibody characterization and enzymatic assays.

    PubMed

    Ghosh, Inca; Sun, Luo; Evans, Thomas C; Xu, Ming-Qun

    2004-10-01

    Synthetic peptides have become an important tool in antibody production and enzyme characterization. The small size of peptides, however, has hindered their use in assays systems, such as Western blots, and as immunogens. Here, we present a facile method to improve the properties of peptides for multiple applications by ligating the peptides to intein-generated carrier proteins. The stoichiometric ligation of peptide and carrier achieved by intein-mediated protein ligation (IPL) results in the ligation product migrating as a single band on a SDS-PAGE gel. The carrier proteins, HhaI methylase (M.HhaI) and maltose-binding protein (MBP), were ligated to various peptides; the ligated carrier-peptide products gave sharp, reproducible bands when used as positive controls for antibodies raised against the same peptides during Western blot analysis. We further show that ligation of the peptide antigens to a different thioester-tagged carrier protein, paramyosin, produced immunogens for the production of antisera in rabbits or mice. Furthermore, we demonstrate the generation of a substrate for enzymatic assays by ligating a peptide containing the phosphorylation site for Abl protein tyrosine kinase to a carrier protein. This carrier-peptide protein was used as a kinase substrate that could easily be tested for phosphorylation using a phosphotyrosine antibody in Western blot analysis. These techniques do not require sophisticated equipment, reagents, or skills thereby providing a simple method for research and development.

  12. Clinical Value of Natriuretic Peptides in Predicting Time to Dialysis in Stage 4 and 5 Chronic Kidney Disease Patients

    PubMed Central

    Sundqvist, Sofia; Larson, Thomas; Cauliez, Bruno; Bauer, Fabrice; Dumont, Audrey; Le Roy, Frank; Hanoy, Mélanie; Fréguin-Bouilland, Caroline; Godin, Michel

    2016-01-01

    Background Anticipating the time to renal replacement therapy (RRT) in chronic kidney disease (CKD) patients is an important but challenging issue. Natriuretic peptides are biomarkers of ventricular dysfunction related to poor outcome in CKD. We comparatively investigated the value of B-type natriuretic peptide (BNP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) as prognostic markers for the risk of RRT in stage 4 and 5 CKD patients, and in foretelling all-cause mortality and major cardiovascular events within a 5-year follow-up period. Methods Baseline plasma BNP (Triage, Biosite) and NT-proBNP (Elecsys, Roche) were measured at inclusion. Forty-three patients were followed-up during 5 years. Kaplan-Meier analysis, with log-rank testing and hazard ratios (HR), were calculated to evaluate survival without RRT, cardiovascular events or mortality. The independent prognostic value of the biomarkers was estimated in separate Cox multivariate analysis, including estimated glomerular filtration rate (eGFR), creatininemia and comorbidities. Results During the first 12-month follow-up period, 16 patients started RRT. NT-proBNP concentration was higher in patients who reached endpoint (3221 ng/L vs 777 ng/L, p = 0.02). NT-proBNP concentration > 1345 ng/L proved significant predictive value on survival analysis for cardiovascular events (p = 0.04) and dialysis within 60 months follow-up (p = 0.008). BNP concentration > 140 ng/L was an independent predictor of RRT after 12 months follow-up (p<0.005), and of significant predictive value for initiation of dialysis within 60 months follow-up. Conclusions Our results indicate a prognostic value for BNP and NT-proBNP in predicting RRT in stage 4 and 5 CKD patients, regarding both short- and long-term periods. NT-proBNP also proved a value in predicting cardiovascular events. Natriuretic peptides could be useful predictive biomarkers for therapeutic guidance in CKD. PMID:27548064

  13. Methods to follow intracellular trafficking of cell-penetrating peptides.

    PubMed

    Pärnaste, Ly; Arukuusk, Piret; Zagato, Elisa; Braeckmans, Kevin; Langel, Ülo

    2016-01-01

    Cell-penetrating peptides (CPPs) are efficient vehicles to transport bioactive molecules into the cells. Despite numerous studies the exact mechanism by which CPPs facilitate delivery of cargo to its intracellular target is still debated. The current work presents methods that can be used for tracking CPP/pDNA complexes through endosomal transport and show the role of endosomal transport in the delivery of cargo. Separation of endosomal vesicles by differential centrifugation enables to pinpoint the localization of delivered cargo without labeling it and gives important quantitative information about pDNA trafficing in certain endosomal compartments. Single particle tracking (SPT) allows following individual CPP/cargo complex through endosomal path in live cells, using fluoresently labled cargo and green fluoresent protein expressing cells. These two different methods show similar results about tested NickFect/pDNA complexes intracellular trafficing. NF51 facilitates rapid internalization of complexes into the cells, prolongs their stay in early endosomes and promotes release to cytosol. NF1 is less capable to induce endosomal release and higher amount of complexes are routed to lysosomes for degradation. Our findings offer potential delivery vector for in vivo applications, NF51, where endosomal entrapment has been allayed. Furthermore, these methods are valuable tools to study other CPP-based delivery systems.

  14. Cyanine-based probe\\tag-peptide pair fluorescence protein imaging and fluorescence protein imaging methods

    DOEpatents

    Mayer-Cumblidge, M. Uljana; Cao, Haishi

    2013-01-15

    A molecular probe comprises two arsenic atoms and at least one cyanine based moiety. A method of producing a molecular probe includes providing a molecule having a first formula, treating the molecule with HgOAc, and subsequently transmetallizing with AsCl.sub.3. The As is liganded to ethanedithiol to produce a probe having a second formula. A method of labeling a peptide includes providing a peptide comprising a tag sequence and contacting the peptide with a biarsenical molecular probe. A complex is formed comprising the tag sequence and the molecular probe. A method of studying a peptide includes providing a mixture containing a peptide comprising a peptide tag sequence, adding a biarsenical probe to the mixture, and monitoring the fluorescence of the mixture.

  15. A novel method to identify and characterise peptide mimotopes of heat shock protein 70-associated antigens.

    PubMed

    Arnaiz, Blanca; Madrigal-Estebas, Laura; Todryk, Stephen; James, Tharappel C; Doherty, Derek G; Bond, Ursula

    2006-04-08

    The heat shock protein, Hsp70, has been shown to play an important role in tumour immunity. Vaccination with Hsp70-peptide complexes (Hsp70-PCs), isolated from autologous tumour cells, can induce protective immune responses. We have developed a novel method to identify synthetic mimic peptides of Hsp70-PCs and to test their ability to activate T-cells. Peptides (referred to as "recognisers") that bind to Hsp70-PCs from the human breast carcinoma cell line, MDA-MB-231, were identified by bio-panning a random peptide M13 phage display library. Synthetic recogniser peptides were subsequently used as bait in a reverse bio-panning experiment to identify potential Hsp70-PC mimic peptides. The ability of the recogniser and mimic peptides to prime human lymphocyte responses against tumour cell antigens was tested by stimulating lymphocytes with autologous peptide-loaded monocyte-derived dendritic cells (DCs). Priming and subsequent stimulation with either the recogniser or mimic peptide resulted in interferon-gamma (IFN-gamma) secretion by the lymphocytes. Furthermore, DCs loaded with Hsp70, Hsp70-PC or the recogniser or the mimic peptide primed the lymphocytes to respond to soluble extracts from breast cells. These results highlight the potential application of synthetic peptide-mimics of Hsp70-PCs, as modulators of the immune response against tumours.

  16. Histidine-containing peptide catalysts developed by a facile library screening method.

    PubMed

    Akagawa, Kengo; Sakai, Nobutaka; Kudo, Kazuaki

    2015-02-02

    Although peptide catalysts have a high potential for the use as organocatalysts, the optimization of peptide sequences is laborious and time-consuming. To address this issue, a facile screening method for finding efficient aminocatalysts from a peptide library has been developed. In the screening for the Michael addition of a malonate to an enal, a dye-labeled product is immobilized on resin-bound peptides through reductive amination to visualize active catalysts. This procedure allows for the monitoring of the reactivity of entire peptides without modifying the resin beads beforehand. Peptides containing histidine at an appropriate position were identified by this method. A novel function of the histidyl residue, which enhances the binding of a substrate to the catalyst by capturing an iminium intermediate, was indicated.

  17. A method for the 32P labeling of peptides or peptide nucleic acid oligomers

    NASA Technical Reports Server (NTRS)

    Kozlov, I. A.; Nielsen, P. E.; Orgel, L. E.; Bada, J. L. (Principal Investigator)

    1998-01-01

    A novel approach to the radioactive labeling of peptides and PNA oligomers is described. It is based on the conjugation of a deoxynucleoside 3'-phosphate with the terminal amine of the substrate, followed by phosphorylation of the 5'-hydroxyl group of the nucleotide using T4 polynucleotide kinase and [gamma-32P]ATP.

  18. Methods for studying transmembrane peptides in bicelles: consequences of hydrophobic mismatch and peptide sequence

    NASA Astrophysics Data System (ADS)

    Whiles, Jennifer A.; Glover, Kerney J.; Vold, Regitze R.; Komives, Elizabeth A.

    2002-09-01

    We have shown that bicelles prepared from dilauryl phosphatidylcholine (DLPC) and dipalmitoyl phosphatidylcholine (DPPC) align in a magnetic field under conditions similar to the more common dimyristoyl phosphatidylcholine (DMPC) bicelles. In addition, a model transmembrane peptide, P16, with a hydrophobic stretch of 24 Å, and specific alanine-d 3 labels, was incorporated into all of the different bicelles. The long-chain phospholipid (DLPC, DMPC, or DPPC) remained unperturbed upon incorporation of the peptide while the quadrupolar splitting of the short-chain phospholipid along the bicelle rim increased by varying degrees in the different bicelle systems. The change in quadrupolar splitting of the short-chain phospholipids was attributed to changes in either fluidity of the planar region of the bicelle or differences in overall lipid packing. When the hydrophobic stretch of the bilayer was 22.8 (DMPC) or 26.3 Å (DPPC), the peptide tilt was found to be transmembrane (33-35° with respect to the bicelle normal). When the hydrophobic stretch of the bilayer was 19.5 Å (DLPC), the peptide quadrupolar splittings suggested a loss of transmembrane orientation. When tryptophan was incorporated in the middle of the transmembrane region, the transmembrane orientation was also lost.

  19. Bioinformatic prediction of arthropod/nematode-like peptides in non-arthropod, non-nematode members of the Ecdysozoa.

    PubMed

    Christie, Andrew E; Nolan, Daniel H; Garcia, Zachery A; McCoole, Matthew D; Harmon, Sarah M; Congdon-Jones, Benjamin; Ohno, Paul; Hartline, Niko; Congdon, Clare Bates; Baer, Kevin N; Lenz, Petra H

    2011-02-01

    The Onychophora, Priapulida and Tardigrada, along with the Arthropoda, Nematoda and several other small phyla, form the superphylum Ecdysozoa. Numerous peptidomic studies have been undertaken for both the arthropods and nematodes, resulting in the identification of many peptides from each group. In contrast, little is known about the peptides used as paracrines/hormones by species from the other ecdysozoan taxa. Here, transcriptome mining and bioinformatic peptide prediction were used to identify peptides in members of the Onychophora, Priapulida and Tardigrada, the only non-arthropod, non-nematode members of the Ecdysozoa for which there are publicly accessible expressed sequence tags (ESTs). The extant ESTs for each phylum were queried using 106 arthropod/nematode peptide precursors. Transcripts encoding calcitonin-like diuretic hormone and pigment-dispersing hormone (PDH) were identified for the onychophoran Peripatopsis sedgwicki, with transcripts encoding C-type allatostatin (C-AST) and FMRFamide-like peptide identified for the priapulid Priapulus caudatus. For the Tardigrada, transcripts encoding members of the A-type allatostatin, C-AST, insect kinin, orcokinin, PDH and tachykinin-related peptide families were identified, all but one from Hypsibius dujardini (the exception being a Milnesium tardigradum orcokinin-encoding transcript). The proteins deduced from these ESTs resulted in the prediction of 48 novel peptides, six onychophoran, eight priapulid and 34 tardigrade, which are the first described from these phyla.

  20. In Silico Prediction of Neuropeptides/Peptide Hormone Transcripts in the Cheilostome Bryozoan Bugula neritina

    PubMed Central

    Zhang, Gen; He, Li-Sheng; Qian, Pei-Yuan

    2016-01-01

    The bryozoan Bugula neritina has a biphasic life cycle that consists of a planktonic larval stage and a sessile juvenile/adult stage. The transition between these two stages is crucial for the development and recruitment of B. neritina. Metamorphosis in B. neritina is mediated by both the nervous system and the release of developmental signals. However, no research has been conducted to investigate the expression of neuropeptides (NP)/peptide hormones in B. neritina larvae. Here, we report a comprehensive study of the NP/peptide hormones in the marine bryozoan B. neritina based on in silico identification methods. We recovered 22 transcripts encompassing 11 NP/peptide hormone precursor transcript sequences. The transcript sequences of the 11 isolated NP precursors were validated by cDNA cloning using gene-specific primers. We also examined the expression of three peptide hormone precursor transcripts (BnFDSIG, BnILP1, BnGPB) in the coronate larvae of B. neritina, demonstrating their distinct expression patterns in the larvae. Overall, our findings serve as an important foundation for subsequent investigations of the peptidergic control of bryozoan larval behavior and settlement. PMID:27537380

  1. Analysis and prediction of the critical regions of antimicrobial peptides based on conditional random fields.

    PubMed

    Chang, Kuan Y; Lin, Tung-pei; Shih, Ling-Yi; Wang, Chien-Kuo

    2015-01-01

    Antimicrobial peptides (AMPs) are potent drug candidates against microbes such as bacteria, fungi, parasites, and viruses. The size of AMPs ranges from less than ten to hundreds of amino acids. Often only a few amino acids or the critical regions of antimicrobial proteins matter the functionality. Accurately predicting the AMP critical regions could benefit the experimental designs. However, no extensive analyses have been done specifically on the AMP critical regions and computational modeling on them is either non-existent or settled to other problems. With a focus on the AMP critical regions, we thus develop a computational model AMPcore by introducing a state-of-the-art machine learning method, conditional random fields. We generate a comprehensive dataset of 798 AMPs cores and a low similarity dataset of 510 representative AMP cores. AMPcore could reach a maximal accuracy of 90% and 0.79 Matthew's correlation coefficient on the comprehensive dataset and a maximal accuracy of 83% and 0.66 MCC on the low similarity dataset. Our analyses of AMP cores follow what we know about AMPs: High in glycine and lysine, but low in aspartic acid, glutamic acid, and methionine; the abundance of α-helical structures; the dominance of positive net charges; the peculiarity of amphipathicity. Two amphipathic sequence motifs within the AMP cores, an amphipathic α-helix and an amphipathic π-helix, are revealed. In addition, a short sequence motif at the N-terminal boundary of AMP cores is reported for the first time: arginine at the P(-1) coupling with glycine at the P1 of AMP cores occurs the most, which might link to microbial cell adhesion.

  2. Folding peptides and proteins with all-atom physics: methods and applications

    NASA Astrophysics Data System (ADS)

    Shell, M. Scott

    2008-03-01

    Computational methods offer powerful tools for investigating proteins and peptides at the molecular-level; however, it has proven challenging to reproduce the long time scale folding processes of these molecules at a level that is both faithful to the atomic driving forces and attainable with modern commodity cluster computing. Alternatively, the past decade has seen significant progress in using bioinformatics-based approaches to infer the three dimensional native structures of proteins, drawing upon extensive knowledge databases of known protein structures [1]. These methods work remarkably well when a homologous protein can be found to provide a structural template for a candidate sequence. However, in cases where homology to database proteins is low, where the folding pathway is of interest, or where conformational flexibility is substantial---as in many emerging protein and peptide technologies---bioinformatics methods perform poorly. There is therefore great interest in seeing purely physics-based approaches succeed. We discuss a purely physics-based, database-free folding method, relying on proper thermal sampling (replica exchange molecular dynamics) and molecular potential energy functions. In order to surmount the tremendous computational demands of all-atom folding simulations, our approach implements a conformational search strategy based on a putative protein folding mechanism called zipping and assembly [2-4]. That is, we explicitly seek out potential folding pathways inferred from short simulations, and iteratively pursue all such routes by coaxing a polypeptide chain along them. The method is called the Zipping and Assembly Method (ZAM) and it works in two parts: (1) the full polypeptide chain is broken into small fragments that are first simulated independently and then successively re-assembled into larger segments with further sampling, and (2) consistently stable structure in fragments is detected and locked into place, in order to avoid re

  3. Hierarchical Ensemble Methods for Protein Function Prediction

    PubMed Central

    2014-01-01

    Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research. PMID:25937954

  4. Methods for Predicting Submersible Hydrodynamic Characteristics

    DTIC Science & Technology

    1978-07-01

    TO PREDICT COMPLETE CONFIGURATION CHARACTERISTICS In this section the previously developed methods are combined to determine... characteristics of individual vehicle components (bodies, tails ), and with their mutual interactions when combined into complete configurations . Each method is...approach used successfully in mis- sile aerodynamics , a set of models was built and tested to obtain systematic data over relevant ranges of geometry

  5. Random vibration ESS adequacy prediction method

    NASA Astrophysics Data System (ADS)

    Lambert, Ronald G.

    Closed form analytical expressions have been derived and are used as part of the proposed method to quantitatively predict the adequacy of the random vibration portion of an Environmental Stress Screen (ESS) to meet its main objective for screening typical avionics electronic assemblies for workmanship defects without consuming excessive useful life. This method is limited to fatigue related defects (including initial damage/Fracture Mechanics effects) and requires defect fatigue and service environment parameter values. Examples are given to illustrate the method.

  6. Design of a predicted MHC restricted short peptide immunodiagnostic and vaccine candidate for Fowl adenovirus C in chicken infection.

    PubMed

    Valdivia-Olarte, Hugo; Requena, David; Ramirez, Manuel; Saravia, Luis E; Izquierdo, Ray; Falconi-Agapito, Francesca; Zavaleta, Milagros; Best, Iván; Fernández-Díaz, Manolo; Zimic, Mirko

    2015-01-01

    Fowl adenoviruses (FAdVs) are the ethiologic agents of multiple pathologies in chicken. There are five different species of FAdVs grouped as FAdV-A, FAdV-B, FAdV-C, FAdV-D, and FAdV-E. It is of interest to develop immunodiagnostics and vaccine candidate for Peruvian FAdV-C in chicken infection using MHC restricted short peptide candidates. We sequenced the complete genome of one FAdV strain isolated from a chicken of a local farm. A total of 44 protein coding genes were identified in each genome. We sequenced twelve Cobb chicken MHC alleles from animals of different farms in the central coast of Peru, and subsequently determined three optimal human MHC-I and four optimal human MHC-II substitute alleles for MHC-peptide prediction. The potential MHC restricted short peptide epitope-like candidates were predicted using human specific (with determined suitable chicken substitutes) NetMHC MHC-peptide prediction model with web server features from all the FAdV genomes available. FAdV specific peptides with calculated binding values to known substituted chicken MHC-I and MHC-II were further filtered for diagnostics and potential vaccine epitopes. Promiscuity to the 3/4 optimal human MHC-I/II alleles and conservation among the available FAdV genomes was considered in this analysis. The localization on the surface of the protein was considered for class II predicted peptides. Thus, a set of class I and class II specific peptides from FAdV were reported in this study. Hence, a multiepitopic protein was built with these peptides, and subsequently tested to confirm the production of specific antibodies in chicken.

  7. Design of a predicted MHC restricted short peptide immunodiagnostic and vaccine candidate for Fowl adenovirus C in chicken infection

    PubMed Central

    Valdivia-Olarte, Hugo; Requena, David; Ramirez, Manuel; Saravia, Luis E; Izquierdo, Ray; Falconi-Agapito, Francesca; Zavaleta, Milagros; Best, Iván; Fernández-Díaz, Manolo; Zimic, Mirko

    2015-01-01

    Fowl adenoviruses (FAdVs) are the ethiologic agents of multiple pathologies in chicken. There are five different species of FAdVs grouped as FAdV-A, FAdV-B, FAdV-C, FAdV-D, and FAdV-E. It is of interest to develop immunodiagnostics and vaccine candidate for Peruvian FAdV-C in chicken infection using MHC restricted short peptide candidates. We sequenced the complete genome of one FAdV strain isolated from a chicken of a local farm. A total of 44 protein coding genes were identified in each genome. We sequenced twelve Cobb chicken MHC alleles from animals of different farms in the central coast of Peru, and subsequently determined three optimal human MHC-I and four optimal human MHC-II substitute alleles for MHC-peptide prediction. The potential MHC restricted short peptide epitope-like candidates were predicted using human specific (with determined suitable chicken substitutes) NetMHC MHC-peptide prediction model with web server features from all the FAdV genomes available. FAdV specific peptides with calculated binding values to known substituted chicken MHC-I and MHC-II were further filtered for diagnostics and potential vaccine epitopes. Promiscuity to the 3/4 optimal human MHC-I/II alleles and conservation among the available FAdV genomes was considered in this analysis. The localization on the surface of the protein was considered for class II predicted peptides. Thus, a set of class I and class II specific peptides from FAdV were reported in this study. Hence, a multiepitopic protein was built with these peptides, and subsequently tested to confirm the production of specific antibodies in chicken. PMID:26664030

  8. Improved pan-specific MHC class I peptide-binding predictions using a novel representation of the MHC-binding cleft environment.

    PubMed

    Carrasco Pro, S; Zimic, M; Nielsen, M

    2014-02-01

    Major histocompatibility complex (MHC) molecules play a key role in cell-mediated immune responses presenting bounded peptides for recognition by the immune system cells. Several in silico methods have been developed to predict the binding affinity of a given peptide to a specific MHC molecule. One of the current state-of-the-art methods for MHC class I is NetMHCpan, which has a core ingredient for the representation of the MHC class I molecule using a pseudo-sequence representation of the binding cleft amino acid environment. New and large MHC-peptide-binding data sets are constantly being made available, and also new structures of MHC class I molecules with a bound peptide have been published. In order to test if the NetMHCpan method can be improved by integrating this novel information, we created new pseudo-sequence definitions for the MHC-binding cleft environment from sequence and structural analyses of different MHC data sets including human leukocyte antigen (HLA), non-human primates (chimpanzee, macaque and gorilla) and other animal alleles (cattle, mouse and swine). From these constructs, we showed that by focusing on MHC sequence positions found to be polymorphic across the MHC molecules used to train the method, the NetMHCpan method achieved a significant increase in the predictive performance, in particular, of non-human MHCs. This study hence showed that an improved performance of MHC-binding methods can be achieved not only by the accumulation of more MHC-peptide-binding data but also by a refined definition of the MHC-binding environment including information from non-human species.

  9. Destabilization of a model membrane by a predicted fusion peptide of fertilin α

    NASA Astrophysics Data System (ADS)

    Schanck, A.; Brasseur, R.; Peuvot, J.

    1998-02-01

    The subunit of the guinea pig fertilin (previously known as PH-30, an integral membrane protein involved in sperm-egg binding and fusion) is predicted to be a potential fusion protein. The structure of this putative fusion protein was analysed by molecular modeling and we have found a peptidic sequence of 17 residues (D83{-P99}) organized in helix that inserts obliquely in lipid phases. The effect of this synthesized peptide was studied on a model membrane by 31P NMR and light scattering. It appears to increase the size of lipid vesicles and induces structural modifications. We interpret these observations as a destabilization of the lipid organization by this peptide because of its tilted insertion in phospholipid layers. This destabilization could favor membrane fusion. La sous-unité α de la fertiline du cochon d'inde (précédemment appelée PH-30, une protéine membranaire impliquée dans la liaison et la fusion ovule-spermatozoïde) est prédite comme étant une protéine de fusion potentielle. Nous avons analysé la structure de cette protéine par modélisation moléculaire et nous avons trouvé une séquence peptidique de 17 résidus (D83 P99) organisée en hélice qui s'insère de façon oblique dans une phase lipidique. L'effet de ce peptide synthétique a été étudié sur membrane modèle par RMN du 31P et par diffusion de la lumière. Il provoque une augmentation de taille de vésicules lipidiques et induit des modifications structurales. Nous interprétons ces observations en termes de déstabilisation de l'organisation lipidique par ce peptide à cause de son insertion oblique dans la couche lipidique. Cette déstabilisation pourrait favoriser la fusion membranaire.

  10. Prediction of cross-recognition of peptide-HLA A2 by Melan-A-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships.

    PubMed

    Fagerberg, Theres; Zoete, Vincent; Viatte, Sebastien; Baumgaertner, Petra; Alves, Pedro M; Romero, Pedro; Speiser, Daniel E; Michielin, Olivier

    2013-01-01

    The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521-46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347-59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q (2)) is used as the fitness criterion and all obtained models are evaluated based on their q (2) values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the

  11. Soft Computing Methods for Disulfide Connectivity Prediction

    PubMed Central

    Márquez-Chamorro, Alfonso E.; Aguilar-Ruiz, Jesús S.

    2015-01-01

    The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods. PMID:26523116

  12. A novel method to measure HLA-DM-susceptibility of peptides bound to MHC class II molecules based on peptide binding competition assay and differential IC(50) determination.

    PubMed

    Yin, Liusong; Stern, Lawrence J

    2014-04-01

    HLA-DM (DM) functions as a peptide editor that mediates the exchange of peptides loaded onto MHCII molecules by accelerating peptide dissociation and association kinetics. The relative DM-susceptibility of peptides bound to MHCII molecules correlates with antigen presentation and immunodominance hierarchy, and measurement of DM-susceptibility has been a key effort in this field. Current assays of DM-susceptibility, based on differential peptide dissociation rates measured for individually labeled peptides over a long time base, are difficult and cumbersome. Here, we present a novel method to measure DM-susceptibility based on peptide binding competition assays performed in the presence and absence of DM, reported as a delta-IC(50) (change in 50% inhibition concentration) value. We simulated binding competition reactions of peptides with various intrinsic and DM-catalyzed kinetic parameters and found that under a wide range of conditions the delta-IC(50) value is highly correlated with DM-susceptibility as measured in off-rate assay. We confirmed experimentally that DM-susceptibility measured by delta-IC(50) is comparable to that measured by traditional off-rate assay for peptides with known DM-susceptibility hierarchy. The major advantage of this method is that it allows simple, fast and high throughput measurement of DM-susceptibility for a large set of unlabeled peptides in studies of the mechanism of DM action and for identification of CD4+ T cell epitopes.

  13. Novel hyperspectral prediction method and apparatus

    NASA Astrophysics Data System (ADS)

    Kemeny, Gabor J.; Crothers, Natalie A.; Groth, Gard A.; Speck, Kathy A.; Marbach, Ralf

    2009-05-01

    Both the power and the challenge of hyperspectral technologies is the very large amount of data produced by spectral cameras. While off-line methodologies allow the collection of gigabytes of data, extended data analysis sessions are required to convert the data into useful information. In contrast, real-time monitoring, such as on-line process control, requires that compression of spectral data and analysis occur at a sustained full camera data rate. Efficient, high-speed practical methods for calibration and prediction are therefore sought to optimize the value of hyperspectral imaging. A novel method of matched filtering known as science based multivariate calibration (SBC) was developed for hyperspectral calibration. Classical (MLR) and inverse (PLS, PCR) methods are combined by spectroscopically measuring the spectral "signal" and by statistically estimating the spectral "noise." The accuracy of the inverse model is thus combined with the easy interpretability of the classical model. The SBC method is optimized for hyperspectral data in the Hyper-CalTM software used for the present work. The prediction algorithms can then be downloaded into a dedicated FPGA based High-Speed Prediction EngineTM module. Spectral pretreatments and calibration coefficients are stored on interchangeable SD memory cards, and predicted compositions are produced on a USB interface at real-time camera output rates. Applications include minerals, pharmaceuticals, food processing and remote sensing.

  14. Midregional pro-atrial natriuretic peptide and procalcitonin improve survival prediction in VAP.

    PubMed

    Boeck, L; Eggimann, P; Smyrnios, N; Pargger, H; Thakkar, N; Siegemund, M; Marsch, S; Rakic, J; Tamm, M; Stolz, D

    2011-03-01

    Ventilator-associated pneumonia (VAP) affects mortality, morbidity and cost of critical care. Reliable risk estimation might improve end-of-life decisions, resource allocation and outcome. Several scoring systems for survival prediction have been established and optimised over the last decades. Recently, new biomarkers have gained interest in the prognostic field. We assessed whether midregional pro-atrial natriuretic peptide (MR-proANP) and procalcitonin (PCT) improve the predictive value of the Simplified Acute Physiologic Score (SAPS) II and Sequential Related Organ Failure Assessment (SOFA) in VAP. Specified end-points of a prospective multinational trial including 101 patients with VAP were analysed. Death <28 days after VAP onset was the primary end-point. MR-proANP and PCT were elevated at the onset of VAP in nonsurvivors compared with survivors (p = 0.003 and p = 0.017, respectively) and their slope of decline differed significantly (p = 0.018 and p = 0.039, respectively). Patients with the highest MR-proANP quartile at VAP onset were at increased risk for death (log rank p = 0.013). In a logistic regression model, MR-proANP was identified as the best predictor of survival. Adding MR-proANP and PCT to SAPS II and SOFA improved their predictive properties (area under the curve 0.895 and 0.880). We conclude that the combination of two biomarkers, MR-proANP and PCT, improve survival prediction of clinical severity scores in VAP.

  15. Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample

    PubMed Central

    Radman, Andreja; Gredičak, Matija; Kopriva, Ivica; Jerić, Ivanka

    2011-01-01

    Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met) with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel) support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM) regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample. PMID:22272081

  16. Air oxidation method employed for the disulfide bond formation of natural and synthetic peptides.

    PubMed

    Calce, Enrica; Vitale, Rosa Maria; Scaloni, Andrea; Amodeo, Pietro; De Luca, Stefania

    2015-08-01

    Among the available protocols, chemically driven approaches to oxidize cysteine may not be required for molecules that, under the native-like conditions, naturally fold in conformations ensuring an effective pairing of the right disulfide bridge pattern. In this contest, we successfully prepared the distinctin, a natural heterodimeric peptide, and some synthetic cyclic peptides that are inhibitors of the CXCR4 receptor. In the first case, the air oxidation reaction allowed to connect two peptide chains via disulfide bridge, while in the second case allowed the cyclization of rationally designed peptides by an intramolecular disulfide bridge. Computational approaches helped to either drive de-novo design or suggest structural modifications and optimal oxidization protocols for disulfide-containing molecules. They are able to both predict and to rationalize the propensity of molecules to spontaneously fold in suitable conformations to achieve the right disulfide bridges.

  17. Chemical methods for producing disulfide bonds in peptides and proteins to study folding regulation.

    PubMed

    Okumura, Masaki; Shimamoto, Shigeru; Hidaka, Yuji

    2014-04-01

    Disulfide bonds play a critical role in the folding of secretory and membrane proteins. Oxidative folding reactions of disulfide bond-containing proteins typically require several hours or days, and numerous misbridged disulfide isomers are often observed as intermediates. The rate-determining step in refolding is thought to be the disulfide-exchange reaction from nonnative to native disulfide bonds in folding intermediates, which often precipitate during the refolding process because of their hydrophobic properties. To overcome this, chemical additives or a disulfide catalyst, protein disulfide isomerase (PDI), are generally used in refolding experiments to regulate disulfide-coupled peptide and protein folding. This unit describes such methods in the context of the thermodynamic and kinetic control of peptide and protein folding, including (1) regulation of disulfide-coupled peptides and protein folding assisted by chemical additives, (2) reductive unfolding of disulfide-containing peptides and proteins, and (3) regulation of disulfide-coupled peptide and protein folding using PDI.

  18. Self-assembling peptide amphiphiles and related methods for growth factor delivery

    DOEpatents

    Stupp, Samuel I.; Donners, Jack J. J. M.; Silva, Gabriel A.; Behanna, Heather A.; Anthony, Shawn G.

    2009-06-09

    Amphiphilic peptide compounds comprising one or more epitope sequences for binding interaction with one or more corresponding growth factors, micellar assemblies of such compounds and related methods of use.

  19. Self-assembling peptide amphiphiles and related methods for growth factor delivery

    DOEpatents

    Stupp, Samuel I [Chicago, IL; Donners, Jack J. J. M.; Silva, Gabriel A [Chicago, IL; Behanna, Heather A [Chicago, IL; Anthony, Shawn G [New Stanton, PA

    2012-03-20

    Amphiphilic peptide compounds comprising one or more epitope sequences for binding interaction with one or more corresponding growth factors, micellar assemblies of such compounds and related methods of use.

  20. Self-assembling peptide amphiphiles and related methods for growth factor delivery

    DOEpatents

    Stupp, Samuel I; Donners, Jack J.J.M.; Silva, Gabriel A; Behanna, Heather A; Anthony, Shawn G

    2013-11-12

    Amphiphilic peptide compounds comprising one or more epitope sequences for binding interaction with one or more corresponding growth factors, micellar assemblies of such compounds and related methods of use.

  1. Homology modelling of frequent HLA class-II alleles: A perspective to improve prediction of HLA binding peptide and understand the HLA associated disease susceptibility.

    PubMed

    Kashyap, Manju; Farooq, Umar; Jaiswal, Varun

    2016-10-01

    Human leukocyte antigen (HLA) plays significant role via the regulation of immune system and contribute in the progression and protection of many diseases. HLA molecules bind and present peptides to T- cell receptors which generate the immune response. HLA peptide interaction and molecular function of HLA molecule is the key to predict peptide binding and understanding its role in different diseases. The availability of accurate three dimensional (3D) structures is the initial step towards this direction. In the present work, homology modelling of important and frequent HLA-DRB1 alleles (07:01, 11:01 and 09:01) was done and acceptable models were generated. These modelled alleles were further refined and cross validated by using several methods including Ramachandran plot, Z-score, ERRAT analysis and root mean square deviation (RMSD) calculations. It is known that numbers of allelic variants are related to the susceptibility or protection of various infectious diseases. Difference in amino acid sequences and structures of alleles were also studied to understand the association of HLA with disease susceptibility and protection. Susceptible alleles showed more amino acid variations than protective alleles in three selected diseases caused by different pathogens. Amino acid variations at binding site were found to be more than other part of alleles. RMSD values were also higher at variable positions within binding site. Higher RMSD values indicate that mutations occurring at peptide binding site alter protein structure more than rest of the protein. Hence, these findings and modelled structures can be used to design HLA-DRB1 binding peptides to overcome low prediction accuracy of HLA class II binding peptides. Furthermore, it may help to understand the allele specific molecular mechanisms involved in susceptibility/resistance against pathogenic diseases.

  2. Relaxation time prediction for a light switchable peptide by molecular dynamics.

    PubMed

    Denschlag, Robert; Schreier, Wolfgang J; Rieff, Benjamin; Schrader, Tobias E; Koller, Florian O; Moroder, Luis; Zinth, Wolfgang; Tavan, Paul

    2010-06-21

    We study a monocyclic peptide called cAPB, whose conformations are light switchable due to the covalent integration of an azobenzene dye. Molecular dynamics (MD) simulations using the CHARMM22 force field and its CMAP extension serve us to sample the two distinct conformational ensembles of cAPB, which belong to the cis and trans isomers of the dye, at room temperature. For gaining sufficient statistics we apply a novel replica exchange technique. We find that the well-known NMR distance restraints are much better described by CMAP than by CHARMM22. In cAPB, the ultrafast cis/trans photoisomerization of the dye elicits a relaxation dynamics of the peptide backbone. Experimentally, we probe this relaxation at picosecond time resolution by IR spectroscopy in the amide I range up to 3 ns after the UV/vis pump flash. We interpret the spectroscopically identified decay kinetics using ensembles of non-equilibrium MD simulations, which provide kinetic data on conformational transitions well matching the observed kinetics. Whereas spectroscopy solely indicates that the relaxation toward the equilibrium trans ensemble is by no means complete after 3 ns, the 20 ns MD simulations of the process predict, independently of the applied force field, that the final relaxation into the trans-ensemble proceeds on a time scale of 23 ns. Overall our explicit solvent simulations cover more than 6 micros.

  3. The investigation of the secondary structures of various peptide sequences of β-casein by the multicanonical simulation method

    NASA Astrophysics Data System (ADS)

    Yaşar, F.; Çelik, S.; Köksel, H.

    2006-05-01

    The structural properties of Arginine-Glutamic acid-Leucine-Glutamic acid-Glutamic acid-Leucine-Asparagine-Valine-Proline-Glycine (RELEELNVPG, in one letter code), Glutamic acid-Glutamic acid-Glutamine-Glutamine-Glutamine-Threonine-Glutamic acid (EEQQQTE) and Glutamic acid-Aspartic acid-Glutamic acid-Leucine-Glutamine-Aspartic acid-Lysine-Isoleucine (EDELQDKI) peptide sequences of β-casein were studied by three-dimensional molecular modeling. In this work, the three-dimensional conformations of each peptide from their primary sequences were obtained by multicanonical simulations. With using major advantage of this simulation technique, Ramachandran plots were prepared and analysed to predict the relative occurrence probabilities of β-turn, γ-turn and helical structures. Structural predictions of these sequences of β-casein molecule indicate the presence of high level of helical structures and βIII-turns. The occurrence probabilities of inverse and classical β-turns were low. The probability of helical structure of each sequence significantly decreased when the temperature increased. Our results show these peptides have highly helical structure and better agreement with the results of spectroscopic techniques and other prediction methods.

  4. Mass spectrometry methods for predicting antibiotic resistance.

    PubMed

    Charretier, Yannick; Schrenzel, Jacques

    2016-10-01

    Developing elaborate techniques for clinical applications can be a complicated process. Whole-cell MALDI-TOF MS revolutionized reliable microorganism identification in clinical microbiology laboratories and is now replacing phenotypic microbial identification. This technique is a generic, accurate, rapid, and cost-effective growth-based method. Antibiotic resistance keeps emerging in environmental and clinical microorganisms, leading to clinical therapeutic challenges, especially for Gram-negative bacteria. Antimicrobial susceptibility testing is used to reliably predict antimicrobial success in treating infection, but it is inherently limited by the need to isolate and grow cultures, delaying the application of appropriate therapies. Antibiotic resistance prediction by growth-independent methods is expected to reduce the turnaround time. Recently, the potential of next-generation sequencing and microarrays in predicting microbial resistance has been demonstrated, and this review evaluates the potential of MS in this field. First, technological advances are described, and the possibility of predicting antibiotic resistance by MS is then illustrated for three prototypical human pathogens: Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. Clearly, MS methods can identify antimicrobial resistance mediated by horizontal gene transfers or by mutations that affect the quantity of a gene product, whereas antimicrobial resistance mediated by target mutations remains difficult to detect.

  5. A peptide N-terminal protection strategy for comprehensive glycoproteome analysis using hydrazide chemistry based method.

    PubMed

    Huang, Junfeng; Qin, Hongqiang; Sun, Zhen; Huang, Guang; Mao, Jiawei; Cheng, Kai; Zhang, Zhang; Wan, Hao; Yao, Yating; Dong, Jing; Zhu, Jun; Wang, Fangjun; Ye, Mingliang; Zou, Hanfa

    2015-05-11

    Enrichment of glycopeptides by hydrazide chemistry (HC) is a popular method for glycoproteomics analysis. However, possible side reactions of peptide backbones during the glycan oxidation in this method have not been comprehensively studied. Here, we developed a proteomics approach to locate such side reactions and found several types of the side reactions that could seriously compromise the performance of glycoproteomics analysis. Particularly, the HC method failed to identify N-terminal Ser/Thr glycopeptides because the oxidation of vicinal amino alcohol on these peptides generates aldehyde groups and after they are covalently coupled to HC beads, these peptides cannot be released by PNGase F for identification. To overcome this drawback, we apply a peptide N-terminal protection strategy in which primary amine groups on peptides are chemically blocked via dimethyl labeling, thus the vicinal amino alcohols on peptide N-termini are eliminated. Our results showed that this strategy successfully prevented the oxidation of peptide N-termini and significantly improved the coverage of glycoproteome.

  6. Predicting abrasive wear with coupled Lagrangian methods

    NASA Astrophysics Data System (ADS)

    Beck, Florian; Eberhard, Peter

    2015-05-01

    In this paper, a mesh-less approach for the simulation of a fluid with particle loading and the prediction of abrasive wear is presented. We are using the smoothed particle hydrodynamics (SPH) method for modeling the fluid and the discrete element method (DEM) for the solid particles, which represent the loading of the fluid. These Lagrangian methods are used to describe heavily sloshing fluids with their free surfaces as well as the interface between the fluid and the solid particles accurately. A Reynolds-averaged Navier-Stokes equations model is applied for handling turbulences. We are predicting abrasive wear on the boundary geometry with two different wear models taking cutting and deformation mechanisms into account. The boundary geometry is discretized with special DEM particles. In doing so, it is possible to use the same particle type for both the calculation of the boundary conditions for the SPH method as well as the DEM and for predicting the abrasive wear. After a brief introduction to the SPH method and the DEM, the handling of the boundary and the coupling of the fluid and the solid particles are discussed. Then, the applied wear models are presented and the simulation scenarios are described. The first numerical experiment is the simulation of a fluid with loading which is sloshing inside a tank. The second numerical experiment is the simulation of the impact of a free jet with loading to a simplified pelton bucket. We are especially investigating the wear patterns inside the tank and the bucket.

  7. Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms.

    PubMed

    González-Díaz, Humberto; Pérez-Montoto, Lázaro G; Ubeira, Florencio M

    2014-01-01

    Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design.

  8. Computational prediction of the pKas of small peptides through Conceptual DFT descriptors

    NASA Astrophysics Data System (ADS)

    Frau, Juan; Hernández-Haro, Noemí; Glossman-Mitnik, Daniel

    2017-03-01

    The experimental pKa of a group of simple amines have been plotted against several Conceptual DFT descriptors calculated by means of different density functionals, basis sets and solvation schemes. It was found that the best fits are those that relate the pKa of the amines with the global hardness η through the MN12SX density functional in connection with the Def2TZVP basis set and the SMD solvation model, using water as a solvent. The parameterized equation resulting from the linear regression analysis has then been used for the prediction of the pKa of small peptides of interest in the study of diabetes and Alzheimer disease. The accuracy of the results is relatively good, with a MAD of 0.36 units of pKa.

  9. Prediction of radiological outcome in early rheumatoid arthritis in clinical practice: role of antibodies to citrullinated peptides (anti-CCP)

    PubMed Central

    Forslind, K; Ahlmen, M; Eberhardt, K; Hafstrom, I; Svensson, B

    2004-01-01

    Objective: To investigate the role of anti-cyclic citrullinated peptide antibody (anti-CCP) for the prediction of radiological outcome in patients with early rheumatoid arthritis. Methods: Anti-CCP was assessed at baseline in 379 patients with early rheumatoid arthritis (disease duration <1 year). Radiological joint damage and progression were assessed by Larsen score after two years of follow up (end point) and used as outcome variables. The prognostic value of anti-CCP and other demographic and disease related baseline variables were assessed by univariate and multivariate analyses, including calculation of odds ratios (OR), predictive values, and multiple logistic regression models. Results: The presence of anti-CCP was associated with significantly higher Larsen score both at baseline and at end point. Univariate predictor analysis showed that anti-CCP had the highest significant OR for radiological joint damage and progression after baseline Larsen score, followed by rheumatoid factor, erythrocyte sedimentation rate (ESR), C reactive protein, age, smoking status, and sex. In stepwise multiple regression analyses, baseline Larsen score, anti-CCP, and ESR were selected as significant independent predictors of the radiological outcomes. Conclusions: There is good evidence for an association of anti-CCP with radiological joint changes in rheumatoid arthritis. Anti-CCP is an independent predictor of radiological damage and progression. Though prediction in early rheumatoid arthritis is still far from perfect, the use of anti-CCP in clinical practice should make it easier for rheumatologists to reach judicious treatment decisions. PMID:15308518

  10. Predicting the composition of red wine blends using an array of multicomponent Peptide-based sensors.

    PubMed

    Ghanem, Eman; Hopfer, Helene; Navarro, Andrea; Ritzer, Maxwell S; Mahmood, Lina; Fredell, Morgan; Cubley, Ashley; Bolen, Jessica; Fattah, Rabia; Teasdale, Katherine; Lieu, Linh; Chua, Tedmund; Marini, Federico; Heymann, Hildegarde; Anslyn, Eric V

    2015-05-20

    Differential sensing using synthetic receptors as mimics of the mammalian senses of taste and smell is a powerful approach for the analysis of complex mixtures. Herein, we report on the effectiveness of a cross-reactive, supramolecular, peptide-based sensing array in differentiating and predicting the composition of red wine blends. Fifteen blends of Cabernet Sauvignon, Merlot and Cabernet Franc, in addition to the mono varietals, were used in this investigation. Linear Discriminant Analysis (LDA) showed a clear differentiation of blends based on tannin concentration and composition where certain mono varietals like Cabernet Sauvignon seemed to contribute less to the overall characteristics of the blend. Partial Least Squares (PLS) Regression and cross validation were used to build a predictive model for the responses of the receptors to eleven binary blends and the three mono varietals. The optimized model was later used to predict the percentage of each mono varietal in an independent test set composted of four tri-blends with a 15% average error. A partial least square regression model using the mouth-feel and taste descriptive sensory attributes of the wine blends revealed a strong correlation of the receptors to perceived astringency, which is indicative of selective binding to polyphenols in wine.

  11. Statistical estimation of statistical mechanical models: helix-coil theory and peptide helicity prediction.

    PubMed

    Schmidler, Scott C; Lucas, Joseph E; Oas, Terrence G

    2007-12-01

    Analysis of biopolymer sequences and structures generally adopts one of two approaches: use of detailed biophysical theoretical models of the system with experimentally-determined parameters, or largely empirical statistical models obtained by extracting parameters from large datasets. In this work, we demonstrate a merger of these two approaches using Bayesian statistics. We adopt a common biophysical model for local protein folding and peptide configuration, the helix-coil model. The parameters of this model are estimated by statistical fitting to a large dataset, using prior distributions based on experimental data. L(1)-norm shrinkage priors are applied to induce sparsity among the estimated parameters, resulting in a significantly simplified model. Formal statistical procedures for evaluating support in the data for previously proposed model extensions are presented. We demonstrate the advantages of this approach including improved prediction accuracy and quantification of prediction uncertainty, and discuss opportunities for statistical design of experiments. Our approach yields a 39% improvement in mean-squared predictive error over the current best algorithm for this problem. In the process we also provide an efficient recursive algorithm for exact calculation of ensemble helicity including sidechain interactions, and derive an explicit relation between homo- and heteropolymer helix-coil theories and Markov chains and (non-standard) hidden Markov models respectively, which has not appeared in the literature previously.

  12. A Method for Selective Enrichment and Analysis of Nitrotyrosine-Containing Peptides in Complex Proteome Samples

    SciTech Connect

    Zhang, Qibin; Qian, Weijun; Knyushko, Tanya V.; Clauss, Therese RW; Purvine, Samuel O.; Moore, Ronald J.; Sacksteder, Colette A.; Chin, Mark H.; Smith, Desmond J.; Camp, David G.; Bigelow, Diana J.; Smith, Richard D.

    2007-06-01

    Elevated levels of protein tyrosine nitration have been found in various neurodegenerative diseases and aging related pathologies; however, the lack of an efficient enrichment method has prevented the analysis of this important low level protein modification. We have developed an efficient method for specific enrichment of nitrotyrosine containing peptides that permits nitrotyrosine peptides and specific nitration sites to be unambiguously identified with LC-MS/MS. The method is based on the derivatization of nitrotyrosine into free sulfhydryl groups followed by high efficiency enrichment of sulfhydryl-containing peptides with thiopropyl sepharose beads. The derivatization process starts with acetylation with acetic anhydride to block all primary amines, followed by reduction of nitrotyrosine to aminotyrosine, then derivatization of aminotyrosine with N-Succinimidyl S-Acetylthioacetate (SATA), and finally deprotecting of S-acetyl on SATA to form free sulfhydryl groups. This method was evaluated using nitrotyrosine containing peptides, in-vitro nitrated human histone 1.2, and bovine serum albumin (BSA). 91% and 62% of the identified peptides from enriched histone and BSA samples were nitrotyrosine derivatized peptides, respectively, suggesting relative high specificity of the enrichment method. The application of this method to in-vitro nitrated mouse brain homogenate resulted in 35% of identified peptides containing nitrotyrosine (compared to only 5.9% observed from the global analysis of unenriched sample), and a total of 150 unique nitrated peptides covering 102 proteins were identified with a false discovery rate estimated at 3.3% from duplicate LC-MS/MS analyses of a single enriched sample.

  13. State of the art of immunoassay methods for B-type natriuretic peptides: An update.

    PubMed

    Clerico, Aldo; Franzini, Maria; Masotti, Silvia; Prontera, Concetta; Passino, Claudio

    2015-01-01

    The aim of this review article is to give an update on the state of the art of the immunoassay methods for the measurement of B-type natriuretic peptide (BNP) and its related peptides. Using chromatographic procedures, several studies reported an increasing number of circulating peptides related to BNP in human plasma of patients with heart failure. These peptides may have reduced or even no biological activity. Furthermore, other studies have suggested that, using immunoassays that are considered specific for BNP, the precursor of the peptide hormone, proBNP, constitutes a major portion of the peptide measured in plasma of patients with heart failure. Because BNP immunoassay methods show large (up to 50%) systematic differences in values, the use of identical decision values for all immunoassay methods, as suggested by the most recent international guidelines, seems unreasonable. Since proBNP significantly cross-reacts with all commercial immunoassay methods considered specific for BNP, manufacturers should test and clearly declare the degree of cross-reactivity of glycosylated and non-glycosylated proBNP in their BNP immunoassay methods. Clinicians should take into account that there are large systematic differences between methods when they compare results from different laboratories that use different BNP immunoassays. On the other hand, clinical laboratories should take part in external quality assessment (EQA) programs to evaluate the bias of their method in comparison to other BNP methods. Finally, the authors believe that the development of more specific methods for the active peptide, BNP1-32, should reduce the systematic differences between methods and result in better harmonization of results.

  14. Modern Prediction Methods for Turbomachine Performance

    DTIC Science & Technology

    1976-01-01

    the Consultan’. and Exechange Programme. Propulsion system development costs may be significantly reduced by improvement of methods tor prediction of...of Science and Technology Ames, Iowa 50011 United States of America Aircraft propulsion system development time and cost could be significantly reduced...information is required about things like %tart up performance, wind - milling and altitude light up capability, rapid thrust changes etc. The

  15. Discovery of peptidic miR-21 processing inhibitor by mirror image phage display: A novel method to generate RNA binding D-peptides.

    PubMed

    Sakamoto, Kotaro; Otake, Kentaro; Umemoto, Tadashi

    2017-02-15

    A novel method to generate RNA binding D-peptide has been developed. To achieve the screening method, phage display was applied to "Mirrored" RNA (L-enantiomer of RNA). We have selected pre-miR21 as an initial screening target to demonstrate the method. The mirrored pre-miR-21 binding peptide sequences were successfully obtained, and were chemically synthesized using D-amino acids. D-peptide is expected to have favorable properties as a drug candidate such as protease resistance and low immunogenicity. As a result of binding evaluation of the D-peptide to pre-miR-21, the EC50 value was 440nM. In addition, the D-peptide possessed inhibition activity to miR-21 processing.

  16. A manual sequence method of peptides and phosphopeptides using 4-(1'-cyanoisoindolyl)phenylisothiocyanate.

    PubMed

    Shibata, Takayuki; Wainaina, Moses N; Miyoshi, Takayuki; Kabashima, Tsutomu; Kai, Masaaki

    2011-06-17

    A method for sequence analysis and identification of phosphoamino acids in peptides based on high performance liquid chromatography (HPLC) is described. The peptides were derivatized with an Edman type reagent, 4-(1'-cyanoisoindolyl)phenylisothiocyanate (CIPIC) and subsequently cleaved to generate stable and fluorescent 4-(1'-cyanoisoindolyl)phenylthiazolinone (CIP-TZ)-amino acids. Several experimental factors that affected derivatization on membranes were examined. Under the optimized conditions, the CIP-TZ derivatives of Try(p), Thr(p) and Ser(p) were obtained and separated from their parent amino acids with baseline resolution using an isocratic elution system. Up to the 4th residue of phosphorylated pentapeptides was successfully identified, whereas phosphoamino acid residues could not be detected by the conventional procedure using phenylisothiocyanate (PITC). The results demonstrated the potential of CIPIC as a derivatization reagent for peptide sequencing and the applicability of the method for the study and identification of phosphoamino acids in peptides.

  17. Machine learning study of classifiers trained with biophysiochemical properties of amino acids to predict fibril forming Peptide motifs.

    PubMed

    Kumaran Nair, Smitha Sunil; Subba Reddy, N V; Hareesha, K S

    2012-09-01

    It is important to understand the cause of amyloid illnesses by predicting the short protein fragments capable of forming amyloid-like fibril motifs aiding in the discovery of sequence-targeted anti-aggregation drugs. It is extremely desirable to design computational tools to provide affordable in silico predictions owing to the limitations of molecular techniques for their identification. In this research article, we tried to study, from a machine learning perspective, the performance of several machine learning classifiers that use heterogenous features based on biochemical and biophysical properties of amino acids to discriminate between amyloidogenic and non-amyloidogenic regions in peptides. Four conventional machine learning classifiers namely Support Vector Machine, Neural network, Decision tree and Random forest were trained and tested to find the best classifier that fits the problem domain well. Prior to classification, novel implementations of two biologically-inspired feature optimization techniques based on evolutionary algorithms and methodologies that mimic social life and a multivariate method based on projection are utilized in order to remove the unimportant and uninformative features. Among the dimenionality reduction algorithms considered under the study, prediction results show that algorithms based on evolutionary computation is the most effective. SVM best suits the problem domain in its fitment among the classifiers considered. The best classifier is also compared with an online predictor to evidence the equilibrium maintained between true positive rates and false positive rates in the proposed classifier. This exploratory study suggests that these methods are promising in providing amyloidogenity prediction and may be further extended for large-scale proteomic studies.

  18. A numerical method for predicting hypersonic flowfields

    NASA Technical Reports Server (NTRS)

    Maccormack, Robert W.; Candler, Graham V.

    1989-01-01

    The flow about a body traveling at hypersonic speed is energetic enough to cause the atmospheric gases to chemically react and reach states in thermal nonequilibrium. The prediction of hypersonic flowfields requires a numerical method capable of solving the conservation equations of fluid flow, the chemical rate equations for specie formation and dissociation, and the transfer of energy relations between translational and vibrational temperature states. Because the number of equations to be solved is large, the numerical method should also be as efficient as possible. The proposed paper presents a fully implicit method that fully couples the solution of the fluid flow equations with the gas physics and chemistry relations. The method flux splits the inviscid flow terms, central differences of the viscous terms, preserves element conservation in the strong chemistry source terms, and solves the resulting block matrix equation by Gauss Seidel line relaxation.

  19. B-Type Natriuretic Peptide Levels Predict Ventricular Arrhythmia Post Left Ventricular Assist Device Implantation.

    PubMed

    Hellman, Yaron; Malik, Adnan S; Lin, Hongbo; Shen, Changyu; Wang, I-Wen; Wozniak, Thomas C; Hashmi, Zubair A; Pickrell, Jeanette; Jani, Milena; Caccamo, Marco A; Gradus-Pizlo, Irmina; Hadi, Azam

    2015-12-01

    B-type natriuretic peptide (BNP) levels have been shown to predict ventricular arrhythmia (VA) and sudden death in patients with heart failure. We sought to determine whether BNP levels before left ventricular assist device (LVAD) implantation can predict VA post LVAD implantation in advanced heart failure patients. We conducted a retrospective study consisting of patients who underwent LVAD implantation in our institution during the period of May 2009-March 2013. The study was limited to patients receiving a HeartMate II or HeartWare LVAD. Acute myocardial infarction patients were excluded. We compared between the patients who developed VA within 15 days post LVAD implantation to the patients without VA. A total of 85 patients underwent LVAD implantation during the study period. Eleven patients were excluded (five acute MI, four without BNP measurements, and two discharged earlier than 13 days post LVAD implantation). The incidence of VA was 31%, with 91% ventricular tachycardia (VT) and 9% ventricular fibrillation. BNP remained the single most powerful predictor of VA even after adjustment for other borderline significant factors in a multivariate logistic regression model (P < 0.05). BNP levels are a strong predictor of VA post LVAD implantation, surpassing previously described risk factors such as age and VT in the past.

  20. Inhibition of the Hantavirus Fusion Process by Predicted Domain III and Stem Peptides from Glycoprotein Gc.

    PubMed

    Barriga, Gonzalo P; Villalón-Letelier, Fernando; Márquez, Chantal L; Bignon, Eduardo A; Acuña, Rodrigo; Ross, Breyan H; Monasterio, Octavio; Mardones, Gonzalo A; Vidal, Simon E; Tischler, Nicole D

    2016-07-01

    Hantaviruses can cause hantavirus pulmonary syndrome or hemorrhagic fever with renal syndrome in humans. To enter cells, hantaviruses fuse their envelope membrane with host cell membranes. Previously, we have shown that the Gc envelope glycoprotein is the viral fusion protein sharing characteristics with class II fusion proteins. The ectodomain of class II fusion proteins is composed of three domains connected by a stem region to a transmembrane anchor in the viral envelope. These fusion proteins can be inhibited through exogenous fusion protein fragments spanning domain III (DIII) and the stem region. Such fragments are thought to interact with the core of the fusion protein trimer during the transition from its pre-fusion to its post-fusion conformation. Based on our previous homology model structure for Gc from Andes hantavirus (ANDV), here we predicted and generated recombinant DIII and stem peptides to test whether these fragments inhibit hantavirus membrane fusion and cell entry. Recombinant ANDV DIII was soluble, presented disulfide bridges and beta-sheet secondary structure, supporting the in silico model. Using DIII and the C-terminal part of the stem region, the infection of cells by ANDV was blocked up to 60% when fusion of ANDV occurred within the endosomal route, and up to 95% when fusion occurred with the plasma membrane. Furthermore, the fragments impaired ANDV glycoprotein-mediated cell-cell fusion, and cross-inhibited the fusion mediated by the glycoproteins from Puumala virus (PUUV). The Gc fragments interfered in ANDV cell entry by preventing membrane hemifusion and pore formation, retaining Gc in a non-resistant homotrimer stage, as described for DIII and stem peptide inhibitors of class II fusion proteins. Collectively, our results demonstrate that hantavirus Gc shares not only structural, but also mechanistic similarity with class II viral fusion proteins, and will hopefully help in developing novel therapeutic strategies against hantaviruses.

  1. Inhibition of the Hantavirus Fusion Process by Predicted Domain III and Stem Peptides from Glycoprotein Gc

    PubMed Central

    Barriga, Gonzalo P.; Villalón-Letelier, Fernando; Márquez, Chantal L.; Bignon, Eduardo A.; Acuña, Rodrigo; Ross, Breyan H.; Monasterio, Octavio; Mardones, Gonzalo A.; Vidal, Simon E.; Tischler, Nicole D.

    2016-01-01

    Hantaviruses can cause hantavirus pulmonary syndrome or hemorrhagic fever with renal syndrome in humans. To enter cells, hantaviruses fuse their envelope membrane with host cell membranes. Previously, we have shown that the Gc envelope glycoprotein is the viral fusion protein sharing characteristics with class II fusion proteins. The ectodomain of class II fusion proteins is composed of three domains connected by a stem region to a transmembrane anchor in the viral envelope. These fusion proteins can be inhibited through exogenous fusion protein fragments spanning domain III (DIII) and the stem region. Such fragments are thought to interact with the core of the fusion protein trimer during the transition from its pre-fusion to its post-fusion conformation. Based on our previous homology model structure for Gc from Andes hantavirus (ANDV), here we predicted and generated recombinant DIII and stem peptides to test whether these fragments inhibit hantavirus membrane fusion and cell entry. Recombinant ANDV DIII was soluble, presented disulfide bridges and beta-sheet secondary structure, supporting the in silico model. Using DIII and the C-terminal part of the stem region, the infection of cells by ANDV was blocked up to 60% when fusion of ANDV occurred within the endosomal route, and up to 95% when fusion occurred with the plasma membrane. Furthermore, the fragments impaired ANDV glycoprotein-mediated cell-cell fusion, and cross-inhibited the fusion mediated by the glycoproteins from Puumala virus (PUUV). The Gc fragments interfered in ANDV cell entry by preventing membrane hemifusion and pore formation, retaining Gc in a non-resistant homotrimer stage, as described for DIII and stem peptide inhibitors of class II fusion proteins. Collectively, our results demonstrate that hantavirus Gc shares not only structural, but also mechanistic similarity with class II viral fusion proteins, and will hopefully help in developing novel therapeutic strategies against hantaviruses

  2. Insights from the analysis of predicted Rv0679c protein peptide from Mycobacterium tuberculosis with Toll like Receptors in host

    PubMed Central

    Lavarti, Rupa; Ganugapati, Jayasree; Ratcha, Shirisa; Rao, Lakshmana SS; SivaSai, Krovvidi SR

    2016-01-01

    Peptides of Rv0679c a membrane protein of the cell envelope (16.6 KDa) of Mycobacterium tuberculosis (M. tb), inhibited entry of live bacilli into epithelial (A549) and macrophage (U937) cell lines in vitro, suggesting a possible role in invasion. Receptors associated with Rv0679c antigen entry into cell lines were not characterized. We are reporting that Rv0679c peptides could bind to Toll like receptors (TLRs), the principal class of pathogen recognition receptors on host cells (PRR) by docking studies. Peptide structures were predicted using PEP FOLD and docking of truncated peptides with TLR’s was performed using Cluspro 2.0. Docked complexes were analyzed using Swiss-PDB Viewer. Nine peptides of Rv0679c protein assessed were able to bind to TLR2-1 and TLR 4-MD2; however the binding energy was better with TLR 4-MD2. Peptide 30985 (-866.4 kcal/mol) has better binding energy with TLR2-1, in contrast peptide 30982 showed a better binding energy to TLR 4-MD2 dimer with a score of -1291.7 kcal/mol. Interactive residue analysis revealed that GLU 173 and SER 454 of TLR 1; ARG 447 and ARG 486 of TLR2; ARG 264 of TLR 4 and SER 120, LYS 122 and GLU 92 of MD2 region are predominant residues interacting with peptides of Rv0679c protein. Our study suggests that predominant residues and receptors of TLR2 and TLR4 are important for Rv0679c protein binding, which could further lead to invasion of M. tb into the host cell. PMID:28246463

  3. Airframe Noise Prediction Using the Sngr Method

    NASA Astrophysics Data System (ADS)

    Chen, Rongqian; Wu, Yizhao; Xia, Jian

    In this paper, the Stochastic Noise Generation and Radiation method (SNGR) is used to predict airframe noise. The SNGR method combines a stochastic model with Computational Fluid Dynamics (CFD), and it can give acceptable noise results while the computation cost is relatively low. In the method, the time-averaged mean flow field is firstly obtained by solving Reynolds Averaged Navier-Stokes equations (RANS), and a stochastic velocity is generated based on the obtained information. Then the turbulent field is used to generate the source for the Acoustic Perturbation Equations (APEs) that simulate the noise propagation. For numerical methods, timeaveraged RANS equations are solved by finite volume method, and the turbulent model is K - ɛ model; APEs are solved by finite difference method, and the numerical scheme is the Dispersion-Relation-Preserving (DRP) scheme, with explicit optimized 5-stage Rung-Kutta scheme time step. In order to test the APE solver, propagation of a Gaussian pulse in a uniform mean flow is firstly simulated and compared with the analytical solution. Then, using the method, the trailing edge noise of NACA0012 airfoil is calculated. The results are compared with reference data, and good agreements are demonstrated.

  4. Computational predictive methods for fracture and fatigue

    NASA Technical Reports Server (NTRS)

    Cordes, J.; Chang, A. T.; Nelson, N.; Kim, Y.

    1994-01-01

    The damage-tolerant design philosophy as used by aircraft industries enables aircraft components and aircraft structures to operate safely with minor damage, small cracks, and flaws. Maintenance and inspection procedures insure that damages developed during service remain below design values. When damage is found, repairs or design modifications are implemented and flight is resumed. Design and redesign guidelines, such as military specifications MIL-A-83444, have successfully reduced the incidence of damage and cracks. However, fatigue cracks continue to appear in aircraft well before the design life has expired. The F16 airplane, for instance, developed small cracks in the engine mount, wing support, bulk heads, the fuselage upper skin, the fuel shelf joints, and along the upper wings. Some cracks were found after 600 hours of the 8000 hour design service life and design modifications were required. Tests on the F16 plane showed that the design loading conditions were close to the predicted loading conditions. Improvements to analytic methods for predicting fatigue crack growth adjacent to holes, when multiple damage sites are present, and in corrosive environments would result in more cost-effective designs, fewer repairs, and fewer redesigns. The overall objective of the research described in this paper is to develop, verify, and extend the computational efficiency of analysis procedures necessary for damage tolerant design. This paper describes an elastic/plastic fracture method and an associated fatigue analysis method for damage tolerant design. Both methods are unique in that material parameters such as fracture toughness, R-curve data, and fatigue constants are not required. The methods are implemented with a general-purpose finite element package. Several proof-of-concept examples are given. With further development, the methods could be extended for analysis of multi-site damage, creep-fatigue, and corrosion fatigue problems.

  5. Computational predictive methods for fracture and fatigue

    NASA Astrophysics Data System (ADS)

    Cordes, J.; Chang, A. T.; Nelson, N.; Kim, Y.

    1994-09-01

    The damage-tolerant design philosophy as used by aircraft industries enables aircraft components and aircraft structures to operate safely with minor damage, small cracks, and flaws. Maintenance and inspection procedures insure that damages developed during service remain below design values. When damage is found, repairs or design modifications are implemented and flight is resumed. Design and redesign guidelines, such as military specifications MIL-A-83444, have successfully reduced the incidence of damage and cracks. However, fatigue cracks continue to appear in aircraft well before the design life has expired. The F16 airplane, for instance, developed small cracks in the engine mount, wing support, bulk heads, the fuselage upper skin, the fuel shelf joints, and along the upper wings. Some cracks were found after 600 hours of the 8000 hour design service life and design modifications were required. Tests on the F16 plane showed that the design loading conditions were close to the predicted loading conditions. Improvements to analytic methods for predicting fatigue crack growth adjacent to holes, when multiple damage sites are present, and in corrosive environments would result in more cost-effective designs, fewer repairs, and fewer redesigns. The overall objective of the research described in this paper is to develop, verify, and extend the computational efficiency of analysis procedures necessary for damage tolerant design. This paper describes an elastic/plastic fracture method and an associated fatigue analysis method for damage tolerant design. Both methods are unique in that material parameters such as fracture toughness, R-curve data, and fatigue constants are not required. The methods are implemented with a general-purpose finite element package. Several proof-of-concept examples are given. With further development, the methods could be extended for analysis of multi-site damage, creep-fatigue, and corrosion fatigue problems.

  6. Computational approach for designing tumor homing peptides

    PubMed Central

    Sharma, Arun; Kapoor, Pallavi; Gautam, Ankur; Chaudhary, Kumardeep; Kumar, Rahul; Chauhan, Jagat Singh; Tyagi, Atul; Raghava, Gajendra P. S.

    2013-01-01

    Tumor homing peptides are small peptides that home specifically to tumor and tumor associated microenvironment i.e. tumor vasculature, after systemic delivery. Keeping in mind the huge therapeutic importance of these peptides, we have made an attempt to analyze and predict tumor homing peptides. It was observed that certain types of residues are preferred in tumor homing peptides. Therefore, we developed support vector machine based models for predicting tumor homing peptides using amino acid composition and binary profiles of peptides. Amino acid composition, dipeptide composition and binary profile-based models achieved a maximum accuracy of 86.56%, 82.03%, and 84.19% respectively. These methods have been implemented in a user-friendly web server, TumorHPD. We anticipate that this method will be helpful to design novel tumor homing peptides. TumorHPD web server is freely accessible at http://crdd.osdd.net/raghava/tumorhpd/. PMID:23558316

  7. Estimation of angiotensin peptides in biological samples by LC/MS method.

    PubMed

    Ali, Quaisar; Wu, Yonnie; Nag, Sourashish; Hussain, Tahir

    2014-01-21

    The low abundance of angiotensin peptides in biological tissues such as the kidney cortex, adipose tissue, urine and plasma makes their detection and quantification a challenge. A few available methods used to quantify these peptides involve lengthy processes of sample preparation and are hardly quantitative. Here, we report a mass spectrometry approach for quantifying angiotensin peptides [Ang II, Ang-(1-7)] in the kidney cortex, epididymal white adipose tissue (eWAT), urine and plasma of male mice. Tissue homogenates, urine and plasma samples were solid-phase extracted with C18 Sep-Pak cartridges and eluted off proteinaceous compounds. These extracted peptide samples were separated on C18 column with a linear acetonitrile gradient and detected by Q-ToF mass analyzer in ESI+-MS ion mode based on their retention time, accurate mass measurement of peptides, the isotope pattern of doubly charged molecular ion, and quantitation of peak area (or ion count) when referencing to the angiotensin peptide standards. The lower limit of quantitation for each angiotensin peptide was 10 pgmg(-1) with the percent recovery at 100.6%. The intra-batch precision for Ang-(1-7) and Ang II were 24.0 and 12.7%, accuracy 84.0-123.0% and 100.2-116.0% respectively. Using this method, we determined the levels of Ang II and Ang-(1-7) in the kidney cortex, eWAT, urine and plasma. Quantification of angiotensin peptides could help target subtle therapeutics changes against pathophysiological conditions such as obesity, kidney disease and hypertension.

  8. Pro-A-type natriuretic peptide and pro-adrenomedullin predict progression of chronic kidney disease: the MMKD Study.

    PubMed

    Dieplinger, Benjamin; Mueller, Thomas; Kollerits, Barbara; Struck, Joachim; Ritz, Eberhard; von Eckardstein, Arnold; Haltmayer, Meinhard; Kronenberg, Florian

    2009-02-01

    A-type natriuretic peptide (ANP) and adrenomedullin (ADM) are potent hypotensive, diuretic, and natriuretic peptides involved in maintaining cardiovascular and renal homeostasis. We conducted a prospective 7-year study of 177 nondiabetic patients with primary chronic kidney disease to see if ANP and ADM plasma concentrations predict the progression of their disease, using novel sandwich immunoassays covering the midregional epitopes of the stable prohormones (MRproANP and MR-proADM). Progression of chronic kidney disease was defined as doubling of baseline serum creatinine and/or terminal renal failure, which occurred in 65 patients. Analysis of the receiver operating characteristic curve for the prediction of renal endpoints showed similar areas under the curve for the glomerular filtration rate (GFR) (0.838), MR-proANP (0.810), and MRproADM (0.876), respectively, as did the Kaplan-Meier curve analyses of the patients stratified according to the median of the respective markers. In separate multiple Cox-proportional hazard regression analyses, increased plasma concentrations of both peptides were each strongly predictive of the progression of chronic kidney disease after adjustments for age, gender, GFR, proteinuria and amino-terminal pro-B-type natriuretic peptide. Our study suggests that MR-proANP and MR-proADM are useful new markers of progression of primary nondiabetic chronic kidney disease.

  9. Drug permeability prediction using PMF method.

    PubMed

    Meng, Fancui; Xu, Weiren

    2013-03-01

    Drug permeability determines the oral availability of drugs via cellular membranes. Poor permeability makes a drug unsuitable for further development. The permeability may be estimated as the free energy change that the drug should overcome through crossing membrane. In this paper the drug permeability was simulated using molecular dynamics method and the potential energy profile was calculated with potential of mean force (PMF) method. The membrane was simulated using DPPC bilayer and three drugs with different permeability were tested. PMF studies on these three drugs show that doxorubicin (low permeability) should pass higher free energy barrier from water to DPPC bilayer center while ibuprofen (high permeability) has a lower energy barrier. Our calculation indicates that the simulation model we built is suitable to predict drug permeability.

  10. Quantum chemical calculations predict biological function: The case of T cell receptor interaction with a peptide/MHC class I

    NASA Astrophysics Data System (ADS)

    Antipas, Georgios S. E.; Germenis, Anastasios

    2015-02-01

    A combination of atomic correlation statistics and quantum chemical calculations are shown to predict biological function. In the present study, various antigenic peptide-Major Histocompatibility Complex (pMHC) ligands with near-identical stereochemistries, in complexation with the same T cell receptor (TCR), were found to consistently induce distinctly different quantum chemical behavior, directly dependent on the peptide’s electron spin density and intrinsically expressed by the protonation state of the peptide’s N-terminus. Furthermore, the cumulative coordination difference of any variant in respect to the native peptide was found to accurately reflect peptide biological function and immerges as the physical observable which is directly related to the immunological end-effect of pMHC-TCR interaction.

  11. [A method of determining fragments of peptide bioregulators responsible for interaction with receptors].

    PubMed

    Golubovich, V P; Drboglav, V V

    1989-01-01

    A method for investigating the relationship between chemical structure of peptide molecules and their biological activity is suggested. It is based on a few statistical algorithms which are described. The results of the method testing on the thyrotropin-releasing hormone analogs are presented.

  12. Phase analysis method for burst onset prediction

    NASA Astrophysics Data System (ADS)

    Stellino, Flavio; Mazzoni, Alberto; Storace, Marco

    2017-02-01

    The response of bursting neurons to fluctuating inputs is usually hard to predict, due to their strong nonlinearity. For the same reason, decoding the injected stimulus from the activity of a bursting neuron is generally difficult. In this paper we propose a method describing (for neuron models) a mechanism of phase coding relating the burst onsets with the phase profile of the input current. This relation suggests that burst onset may provide a way for postsynaptic neurons to track the input phase. Moreover, we define a method of phase decoding to solve the inverse problem and estimate the likelihood of burst onset given the input state. Both methods are presented here in a unified framework, describing a complete coding-decoding procedure. This procedure is tested by using different neuron models, stimulated with different inputs (stochastic, sinusoidal, up, and down states). The results obtained show the efficacy and broad range of application of the proposed methods. Possible applications range from the study of sensory information processing, in which phase-of-firing codes are known to play a crucial role, to clinical applications such as deep brain stimulation, helping to design stimuli in order to trigger or prevent neural bursting.

  13. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex

    PubMed Central

    Lamiable, Alexis; Thévenet, Pierre; Rey, Julien; Vavrusa, Marek; Derreumaux, Philippe; Tufféry, Pierre

    2016-01-01

    Structure determination of linear peptides of 5–50 amino acids in aqueous solution and interacting with proteins is a key aspect in structural biology. PEP-FOLD3 is a novel computational framework, that allows both (i) de novo free or biased prediction for linear peptides between 5 and 50 amino acids, and (ii) the generation of native-like conformations of peptides interacting with a protein when the interaction site is known in advance. PEP-FOLD3 is fast, and usually returns solutions in a few minutes. Testing PEP-FOLD3 on 56 peptides in aqueous solution led to experimental-like conformations for 80% of the targets. Using a benchmark of 61 peptide–protein targets starting from the unbound form of the protein receptor, PEP-FOLD3 was able to generate peptide poses deviating on average by 3.3Å from the experimental conformation and return a native-like pose in the first 10 clusters for 52% of the targets. PEP-FOLD3 is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3. PMID:27131374

  14. Cardiac natriuretic peptides are related to left ventricular mass and function and predict mortality in dialysis patients.

    PubMed

    Zoccali, C; Mallamaci, F; Benedetto, F A; Tripepi, G; Parlongo, S; Cataliotti, A; Cutrupi, S; Giacone, G; Bellanuova, I; Cottini, E; Malatino, L S

    2001-07-01

    This study was designed to investigate the relationship among brain natriuretic peptide (BNP) and atrial natriuretic peptide (ANP) and left ventricular mass (LVM), ejection fraction, and LV geometry in a large cohort of dialysis patients without heart failure (n = 246) and to test the prediction power of these peptides for total and cardiovascular mortality. In separate multivariate models of LVM, BNP and ANP were the strongest independent correlates of the LVM index. In these models, the predictive power of BNP was slightly stronger than that of ANP. Both natriuretic peptides also were the strongest independent predictors of ejection fraction, and again BNP was a slightly better predictor of ejection fraction than ANP. In separate multivariate Cox models, the relative risk of death was significantly higher in patients of the third tertile of the distribution of BNP and ANP than in those of the first tertile (BNP, 7.14 [95% confidence interval (CI), 2.83 to 18.01, P = 0.00001]; ANP, 4.22 [95% CI, 1.79 to 9.92, P = 0.001]), and a similar difference was found for cardiovascular death (BNP, 6.72 [95% CI, 2.44 to 18.54, P = 0.0002]; ANP, 3.80 [95% CI, 1.44 to 10.03, P = 0.007]). BNP but not ANP remained as an independent predictor of death in a Cox's model including LVM and ejection fraction. Cardiac natriuretic peptides are linked independently to LVM and function in dialysis patients and predict overall and cardiovascular mortality. The measurement of the plasma concentration of BNP and ANP may be useful for risk stratification in these patients.

  15. Predicting three-dimensional conformations of peptides constructed of only glycine, alanine, aspartic acid, and valine.

    PubMed

    Oda, Akifumi; Fukuyoshi, Shuichi

    2015-06-01

    The GADV hypothesis is a form of the protein world hypothesis, which suggests that life originated from proteins (Lacey et al. 1999; Ikehara 2002; Andras 2006). In the GADV hypothesis, life is thought to have originated from primitive proteins constructed of only glycine, alanine, aspartic acid, and valine ([GADV]-proteins). In this study, the three-dimensional (3D) conformations of randomly generated short [GADV]-peptides were computationally investigated using replica-exchange molecular dynamics (REMD) simulations (Sugita and Okamoto 1999). Because the peptides used in this study consisted of only 20 residues each, they could not form certain 3D structures. However, the conformational tendencies of the peptides were elucidated by analyzing the conformational ensembles generated by REMD simulations. The results indicate that secondary structures can be formed in several randomly generated [GADV]-peptides. A long helical structure was found in one of the hydrophobic peptides, supporting the conjecture of the GADV hypothesis that many peptides aggregated to form peptide multimers with enzymatic activity in the primordial soup. In addition, these results indicate that REMD simulations can be used for the structural investigation of short peptides.

  16. Predicting Three-Dimensional Conformations of Peptides Constructed of Only Glycine, Alanine, Aspartic Acid, and Valine

    NASA Astrophysics Data System (ADS)

    Oda, Akifumi; Fukuyoshi, Shuichi

    2015-06-01

    The GADV hypothesis is a form of the protein world hypothesis, which suggests that life originated from proteins (Lacey et al. 1999; Ikehara 2002; Andras 2006). In the GADV hypothesis, life is thought to have originated from primitive proteins constructed of only glycine, alanine, aspartic acid, and valine ([GADV]-proteins). In this study, the three-dimensional (3D) conformations of randomly generated short [GADV]-peptides were computationally investigated using replica-exchange molecular dynamics (REMD) simulations (Sugita and Okamoto 1999). Because the peptides used in this study consisted of only 20 residues each, they could not form certain 3D structures. However, the conformational tendencies of the peptides were elucidated by analyzing the conformational ensembles generated by REMD simulations. The results indicate that secondary structures can be formed in several randomly generated [GADV]-peptides. A long helical structure was found in one of the hydrophobic peptides, supporting the conjecture of the GADV hypothesis that many peptides aggregated to form peptide multimers with enzymatic activity in the primordial soup. In addition, these results indicate that REMD simulations can be used for the structural investigation of short peptides.

  17. Systems and methods for predicting materials properties

    DOEpatents

    Ceder, Gerbrand; Fischer, Chris; Tibbetts, Kevin; Morgan, Dane; Curtarolo, Stefano

    2007-11-06

    Systems and methods for predicting features of materials of interest. Reference data are analyzed to deduce relationships between the input data sets and output data sets. Reference data includes measured values and/or computed values. The deduced relationships can be specified as equations, correspondences, and/or algorithmic processes that produce appropriate output data when suitable input data is used. In some instances, the output data set is a subset of the input data set, and computational results may be refined by optionally iterating the computational procedure. To deduce features of a new material of interest, a computed or measured input property of the material is provided to an equation, correspondence, or algorithmic procedure previously deduced, and an output is obtained. In some instances, the output is iteratively refined. In some instances, new features deduced for the material of interest are added to a database of input and output data for known materials.

  18. High-yield peptide-extraction method for the discovery of subnanomolar biomarkers from small serum samples.

    PubMed

    Kawashima, Yusuke; Fukutomi, Toshiyuki; Tomonaga, Takeshi; Takahashi, Hiroki; Nomura, Fumio; Maeda, Tadakazu; Kodera, Yoshio

    2010-04-05

    Serum proteins/peptides reflect physiological or pathological states in humans and are an attractive target for the discovery of disease biomarkers. However, the existence of high-abundance proteins and the large dynamic range of serum proteins/peptides make any quantitative analysis of low-abundance proteins/peptides challenging. Furthermore, analyses of peptides, including the cleaved fragments of proteins, are difficult because of carrier protein binding. Here, we developed a differential solubilization (DS) method to extract low-molecular-weight proteins/peptides in serum with good reproducibility and yield as compared to typical peptide-extraction methods such as organic solvent precipitation and ultrafiltration. Using the DS method combined with reverse-phase HPLC fractionation followed by MALDI-TOF-MS, we performed high-quality comparative analyses of more than 1500 peptides from 1 microL of serum samples, including low-abundance peptides in the subnanomolar range and containing many peptides bound to carrier proteins such as albumin. We applied this method and successfully discovered four new biomarker candidates of colon cancer, none of which have previously been observed in serum and one of which is a fragment of the protein zyxin that possibly originated from tumor cells. Our results indicate that serum peptide analyses based on the DS method should greatly contribute to the discovery of novel low-abundance biomarkers.

  19. A method to predict circulation control noise

    NASA Astrophysics Data System (ADS)

    Reger, Robert W.

    Underwater vehicles suffer from reduced maneuverability with conventional lifting append-\\ ages due to the low velocity of operation. Circulation control offers a method to increase maneuverability independent of vehicle speed. However, with circulation control comes additional noise sources, which are not well understood. To better understand these noise sources, a modal-based prediction method is developed, potentially offering a quantitative connection between flow structures and far-field noise. This method involves estimation of the velocity field, surface pressure field, and far-field noise, using only non-time-resolved velocity fields and time-resolved probe measurements. Proper orthogonal decomposition, linear stochastic estimation and Kalman smoothing are employed to estimate time-resolved velocity fields. Poisson's equation is used to calculate time-resolved pressure fields from velocity. Curle's analogy is then used to propagate the surface pressure forces to the far field. This method is developed on a direct numerical simulation of a two-dimensional cylinder at a low Reynolds number (150). Since each of the fields to be estimated are also known from the simulation, a means of obtaining the error from using the methodology is provided. The velocity estimation and the simulated velocity match well when the simulated additive measurement noise is low. The pressure field suffers due to a small domain size; however, the surface pressures estimates fare much better. The far-field estimation contains similar frequency content with reduced magnitudes, attributed to the exclusion of the viscous forces in Curle's analogy. In the absence of added noise, the estimation procedure performs quite nicely for this model problem. The method is tested experimentally on a 650,000 chord-Reynolds-number flow over a 2-D, 20% thick, elliptic circulation control airfoil. Slot jet momentum coefficients of 0 and 0.10 are investigated. Particle image velocimetry, unsteady

  20. Structure of Trichamide, a Cyclic Peptide from the Bloom-Forming Cyanobacterium Trichodesmium erythraeum, Predicted from the Genome Sequence†

    PubMed Central

    Sudek, Sebastian; Haygood, Margo G.; Youssef, Diaa T. A.; Schmidt, Eric W.

    2006-01-01

    A gene cluster for the biosynthesis of a new small cyclic peptide, dubbed trichamide, was discovered in the genome of the global, bloom-forming marine cyanobacterium Trichodesmium erythraeum ISM101 because of striking similarities to the previously characterized patellamide biosynthesis cluster. The tri cluster consists of a precursor peptide gene containing the amino acid sequence for mature trichamide, a putative heterocyclization gene, an oxidase, two proteases, and hypothetical genes. Based upon detailed sequence analysis, a structure was predicted for trichamide and confirmed by Fourier transform mass spectrometry. Trichamide consists of 11 amino acids, including two cysteine-derived thiazole groups, and is cyclized by an N—C terminal amide bond. As the first natural product reported from T. erythraeum, trichamide shows the power of genome mining in the prediction and discovery of new natural products. PMID:16751554

  1. A Novel Method for Anti-HLA Antibody Detection Using Personalized Peptide Arrays

    PubMed Central

    Liu, Pan; Souma, Tomokazu; Wei, Andrew Zu-Sern; Xie, Xueying; Luo, Xunrong; Jin, Jing

    2016-01-01

    Background HLA mismatches are the primary cause of alloantibody-mediated rejection (AMR) in organ transplantation. To delineate antigenic and immunogenic potentials among individual HLA mismatches, information regarding antibody specificity at the epitope level, instead of the allelic level, is needed. Methods This study explores a direct screening method for HLA linear epitopes in kidney transplant patients. We custom synthesized a large panel of 15-residue HLA peptides in an array format and measured alloantibody reactivity to these peptides from the sera of post and/or pretransplant patients. Two design concepts for the arrays were followed: a standard array of a fixed panel of peptides or personalized arrays. The standard array contains 420 peptides derived from a predetermined set of HLA-DQ allelic antigens based on templates also used in the single-antigen beads assay. Results The array detected distinct antiserum patterns among transplant subjects and revealed epitope levels of specificity largely in accordance with the single-antigen results. Two personalized arrays that each included donor-derived peptides of HLA-A, -B, -C, -DQ, and -DR sequences were separately designed for 2 transplant subjects. The personalized arrays detected de novo antibodies following transplantation. The new method also showed superior sensitivity to a single-antigen assay in one of the cases whose pathological diagnosis of AMR occurred before single-antigen assay could detect antibodies. Conclusions This pilot study proved the feasibility of using personalized peptide arrays to achieve detection of alloantibodies for linear HLA epitopes associated with distinct donor-recipient mismatches. Single or multiple reactive epitopes may occur on an individual HLA molecule, and donor-specific HLA-DQ-reactivity among 5 kidney transplant subjects revealed patterns of shared epitopes. PMID:27826602

  2. High-performance liquid chromatographic method for the determination of prolyl peptides in urine.

    PubMed

    Codini, M; Palmerini, C A; Fini, C; Lucarelli, C; Floridi, A

    1991-01-04

    A rapid and accurate method is described for the determination of prolyl peptides in urine, with specific reference to the dipeptide prolylhydroxyproline, and free hydroxyproline and proline. Free amino acids and peptides were isolated from urine on cation-exchange minicolumns, and free imino acids and prolyl-N-terminal peptides were selectively derivatized with 4-chloro-7-nitrobenzofurazan, after reaction of amino acids and N-terminal aminoacyl peptides with o-phthalaldehyde. The highly fluorescent adducts of imino acids and prolyl peptides were separated on a Spherisorb ODS 2 column by isocratic elution for 12 min using as mobile phase 17.5 mM aqueous trifluoracetic acid solution containing 12.5% acetonitrile (eluent A), followed by gradient elution from eluent A to 40% of 17.5 mM aqueous trifluoroacetic acid solution containing 80% acetonitrile in 20 min. Analytes of interest, in particular the dipeptide prolylhydroxyproline, can be easily quantified by fluorimetric detection (epsilon ex = 470 nm, epsilon em = 530 nm) without interference from primary amino-containing compounds.

  3. Cyanine-based probe\\tag-peptide pair for fluorescence protein imaging and fluorescence protein imaging methods

    DOEpatents

    Mayer-Cumblidge, M. Uljana; Cao, Haishi

    2010-08-17

    A molecular probe comprises two arsenic atoms and at least one cyanine based moiety. A method of producing a molecular probe includes providing a molecule having a first formula, treating the molecule with HgOAc, and subsequently transmetallizing with AsCl.sub.3. The As is liganded to ethanedithiol to produce a probe having a second formula. A method of labeling a peptide includes providing a peptide comprising a tag sequence and contacting the peptide with a biarsenical molecular probe. A complex is formed comprising the tag sequence and the molecular probe. A method of studying a peptide includes providing a mixture containing a peptide comprising a peptide tag sequence, adding a biarsenical probe to the mixture, and monitoring the fluorescence of the mixture.

  4. Anti-citrullinated peptide antibodies and their value for predicting responses to biologic agents: a review.

    PubMed

    Martin-Mola, Emilio; Balsa, Alejandro; García-Vicuna, Rosario; Gómez-Reino, Juan; González-Gay, Miguel Angel; Sanmartí, Raimon; Loza, Estíbaliz

    2016-08-01

    Anti-citrullinated peptide antibodies (ACPAs) play an important pathogenic role both at the onset and during the disease course. These antibodies precede the clinical appearance of rheumatoid arthritis (RA) and are associated with a less favorable prognosis, both clinically and radiologically. The objective of this work was to conduct a comprehensive review of studies published through September 2015 of ACPAs' role as a predictor of the therapeutic response to the biological agents in RA patients. The review also includes summary of the biology and detection of ACPAs as well as ACPAs in relation to joint disease and CV disease and the possible role of seroconversion. The reviews of studies examining TNF inhibitors and tocilizumab yielded negative results. In the case of rituximab, the data indicated a greater probability of clinical benefit in ACPA(+) patients versus ACPA(-) patients, as has been previously described for rheumatoid factor. Nonetheless, the effect is discreet and heterogeneous. Another drug that may have greater effectiveness in ACPA(+) patients is abatacept. Some studies have suggested that the drug is more efficient in ACPA(+) patients and that those patients show greater drug retention. In a subanalysis of the AMPLE trial, patients with very high ACPA titers who were treated with abatacept had a statistically significant response compared to patients with lower titers. In summary, the available studies suggest that the presence of or high titers of ACPA may predict a better response to rituximab and/or abatacept. Evidence regarding TNFi and tocilizumab is lacking. However, there is a lack of studies with appropriate designs to demonstrate that some drugs are superior to others for ACPA(+) patients.

  5. Prediction Methods in Solar Sunspots Cycles

    PubMed Central

    Ng, Kim Kwee

    2016-01-01

    An understanding of the Ohl’s Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to the geomagnetic activity index in the prior solar cycle. The correlation factors are significant and they are respectively found to be 0.91 ± 0.13 and 0.85 ± 0.17. The projected result is consistent with the current observation of solar cycle 24 which appears to have attained at least Rz(max) at 78.7 ± 11.7 in March 2014. Moreover, in a statistical study of the time-delayed solar events, the average time between the peak in the monthly geomagnetic index and the peak in the monthly sunspots numbers in the succeeding ascending phase of the sunspot activity is found to be 57.6 ± 3.1 months. The statistically determined time-delayed interval confirms earlier observational results by others that the Sun’s electromagnetic dipole is moving toward the Sun’s Equator during a solar cycle. PMID:26868269

  6. The Predikin webserver: improved prediction of protein kinase peptide specificity using structural information

    PubMed Central

    Saunders, Neil F. W.

    2008-01-01

    The Predikin webserver allows users to predict substrates of protein kinases. The Predikin system is built from three components: a database of protein kinase substrates that links phosphorylation sites with specific protein kinase sequences; a perl module to analyse query protein kinases and a web interface through which users can submit protein kinases for analysis. The Predikin perl module provides methods to (i) locate protein kinase catalytic domains in a sequence, (ii) classify them by type or family, (iii) identify substrate-determining residues, (iv) generate weighted scoring matrices using three different methods, (v) extract putative phosphorylation sites in query substrate sequences and (vi) score phosphorylation sites for a given kinase, using optional filters. The web interface provides user-friendly access to each of these functions and allows users to obtain rapidly a set of predictions that they can export for further analysis. The server is available at http://predikin.biosci.uq.edu.au. PMID:18477637

  7. Method For Determining And Modifying Protein/Peptide Solubilty

    SciTech Connect

    Waldo, Geoffrey S.

    2005-03-15

    A solubility reporter for measuring a protein's solubility in vivo or in vitro is described. The reporter, which can be used in a single living cell, gives a specific signal suitable for determining whether the cell bears a soluble version of the protein of interest. A pool of random mutants of an arbitrary protein, generated using error-prone in vitro recombination, may also be screened for more soluble versions using the reporter, and these versions may be recombined to yield variants having further-enhanced solubility. The method of the present invention includes "irrational" (random mutagenesis) methods, which do not require a priori knowledge of the three-dimensional structure of the protein of interest. Multiple sequences of mutation/genetic recombination and selection for improved solubility are demonstrated to yield versions of the protein which display enhanced solubility.

  8. A Support Vector Machine model for the prediction of proteotypic peptides for accurate mass and time proteomics

    SciTech Connect

    Webb-Robertson, Bobbie-Jo M.; Cannon, William R.; Oehmen, Christopher S.; Shah, Anuj R.; Gurumoorthi, Vidhya; Lipton, Mary S.; Waters, Katrina M.

    2008-07-01

    Motivation: The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares these profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic). Results: We present a Support Vector Machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity, and polarity for the quantitative prediction of proteotypic peptides. Using three independently derived AMT databases (Shewanella oneidensis, Salmonella typhimurium, Yersinia pestis) for training and validation within and across species, the SVM resulted in an average accuracy measure of ~0.8 with a standard deviation of less than 0.025. Furthermore, we demonstrate that these results are achievable with a small set of 12 variables and can achieve high proteome coverage. Availability: http://omics.pnl.gov/software/STEPP.php

  9. Pro-atrial natriuretic peptide and prediction of atrial fibrillation and stroke: The Malmö Preventive Project.

    PubMed

    Berntsson, John; Smith, J Gustav; Nilsson, Peter M; Hedblad, Bo; Melander, Olle; Engström, Gunnar

    2017-01-01

    Background The increasing prevalence of atrial fibrillation and novel therapeutic tools to prevent cardioembolic stroke has increased the need for risk markers. Objectives This study explored the relationship between the midregional sequence of pro-atrial natriuretic peptide (MR-proANP) levels with the risk of atrial fibrillation and stroke, and whether measurement of MR-proANP improves the prediction of these outcomes. Methods MR-proANP was measured in fasting blood samples of 5130 subjects (69% men, mean age 69.2 ± 6.2 years) without a history of atrial fibrillation or stroke from the general population. The incidence of atrial fibrillation and stroke was monitored over a median follow-up of 5.6 years. C-statistics and net reclassification improvement was used to assess the predictive ability of MR-proANP in addition to conventional risk factors. Results Log-normalized MR-proANP was significantly associated with the incidence of atrial fibrillation ( n = 362; hazard ratio (HR); 95% confidence interval (CI) per 1 standard deviation (SD) 2.05, 1.86-2.27) and stroke from all causes ( n = 195; HR 1.30; 95% CI 1.12-1.50). The HR for stroke events related to atrial fibrillation was 1.79 (95% CI 1.25-2.58) per 1 SD. MR-proANP significantly improved the prediction of atrial fibrillation when added to a risk score of conventional risk factors (C statistic 0.69 vs. 0.75), mainly by down-classifying subjects who did not develop atrial fibrillation. A smaller improvement in predictive ability was observed for stroke (C statistic 0.66 vs. 0.68). Conclusion High plasma levels of MR-proANP are associated with the incidence of atrial fibrillation and stroke in the middle-aged and elderly population. MR-proANP may be useful to identify individuals with an increased risk of atrial fibrillation.

  10. A method for high-sensitivity peptide sequencing using postsource decay matrix-assisted laser desorption ionization mass spectrometry

    PubMed Central

    Keough, T.; Youngquist, R. S.; Lacey, M. P.

    1999-01-01

    A method has been developed for de novo peptide sequencing using matrix-assisted laser desorption ionization mass spectrometry. This method will facilitate biological studies that require rapid determination of peptide or protein sequences, e.g., determination of posttranslational modifications, identification of active compounds isolated from combinatorial peptide libraries, and the selective identification of proteins as part of proteome studies. The method involves fast, one-step addition of a sulfonic acid group to the N terminus of tryptic peptides followed by acquisition of postsource decay (PSD) fragment ion spectra. The derivatives are designed to promote efficient charge site-initiated fragmentation of the backbone amide bonds and to selectively enhance the detection of a single fragment ion series that contains the C terminus of the molecule (y-ions). The overall method has been applied to pmol quantities of peptides. The resulting PSD fragment ion spectra often exhibit uninterrupted sequences of 20 or more amino acid residues. However, fragmentation efficiency decreases considerably at amide bonds on the C-terminal side of Pro. The spectra are simple enough that de novo sequence tagging is routine. The technique has been successfully applied to peptide mixtures, to high-mass peptides (up to 3,600 Da) and to the unambiguous identification of proteins isolated from two-dimensional gel electrophoresis. The PSD spectra of these derivatized peptides often allow far more selective protein sequence database searches than those obtained from the spectra of native peptides. PMID:10377380

  11. Functional genomics of intracellular peptide recognition domains with combinatorial biology methods.

    PubMed

    Sidhu, Sachdev S; Bader, Gary D; Boone, Charles

    2003-02-01

    Phage-displayed peptide libraries have been used to identify specific ligands for peptide-binding domains that mediate intracellular protein-protein interactions. These studies have provided significant insights into the specificities of particular domains. For PDZ domains that recognize C-terminal sequences, the information has proven useful in identifying natural binding partners from genomic databases. For SH3 domains that recognize internal proline-rich motifs, the results of database searches with phage-derived ligands have been compared with the results of yeast-two-hybrid experiments to produce overlap networks that reliably predict natural protein-protein interactions. In addition, libraries of phage-displayed PDZ and SH3 domains have been used to identify the residues responsible for ligand recognition, and also to engineer domains with altered specificities.

  12. Size-exclusion HPLC as a sensitive and calibrationless method for complex peptide mixtures quantification.

    PubMed

    Bodin, Alice; Framboisier, Xavier; Alonso, Dominique; Marc, Ivan; Kapel, Romain

    2015-12-01

    This work describes an original methodology to quantify complex peptide mixtures by size-exclusion high-performance liquid chromatography (SE-HPLC). The methodology was first tested on simulated elutions of peptide mixtures. For this set of experiments, a good estimation of the total peptide concentration was observed (error less than 10 %). Then 30 fractions obtained by ultrafiltration of hydrolysates from two different sources were titrated by Kjeldahl or BCA analysis and analysed by SE-HPLC for an experimental validation of the methodology. Very good matchs between methods were obtained. The linear working range depends on the hydrolysate but is generally between 0.2 and 4gL(-1) (i.e. between 10 and 200μg). Moreover, the presence of organic solvents or salts in samples does not impact the accuracy of the methodology contrary to common quantification methods. Hence, the findings of this study show that total concentration of complex peptide mixture can be efficiently determinate by the proposed methodology using simple SE-HPLC analysis.

  13. Computational Prediction and Experimental Validation of Signal Peptide Cleavage in the Extracellular Proteome of a Natural Microbial Community

    SciTech Connect

    Erickson, Brian K; Mueller, Ryan; Verberkmoes, Nathan C; Shah, Manesh B; Singer, Steven; Thelen, Michael P.; Banfield, Jillian F.; Hettich, Robert {Bob} L

    2010-01-01

    An integrated computational/experimental approach was used to predict and identify signal peptide cleavages among microbial proteins of environmental biofilm communities growing in acid mine drainage (AMD). SignalP-3.0 was employed to computationally query the AMD protein database of >16,000 proteins, which resulted in 1,480 predicted signal peptide cleaved proteins. LC-MS/MS analyses of extracellular (secretome) microbial preparations from different locations and developmental states empirically confirmed 531 of these signal peptide cleaved proteins. The majority of signal-cleavage proteins (58.4%) are annotated to have unknown functions; however, Pfam domain analysis revealed that many may be involved in extracellular functions expected within the AMD system. Examination of the abundances of signal-cleaved proteins across 28 proteomes from biofilms collected over a 4-year period demonstrated a strong correlation with the developmental state of the biofilm. For example, class I cytochromes are abundant in early growth states, whereas cytochrome oxidases from the same organism increase in abundance later in development. These results likely reflect shifts in metabolism that occur as biofilms thicken and communities diversify. In total, these results provide experimental confirmation of proteins that are designed to function in the extreme acidic extracellular environment and will serve as targets for future biochemical analysis.

  14. Simple and scalable method for peptide inhalable powder production.

    PubMed

    Schoubben, Aurélie; Blasi, Paolo; Giovagnoli, Stefano; Ricci, Maurizio; Rossi, Carlo

    2010-01-31

    The aim of this work was to produce capreomycin dry powder and capreomycin loaded PLGA microparticles intended for tuberculosis inhalation therapy, using simple and scalable methods. Capreomycin physico-chemical characteristics have been modified by hydrophobic ion pairing with oleate. The powder suspension was processed by high pressure homogenization and spray-dried. Spray-drying was also used to prepare capreomycin oleate (CO) loaded PLGA microparticles. CO powder was suspended in the organic phase containing PLGA and the suspension was spray-dried. Particle dimensions were determined using photon correlation spectroscopy and Accusizer C770. Morphology was investigated by scanning electron microscopy (SEM) and capreomycin content by spectrophotometry. Capreomycin properties were modified to increase polymeric microparticle content and obtain respirable CO powder. High pressure homogenization allowed to reduce CO particle dimensions obtaining a population in the micrometric (6.18 microm) and one in the nanometric (approximately 317 nm) range. SEM pictures showed not perfectly spherical particles with a wrinkled surface, generally suitable for inhalation. PLGA particles were characterized by a high encapsulation efficiency (about 90%) and dimensions (approximately 6.69 microm) suitable for inhalation. Concluding, two different formulations were successfully developed for capreomycin pulmonary delivery. The hydrophobic ion pair strategy led to a noticeable drug content increase.

  15. Nonlinear Methods Applied to Atmospheric Prediction

    DTIC Science & Technology

    2007-11-02

    states being a minimum and the spatial correlation being a maximum to determine the best analogs. They also started exploring the value of finding...the best analog of each of the trajectories in the ensemble of numerical predictions from the start of the prediction to the verification time. These...Benard convection, with the fluid layer heated below and cooled above, cellular convection occurs with cells of width very nearly equal to their

  16. A modern approach for epitope prediction: identification of foot-and-mouth disease virus peptides binding bovine leukocyte antigen (BoLA) class I molecules.

    PubMed

    Pandya, Mital; Rasmussen, Michael; Hansen, Andreas; Nielsen, Morten; Buus, Soren; Golde, William; Barlow, John

    2015-11-01

    Major histocompatibility complex (MHC) class Imolecules regulate adaptive immune responses through the presentation of antigenic peptides to CD8+ T cells. Polymorphisms in the peptide binding region of class I molecules determine peptide binding affinity and stability during antigen presentation, and different antigen peptide motifs are associated with specific genetic sequences of class I molecules. Understanding bovine leukocyte antigen (BoLA), peptide-MHC class I binding specificities may facilitate development of vaccines or reagents for quantifying the adaptive immune response to intracellular pathogens, such as foot-and-mouth disease virus (FMDV). Six synthetic BoLA class I (BoLA-I) molecules were produced, and the peptide binding motif was generated for five of the six molecules using a combined approach of positional scanning combinatorial peptide libraries (PSCPLs) and neural network-based predictions (NetMHCpan). The updated NetMHCpan server was used to predict BoLA-I binding peptides within the P1 structural polyprotein sequence of FMDV (strain A24 Cruzeiro) for Bo-LA-1*01901, BoLA-2*00801, BoLA-2*01201, and BoLA-4*02401. Peptide binding affinity and stability were determined for these BoLA-I molecules using the luminescent oxygen channeling immunoassay (LOCI) and scintillation proximity assay (SPA). The functional diversity of known BoLA alleles was predicted using theMHCcluster tool, and functional predictions for peptide motifs were compared to observed data from this and prior studies. The results of these analyses showed that BoLA alleles cluster into three distinct groups with the potential to define BBoLA supertypes.^ This streamlined approach identifies potential T cell epitopes from pathogens, such as FMDV, and provides insight into T cell immunity following infection or vaccination.

  17. Earthquake prediction: Simple methods for complex phenomena

    NASA Astrophysics Data System (ADS)

    Luen, Bradley

    2010-09-01

    Earthquake predictions are often either based on stochastic models, or tested using stochastic models. Tests of predictions often tacitly assume predictions do not depend on past seismicity, which is false. We construct a naive predictor that, following each large earthquake, predicts another large earthquake will occur nearby soon. Because this "automatic alarm" strategy exploits clustering, it succeeds beyond "chance" according to a test that holds the predictions _xed. Some researchers try to remove clustering from earthquake catalogs and model the remaining events. There have been claims that the declustered catalogs are Poisson on the basis of statistical tests we show to be weak. Better tests show that declustered catalogs are not Poisson. In fact, there is evidence that events in declustered catalogs do not have exchangeable times given the locations, a necessary condition for the Poisson. If seismicity followed a stochastic process, an optimal predictor would turn on an alarm when the conditional intensity is high. The Epidemic-Type Aftershock (ETAS) model is a popular point process model that includes clustering. It has many parameters, but is still a simpli_cation of seismicity. Estimating the model is di_cult, and estimated parameters often give a non-stationary model. Even if the model is ETAS, temporal predictions based on the ETAS conditional intensity are not much better than those of magnitude-dependent automatic (MDA) alarms, a much simpler strategy with only one parameter instead of _ve. For a catalog of Southern Californian seismicity, ETAS predictions again o_er only slight improvement over MDA alarms

  18. Spectrum-based method to generate good decoy libraries for spectral library searching in peptide identifications.

    PubMed

    Cheng, Chia-Ying; Tsai, Chia-Feng; Chen, Yu-Ju; Sung, Ting-Yi; Hsu, Wen-Lian

    2013-05-03

    As spectral library searching has received increasing attention for peptide identification, constructing good decoy spectra from the target spectra is the key to correctly estimating the false discovery rate in searching against the concatenated target-decoy spectral library. Several methods have been proposed to construct decoy spectral libraries. Most of them construct decoy peptide sequences and then generate theoretical spectra accordingly. In this paper, we propose a method, called precursor-swap, which directly constructs decoy spectral libraries directly at the "spectrum level" without generating decoy peptide sequences by swapping the precursors of two spectra selected according to a very simple rule. Our spectrum-based method does not require additional efforts to deal with ion types (e.g., a, b or c ions), fragment mechanism (e.g., CID, or ETD), or unannotated peaks, but preserves many spectral properties. The precursor-swap method is evaluated on different spectral libraries and the results of obtained decoy ratios show that it is comparable to other methods. Notably, it is efficient in time and memory usage for constructing decoy libraries. A software tool called Precursor-Swap-Decoy-Generation (PSDG) is publicly available for download at http://ms.iis.sinica.edu.tw/PSDG/.

  19. High throughput peptide mapping method for analysis of site specific monoclonal antibody oxidation.

    PubMed

    Li, Xiaojuan; Xu, Wei; Wang, Yi; Zhao, Jia; Liu, Yan-Hui; Richardson, Daisy; Li, Huijuan; Shameem, Mohammed; Yang, Xiaoyu

    2016-08-19

    Oxidation of therapeutic monoclonal antibodies (mAbs) often occurs on surface exposed methionine and tryptophan residues during their production in cell culture, purification, and storage, and can potentially impact the binding to their targets. Characterization of site specific oxidation is critical for antibody quality control. Antibody oxidation is commonly determined by peptide mapping/LC-MS methods, which normally require a long (up to 24h) digestion step. The prolonged sample preparation procedure could result in oxidation artifacts of susceptible methionine and tryptophan residues. In this paper, we developed a rapid and simple UV based peptide mapping method that incorporates an 8-min trypsin in-solution digestion protocol for analysis of oxidation. This method is able to determine oxidation levels at specific residues of a mAb based on the peptide UV traces within <1h, from either TBHP treated or UV light stressed samples. This is the simplest and fastest method reported thus far for site specific oxidation analysis, and can be applied for routine or high throughput analysis of mAb oxidation during various stability and degradation studies. By using the UV trace, the method allows more accurate measurement than mass spectrometry and can be potentially implemented as a release assay. It has been successfully used to monitor antibody oxidation in real time stability studies.

  20. Potential Method of Predicting Coronal Mass Ejection

    NASA Astrophysics Data System (ADS)

    Imholt, Timothy

    2001-10-01

    Coronal Mass Ejections (CME) may be described as a blast of gas and highly charged solar mass fragments ejected into space. These ejections, when directed toward Earth, have many different effects on terrestrial systems ranging from the Aurora Borealis to changes in wireless communication. The early prediction of these solar events cannot be overlooked. There are several models currently accepted and utilized to predict these events, however, with earlier prediction of both the event and the location on the sun where the event occurs allows us to have earlier warnings as to when they will affect man-made systems. A better prediction could perhaps be achieved by utilizing low angular resolution radio telescope arrays to catalog data from the sun at different radio frequencies on a regular basis. Once this data is cataloged a better predictor for these CME’s could be found. We propose a model that allows a prediction to be made that appears to be longer than 24 hours.

  1. Potential Method of Predicting Coronal Mass Ejection

    NASA Astrophysics Data System (ADS)

    Imholt, Timothy; Roberts, J. A.; Scott, J. B.; University Of North Texas Team

    2000-10-01

    Coronal Mass Ejections (CME) may be described as a blast of gas and highly charged solar mass fragments ejected into space. These ejections, when directed toward Earth, have many different effects on terrestrial systems ranging from the Aurora Borealis to changes in wireless communications. The importance of an early prediction of these solar events cannot be overlooked. There are several models currently accepted and utilized to predict these events, however, with earlier prediction of both the event and the location on the sun where the event occur allows us to have earlier warnings as to when they will effect man-made systems. A better prediction could perhaps be achieved by utilizing low angular resolution radio telescope arrays to catalog data from the sun at different radio frequencies on a regular basis. Once this data is cataloged a better predictor for these CME's could be found. We propose a model that allows a prediction to be made that appears to be longer than 24 hours.

  2. Integrated method for combustion stability prediction

    NASA Astrophysics Data System (ADS)

    Yu, Y. C.; O'Hara, L.; Smith, R. J.; Anderson, W. E.; Merkle, C. L.

    2011-10-01

    Major obstacles in overcoming combustion instability include the absence of a mechanistic and a priori prediction capability, and the difficulty in studying instability in the laboratory due to the perceived need for testing at the full-scale pressure and geometry to ensure that important processes are maintained. A hierarchal approach toward combustion instability is described that comprises experiment, analysis, and highfidelity computation to develop combustion response submodels that can be used in engineering-level design analysis. The paper provides an illustrative example of how these elements are used to develop a prediction for growth rates in model rocket combustors that generate spontaneous longitudinal combustion instabilities.

  3. Peptide synthesis triggered by comet impacts: A possible method for peptide delivery to the early Earth and icy satellites

    NASA Astrophysics Data System (ADS)

    Sugahara, Haruna; Mimura, Koichi

    2015-09-01

    We performed shock experiments simulating natural comet impacts in an attempt to examine the role that comet impacts play in peptide synthesis. In the present study, we selected a mixture of alanine (DL-alanine), water ice, and silicate (forsterite) to make a starting material for the experiments. The shock experiments were conducted under cryogenic conditions (77 K), and the shock pressure range achieved in the experiments was 4.8-25.8 GPa. The results show that alanine is oligomerized into peptides up to tripeptides due to the impact shock. The synthesized peptides were racemic, indicating that there was no enantioselective synthesis of peptides from racemic amino acids due to the impact shock. We also found that the yield of linear peptides was a magnitude higher than those of cyclic diketopiperazine. Furthermore, we estimated the amount of cometary-derived peptides to the early Earth based on two models (the Lunar Crating model and the Nice model) during the Late Heavy Bombardment (LHB) using our experimental data. The estimation based on the Lunar Crating model gave 3 × 109 mol of dialanine, 4 × 107 mol of trialanine, and 3 × 108 mol of alanine-diketopiperazine. Those based on the Nice model, in which the main impactor of LHB is comets, gave 6 × 1010 mol of dialanine, 1 × 109 mol of trialanine, and 8 × 109 mol of alanine-diketopiperazine. The estimated amounts were comparable to those originating from terrestrial sources (Cleaves, H.J., Aubrey, A.D., Bada, J.L. [2009]. Orig. Life Evol. Biosph. 39, 109-126). Our results indicate that comet impacts played an important role in chemical evolution as a supplier of linear peptides, which are important for further chemical evolution on the early Earth. Our study also highlights the importance of icy satellites, which were formed by comet accumulation, as prime targets for missions searching for extraterrestrial life.

  4. A Novel Method for Satellite Maneuver Prediction

    NASA Astrophysics Data System (ADS)

    Shabarekh, C.; Kent-Bryant, J.; Keselman, G.; Mitidis, A.

    2016-09-01

    A space operations tradecraft consisting of detect-track-characterize-catalog is insufficient for maintaining Space Situational Awareness (SSA) as space becomes increasingly congested and contested. In this paper, we apply analytical methodology from the Geospatial-Intelligence (GEOINT) community to a key challenge in SSA: predicting where and when a satellite may maneuver in the future. We developed a machine learning approach to probabilistically characterize Patterns of Life (PoL) for geosynchronous (GEO) satellites. PoL are repeatable, predictable behaviors that an object exhibits within a context and is driven by spatio-temporal, relational, environmental and physical constraints. An example of PoL are station-keeping maneuvers in GEO which become generally predictable as the satellite re-positions itself to account for orbital perturbations. In an earlier publication, we demonstrated the ability to probabilistically predict maneuvers of the Galaxy 15 (NORAD ID: 28884) satellite with high confidence eight days in advance of the actual maneuver. Additionally, we were able to detect deviations from expected PoL within hours of the predicted maneuver [6]. This was done with a custom unsupervised machine learning algorithm, the Interval Similarity Model (ISM), which learns repeating intervals of maneuver patterns from unlabeled historical observations and then predicts future maneuvers. In this paper, we introduce a supervised machine learning algorithm that works in conjunction with the ISM to produce a probabilistic distribution of when future maneuvers will occur. The supervised approach uses a Support Vector Machine (SVM) to process the orbit state whereas the ISM processes the temporal intervals between maneuvers and the physics-based characteristics of the maneuvers. This multiple model approach capitalizes on the mathematical strengths of each respective algorithm while incorporating multiple features and inputs. Initial findings indicate that the combined

  5. From Protein-RNA Predictions toward a Peptide-RNA Code.

    PubMed

    Brannan, Kristopher W; Yeo, Gene W

    2016-11-03

    The RNA field is undergoing a renaissance, with a deluge of proteins being identified to bind RNA. Two reports now introduce proteome-wide approaches that identify the peptides that are crosslinked to RNA (Castello et al., 2016; He et al., 2016).

  6. Molecular modeling of peptides.

    PubMed

    Kuczera, Krzysztof

    2015-01-01

    This article presents a review of the field of molecular modeling of peptides. The main focus is on atomistic modeling with molecular mechanics potentials. The description of peptide conformations and solvation through potentials is discussed. Several important computer simulation methods are briefly introduced, including molecular dynamics, accelerated sampling approaches such as replica-exchange and metadynamics, free energy simulations and kinetic network models like Milestoning. Examples of recent applications for predictions of structure, kinetics, and interactions of peptides with complex environments are described. The reliability of current simulation methods is analyzed by comparison of computational predictions obtained using different models with each other and with experimental data. A brief discussion of coarse-grained modeling and future directions is also presented.

  7. Gene structure prediction by linguistic methods

    SciTech Connect

    Dong, S.; Searls, D.B.

    1994-10-01

    The higher-order structure of genes and other features of biological sequences can be described by means of formal grammars. These grammars can then be used by general-purpose parsers to detect and to assemble such structures by means of syntactic pattern recognition. We describe a grammar and parser for eukaryotic protein-encoding genes, which by some measures is as effective as current connectionist and combinatorial algorithms in predicting gene structures for sequence database entries. Parameters of the grammar rules are optimized for several different species, and mixing experiments are performed to determine the degree of species specificity and the relative importance of compositional, signal-based, and syntactic components in gene prediction. 24 refs., 5 figs., 3 tabs.

  8. A Method for Structure–Activity Analysis of Quorum-Sensing Signaling Peptides from Naturally Transformable Streptococci

    PubMed Central

    2009-01-01

    Many species of streptococci secrete and use a competence-stimulating peptide (CSP) to initiate quorum sensing for induction of genetic competence, bacteriocin production, and other activities. These signaling molecules are small, unmodified peptides that induce powerful strain-specific activity at nano-molar concentrations. This feature has provided an excellent opportunity to explore their structure–function relationships. However, CSP variants have also been identified in many species, and each specifically activates its cognate receptor. How such minor changes dramatically affect the specificity of these peptides remains unclear. Structure–activity analysis of these peptides may provide clues for understanding the specificity of signaling peptide–receptor interactions. Here, we use the Streptococcus mutans CSP as an example to describe methods of analyzing its structure–activity relationship. The methods described here may provide a platform for studying quorum-sensing signaling peptides of other naturally transformable streptococci. PMID:19517207

  9. Usefulness of ELISA Methods for Assessing LPS Interactions with Proteins and Peptides

    PubMed Central

    Martínez-Sernández, Victoria; Orbegozo-Medina, Ricardo A.; Romarís, Fernanda; Paniagua, Esperanza; Ubeira, Florencio M.

    2016-01-01

    Lipopolysaccharide (LPS), the major constituent of the outer membrane of Gram-negative bacteria, can trigger severe inflammatory responses during bacterial infections, possibly leading to septic shock. One approach to combatting endotoxic shock is to neutralize the most conserved part and major mediator of LPS activity (lipid A) with LPS-binding proteins or peptides. Although several available assays evaluate the biological activity of these molecules on LPS (e.g. inhibition of LPS-induced TNF-α production in macrophages), the development of simple and cost-effective methods that would enable preliminary screening of large numbers of potential candidate molecules is of great interest. Moreover, it would be also desirable that such methods could provide information about the possible biological relevance of the interactions between proteins and LPS, which may enhance or neutralize LPS-induced inflammatory responses. In this study, we designed and evaluated different types of ELISA that could be used to study possible interactions between LPS and any protein or peptide. We also analysed the usefulness and limitations of the different ELISAs. Specifically, we tested the capacity of several proteins and peptides to bind FITC-labeled LPSs from Escherichia coli serotypes O111:B4 and O55:B5 in an indirect ELISA and in two competitive ELISAs including casein hydrolysate (hCAS) and biotinylated polymyxin B (captured by deglycosylated avidin; PMX) as LPS-binding agents in the solid phase. We also examined the influence of pH, detergents and different blocking agents on LPS binding. Our results showed that the competitive hCAS-ELISA performed under mildly acidic conditions can be used as a general method for studying LPS interactions, while the more restrictive PMX-ELISA may help to identify proteins/peptides that are likely to have neutralizing properties in vitro or in vivo. PMID:27249227

  10. Analysis of peptide-protein binding using amino acid descriptors: prediction and experimental verification for human histocompatibility complex HLA-A0201.

    PubMed

    Guan, Pingping; Doytchinova, Irini A; Walshe, Valerie A; Borrow, Persephone; Flower, Darren R

    2005-11-17

    Amino acid descriptors are often used in quantitative structure-activity relationship (QSAR) analysis of proteins and peptides. In the present study, descriptors were used to characterize peptides binding to the human MHC allele HLA-A0201. Two sets of amino acid descriptors were chosen: 93 descriptors taken from the amino acid descriptor database AAindex and the z descriptors defined by Wold and Sandberg. Variable selection techniques (SIMCA, genetic algorithm, and GOLPE) were applied to remove redundant descriptors. Our results indicate that QSAR models generated using five z descriptors had the highest predictivity and explained variance (q2 between 0.6 and 0.7 and r2 between 0.6 and 0.9). Further to the QSAR analysis, 15 peptides were synthesized and tested using a T2 stabilization assay. All peptides bound to HLA-A0201 well, and four peptides were identified as high-affinity binders.

  11. Nonprotein Based Enrichment Method to Analyze Peptide Cross-Linking in Protein Complexes

    PubMed Central

    Yan, Funing; Che, Fa-Yun; Rykunov, Dmitry; Nieves, Edward; Fiser, Andras; Weiss, Louis M.; Angeletti, Ruth Hogue

    2009-01-01

    Cross-linking analysis of protein complexes and structures by tandem mass spectrometry (MS/MS) has advantages in speed, sensitivity, specificity, and the capability of handling complicated protein assemblies. However, detection and accurate assignment of the cross-linked peptides are often challenging due to their low abundance and complicated fragmentation behavior in collision-induced dissociation (CID). To simplify the MS analysis and improve the signal-to-noise ratio of the cross-linked peptides, we developed a novel peptide enrichment strategy that utilizes a cross-linker with a cryptic thiol group and using beads modified with a photocleavable cross-linker. The functional cross-linkers were designed to react with the primary amino groups in proteins. Human serum albumin was used as a model protein to detect intra- and intermolecular cross-linkages. Use of this protein-free selective retrieval method eliminates the contamination that can result from avidin–biotin based retrieval systems and simplifies data analysis. These features may make the method suitable to investigate protein–protein interactions in biological samples. PMID:19642656

  12. Methods for the creation of cyclic Peptide libraries for use in lead discovery.

    PubMed

    Foster, Andrew D; Ingram, James D; Leitch, Eilidh K; Lennard, Katherine R; Osher, Eliot L; Tavassoli, Ali

    2015-06-01

    The identification of initial hits is a crucial stage in the drug discovery process. Although many projects adopt high-throughput screening of small-molecule libraries at this stage, there is significant potential for screening libraries of macromolecules created using chemical biology approaches. Not only can the production of the library be directly interfaced with a cell-based assay, but these libraries also require significantly fewer resources to generate and maintain. In this context, cyclic peptides are increasingly viewed as ideal scaffolds and have proven capability against challenging targets such as protein-protein interactions. Here we discuss a range of methods used for the creation of cyclic peptide libraries and detail examples of their successful implementation.

  13. MARQUIS: A Multiplex Method for Absolute Quantification of Peptides and Post-Translational Modifications

    PubMed Central

    Curran, Timothy G; Zhang, Yi; Ma, Daniel J.; Sarkaria, Jann N.; White, Forest M

    2014-01-01

    Absolute quantification of protein expression and post-translational modifications by mass spectrometry has been challenging due to a variety of factors, including the potentially large dynamic range of phosphorylation response. To address these issues, we have developed MARQUIS — Multiplex Absolute Regressed Quantification with Internal Standards — a novel mass spectrometry-based approach using a combination of isobaric tags and heavy-labeled standard peptides to construct internal standard curves for peptides derived from key nodes in signal transduction networks. We applied MARQUIS to quantify phosphorylation dynamics within the EGFR network at multiple time points following stimulation with several ligands, enabling a quantitative comparison of EGFR phosphorylation sites and demonstrating that receptor phosphorylation is qualitatively similar but quantitatively distinct for each EGFR ligand tested. MARQUIS was also applied to quantify the effect of EGFR kinase inhibition on glioblastoma patient derived xenografts. MARQUIS is a versatile method, broadly applicable and extendable to multiple mass spectrometric platforms. PMID:25581283

  14. Immunogenicity and immune modulatory effects of in silico predicted L. donovani candidate peptide vaccines.

    PubMed

    Elfaki, Mona E E; Khalil, Eltahir A G; De Groot, Anne S; Musa, Ahmed M; Gutierrez, Andres; Younis, Brima M; Salih, Kawthar A M; El-Hassan, Ahmed M

    2012-12-01

    Visceral leishmaniasis (VL) is a serious parasitic disease for which control measures are limited and drug resistance is increasing. First and second generation vaccine candidates have not been successful. The goal of the present study was to select possibly immunogenic L. donovani donovani GP63 peptides using immunoinformatics tools and to test their immunogenicity in vitro. The amino acid sequence of L. donovani donovani GP63 [GenBank accession: ACT31401] was screened using the EpiMatrix algorithm for putative T cell epitopes that would bind to the most common HLA class II alleles (DRB1*1101 and DRB1*0804) among at-risk populations. Four T cell epitopes were selected from nine potential candidates. Stimulation of whole blood from healthy volunteers using the peptides separately produced mean IFN-γ and IL-4 levels that were not significantly different from negative controls, while the pooled peptides produced a moderate IFN-γ increase in some volunteers. However, mean IL-10 levels were significantly reduced for all individuals compared with controls. The immunogenicity of these epitopes may be harnessed most effectively in a vaccine delivered in combination with immune-modulating adjuvants.

  15. Machine learning-based methods for prediction of linear B-cell epitopes.

    PubMed

    Wang, Hsin-Wei; Pai, Tun-Wen

    2014-01-01

    B-cell epitope prediction facilitates immunologists in designing peptide-based vaccine, diagnostic test, disease prevention, treatment, and antibody production. In comparison with T-cell epitope prediction, the performance of variable length B-cell epitope prediction is still yet to be satisfied. Fortunately, due to increasingly available verified epitope databases, bioinformaticians could adopt machine learning-based algorithms on all curated data to design an improved prediction tool for biomedical researchers. Here, we have reviewed related epitope prediction papers, especially those for linear B-cell epitope prediction. It should be noticed that a combination of selected propensity scales and statistics of epitope residues with machine learning-based tools formulated a general way for constructing linear B-cell epitope prediction systems. It is also observed from most of the comparison results that the kernel method of support vector machine (SVM) classifier outperformed other machine learning-based approaches. Hence, in this chapter, except reviewing recently published papers, we have introduced the fundamentals of B-cell epitope and SVM techniques. In addition, an example of linear B-cell prediction system based on physicochemical features and amino acid combinations is illustrated in details.

  16. Gaussian mixture models as flux prediction method for central receivers

    NASA Astrophysics Data System (ADS)

    Grobler, Annemarie; Gauché, Paul; Smit, Willie

    2016-05-01

    Flux prediction methods are crucial to the design and operation of central receiver systems. Current methods such as the circular and elliptical (bivariate) Gaussian prediction methods are often used in field layout design and aiming strategies. For experimental or small central receiver systems, the flux profile of a single heliostat often deviates significantly from the circular and elliptical Gaussian models. Therefore a novel method of flux prediction was developed by incorporating the fitting of Gaussian mixture models onto flux profiles produced by flux measurement or ray tracing. A method was also developed to predict the Gaussian mixture model parameters of a single heliostat for a given time using image processing. Recording the predicted parameters in a database ensures that more accurate predictions are made in a shorter time frame.

  17. A survey of the broadband shock associated noise prediction methods

    NASA Technical Reports Server (NTRS)

    Kim, Chan M.; Krejsa, Eugene A.; Khavaran, Abbas

    1992-01-01

    Several different prediction methods to estimate the broadband shock associated noise of a supersonic jet are introduced and compared with experimental data at various test conditions. The nozzle geometries considered for comparison include a convergent and a convergent-divergent nozzle, both axisymmetric. Capabilities and limitations of prediction methods in incorporating the two nozzle geometries, flight effect, and temperature effect are discussed. Predicted noise field shows the best agreement for a convergent nozzle geometry under static conditions. Predicted results for nozzles in flight show larger discrepancies from data and more dependable flight data are required for further comparison. Qualitative effects of jet temperature, as observed in experiment, are reproduced in predicted results.

  18. Adaptive method for electron bunch profile prediction

    SciTech Connect

    Scheinker, Alexander; Gessner, Spencer

    2015-10-01

    We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. The simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrial control system. The main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET. © 2015 authors. Published by the American Physical Society.

  19. Adaptive method for electron bunch profile prediction

    NASA Astrophysics Data System (ADS)

    Scheinker, Alexander; Gessner, Spencer

    2015-10-01

    We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. The simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrial control system. The main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET.

  20. Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models.

    PubMed

    Hou, Tingjun; Li, Nan; Li, Youyong; Wang, Wei

    2012-05-04

    Determination of the binding specificity of SH3 domain, a peptide recognition module (PRM), is important to understand their biological functions and reconstruct the SH3-mediated protein-protein interaction network. In the present study, the SH3-peptide interactions for both class I and II SH3 domains were characterized by the intermolecular residue-residue interaction network. We developed generic MIEC-SVM models to infer SH3 domain-peptide recognition specificity that achieved satisfactory prediction accuracy. By investigating the domain-peptide recognition mechanisms at the residue level, we found that the class-I and class-II binding peptides have different binding modes even though they occupy the same binding site of SH3. Furthermore, we predicted the potential binding partners of SH3 domains in the yeast proteome and constructed the SH3-mediated protein-protein interaction network. Comparison with the experimentally determined interactions confirmed the effectiveness of our approach. This study showed that our sophisticated computational approach not only provides a powerful platform to decipher protein recognition code at the molecular level but also allows identification of peptide-mediated protein interactions at a proteomic scale. We believe that such an approach is general to be applicable to other domain-peptide interactions.

  1. Characterization of Domain–Peptide Interaction Interface: Prediction of SH3 Domain-Mediated Protein–Protein Interaction Network in Yeast by Generic Structure-Based Models

    PubMed Central

    Hou, Tingjun; Li, Nan; Li, Youyong; Wang, Wei

    2012-01-01

    Determination of the binding specificity of SH3 domain, a peptide recognition module (PRM), is important to understand their biological functions and reconstruct the SH3-mediated protein–protein interaction network. In the present study, the SH3-peptide interactions for both class I and II SH3 domains were characterized by the intermolecular residue–residue interaction network. We developed generic MIEC-SVM models to infer SH3 domain-peptide recognition specificity that achieved satisfactory prediction accuracy. By investigating the domain–peptide recognition mechanisms at the residue level, we found that the class-I and class-II binding peptides have different binding modes even though they occupy the same binding site of SH3. Furthermore, we predicted the potential binding partners of SH3 domains in the yeast proteome and constructed the SH3-mediated protein–protein interaction network. Comparison with the experimentally determined interactions confirmed the effectiveness of our approach. This study showed that our sophisticated computational approach not only provides a powerful platform to decipher protein recognition code at the molecular level but also allows identification of peptide-mediated protein interactions at a proteomic scale. We believe that such an approach is general to be applicable to other domain–peptide interactions. PMID:22468754

  2. Development of a method for environmentally friendly chemical peptide synthesis in water using water-dispersible amino acid nanoparticles

    PubMed Central

    2011-01-01

    Due to the vast importance of peptides in biological processes, there is an escalating need for synthetic peptides to be used in a wide variety of applications. However, the consumption of organic solvent is extremely large in chemical peptide syntheses because of the multiple condensation steps in organic solvents. That is, the current synthesis method is not environmentally friendly. From the viewpoint of green sustainable chemistry, we focused on developing an organic solvent-free synthetic method using water, an environmentally friendly solvent. Here we described in-water synthesis technology using water-dispersible protected amino acids. PMID:21867548

  3. Immune reactivity against a novel HLA-A3-restricted influenza virus peptide identified by predictive algorithms and interferon-gamma quantitative PCR.

    PubMed

    Trojan, Andreas; Urosevic, Mirjana; Hummerjohann, Jörg; Giger, Robin; Schanz, Urs; Stahel, Rolf A

    2003-01-01

    The use of appropriate antigenic peptides for the most common human major histocompatibility complex (MHC) alleles is required for the amplification of the autologous cytotoxic compartment and the development of cytotoxic T cell-mediated immunity. The human A2 allele of the MHC plays an important role for the identification of peptide-specific cytotoxic T cells (CTL) against tumor and viral epitopes. Computer-based prediction algorithms, which are available on the Internet, have already proved to be applicable for the identification of novel CTL epitopes. Using the bioinformatics approach, the authors have identified the novel influenza matrix protein-derived and HLA-A3-restricted 9-mer peptide RLEDVFAGK capable of inducing peptide specific CTL reactivity. Peripheral blood mononuclear cells (PBMC) from healthy individuals and patients with lung cancer were pulsed with this peptide and with the well-characterized HLA-A2-restricted influenza A virus matrix peptide GILGFVFTL. Using quantitative PCR (TaqMan; Applied Biosystems, Foster City, CA, U.S.A), reactivity for both peptides was determined by measuring the change in type 1 cytokine (IFN-gamma) expression upon in vitro stimulation. Peptide-specific reactivity matched well with the subsequently determined MHC-class I alleles of the tested individuals. Results from this study indicate that the use of bioinformatics and the PCR-based screening system for the monitoring of T cell reactivity may allow for the identification of novel CTL epitopes.

  4. Improved method for predicting protein fold patterns with ensemble classifiers.

    PubMed

    Chen, W; Liu, X; Huang, Y; Jiang, Y; Zou, Q; Lin, C

    2012-01-27

    Protein folding is recognized as a critical problem in the field of biophysics in the 21st century. Predicting protein-folding patterns is challenging due to the complex structure of proteins. In an attempt to solve this problem, we employed ensemble classifiers to improve prediction accuracy. In our experiments, 188-dimensional features were extracted based on the composition and physical-chemical property of proteins and 20-dimensional features were selected using a coupled position-specific scoring matrix. Compared with traditional prediction methods, these methods were superior in terms of prediction accuracy. The 188-dimensional feature-based method achieved 71.2% accuracy in five cross-validations. The accuracy rose to 77% when we used a 20-dimensional feature vector. These methods were used on recent data, with 54.2% accuracy. Source codes and dataset, together with web server and software tools for prediction, are available at: http://datamining.xmu.edu.cn/main/~cwc/ProteinPredict.html.

  5. Method for Predicting Which Customers' Time Deposit Balances Will Increase

    NASA Astrophysics Data System (ADS)

    Ono, Toshiyuki; Yoshikawa, Hiroshi; Morita, Masahiro; Komoda, Norihisa

    This paper proposes a method of predicting which customers' account balances will increase by using data mining to effectively and efficiently promote sales. Prediction by mining all the data in a business is difficult because of much time required to collect, process, and calculate it. The selection of which features are used for prediction is a critical issue. We propose a method of selecting features to improve the accuracy of prediction within practical time limits. It consists of three parts: (1) converting collected features into financial behavior features that reflect customer actions, (2) extracting features affecting increases in account balances from these collected and financial behavior features, and (3) predicting customers whose account balances will increase based on the extracted features. We found the accuracy of prediction in an experiment with our method to be higher than with other conventional methods.

  6. Efficient Methods to Compute Genomic Predictions

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and simultaneously estimate thousands of marker effects. Algorithms were derived and computer programs tested on simulated data for 50,000 markers and 2,967 bulls. Accurate estimates of ...

  7. Drawability Prediction Method using Continuous Texture Evolution Model

    NASA Astrophysics Data System (ADS)

    Morimoto, Toshiharu; Yanagimoto, Jun

    2011-08-01

    Drawability is one of steel strip properties which control press forming. Many predicted method for the Lankford value have been proposed. First, we predict recystallization texture based on the idea that total amount of microscopic crystal slips is proportional to accumulated dislocation density in grain boundaries. Next, we can predict Lankford value of ultra low carbon strips and ferritic stainless steel strips using Sachs model. Our method is very practical to use in hot and cold steel rolling industry.

  8. How to address CPP and AMP translocation? Methods to detect and quantify peptide internalization in vitro and in vivo (Review).

    PubMed

    Henriques, Sónia Troeira; Melo, Manuel Nuno; Castanho, Miguel A R B

    2007-01-01

    Membrane translocation is a crucial issue when addressing the activity of both cell-penetrating and antimicrobial peptides. Translocation is responsible for the therapeutic potential of cell-penetrating peptides as drug carriers and can dictate the killing mechanisms, selectivity and efficiency of antimicrobial peptides. It is essential to evaluate if the internalization of cell-penetrating peptides is mediated by endocytosis and if it is able to internalize attached cargoes. The mode of action of an antimicrobial peptide cannot be fully understood if it is not known whether the peptide acts exclusively at the membrane level or also at the cytoplasm. Therefore, experimental methods to evaluate and quantify translocation processes are of first importance. In this work, over 20 methods described in the literature for the assessment of peptide translocation in vivo and in vitro, with and without attached macromolecular cargoes, are discussed and their applicability, advantages and disadvantages reviewed. In addition, a classification of these methods is proposed, based on common approaches to detect translocation.

  9. Statistical energy analysis response prediction methods for structural systems

    NASA Technical Reports Server (NTRS)

    Davis, R. F.

    1979-01-01

    The results of an effort to document methods for accomplishing response predictions for commonly encountered aerospace structural configurations is presented. Application of these methods to specified aerospace structure to provide sample analyses is included. An applications manual, with the structural analyses appended as example problems is given. Comparisons of the response predictions with measured data are provided for three of the example problems.

  10. A Versatile Nonlinear Method for Predictive Modeling

    NASA Technical Reports Server (NTRS)

    Liou, Meng-Sing; Yao, Weigang

    2015-01-01

    As computational fluid dynamics techniques and tools become widely accepted for realworld practice today, it is intriguing to ask: what areas can it be utilized to its potential in the future. Some promising areas include design optimization and exploration of fluid dynamics phenomena (the concept of numerical wind tunnel), in which both have the common feature where some parameters are varied repeatedly and the computation can be costly. We are especially interested in the need for an accurate and efficient approach for handling these applications: (1) capturing complex nonlinear dynamics inherent in a system under consideration and (2) versatility (robustness) to encompass a range of parametric variations. In our previous paper, we proposed to use first-order Taylor expansion collected at numerous sampling points along a trajectory and assembled together via nonlinear weighting functions. The validity and performance of this approach was demonstrated for a number of problems with a vastly different input functions. In this study, we are especially interested in enhancing the method's accuracy; we extend it to include the second-orer Taylor expansion, which however requires a complicated evaluation of Hessian matrices for a system of equations, like in fluid dynamics. We propose a method to avoid these Hessian matrices, while maintaining the accuracy. Results based on the method are presented to confirm its validity.

  11. New Methods for Estimating Seasonal Potential Climate Predictability

    NASA Astrophysics Data System (ADS)

    Feng, Xia

    This study develops two new statistical approaches to assess the seasonal potential predictability of the observed climate variables. One is the univariate analysis of covariance (ANOCOVA) model, a combination of autoregressive (AR) model and analysis of variance (ANOVA). It has the advantage of taking into account the uncertainty of the estimated parameter due to sampling errors in statistical test, which is often neglected in AR based methods, and accounting for daily autocorrelation that is not considered in traditional ANOVA. In the ANOCOVA model, the seasonal signals arising from external forcing are determined to be identical or not to assess any interannual variability that may exist is potentially predictable. The bootstrap is an attractive alternative method that requires no hypothesis model and is available no matter how mathematically complicated the parameter estimator. This method builds up the empirical distribution of the interannual variance from the resamplings drawn with replacement from the given sample, in which the only predictability in seasonal means arises from the weather noise. These two methods are applied to temperature and water cycle components including precipitation and evaporation, to measure the extent to which the interannual variance of seasonal means exceeds the unpredictable weather noise compared with the previous methods, including Leith-Shukla-Gutzler (LSG), Madden, and Katz. The potential predictability of temperature from ANOCOVA model, bootstrap, LSG and Madden exhibits a pronounced tropical-extratropical contrast with much larger predictability in the tropics dominated by El Nino/Southern Oscillation (ENSO) than in higher latitudes where strong internal variability lowers predictability. Bootstrap tends to display highest predictability of the four methods, ANOCOVA lies in the middle, while LSG and Madden appear to generate lower predictability. Seasonal precipitation from ANOCOVA, bootstrap, and Katz, resembling that

  12. A numerical method for predicting hypersonic flowfields

    NASA Technical Reports Server (NTRS)

    Maccormack, Robert W.; Candler, Graham V.

    1988-01-01

    The flow about a body traveling at hypersonic speed is energetic enough to cause the atmospheric gases to react chemically and reach states in thermal nonequilibrium. In this paper, a new procedure based on Gauss-Seidel line relaxation is shown to solve the equations of hypersonic flow fields containing finite reaction rate chemistry and thermal nonequilibrium. The method requires a few hundred time steps and small computer times for axisymmetric flows about simple body shapes. The extension to more complex two-dimensional body geometries appears straightforward.

  13. Urinary C-peptide as a method for monitoring body mass changes in captive bonobos (Pan paniscus).

    PubMed

    Deschner, Tobias; Kratzsch, Jürgen; Hohmann, Gottfried

    2008-11-01

    In recent years methodological improvements have allowed for more precise estimates of nutrient intake in wild primates. However, estimates of energetic condition have remained relatively imprecise due to the difficulties of estimating digestive efficiency and energy expenditure in these animals. In the absence of a reliable intake-expenditure calculation, a method is needed that directly links changes in energetic condition, such as body mass, to physiological changes that can be detected via markers in body excretions such as urine or feces. One promising marker is C-peptide, a metabolic byproduct of insulin synthesis. Here we present the results of a food restriction experiment carried out in a group of captive bonobos (Pan paniscus). We measured changes in food availability and body mass and determined urinary C-peptide levels with the help of a time-resolved fluoroimmunoassay routinely used for measuring C-peptide in human blood. Urinary C-peptide levels decreased during a period of food restriction and increased again when food availability was continuously increased. During this refeeding phase an increase in body mass was significantly correlated with an increase in urinary C-peptide levels. Our results suggest that urinary C-peptide levels are an accurate indicator of individual energy balance. In conclusion, measuring C-peptide in urine is a promising method to quantify the energetic condition of wild apes.

  14. A novel prefractionation method combining protein and peptide isoelectric focusing in immobilized pH gradient strips.

    PubMed

    Pernemalm, Maria; Lehtiö, Janne

    2013-02-01

    To increase sensitivity and analytical depth in shotgun proteomics, prefractionation of complex samples is often used. Here we describe a novel prefractionation method, Sandwich high resolution isoelectric focusing, which combines both protein and peptide isoelectric focusing. In the first step, intact proteins are separated on the basis of isoelectric point (pI) using traditional immobilized pH gradient (IPG) strips. Segments in the IPG-strip containing proteins of interest are subsequently cut out and applied to in-strip digestion, without subsequent peptide elution. In the second peptide isoelectric focusing step, the strip segments are used as loading bridges. The peptides are thereby directly applied to the peptide isoelectric focusing, without an intermediate elution step, and separated on narrow range IPG strips to reduce the complexity on the peptide level. In the final step, the peptides are eluted into 96-well plates and analyzed with mass spectrometry. In a proof of principle experiment, using this method to zoom in on pI regions of interest in human plasma, we identify over 800 proteins, with concentrations spanning over 6 orders of magnitude.

  15. Scaling--which methods best predict performance?

    PubMed

    Comfort, Paul; Pearson, Stephen J

    2014-06-01

    Athletes with a higher body mass (BM) tend to be stronger, with ratio scaling possibly eliminating this effect. The aim of this study was to compare relationships between sprint performances with scaled measures of strength and power. Fifteen professional rugby league players (age, 26.27 6 3.87 years; height, 183.33 6 6.37 cm; BM, 96.86 6 11.49 kg) performed 1 repetition maximum back squats, power cleans, squat jumps, and sprints (5, 10, and 20 m). Heavier athletes (forward) generated significantly greater absolute levels of power during the squat jump (5,659.11 6 710.35 vs.4,740.16 6 558.61 W; p , 0.001); however, when power data were scaled no differences were observed. Squat performance indicated no differences in absolute ability between the subgroups (190.6 6 14.25 vs. 205.7 6 18.35 kg), although the lighter group was significantly (p # 0.05) stronger than the heavier group when using ratio and allometric methods (2.1 vs. 1.9 kg · kg(-1) and 10.42 vs. 9.87 kg · kg(0.28)), respectively. Significant relationships with 5-m sprints were only observed for ratio and allometrically scaled power cleans (r = 20.625, p , 0.02; r = 20.675, p , 0.02), with similar correlations between allometrically scaled 10-m sprint and both back squat and power clean performances. Scaled power clean performances were also inversely correlated with 20-m sprints (r = 20.620, r = 20.638, p , 0.02). Where differences in absolute strength are apparent between individuals of different BM, then the use of scaling is required. Because of the similarity between ratio and allometric methods, simple ratio scaling is recommended.

  16. Methods for Elucidating the Mechanism of Action of Proline-Rich and Other Non-lytic Antimicrobial Peptides.

    PubMed

    Benincasa, Monica; Runti, Giulia; Mardirossian, Mario; Gennaro, Renato; Scocchi, Marco

    2017-01-01

    A distinct group of antimicrobial peptides kills bacteria by interfering with internal cellular functions and without concurrent lytic effects on cell membranes. Here we describe some methods to investigate the mechanisms of action of these antimicrobial peptides. They include assays to detect the possible temporal separation between membrane permeabilization and bacterial killing events, to assess the capacity of antimicrobial peptides to cross the bacterial membranes and reside in the cytoplasm, and later to inhibit vital cell functions such as DNA transcription and protein translation.

  17. Screening and selection of synthetic peptides for a novel and optimized endotoxin detection method.

    PubMed

    Mujika, M; Zuzuarregui, A; Sánchez-Gómez, S; Martínez de Tejada, G; Arana, S; Pérez-Lorenzo, E

    2014-09-30

    The current validated endotoxin detection methods, in spite of being highly sensitive, present several drawbacks in terms of reproducibility, handling and cost. Therefore novel approaches are being carried out in the scientific community to overcome these difficulties. Remarkable efforts are focused on the development of endotoxin-specific biosensors. The key feature of these solutions relies on the proper definition of the capture protocol, especially of the bio-receptor or ligand. The aim of the presented work is the screening and selection of a synthetic peptide specifically designed for LPS detection, as well as the optimization of a procedure for its immobilization onto gold substrates for further application to biosensors.

  18. Empirical Methods for Identifying Specific Peptide-protein Interactions for Smart Reagent Development

    DTIC Science & Technology

    2012-09-01

    virion surface,” Science, 228(4705), 1315-7 (1985). [9] C. Santini, D. Brennan, C. Mennuni et al., “Efficient display of an HCV cDNA expression...in indirect enzyme-linked immunosorbent assays (ELISA),” Journal of Immunological Methods, 208(2), 141-149 (1997). [17] M. Sallberg, M. Blixt, Z...X. Zhang et al., “Passive Adsorption of Immunologically Active and Inactive Synthetic Peptides to Poylystyrene is Influenced by the Proportion of

  19. Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic

    NASA Astrophysics Data System (ADS)

    Maiwald, Thomas; Winterhalder, Matthias; Aschenbrenner-Scheibe, Richard; Voss, Henning U.; Schulze-Bonhage, Andreas; Timmer, Jens

    2004-07-01

    Epilepsy is characterized by the spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. The predictability of these seizures would render novel therapeutic approaches possible. Several prediction methods have claimed to be able to predict seizures based on EEG recordings minutes in advance. However, the term seizure prediction is not unequivocally defined, different criteria to assess prediction methods exist, and only little attention has been paid to issues of sensitivity and false prediction rate. We introduce an assessment criterion called the seizure prediction characteristic that incorporates the assessment of sensitivity and false prediction rate. Within this framework, three nonlinear seizure prediction methods were evaluated on a large EEG data pool of 21 patients. Altogether, 582 h intracranial EEG data and 88 seizures were examined. With a rate of 1-3.6 false predictions per day, the “dynamical similarity index” achieves a sensitivity between 21 and 42%, which was the best result of the three methods. Sensitivity was between 18 and 31% for the extended, prospective version of the “accumulated energy” and between 13 and 30% for the “effective correlation dimension”. These results still are not sufficient for clinical applications.

  20. Fluorescence and Absorbance Spectroscopy Methods to Study Membrane Perturbations by Antimicrobial Host Defense Peptides.

    PubMed

    Arias, Mauricio; Vogel, Hans J

    2017-01-01

    Antimicrobial peptides (AMPs) are currently intensely studied because of their potential as new bactericidal and bacteriostatic agents. The mechanism of action of numerous AMPs involves the permeabilization of bacterial membranes. Several methods have been developed to study peptide-membrane interactions; in particular optical spectroscopy methods are widely used. The intrinsic fluorescence properties of the Trp indole ring in Trp-containing AMPs can be exploited by measuring the fluorescence blue shift and acrylamide-induced fluorescence quenching. One important aspect of such studies is the use of distinct models of the bacterial membrane, in most cases large unilamellar vesicles (LUVs) with different, yet well-defined, phospholipid compositions. Deploying LUVs that are preloaded with fluorescent dyes, such as calcein, also allows for the study of vesicle permeabilization by AMPs. In addition, experiments using genetically engineered live Escherichia coli cells can be used to distinguish between the effects of AMPs on the outer and inner membranes of gram-negative bacteria. In combination, these methods can provide a detailed insight into the mode of action of AMPs.

  1. Peptide-derived Method to Transport Genes and Proteins Across Cellular and Organellar Barriers in Plants

    PubMed Central

    Chuah, Jo-Ann; Horii, Yoko; Numata, Keiji

    2016-01-01

    The capacity to introduce exogenous proteins and express (or down-regulate) specific genes in plants provides a powerful tool for fundamental research as well as new applications in the field of plant biotechnology. Viable methods that currently exist for protein or gene transfer into plant cells, namely Agrobacterium and microprojectile bombardment, have disadvantages of low transformation frequency, limited host range, or a high cost of equipment and microcarriers. The following protocol outlines a simple and versatile method, which employs rationally-designed peptides as delivery agents for a variety of nucleic acid- and protein-based cargoes into plants. Peptides are selected as tools for development of the system due to their biodegradability, reduced size, diverse and tunable properties as well as the ability to gain intracellular/organellar access. The preparation, characterization and application of optimized formulations for each type of the wide range of delivered cargoes (plasmid DNA, double-stranded DNA or RNA, and protein) are described. Critical steps within the protocol, possible modifications and existing limitations of the method are also discussed. PMID:28060264

  2. Experimental validation of boundary element methods for noise prediction

    NASA Technical Reports Server (NTRS)

    Seybert, A. F.; Oswald, Fred B.

    1992-01-01

    Experimental validation of methods to predict radiated noise is presented. A combined finite element and boundary element model was used to predict the vibration and noise of a rectangular box excited by a mechanical shaker. The predicted noise was compared to sound power measured by the acoustic intensity method. Inaccuracies in the finite element model shifted the resonance frequencies by about 5 percent. The predicted and measured sound power levels agree within about 2.5 dB. In a second experiment, measured vibration data was used with a boundary element model to predict noise radiation from the top of an operating gearbox. The predicted and measured sound power for the gearbox agree within about 3 dB.

  3. Predicting peptide vaccine candidates against H1N1 influenza virus through theoretical approaches.

    PubMed

    Bello, Martiniano; Campos-Rodriguez, Rafael; Rojas-Hernandez, Saul; Contis-Montes de Oca, Arturo; Correa-Basurto, José

    2015-05-01

    Identification of potential epitopes that might activate the immune system has been facilitated by the employment of algorithms that use experimental data as templates. However, in order to prove the affinity and the map of interactions between the receptor (major histocompatibility complex, MHC, or T-cell receptor) and the potential epitope, further computational studies are required. Docking and molecular dynamics (MDs) simulations have been an effective source of generating structural information at molecular level in immunology. Herein, in order to provide a detailed understanding of the origins of epitope recognition and to select the best peptide candidate to develop an epitope-based vaccine, docking and MDs simulations in combination with MMGBSA free energy calculations and per-residue free energy decomposition were performed, taking as starting complexes those formed between four designed epitopes (P1-P4) from hemagglutinin (HA) of the H1N1 influenza virus and MHC-II anchored in POPC membrane. Our results revealed that the energetic contributions of individual amino acids within the pMHC-II complexes are mainly dictated by van der Waals interactions and the nonpolar part of solvation energy, whereas the electrostatic interactions corresponding to hydrogen bonds and salt bridges determine the binding specificity, being the most favorable interactions formed between p4 and MHC-II. Then, P1-P4 epitopes were synthesized and tested experimentally to compare theoretical and experimental results. Experimental results show that P4 elicited the highest strong humoral immune response to HA of the H1N1 and may induce antibodies that are cross-reactive to other influenza subtypes, suggesting that it could be a good candidate for the development of a peptide-based vaccine.

  4. Critical evaluation of in silico methods for prediction of coiled-coil domains in proteins.

    PubMed

    Li, Chen; Ching Han Chang, Catherine; Nagel, Jeremy; Porebski, Benjamin T; Hayashida, Morihiro; Akutsu, Tatsuya; Song, Jiangning; Buckle, Ashley M

    2016-03-01

    Coiled-coils refer to a bundle of helices coiled together like strands of a rope. It has been estimated that nearly 3% of protein-encoding regions of genes harbour coiled-coil domains (CCDs). Experimental studies have confirmed that CCDs play a fundamental role in subcellular infrastructure and controlling trafficking of eukaryotic cells. Given the importance of coiled-coils, multiple bioinformatics tools have been developed to facilitate the systematic and high-throughput prediction of CCDs in proteins. In this article, we review and compare 12 sequence-based bioinformatics approaches and tools for coiled-coil prediction. These approaches can be categorized into two classes: coiled-coil detection and coiled-coil oligomeric state prediction. We evaluated and compared these methods in terms of their input/output, algorithm, prediction performance, validation methods and software utility. All the independent testing data sets are available at http://lightning.med.monash.edu/coiledcoil/. In addition, we conducted a case study of nine human polyglutamine (PolyQ) disease-related proteins and predicted CCDs and oligomeric states using various predictors. Prediction results for CCDs were highly variable among different predictors. Only two peptides from two proteins were confirmed to be CCDs by majority voting. Both domains were predicted to form dimeric coiled-coils using oligomeric state prediction. We anticipate that this comprehensive analysis will be an insightful resource for structural biologists with limited prior experience in bioinformatics tools, and for bioinformaticians who are interested in designing novel approaches for coiled-coil and its oligomeric state prediction.

  5. Computational methods for prediction of T-cell epitopes--a framework for modelling, testing, and applications.

    PubMed

    Brusic, Vladimir; Bajic, Vladimir B; Petrovsky, Nikolai

    2004-12-01

    Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes.

  6. Overview of Antioxidant Peptides Derived from Marine Resources: The Sources, Characteristic, Purification, and Evaluation Methods.

    PubMed

    Wu, RiBang; Wu, CuiLing; Liu, Dan; Yang, XingHao; Huang, JiaFeng; Zhang, Jiang; Liao, Binqiang; He, HaiLun; Li, Hao

    2015-08-01

    Marine organisms are rich sources of structurally diverse bioactive nitrogenous components. In recent years, numerous bioactive peptides have been identified in a range of marine protein resources, such as antioxidant peptides. Many studies have approved that marine antioxidant peptides have a positive effect on human health and the food industry. Antioxidant activity of peptides can be attributed to free radicals scavenging, inhibition of lipid peroxidation, and metal ion chelating. Moreover, it has also been verified that peptide structure and its amino acid sequence can mainly affect its antioxidant properties. The aim of this review is to summarize kinds of antioxidant peptides from various marine resources. Additionally, the relationship between structure and antioxidant activities of peptides is discussed in this paper. Finally, current technologies used in the preparation, purification, and evaluation of marine-derived antioxidant peptides are also reviewed.

  7. A K-nearest neighbors survival probability prediction method.

    PubMed

    Lowsky, D J; Ding, Y; Lee, D K K; McCulloch, C E; Ross, L F; Thistlethwaite, J R; Zenios, S A

    2013-05-30

    We introduce a nonparametric survival prediction method for right-censored data. The method generates a survival curve prediction by constructing a (weighted) Kaplan-Meier estimator using the outcomes of the K most similar training observations. Each observation has an associated set of covariates, and a metric on the covariate space is used to measure similarity between observations. We apply our method to a kidney transplantation data set to generate patient-specific distributions of graft survival and to a simulated data set in which the proportional hazards assumption is explicitly violated. We compare the performance of our method with the standard Cox model and the random survival forests method.

  8. What Predicts Use of Learning-Centered, Interactive Engagement Methods?

    ERIC Educational Resources Information Center

    Madson, Laura; Trafimow, David; Gray, Tara; Gutowitz, Michael

    2014-01-01

    What makes some faculty members more likely to use interactive engagement methods than others? We use the theory of reasoned action to predict faculty members' use of interactive engagement methods. Results indicate that faculty members' beliefs about the personal positive consequences of using these methods (e.g., "Using interactive…

  9. SPA: Short peptide analyzer of intrinsic disorder status of short peptides

    PubMed Central

    Xue, Bin; Hsu, Wei-Lun; Lee, Jun-Ho; Lu, Hua; Dunker, A. Keith; Uversky, Vladimir N.

    2010-01-01

    Disorder prediction for short peptides is important and difficult. All modern predictors have to be optimized on a preselected dataset prior to prediction. In the succeeding prediction process, the predictor works on a query sequence or its short segment. For implementing the prediction smoothly and obtaining sound prediction results, a specific length of the sequence or segment is usually required. The need of the preselected dataset in the optimization process and the length limitation in the prediction process restrict predictors’ performance. To minimize the influence of these limitations, we developed a method for the prediction of intrinsic disorder in short peptides based on large dataset sampling and statistics. As evident from the data analysis, this method provides more reliable prediction of the intrinsic disorder status of short peptides. PMID:20497238

  10. Interim prediction method for low frequency core engine noise

    NASA Technical Reports Server (NTRS)

    Huff, R. G.; Clark, B. J.; Dorsch, R. G.

    1974-01-01

    A literature survey on low-frequency core engine noise is presented. Possible sources of low frequency internally generated noise in core engines are discussed with emphasis on combustion and component scrubbing noise. An interim method is recommended for predicting low frequency core engine noise that is dominant when jet velocities are low. Suggestions are made for future research on low frequency core engine noise that will aid in improving the prediction method and help define possible additional internal noise sources.

  11. Mimicking Tumors: Toward More Predictive In Vitro Models for Peptide- and Protein-Conjugated Drugs

    PubMed Central

    2017-01-01

    Macromolecular drug candidates and nanoparticles are typically tested in 2D cancer cell culture models, which are often directly followed by in vivo animal studies. The majority of these drug candidates, however, fail in vivo. In contrast to classical small-molecule drugs, multiple barriers exist for these larger molecules that two-dimensional approaches do not recapitulate. In order to provide better mechanistic insights into the parameters controlling success and failure and due to changing ethical perspectives on animal studies, there is a growing need for in vitro models with higher physiological relevance. This need is reflected by an increased interest in 3D tumor models, which during the past decade have evolved from relatively simple tumor cell aggregates to more complex models that incorporate additional tumor characteristics as well as patient-derived material. This review will address tissue culture models that implement critical features of the physiological tumor context such as 3D structure, extracellular matrix, interstitial flow, vascular extravasation, and the use of patient material. We will focus on specific examples, relating to peptide-and protein-conjugated drugs and other nanoparticles, and discuss the added value and limitations of the respective approaches. PMID:28122451

  12. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

    PubMed Central

    Nesvizhskii, Alexey I.

    2010-01-01

    This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues. PMID:20816881

  13. Comparison of prediction performance using statistical postprocessing methods

    NASA Astrophysics Data System (ADS)

    Han, Keunhee; Choi, JunTae; Kim, Chansoo

    2016-11-01

    As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.

  14. Review of methods for determination of total protein and peptide concentration in biological samples.

    PubMed

    Sapan, Christine V; Lundblad, Roger L

    2015-04-01

    Clinical proteomics can be defined as the use of proteomic technologies to identify and measure biomarkers in fluids and tissues. The current work is intended to review various methods used for the determination of the total concentration of protein or peptide in fluids and tissues and the application of such methods to clinical proteomics. Specifically, this article considers the approaches to the measurement of total protein concentration, not the measurement of the concentration of a specific protein or group of proteins in a larger mixture of proteins. The necessity of understanding various concepts such as fit-for-use, quality-by-design, and other regulatory elements is discussed, as is the significance of using suitable standards for the protein quality of various samples.

  15. A method to objectively optimize coral bleaching prediction techniques

    NASA Astrophysics Data System (ADS)

    van Hooidonk, R. J.; Huber, M.

    2007-12-01

    Thermally induced coral bleaching is a global threat to coral reef health. Methodologies, e.g. the Degree Heating Week technique, have been developed to predict bleaching induced by thermal stress by utilizing remotely sensed sea surface temperature (SST) observations. These techniques can be used as a management tool for Marine Protected Areas (MPA). Predictions are valuable to decision makers and stakeholders on weekly to monthly time scales and can be employed to build public awareness and support for mitigation. The bleaching problem is only expected to worsen because global warming poses a major threat to coral reef health. Indeed, predictive bleaching methods combined with climate model output have been used to forecast the global demise of coral reef ecosystems within coming decades due to climate change. Accuracy of these predictive techniques has not been quantitatively characterized despite the critical role they play. Assessments have typically been limited, qualitative or anecdotal, or more frequently they are simply unpublished. Quantitative accuracy assessment, using well established methods and skill scores often used in meteorology and medical sciences, will enable objective optimization of existing predictive techniques. To accomplish this, we will use existing remotely sensed data sets of sea surface temperature (AVHRR and TMI), and predictive values from techniques such as the Degree Heating Week method. We will compare these predictive values with observations of coral reef health and calculate applicable skill scores (Peirce Skill Score, Hit Rate and False Alarm Rate). We will (a) quantitatively evaluate the accuracy of existing coral reef bleaching predictive methods against state-of- the-art reef health databases, and (b) present a technique that will objectively optimize the predictive method for any given location. We will illustrate this optimization technique for reefs located in Puerto Rico and the US Virgin Islands.

  16. Modified-Fibonacci-Dual-Lucas method for earthquake prediction

    NASA Astrophysics Data System (ADS)

    Boucouvalas, A. C.; Gkasios, M.; Tselikas, N. T.; Drakatos, G.

    2015-06-01

    The FDL method makes use of Fibonacci, Dual and Lucas numbers and has shown considerable success in predicting earthquake events locally as well as globally. Predicting the location of the epicenter of an earthquake is one difficult challenge the other being the timing and magnitude. One technique for predicting the onset of earthquakes is the use of cycles, and the discovery of periodicity. Part of this category is the reported FDL method. The basis of the reported FDL method is the creation of FDL future dates based on the onset date of significant earthquakes. The assumption being that each occurred earthquake discontinuity can be thought of as a generating source of FDL time series The connection between past earthquakes and future earthquakes based on FDL numbers has also been reported with sample earthquakes since 1900. Using clustering methods it has been shown that significant earthquakes (<6.5R) can be predicted with very good accuracy window (+-1 day). In this contribution we present an improvement modification to the FDL method, the MFDL method, which performs better than the FDL. We use the FDL numbers to develop possible earthquakes dates but with the important difference that the starting seed date is a trigger planetary aspect prior to the earthquake. Typical planetary aspects are Moon conjunct Sun, Moon opposite Sun, Moon conjunct or opposite North or South Modes. In order to test improvement of the method we used all +8R earthquakes recorded since 1900, (86 earthquakes from USGS data). We have developed the FDL numbers for each of those seeds, and examined the earthquake hit rates (for a window of 3, i.e. +-1 day of target date) and for <6.5R. The successes are counted for each one of the 86 earthquake seeds and we compare the MFDL method with the FDL method. In every case we find improvement when the starting seed date is on the planetary trigger date prior to the earthquake. We observe no improvement only when a planetary trigger coincided with

  17. AVP-IC50 Pred: Multiple machine learning techniques-based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50).

    PubMed

    Qureshi, Abid; Tandon, Himani; Kumar, Manoj

    2015-11-01

    Peptide-based antiviral therapeutics has gradually paved their way into mainstream drug discovery research. Experimental determination of peptides' antiviral activity as expressed by their IC50 values involves a lot of effort. Therefore, we have developed "AVP-IC50 Pred," a regression-based algorithm to predict the antiviral activity in terms of IC50 values (μM). A total of 759 non-redundant peptides from AVPdb and HIPdb were divided into a training/test set having 683 peptides (T(683)) and a validation set with 76 independent peptides (V(76)) for evaluation. We utilized important peptide sequence features like amino-acid compositions, binary profile of N8-C8 residues, physicochemical properties and their hybrids. Four different machine learning techniques (MLTs) namely Support vector machine, Random Forest, Instance-based classifier, and K-Star were employed. During 10-fold cross validation, we achieved maximum Pearson correlation coefficients (PCCs) of 0.66, 0.64, 0.56, 0.55, respectively, for the above MLTs using the best combination of feature sets. All the predictive models also performed well on the independent validation dataset and achieved maximum PCCs of 0.74, 0.68, 0.59, 0.57, respectively, on the best combination of feature sets. The AVP-IC50 Pred web server is anticipated to assist the researchers working on antiviral therapeutics by enabling them to computationally screen many compounds and focus experimental validation on the most promising set of peptides, thus reducing cost and time efforts. The server is available at http://crdd.osdd.net/servers/ic50avp.

  18. Prediction methods of spudcan penetration for jack-up units

    NASA Astrophysics Data System (ADS)

    Zhang, Ai-xia; Duan, Meng-lan; Li, Hai-ming; Zhao, Jun; Wang, Jian-jun

    2012-12-01

    Jack-up units are extensively playing a successful role in drilling engineering around the world, and their safety and efficiency take more and more attraction in both research and engineering practice. An accurate prediction of the spudcan penetration depth is quite instrumental in deciding on whether a jack-up unit is feasible to operate at the site. The prediction of a too large penetration depth may lead to the hesitation or even rejection of a site due to potential difficulties in the subsequent extraction process; the same is true of a too small depth prediction due to the problem of possible instability during operation. However, a deviation between predictive results and final field data usually exists, especially when a strong-over-soft soil is included in the strata. The ultimate decision sometimes to a great extent depends on the practical experience, not the predictive results given by the guideline. It is somewhat risky, but no choice. Therefore, a feasible predictive method for the spudcan penetration depth, especially in strata with strong-over-soft soil profile, is urgently needed by the jack-up industry. In view of this, a comprehensive investigation on methods of predicting spudcan penetration is executed. For types of different soil profiles, predictive methods for spudcan penetration depth are proposed, and the corresponding experiment is also conducted to validate these methods. In addition, to further verify the feasibility of the proposed methods, a practical engineering case encountered in the South China Sea is also presented, and the corresponding numerical and experimental results are also presented and discussed.

  19. Connecting clinical and actuarial prediction with rule-based methods.

    PubMed

    Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

    2015-06-01

    Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods.

  20. The trajectory prediction of spacecraft by grey method

    NASA Astrophysics Data System (ADS)

    Wang, Qiyue; Zhang, Zili; Wang, Zhongyu; Wang, Yanqing; Zhou, Weihu

    2016-08-01

    The real-time and high-precision trajectory prediction of a moving object is a core technology in the field of aerospace engineering. The real-time monitoring and tracking technology are also significant guarantees of aerospace equipment. A dynamic trajectory prediction method called grey dynamic filter (GDF) which combines the dynamic measurement theory and grey system theory is proposed. GDF can use coordinates of the current period to extrapolate coordinates of the following period. At meantime, GDF can also keep the instantaneity of measured coordinates by the metabolism model. In this paper the optimal model length of GDF is firstly selected to improve the prediction accuracy. Then the simulation for uniformly accelerated motion and variably accelerated motion is conducted. The simulation results indicate that the mean composite position error of GDF prediction is one-fifth to that of Kalman filter (KF). By using a spacecraft landing experiment, the prediction accuracy of GDF is compared with the KF method and the primitive grey method (GM). The results show that the motion trajectory of spacecraft predicted by GDF is much closer to actual trajectory than the other two methods. The mean composite position error calculated by GDF is one-eighth to KF and one-fifth to GM respectively.

  1. HOPE: a homotopy optimization method for protein structure prediction.

    PubMed

    Dunlavy, Daniel M; O'Leary, Dianne P; Klimov, Dmitri; Thirumalai, D

    2005-12-01

    We use a homotopy optimization method, HOPE, to minimize the potential energy associated with a protein model. The method uses the minimum energy conformation of one protein as a template to predict the lowest energy structure of a query sequence. This objective is achieved by following a path of conformations determined by a homotopy between the potential energy functions for the two proteins. Ensembles of solutions are produced by perturbing conformations along the path, increasing the likelihood of predicting correct structures. Successful results are presented for pairs of homologous proteins, where HOPE is compared to a variant of Newton's method and to simulated annealing.

  2. Optimization of preparation of antioxidative peptides from pumpkin seeds using response surface method.

    PubMed

    Fan, Sanhong; Hu, Yanan; Li, Chen; Liu, Yanrong

    2014-01-01

    Protein isolates of pumpkin (Cucurbita pepo L) seeds were hydrolyzed by acid protease to prepare antioxidative peptides. The hydrolysis conditions were optimized through Box-Behnken experimental design combined with response surface method (RSM). The second-order model, developed for the DPPH radical scavenging activity of pumpkin seed hydrolysates, showed good fit with the experiment data with a high value of coefficient of determination (0.9918). The optimal hydrolysis conditions were determined as follows: hydrolyzing temperature 50°C, pH 2.5, enzyme amount 6000 U/g, substrate concentration 0.05 g/ml and hydrolyzing time 5 h. Under the above conditions, the scavenging activity of DPPH radical was as high as 92.82%.

  3. Methods, apparatus and system for notification of predictable memory failure

    DOEpatents

    Cher, Chen-Yong; Andrade Costa, Carlos H.; Park, Yoonho; Rosenburg, Bryan S.; Ryu, Kyung D.

    2017-01-03

    A method for providing notification of a predictable memory failure includes the steps of: obtaining information regarding at least one condition associated with a memory; calculating a memory failure probability as a function of the obtained information; calculating a failure probability threshold; and generating a signal when the memory failure probability exceeds the failure probability threshold, the signal being indicative of a predicted future memory failure.

  4. Computational Methods for Failure Analysis and Life Prediction

    NASA Technical Reports Server (NTRS)

    Noor, Ahmed K. (Compiler); Harris, Charles E. (Compiler); Housner, Jerrold M. (Compiler); Hopkins, Dale A. (Compiler)

    1993-01-01

    This conference publication contains the presentations and discussions from the joint UVA/NASA Workshop on Computational Methods for Failure Analysis and Life Prediction held at NASA Langley Research Center 14-15 Oct. 1992. The presentations focused on damage failure and life predictions of polymer-matrix composite structures. They covered some of the research activities at NASA Langley, NASA Lewis, Southwest Research Institute, industry, and universities. Both airframes and propulsion systems were considered.

  5. A simple method of predicting S-wave velocity

    USGS Publications Warehouse

    Lee, M.W.

    2006-01-01

    Prediction of shear-wave velocity plays an important role in seismic modeling, amplitude analysis with offset, and other exploration applications. This paper presents a method for predicting S-wave velocity from the P-wave velocity on the basis of the moduli of dry rock. Elastic velocities of water-saturated sediments at low frequencies can be predicted from the moduli of dry rock by using Gassmann's equation; hence, if the moduli of dry rock can be estimated from P-wave velocities, then S-wave velocities easily can be predicted from the moduli. Dry rock bulk modulus can be related to the shear modulus through a compaction constant. The numerical results indicate that the predicted S-wave velocities for consolidated and unconsolidated sediments agree well with measured velocities if differential pressure is greater than approximately 5 MPa. An advantage of this method is that there are no adjustable parameters to be chosen, such as the pore-aspect ratios required in some other methods. The predicted S-wave velocity depends only on the measured P-wave velocity and porosity. ?? 2006 Society of Exploration Geophysicists.

  6. Three-dimensional protein structure prediction: Methods and computational strategies.

    PubMed

    Dorn, Márcio; E Silva, Mariel Barbachan; Buriol, Luciana S; Lamb, Luis C

    2014-10-12

    A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction.

  7. Prediction of Protein–Protein Interactions by Evidence Combining Methods

    PubMed Central

    Chang, Ji-Wei; Zhou, Yan-Qing; Ul Qamar, Muhammad Tahir; Chen, Ling-Ling; Ding, Yu-Duan

    2016-01-01

    Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy. PMID:27879651

  8. Evaluation of Methods to Predict Reactivity of Gold Nanoparticles

    SciTech Connect

    Allison, Thomas C.; Tong, Yu ye J.

    2011-06-20

    Several methods have appeared in the literature for predicting reactivity on metallic surfaces and on the surface of metallic nanoparticles. All of these methods have some relationship to the concept of frontier molecular orbital theory. The d-band theory of Hammer and Nørskov is perhaps the most widely used predictor of reactivity on metallic surfaces, and it has been successfully applied in many cases. Use of the Fukui function and the condensed Fukui function is well established in organic chemistry, but has not been so widely applied in predicting the reactivity of metallic nanoclusters. In this article, we will evaluate the usefulness of the condensed Fukui function in predicting the reactivity of a family of cubo-octahedral gold nanoparticles and make comparison with the d-band method.

  9. Multiple and sequential data acquisition method: an improved method for fragmentation and detection of cross-linked peptides on a hybrid linear trap quadrupole Orbitrap Velos mass spectrometer.

    PubMed

    Rudashevskaya, Elena L; Breitwieser, Florian P; Huber, Marie L; Colinge, Jacques; Müller, André C; Bennett, Keiryn L

    2013-02-05

    The identification and validation of cross-linked peptides by mass spectrometry remains a daunting challenge for protein-protein cross-linking approaches when investigating protein interactions. This includes the fragmentation of cross-linked peptides in the mass spectrometer per se and following database searching, the matching of the molecular masses of the fragment ions to the correct cross-linked peptides. The hybrid linear trap quadrupole (LTQ) Orbitrap Velos combines the speed of the tandem mass spectrometry (MS/MS) duty circle with high mass accuracy, and these features were utilized in the current study to substantially improve the confidence in the identification of cross-linked peptides. An MS/MS method termed multiple and sequential data acquisition method (MSDAM) was developed. Preliminary optimization of the MS/MS settings was performed with a synthetic peptide (TP1) cross-linked with bis[sulfosuccinimidyl] suberate (BS(3)). On the basis of these results, MSDAM was created and assessed on the BS(3)-cross-linked bovine serum albumin (BSA) homodimer. MSDAM applies a series of multiple sequential fragmentation events with a range of different normalized collision energies (NCE) to the same precursor ion. The combination of a series of NCE enabled a considerable improvement in the quality of the fragmentation spectra for cross-linked peptides, and ultimately aided in the identification of the sequences of the cross-linked peptides. Concurrently, MSDAM provides confirmatory evidence from the formation of reporter ions fragments, which reduces the false positive rate of incorrectly assigned cross-linked peptides.

  10. Assessing Computational Methods of Cis-Regulatory Module Prediction

    PubMed Central

    Su, Jing; Teichmann, Sarah A.; Down, Thomas A.

    2010-01-01

    Computational methods attempting to identify instances of cis-regulatory modules (CRMs) in the genome face a challenging problem of searching for potentially interacting transcription factor binding sites while knowledge of the specific interactions involved remains limited. Without a comprehensive comparison of their performance, the reliability and accuracy of these tools remains unclear. Faced with a large number of different tools that address this problem, we summarized and categorized them based on search strategy and input data requirements. Twelve representative methods were chosen and applied to predict CRMs from the Drosophila CRM database REDfly, and across the human ENCODE regions. Our results show that the optimal choice of method varies depending on species and composition of the sequences in question. When discriminating CRMs from non-coding regions, those methods considering evolutionary conservation have a stronger predictive power than methods designed to be run on a single genome. Different CRM representations and search strategies rely on different CRM properties, and different methods can complement one another. For example, some favour homotypical clusters of binding sites, while others perform best on short CRMs. Furthermore, most methods appear to be sensitive to the composition and structure of the genome to which they are applied. We analyze the principal features that distinguish the methods that performed well, identify weaknesses leading to poor performance, and provide a guide for users. We also propose key considerations for the development and evaluation of future CRM-prediction methods. PMID:21152003

  11. Brain natriuretic peptide predicts forced vital capacity of the lungs, oxygen pulse and peak oxygen consumption in physiological condition.

    PubMed

    Popovic, Dejana; Ostojic, Miodrag C; Popovic, Bojana; Petrovic, Milan; Vujisic-Tesic, Bosiljka; Kocijancic, Aleksandar; Banovic, Marko; Arandjelovic, Aleksandra; Stojiljkovic, Stanimir; Markovic, Vidan; Damjanovic, Svetozar S

    2013-05-01

    Brain natriuretic peptide (NT-pro-BNP) is used as marker of cardiac and pulmonary diseases. However, the predictive value of circulating NT-pro-BNP for cardiac and pulmonary performance is unclear in physiological conditions. Standard echocardiography, tissue Doppler and forced spirometry at rest were used to assess cardiac parameters and forced vital capacity (FVC) in two groups of athletes (16 elite male wrestlers (W), 21 water polo player (WP)), as different stress adaptation models, and 20 sedentary subjects (C) matched for age. Cardiopulmonary test on treadmill (CPET), as acute stress model, was used to measure peak oxygen consumption (peak VO2), maximal heart rate (HRmax) and peak oxygen pulse (peak VO2/HR). NT-pro-BNP was measured by immunoassey sandwich technique 10min before the test - at rest, at the beginning of the test, at maximal effort, at third minute of recovery. FVC was higher in athletes and the highest in W (WP 5.60±0.29 l; W 6.57±1.00 l; C 5.41±0.29 l; p<0.01). Peak VO2 and peak VO2/HR were higher in athletes and the highest in WP. HRmax was not different among groups. In all groups, NT-pro-BNP decreased from rest to the beginning phase, increased in maximal effort and stayed unchanged in recovery. NT-pro-BNP was higher in C than W in all phases; WP had similar values as W and C. On multiple regression analysis, in all three groups together, ΔNT-pro-BNP from rest to the beginning phase independently predicted both peak VO2 and peak VO2/HR (r=0.38, 0.35; B=37.40, 0.19; p=0.007, 0.000, respectively). NT-pro-BNP at rest predicted HRmax (r=-0.32, B=-0.22, p=0.02). Maximal NT-pro-BNP predicted FVC (r=-0.22, B=-0.07, p=0.02). These results show noticeable predictive value of NT-pro-BNP for both cardiac and pulmonary performance in physiological conditions suggesting that NT-pro-BNP could be a common regulatory factor coordinating adaptation of heart and lungs to stress condition.

  12. Predicting Spacecraft Trajectories by the WeavEncke Method

    NASA Technical Reports Server (NTRS)

    Weaver, Jonathan K.; Adamo, Daniel R.

    2011-01-01

    A combination of methods is proposed of predicting spacecraft trajectories that possibly include multiple maneuvers and/or perturbing accelerations, with greater speed, accuracy, and repeatability than were heretofore achievable. The combination is denoted the WeavEncke method because it is based on unpublished studies by Jonathan Weaver of the orbit-prediction formulation of the noted astronomer Johann Franz Encke. Weaver evaluated a number of alternatives that arise within that formulation, arriving at an orbit-predicting algorithm optimized for complex trajectory operations. In the WeavEncke method, Encke's method of prediction of perturbed orbits is enhanced by application of modern numerical methods. Among these methods are efficient Kepler s-equation time-of-flight solutions and self-starting numerical integration with time as the independent variable. Self-starting numerical integration satisfies the requirements for accuracy, reproducibility, and efficiency (and, hence, speed). Self-starting numerical integration also supports fully analytic regulation of integration step sizes, thereby further increasing speed while maintaining accuracy.

  13. Diagnosis of bacterial vaginosis by a new multiplex peptide nucleic acid fluorescence in situ hybridization method

    PubMed Central

    Machado, António; Castro, Joana; Cereija, Tatiana; Almeida, Carina

    2015-01-01

    Bacterial vaginosis (BV) is one of most common vaginal infections. However, its diagnosis by classical methods reveals low specificity. Our goal was to evaluate the accuracy diagnosis of 150 vaginal samples with research gold standard methods and our Peptide Nucleic Acid (PNA) probes by Fluorescence in situ Hybridization (FISH) methodology. Also, we described the first PNA-FISH methodology for BV diagnosis, which provides results in approximately 3 h. The results showed a sensitivity of 84.6% (95% confidence interval (CI), from 64.3 to 95.0%) and a specificity of 97.6% (95% CI [92.6–99.4%]), demonstrating the higher specificity of the PNA-FISH method and showing false positive results in BV diagnosis commonly obtained by the classical methods. This methodology combines the specificity of PNA probes for Lactobacillus species and G. vaginalis visualization and the calculation of the microscopic field by Nugent score, allowing a trustful evaluation of the bacteria present in vaginal microflora and avoiding the occurrence of misleading diagnostics. Therefore, the PNA-FISH methodology represents a valuable alternative for BV diagnosis. PMID:25737820

  14. An analytical method to predict efficiency of aircraft gearboxes

    NASA Technical Reports Server (NTRS)

    Anderson, N. E.; Loewenthal, S. H.; Black, J. D.

    1984-01-01

    A spur gear efficiency prediction method previously developed by the authors was extended to include power loss of planetary gearsets. A friction coefficient model was developed for MIL-L-7808 oil based on disc machine data. This combined with the recent capability of predicting losses in spur gears of nonstandard proportions allows the calculation of power loss for complete aircraft gearboxes that utilize spur gears. The method was applied to the T56/501 turboprop gearbox and compared with measured test data. Bearing losses were calculated with large scale computer programs. Breakdowns of the gearbox losses point out areas for possible improvement.

  15. Preface to the Focus Issue: Chaos Detection Methods and Predictability

    SciTech Connect

    Gottwald, Georg A.; Skokos, Charalampos

    2014-06-01

    This Focus Issue presents a collection of papers originating from the workshop Methods of Chaos Detection and Predictability: Theory and Applications held at the Max Planck Institute for the Physics of Complex Systems in Dresden, June 17–21, 2013. The main aim of this interdisciplinary workshop was to review comprehensively the theory and numerical implementation of the existing methods of chaos detection and predictability, as well as to report recent applications of these techniques to different scientific fields. The collection of twelve papers in this Focus Issue represents the wide range of applications, spanning mathematics, physics, astronomy, particle accelerator physics, meteorology and medical research. This Preface surveys the papers of this Issue.

  16. Preface to the Focus Issue: chaos detection methods and predictability.

    PubMed

    Gottwald, Georg A; Skokos, Charalampos

    2014-06-01

    This Focus Issue presents a collection of papers originating from the workshop Methods of Chaos Detection and Predictability: Theory and Applications held at the Max Planck Institute for the Physics of Complex Systems in Dresden, June 17-21, 2013. The main aim of this interdisciplinary workshop was to review comprehensively the theory and numerical implementation of the existing methods of chaos detection and predictability, as well as to report recent applications of these techniques to different scientific fields. The collection of twelve papers in this Focus Issue represents the wide range of applications, spanning mathematics, physics, astronomy, particle accelerator physics, meteorology and medical research. This Preface surveys the papers of this Issue.

  17. Development of aerodynamic prediction methods for irregular planform wings

    NASA Technical Reports Server (NTRS)

    Benepe, D. B., Sr.

    1983-01-01

    A set of empirical methods was developed to predict low-speed lift, drag and pitching-moment variations with angle of attack for a class of low aspect ratio irregular planform wings suitable for application to advanced aerospace vehicles. The data base, an extensive series of wind-tunnel tests accomplished by the Langley Research Center of the National Aeronautics and Space Administration, is summarized. The approaches used to analyze the wind tunnel data, the evaluation of previously existing methods, data correlation efforts, and the development of the selected methods are presented and discussed. A summary of the methods is also presented to document the equations, computational charts and design guides which have been programmed for digital computer solution. Comparisons of predictions and test data are presented which show that the new methods provide a significant improvement in capability for evaluating the landing characteristics of advanced aerospace vehicles during the preliminary design phase of the configuration development cycle.

  18. An improved method for high-level soluble expression and purification of recombinant amyloid-beta peptide for in vitro studies.

    PubMed

    Chhetri, Gaurav; Pandey, Tripti; Chinta, Ramesh; Kumar, Awanish; Tripathi, Timir

    2015-10-01

    Amyloid-beta (Aβ) peptide mediates several neurodegenerative diseases. The 42 amino acid (Aβ1-42) is the predominant form of peptide found in the neuritic plaques and has been demonstrated to be neurotoxic in vivo and in vitro. The availability of large quantities of Aβ peptide will help in several biochemical and biophysical studies that may help in exploring the aggregation mechanism and toxicity of Aβ peptide. We report a convenient and economical method to obtain such a peptide biologically. Synthetic oligonucleotides encoding Aβ1-42 were constructed and amplified through the polymerase cycling assembly (also known as assembly PCR), followed by the amplification PCR. Aβ1-42 gene was cloned into pET41a(+) vector for expression. Interestingly, the addition of 3% (v/v) ethanol to the culture medium resulted in the production of large amounts of soluble Aβ fusion protein. The Aβ fusion protein was subjected to a Ni-NTA affinity chromatography followed by enterokinase digestion, and the Aβ peptide was purified using glutathione Sepharose affinity chromatography. The peptide yield was ∼15mg/L culture, indicating the utility of this method for high-yield production of soluble Aβ peptide. Sodium dodecyl sulfate polyacrylamide gel electrophoresis analysis and immunoblotting with anti-His antibody confirmed the identity of purified Aβ fusion protein and Aβ peptide. In addition, this method provides an advantage over the chemical synthesis and other conventional methods used for large-scale production of recombinant Aβ peptide.

  19. Development of Improved Surface Integral Methods for Jet Aeroacoustic Predictions

    NASA Technical Reports Server (NTRS)

    Pilon, Anthony R.; Lyrintzis, Anastasios S.

    1997-01-01

    The accurate prediction of aerodynamically generated noise has become an important goal over the past decade. Aeroacoustics must now be an integral part of the aircraft design process. The direct calculation of aerodynamically generated noise with CFD-like algorithms is plausible. However, large computer time and memory requirements often make these predictions impractical. It is therefore necessary to separate the aeroacoustics problem into two parts, one in which aerodynamic sound sources are determined, and another in which the propagating sound is calculated. This idea is applied in acoustic analogy methods. However, in the acoustic analogy, the determination of far-field sound requires the solution of a volume integral. This volume integration again leads to impractical computer requirements. An alternative to the volume integrations can be found in the Kirchhoff method. In this method, Green's theorem for the linear wave equation is used to determine sound propagation based on quantities on a surface surrounding the source region. The change from volume to surface integrals represents a tremendous savings in the computer resources required for an accurate prediction. This work is concerned with the development of enhancements of the Kirchhoff method for use in a wide variety of aeroacoustics problems. This enhanced method, the modified Kirchhoff method, is shown to be a Green's function solution of Lighthill's equation. It is also shown rigorously to be identical to the methods of Ffowcs Williams and Hawkings. This allows for development of versatile computer codes which can easily alternate between the different Kirchhoff and Ffowcs Williams-Hawkings formulations, using the most appropriate method for the problem at hand. The modified Kirchhoff method is developed primarily for use in jet aeroacoustics predictions. Applications of the method are shown for two dimensional and three dimensional jet flows. Additionally, the enhancements are generalized so that

  20. The Energetic Cost of Walking: A Comparison of Predictive Methods

    PubMed Central

    Kramer, Patricia Ann; Sylvester, Adam D.

    2011-01-01

    Background The energy that animals devote to locomotion has been of intense interest to biologists for decades and two basic methodologies have emerged to predict locomotor energy expenditure: those based on metabolic and those based on mechanical energy. Metabolic energy approaches share the perspective that prediction of locomotor energy expenditure should be based on statistically significant proxies of metabolic function, while mechanical energy approaches, which derive from many different perspectives, focus on quantifying the energy of movement. Some controversy exists as to which mechanical perspective is “best”, but from first principles all mechanical methods should be equivalent if the inputs to the simulation are of similar quality. Our goals in this paper are 1) to establish the degree to which the various methods of calculating mechanical energy are correlated, and 2) to investigate to what degree the prediction methods explain the variation in energy expenditure. Methodology/Principal Findings We use modern humans as the model organism in this experiment because their data are readily attainable, but the methodology is appropriate for use in other species. Volumetric oxygen consumption and kinematic and kinetic data were collected on 8 adults while walking at their self-selected slow, normal and fast velocities. Using hierarchical statistical modeling via ordinary least squares and maximum likelihood techniques, the predictive ability of several metabolic and mechanical approaches were assessed. We found that all approaches are correlated and that the mechanical approaches explain similar amounts of the variation in metabolic energy expenditure. Most methods predict the variation within an individual well, but are poor at accounting for variation between individuals. Conclusion Our results indicate that the choice of predictive method is dependent on the question(s) of interest and the data available for use as inputs. Although we used modern

  1. A new method to predict unsteady aeroelastic behavior

    NASA Technical Reports Server (NTRS)

    Strganac, Thomas W.; Mook, Dean T.

    1987-01-01

    A new method for predicting subsonic flutter and static deflections, including divergence, has been developed. The present method accounts for aspect ratio and, in the case of flutter, static deflections. The angle of attack is limited only by the occurrence of stall or vortex bursting near the wing. The innovation in the present method is to integrate simultaneously and interactively the equations of motion of the structure and the flowfield. The present approach employs an iterative scheme based on the predictor-corrector method. The general unsteady vortex-lattice method (UVLM) is used to predict the aerodynamic loads. Because the UVLM predicts the wakes as part of the solution, the history of the motion is taken into account; hysteresis is predicted. The deflection (for both bending and torsion) is expressed as an expansion in terms of the free-vibration modes. The time-dependent coefficients in these expansions serve as the generalized coordinates. Numerical examples illustrating the calculation of static deflections and transient dynamic responses above and below the flutter boundary are included.

  2. Increased Fidelity in Prediction Methods For Landing Gear Noise

    NASA Technical Reports Server (NTRS)

    Lopes, Leonard V.; Brentner, Kenneth S.; Morris, Philip J.; Lockhard, David P.

    2006-01-01

    An aeroacoustic prediction scheme has been developed for landing gear noise. The method is designed to handle the complex landing gear geometry of current and future aircraft. The gear is represented by a collection of subassemblies and simple components that are modeled using acoustic elements. These acoustic elements are generic, but generate noise representative of the physical components on a landing gear. The method sums the noise radiation from each component of the undercarriage in isolation accounting for interference with adjacent components through an estimate of the local upstream and downstream flows and turbulence intensities. The acoustic calculations are made in the code LGMAP, which computes the sound pressure levels at various observer locations. The method can calculate the noise from the undercarriage in isolation or installed on an aircraft for both main and nose landing gear. Comparisons with wind tunnel and flight data are used to initially calibrate the method, then it may be used to predict the noise of any landing gear. In this paper, noise predictions are compared with wind tunnel data for model landing gears of various scales and levels of fidelity, as well as with flight data on fullscale undercarriages. The present agreement between the calculations and measurements suggests the method has promise for future application in the prediction of airframe noise.

  3. Accuracy of Wind Prediction Methods in the California Sea Breeze

    NASA Astrophysics Data System (ADS)

    Sumers, B. D.; Dvorak, M. J.; Ten Hoeve, J. E.; Jacobson, M. Z.

    2010-12-01

    In this study, we investigate the accuracy of measure-correlate-predict (MCP) algorithms and log law/power law scaling using data from two tall towers in coastal environments. We find that MCP algorithms accurately predict sea breeze winds and that log law/power law scaling methods struggle to predict 50-meter wind speeds. MCP methods have received significant attention as the wind industry has grown and the ability to accurately characterize the wind resource has become valuable. These methods are used to produce longer-term wind speed records from short-term measurement campaigns. A correlation is developed between the “target site,” where the developer is interested in building wind turbines, and a “reference site,” where long-term wind data is available. Up to twenty years of prior wind speeds are then are predicted. In this study, two existing MCP methods - linear regression and Mortimer’s method - are applied to predict 50-meter wind speeds at sites in the Salinas Valley and Redwood City, CA. The predictions are then verified with tall tower data. It is found that linear regression is poorly suited to MCP applications as the process produces inaccurate estimates of the cube of the wind speed at 50 meters. Meanwhile, Mortimer’s method, which bins data by direction and speed, is found to accurately predict the cube of the wind speed in both sea breeze and non-sea breeze conditions. We also find that log and power law are unstable predictors of wind speeds. While these methods produced accurate estimates of the average 50-meter wind speed at both sites, they predicted an average cube of the wind speed that was between 1.3 and 1.18 times the observed value. Inspection of time-series error reveals increased error in the mid-afternoon of the summer. This suggests that the cold sea breeze may disrupt the vertical temperature profile, create a stable atmosphere and violate the assumptions that allow log law scaling to work.

  4. Plasma disruption prediction using machine learning methods: DIII-D

    NASA Astrophysics Data System (ADS)

    Lupin-Jimenez, L.; Kolemen, E.; Eldon, D.; Eidietis, N.

    2016-10-01

    Plasma disruption prediction is becoming more important with the development of larger tokamaks, due to the larger amount of thermal and magnetic energy that can be stored. By accurately predicting an impending disruption, the disruption's impact can be mitigated or, better, prevented. Recent approaches to disruption prediction have been through implementation of machine learning methods, which characterize raw and processed diagnostic data to develop accurate prediction models. Using disruption trials from the DIII-D database, the effectiveness of different machine learning methods are characterized. Developed real time disruption prediction approaches are focused on tearing and locking modes. Machine learning methods used include random forests, multilayer perceptrons, and traditional regression analysis. The algorithms are trained with data within short time frames, and whether or not a disruption occurs within the time window after the end of the frame. Initial results from the machine learning algorithms will be presented. Work supported by US DOE under the Science Undergraduate Laboratory Internship (SULI) program, DE-FC02-04ER54698, and DE-AC02-09CH11466.

  5. High accuracy operon prediction method based on STRING database scores.

    PubMed

    Taboada, Blanca; Verde, Cristina; Merino, Enrique

    2010-07-01

    We present a simple and highly accurate computational method for operon prediction, based on intergenic distances and functional relationships between the protein products of contiguous genes, as defined by STRING database (Jensen,L.J., Kuhn,M., Stark,M., Chaffron,S., Creevey,C., Muller,J., Doerks,T., Julien,P., Roth,A., Simonovic,M. et al. (2009) STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res., 37, D412-D416). These two parameters were used to train a neural network on a subset of experimentally characterized Escherichia coli and Bacillus subtilis operons. Our predictive model was successfully tested on the set of experimentally defined operons in E. coli and B. subtilis, with accuracies of 94.6 and 93.3%, respectively. As far as we know, these are the highest accuracies ever obtained for predicting bacterial operons. Furthermore, in order to evaluate the predictable accuracy of our model when using an organism's data set for the training procedure, and a different organism's data set for testing, we repeated the E. coli operon prediction analysis using a neural network trained with B. subtilis data, and a B. subtilis analysis using a neural network trained with E. coli data. Even for these cases, the accuracies reached with our method were outstandingly high, 91.5 and 93%, respectively. These results show the potential use of our method for accurately predicting the operons of any other organism. Our operon predictions for fully-sequenced genomes are available at http://operons.ibt.unam.mx/OperonPredictor/.

  6. A RAPID Method for Blood Processing to Increase the Yield of Plasma Peptide Levels in Human Blood.

    PubMed

    Teuffel, Pauline; Goebel-Stengel, Miriam; Hofmann, Tobias; Prinz, Philip; Scharner, Sophie; Körner, Jan L; Grötzinger, Carsten; Rose, Matthias; Klapp, Burghard F; Stengel, Andreas

    2016-04-28

    Research in the field of food intake regulation is gaining importance. This often includes the measurement of peptides regulating food intake. For the correct determination of a peptide's concentration, it should be stable during blood processing. However, this is not the case for several peptides which are quickly degraded by endogenous peptidases. Recently, we developed a blood processing method employing Reduced temperatures, Acidification, Protease inhibition, Isotopic exogenous controls and Dilution (RAPID) for the use in rats. Here, we have established this technique for the use in humans and investigated recovery, molecular form and circulating concentration of food intake regulatory hormones. The RAPID method significantly improved the recovery for (125)I-labeled somatostatin-28 (+39%), glucagon-like peptide-1 (+35%), acyl ghrelin and glucagon (+32%), insulin and kisspeptin (+29%), nesfatin-1 (+28%), leptin (+21%) and peptide YY3-36 (+19%) compared to standard processing (EDTA blood on ice, p <0.001). High performance liquid chromatography showed the elution of endogenous acyl ghrelin at the expected position after RAPID processing, while after standard processing 62% of acyl ghrelin were degraded resulting in an earlier peak likely representing desacyl ghrelin. After RAPID processing the acyl/desacyl ghrelin ratio in blood of normal weight subjects was 1:3 compared to 1:23 following standard processing (p = 0.03). Also endogenous kisspeptin levels were higher after RAPID compared to standard processing (+99%, p = 0.02). The RAPID blood processing method can be used in humans, yields higher peptide levels and allows for assessment of the correct molecular form.

  7. Developing a comparative docking protocol for the prediction of peptide selectivity profiles: investigation of potassium channel toxins.

    PubMed

    Chen, Po-Chia; Kuyucak, Serdar

    2012-02-01

    During the development of selective peptides against highly homologous targets, a reliable tool is sought that can predict information on both mechanisms of binding and relative affinities. These tools must first be tested on known profiles before application on novel therapeutic candidates. We therefore present a comparative docking protocol in HADDOCK using critical motifs, and use it to "predict" the various selectivity profiles of several major αKTX scorpion toxin families versus K(v)1.1, K(v)1.2 and K(v)1.3. By correlating results across toxins of similar profiles, a comprehensive set of functional residues can be identified. Reasonable models of channel-toxin interactions can be then drawn that are consistent with known affinity and mutagenesis. Without biological information on the interaction, HADDOCK reproduces mechanisms underlying the universal binding of αKTX-2 toxins, and K(v)1.3 selectivity of αKTX-3 toxins. The addition of constraints encouraging the critical lysine insertion confirms these findings, and gives analogous explanations for other families, including models of partial pore-block in αKTX-6. While qualitatively informative, the HADDOCK scoring function is not yet sufficient for accurate affinity-ranking. False minima in low-affinity complexes often resemble true binding in high-affinity complexes, despite steric/conformational penalties apparent from visual inspection. This contamination significantly complicates energetic analysis, although it is usually possible to obtain correct ranking via careful interpretation of binding-well characteristics and elimination of false positives. Aside from adaptations to the broader potassium channel family, we suggest that this strategy of comparative docking can be extended to other channels of interest with known structure, especially in cases where a critical motif exists to improve docking effectiveness.

  8. A Method of Recording and Predicting the Pollen Count.

    ERIC Educational Resources Information Center

    Buck, M.

    1985-01-01

    A hair dryer, plastic funnel, and microscope slide can be used for predicting pollen counts on a day-to-day basis. Materials, methods for assembly, collection technique, meteorological influences, and daily patterns are discussed. Data collected using the apparatus suggest that airborne grass products other than pollen also affect hay fever…

  9. Usefulness of N-terminal pro-brain natriuretic peptide levels to predict success of weaning from intra-aortic balloon pumping.

    PubMed

    Tokita, Yukichi; Yamamoto, Takeshi; Sato, Naoki; Hosokawa, Yusuke; Munakata, Ryo; Akutsu, Koichi; Shimizu, Wataru; Tanaka, Keiji

    2014-09-15

    There is currently no reliable method of predicting the success of weaning from intra-aortic balloon pumping (IABP). The aim of this study was to investigate the ability of serum N-terminal pro-brain natriuretic peptide (NT-proBNP) level to predict the success of weaning from IABP. Consecutive patients scheduled for weaning from IABP were prospectively enrolled. NT-proBNP levels were measured at baseline (before the start of weaning) and cessation (just before cessation of IABP). Changes in NT-proBNP level between baseline and cessation were analyzed in 2 groups of patients: those who were successfully weaned and those who were not successfully weaned for any reason, including a decision to discontinue weaning, worsening of pulmonary edema after cessation of IABP, or unstable hemodynamics after cessation of IABP. A total of 30 patients were enrolled (mean age 66 ± 12 years, 16 men, 16 with acute myocardial infarctions, and 14 with acute exacerbation of chronic heart failure). Median (interquartile range) baseline NT-proBNP levels were not significantly different between the successful and unsuccessful weaning groups (4,200 [1,400 to 8,752] pg/ml vs (5,620 [2,035 to 13,950] pg/ml, p = 0.30). In the unsuccessful weaning group, the median NT-proBNP level was significantly higher at cessation (9,995 [2,920 to 15,100] pg/ml) than at baseline (p = 0.008). All patients with decreases in NT-proBNP level between baseline and cessation were successfully weaned from IABP. In conclusion, these results show that NT-proBNP levels were useful for predicting the success of weaning from IABP. If the NT-proBNP level increases during weaning from IABP, more intense management should be considered.

  10. Respiratory Pattern Variability Analysis Based on Nonlinear Prediction Methods

    DTIC Science & Technology

    2007-11-02

    Brobely. All-night sleep EEG and artificial stochastic control signals have similar correlation dimensions . Electroencephalogr. Clin. Neurophisiol...methods. These methods use the volume signals generated by the respiratory system in order to construct a model of its dynamics, and then to estimate the...definition have been considered. The incidence of different prediction depths and embedding dimensions have been analyzed. A group of 12 patients on

  11. Risk prediction with machine learning and regression methods.

    PubMed

    Steyerberg, Ewout W; van der Ploeg, Tjeerd; Van Calster, Ben

    2014-07-01

    This is a discussion of issues in risk prediction based on the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler.

  12. Computational Methods for Predictive Simulation of Stochastic Turbulence Systems

    DTIC Science & Technology

    2015-11-05

    Enter title and subtitle with volume number and part number, if applicable . On classified documents, enter the title classification in parentheses. 5a...accuracy, and range of applicability of non-intrusive methods, such as stochastic collocation methods, and intrusive techniques, such as stochastic...engineering flows) over a long time interval is not possible within time and resource constraints. Many applications central to predictive CFD today

  13. Hybrid robust predictive optimization method of power system dispatch

    DOEpatents

    Chandra, Ramu Sharat; Liu, Yan; Bose, Sumit; de Bedout, Juan Manuel

    2011-08-02

    A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.

  14. In silico approaches to predict the potential of milk protein-derived peptides as dipeptidyl peptidase IV (DPP-IV) inhibitors.

    PubMed

    Nongonierma, Alice B; Mooney, Catherine; Shields, Denis C; FitzGerald, Richard J

    2014-07-01

    Molecular docking of a library of all 8000 possible tripeptides to the active site of DPP-IV was used to determine their binding potential. A number of tripeptides were selected for experimental testing, however, there was no direct correlation between the Vina score and their in vitro DPP-IV inhibitory properties. While Trp-Trp-Trp, the peptide with the best docking score, was a moderate DPP-IV inhibitor (IC50 216μM), Lineweaver and Burk analysis revealed its action to be non-competitive. This suggested that it may not bind to the active site of DPP-IV as assumed in the docking prediction. Furthermore, there was no significant link between DPP-IV inhibition and the physicochemical properties of the peptides (molecular mass, hydrophobicity, hydrophobic moment (μH), isoelectric point (pI) and charge). LIGPLOTs indicated that competitive inhibitory peptides were predicted to have both hydrophobic and hydrogen bond interactions with the active site of DPP-IV. DPP-IV inhibitory peptides generally had a hydrophobic or aromatic amino acid at the N-terminus, preferentially a Trp for non-competitive inhibitors and a broader range of residues for competitive inhibitors (Ile, Leu, Val, Phe, Trp or Tyr). Two of the potent DPP-IV inhibitors, Ile-Pro-Ile and Trp-Pro (IC50 values of 3.5 and 44.2μM, respectively), were predicted to be gastrointestinally/intestinally stable. This work highlights the needs to test the assumptions (i.e. competitive binding) of any integrated strategy of computational and experimental screening, in optimizing screening. Future strategies targeting allosteric mechanisms may need to rely more on structure-activity relationship modeling, rather than on docking, in computationally selecting peptides for screening.

  15. Statistical Methods for Predicting Malaria Incidences Using Data from Sudan

    PubMed Central

    Awadalla, Khidir E.

    2017-01-01

    Malaria is the leading cause of illness and death in Sudan. The entire population is at risk of malaria epidemics with a very high burden on government and population. The usefulness of forecasting methods in predicting the number of future incidences is needed to motivate the development of a system that can predict future incidences. The objective of this paper is to develop applicable and understood time series models and to find out what method can provide better performance to predict future incidences level. We used monthly incidence data collected from five states in Sudan with unstable malaria transmission. We test four methods of the forecast: (1) autoregressive integrated moving average (ARIMA); (2) exponential smoothing; (3) transformation model; and (4) moving average. The result showed that transformation method performed significantly better than the other methods for Gadaref, Gazira, North Kordofan, and Northern, while the moving average model performed significantly better for Khartoum. Future research should combine a number of different and dissimilar methods of time series to improve forecast accuracy with the ultimate aim of developing a simple and useful model for producing reasonably reliable forecasts of the malaria incidence in the study area. PMID:28367352

  16. Composition and method for self-assembly and mineralization of peptide amphiphiles

    DOEpatents

    Stupp, Samuel I.; Beniash, Elia; Hartgerink, Jeffrey D.

    2009-06-30

    The present invention is directed to a composition useful for making homogeneously mineralized self assembled peptide-amphiphile nanofibers and nanofiber gels. The composition is generally a solution comprised of a positively or negatively charged peptide-amphiphile and a like signed ion from the mineral. Mixing this solution with a second solution containing a dissolved counter-ion of the mineral and/or a second oppositely charged peptide amphiphile, results in the rapid self assembly of the peptide-amphiphiles into a nanofiber gel and templated mineralization of the ions. Templated mineralization of the initially dissolved mineral cations and anions in the mixture occurs with preferential orientation of the mineral crystals along the fiber surfaces within the nanofiber gel. One advantage of the present invention is that it results in homogenous growth of the mineral throughout the nanofiber gel. Another advantage of the present invention is that the nanofiber gel formation and mineralization reactions occur in a single mixing step and under substantially neutral or physiological pH conditions. These homogeneous nanostructured composite materials are useful for medical applications especially the regeneration of damaged bone in mammals. This invention is directed to the synthesis of peptide-amphiphiles with more than one amphiphilic moment and to supramolecular compositions comprised of such multi-dimensional peptide-amphiphiles. Supramolecular compositions can be formed by self assembly of multi-dimensional peptide-amphiphiles by mixing them with a solution comprising a monovalent cation.

  17. Composition and method for self-assembly and mineralization of peptide-amphiphiles

    DOEpatents

    Stupp, Samuel I [Chicago, IL; Beniash, Elia [Newton, MA; Hartgerink, Jeffrey D [Pearland, TX

    2012-02-28

    The present invention is directed to a composition useful for making homogeneously mineralized self assembled peptide-amphiphile nanofibers and nanofiber gels. The composition is generally a solution comprised of a positively or negatively charged peptide-amphiphile and a like signed ion from the mineral. Mixing this solution with a second solution containing a dissolved counter-ion of the mineral and/or a second oppositely charged peptide amphiphile, results in the rapid self assembly of the peptide-amphiphiles into a nanofiber gel and templated mineralization of the ions. Templated mineralization of the initially dissolved mineral cations and anions in the mixture occurs with preferential orientation of the mineral crystals along the fiber surfaces within the nanofiber gel. One advantage of the present invention is that it results in homogenous growth of the mineral throughout the nanofiber gel. Another advantage of the present invention is that the nanofiber gel formation and mineralization reactions occur in a single mixing step and under substantially neutral or physiological pH conditions. These homogeneous nanostructured composite materials are useful for medical applications especially the regeneration of damaged bone in mammals. This invention is directed to the synthesis of peptide-amphiphiles with more than one amphiphilic moment and to supramolecular compositions comprised of such multi-dimensional peptide-amphiphiles. Supramolecular compositions can be formed by self assembly of multi-dimensional peptide-amphiphiles by mixing them with a solution comprising a monovalent cation.

  18. Thermochemical Fragment Energy Method for Biomolecules: Application to a Collagen Model Peptide.

    PubMed

    Suárez, Ernesto; Díaz, Natalia; Suárez, Dimas

    2009-06-09

    Herein, we first review different methodologies that have been proposed for computing the quantum mechanical (QM) energy and other molecular properties of large systems through a linear combination of subsystem (fragment) energies, which can be computed using conventional QM packages. Particularly, we emphasize the similarities among the different methods that can be considered as variants of the multibody expansion technique. Nevertheless, on the basis of thermochemical arguments, we propose yet another variant of the fragment energy methods, which could be useful for, and readily applicable to, biomolecules using either QM or hybrid quantum mechanical/molecular mechanics methods. The proposed computational scheme is applied to investigate the stability of a triple-helical collagen model peptide. To better address the actual applicability of the fragment QM method and to properly compare with experimental data, we compute average energies by carrying out single-point fragment QM calculations on structures generated by a classical molecular dynamics simulation. The QM calculations are done using a density functional level of theory combined with an implicit solvent model. Other free-energy terms such as attractive dispersion interactions or thermal contributions are included using molecular mechanics. The importance of correcting both the intermolecular and intramolecular basis set superposition error (BSSE) in the QM calculations is also discussed in detail. On the basis of the favorable comparison of our fragment-based energies with experimental data and former theoretical results, we conclude that the fragment QM energy strategy could be an interesting addition to the multimethod toolbox for biomolecular simulations in order to investigate those situations (e.g., interactions with metal clusters) that are beyond the range of applicability of common molecular mechanics methods.

  19. Sediment rating curve & Co. - a contest of prediction methods

    NASA Astrophysics Data System (ADS)

    Francke, T.; Zimmermann, A.

    2012-04-01

    In spite of the recent technological progress in sediment monitoring, often the calculation of sediment yield (SSY) still relies on intermittent measurements because of the use of historic records, instrument-failure in continuous recording or financial constraints. Therefore, available measurements are usually inter- and even extrapolated using the sediment rating curve approach, which uses continuously available discharge data to predict sediment concentrations. Extending this idea by further aspects like the inclusion of other predictors (e.g. rainfall, discharge-characteristics, etc.), or the consideration of prediction uncertainty led to a variety of new methods. Now, with approaches such as Fuzzy Logic, Artificial Neural Networks, Tree-based regression, GLMs, etc., the user is left to decide which method to apply. Trying multiple approaches is usually not an option, as considerable effort and expertise may be needed for their application. To establish a helpful guideline in selecting the most appropriate method for SSY-computation, we initiated a study to compare and rank available methods. Depending on problem attributes like hydrological and sediment regime, number of samples, sampling scheme, and availability of ancillary predictors, the performance of different methods is compared. Our expertise allowed us to "register" Random Forests, Quantile Regression Forests and GLMs for the contest. To include many different methods and ensure their sophisticated use we invite scientists that are willing to benchmark their favourite method(s) with us. The more diverse the participating methods are, the more exciting the contest will be.

  20. ESG: extended similarity group method for automated protein function prediction

    PubMed Central

    Chitale, Meghana; Hawkins, Troy; Park, Changsoon; Kihara, Daisuke

    2009-01-01

    Motivation: Importance of accurate automatic protein function prediction is ever increasing in the face of a large number of newly sequenced genomes and proteomics data that are awaiting biological interpretation. Conventional methods have focused on high sequence similarity-based annotation transfer which relies on the concept of homology. However, many cases have been reported that simple transfer of function from top hits of a homology search causes erroneous annotation. New methods are required to handle the sequence similarity in a more robust way to combine together signals from strongly and weakly similar proteins for effectively predicting function for unknown proteins with high reliability. Results: We present the extended similarity group (ESG) method, which performs iterative sequence database searches and annotates a query sequence with Gene Ontology terms. Each annotation is assigned with probability based on its relative similarity score with the multiple-level neighbors in the protein similarity graph. We will depict how the statistical framework of ESG improves the prediction accuracy by iteratively taking into account the neighborhood of query protein in the sequence similarity space. ESG outperforms conventional PSI-BLAST and the protein function prediction (PFP) algorithm. It is found that the iterative search is effective in capturing multiple-domains in a query protein, enabling accurately predicting several functions which originate from different domains. Availability: ESG web server is available for automated protein function prediction at http://dragon.bio.purdue.edu/ESG/ Contact: cspark@cau.ac.kr; dkihara@purdue.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19435743

  1. A Review of Computational Methods for Predicting Drug Targets.

    PubMed

    Huang, Guohua; Yan, Fengxia; Tan, Duoduo

    2016-11-14

    Drug discovery and development is not only a time-consuming and labor-intensive process but also full of risk. Identifying targets of small molecules helps evaluate safety of drugs and find new therapeutic applications. The biotechnology measures a wide variety of properties related to drug and targets from different perspectives, thus generating a large body of data. This undoubtedly provides a solid foundation to explore relationships between drugs and targets. A large number of computational techniques have recently been developed for drug target prediction. In this paper, we summarize these computational methods and classify them into structure-based, molecular activity-based, side-effect-based and multi-omics-based predictions according to the used data for inference. The multi-omics-based methods are further grouped into two types: classifier-based and network-based predictions. Furthermore,the advantages and limitations of each type of methods are discussed. Finally, we point out the future directions of computational predictions for drug targets.

  2. Structure-based Methods for Computational Protein Functional Site Prediction

    PubMed Central

    Dukka, B KC

    2013-01-01

    Due to the advent of high throughput sequencing techniques and structural genomic projects, the number of gene and protein sequences has been ever increasing. Computational methods to annotate these genes and proteins are even more indispensable. Proteins are important macromolecules and study of the function of proteins is an important problem in structural bioinformatics. This paper discusses a number of methods to predict protein functional site especially focusing on protein ligand binding site prediction. Initially, a short overview is presented on recent advances in methods for selection of homologous sequences. Furthermore, a few recent structural based approaches and sequence-and-structure based approaches for protein functional sites are discussed in details. PMID:24688745

  3. Better Methods for Predicting Lifetimes of Seal Materials

    SciTech Connect

    Celina, M.; Gillen, K.T.; Keenan, M.R.

    1999-03-16

    We have been working for many years to develop better methods for predicting the lifetimes of polymer materials. Because of the recent interest in extending the lifetimes of nuclear weapons and the importance of environmental seals (o-rings, gaskets) for protecting weapon interiors against oxygen and water vapor, we have recently turned our attention to seal materials. Perhaps the most important environmental o-ring material is butyl rubber, used in various military applications. Although it is the optimum choice from a water permeability perspective, butyl can be marginal from an aging point-of-view. The purpose of the present work was to derive better methods for predicting seal lifetimes and applying these methods to an important butyl material, Parker compound B6 12-70.

  4. Predictions of Thrombus Formation Using Lattice Boltzmann Method

    NASA Astrophysics Data System (ADS)

    Tamagawa, Masaaki; Matsuo, Sumiaki

    This paper describes the prediction of index of thrombus formation in shear blood flow by computational fluid dynamics (CFD) with Lattice Boltzmann Method (LBM), applying to orifice-pipe blood flow and flow around a cylinder, which is simple model of turbulent shear stress in the high speed rotary blood pumps and complicated geometry of medical fluid machines. The results of the flow field in the orifice-pipe flow using LBM are compared with experimental data and those using finite difference method, and it is found that the reattachment length of the backward facing step flow is predicted as precise as that the experiment and the finite difference method. As for thrombus formation, from the computational data of flow around the cylinder in the channel, the thrombus formation (thickness) is estimated using (1) shear rate and adhesion force (effective distance) to the wall independently, and (2) shear rate function with adhesion force (effective distance), and it is found that the prediction method using shear rate function with adhesion force is more accurate than the method using the former one.

  5. Role of galectin-3 and plasma B type-natriuretic peptide in predicting prognosis in discharged chronic heart failure patients.

    PubMed

    Feola, Mauro; Testa, Marzia; Leto, Laura; Cardone, Marco; Sola, Mario; Rosso, Gian Luca

    2016-06-01

    Galectin-3 demonstrated to be a robust independent marker of cardiovascular mid-term (18-month) outcome in heart failure (HF) patients. The objective of this study was to analyze the value of a predischarged determination of plasma galectin-3 alone and with plasma brain natriuretic peptide (BNP) in predicting mid-term outcome in frequent-flyers (FF) HF (≥2 hospitalization for HF/year)/dead patients discharged after an acute decompensated HF (ADHF) episode.All FF chronic HF subjects discharged alive after an ADHF were enrolled. All patients underwent a determination of BNP and galectin-3, a 6-minute walk test, and an echocardiogram within 48 hours upon hospital discharge. Death by any cause, cardiac transplantation, and worsening HF requiring readmission to hospital were considered cardiovascular events.Eighty-three patients (67 males, age 73.2 ± 8.6 years old) were analyzed (mean follow-up 11.6 ± 5.2 months; range 4-22 months). During the follow-up 38 events (45.7%) were scheduled: (13 cardiac deaths, 35 rehospitalizations for ADHF). According to medical history, in 33 patients (39.8%) a definition of FF HF patients was performed (range 2-4 hospitalization/year). HF patients who suffered an event (FF or death) demonstrated more impaired ventricular function (P = 0.037), higher plasma BNP (P = 0.005), and Gal-3 at predischarge evaluation (P = 0.027). Choosing adequate cut-off points (BNP ≥ 500 pg/mL and Gal-3 ≥ 17.6 ng/mL), the Kaplan-Meier curves depicted the powerful stratification using BNP + Gal-3 in predicting clinical course at mid-term follow-up (log rank 5.65; P = 0.017).Adding Gal-3 to BNP, a single predischarge strategy testing seemed to obtain a satisfactorily predictive value in alive HF patients discharged after an ADHF episode.

  6. Application of linear gauss pseudospectral method in model predictive control

    NASA Astrophysics Data System (ADS)

    Yang, Liang; Zhou, Hao; Chen, Wanchun

    2014-03-01

    This paper presents a model predictive control(MPC) method aimed at solving the nonlinear optimal control problem with hard terminal constraints and quadratic performance index. The method combines the philosophies of the nonlinear approximation model predictive control, linear quadrature optimal control and Gauss Pseudospectral method. The current control is obtained by successively solving linear algebraic equations transferred from the original problem via linearization and the Gauss Pseudospectral method. It is not only of high computational efficiency since it does not need to solve nonlinear programming problem, but also of high accuracy though there are a few discrete points. Therefore, this method is suitable for on-board applications. A design of terminal impact with a specified direction is carried out to evaluate the performance of this method. Augmented PN guidance law in the three-dimensional coordinate system is applied to produce the initial guess. And various cases for target with straight-line movements are employed to demonstrate the applicability in different impact angles. Moreover, performance of the proposed method is also assessed by comparison with other guidance laws. Simulation results indicate that this method is not only of high computational efficiency and accuracy, but also applicable in the framework of guidance design.

  7. Proven in vitro evolution of protease cathepsin E-inhibitors and -activators at pH 4.5 using a paired peptide method.

    PubMed

    Kitamura, Koichiro; Komatsu, Masayuki; Biyani, Madhu; Futakami, Masae; Kawakubo, Tomoyo; Yamamoto, Kenji; Nishigaki, Koichi

    2012-12-01

    Improving a particular function of molecules is often more difficult than identifying such molecules ab initio. Here, a method to acquire higher affinity and/or more functional peptides was developed as a progressive library selection method. The primary library selection products were utilized to build a secondary library composed of blocks of 4 amino acids, of which selection led to peptides with increased activity. These peptides were further converted to randomly generate paired peptides. Cathepsin E-inhibitors thus obtained exhibited the highest activities and affinities (pM order). This was also the case with cathepsin E-activating peptides, proving the methodological effectiveness. The primary, secondary, and tertiary library selections can be regarded as module-finding, module-shuffling, and module-pairing, respectively, which resembles the progression of the natural evolution of proteins. The mode of peptide binding to their target proteins is discussed in analogy to antibodies and epitopes of an antigen.

  8. Influence of the yeast strain on the changes of the amino acids, peptides and proteins during sparkling wine production by the traditional method.

    PubMed

    Martínez-Rodríguez, A J; Carrascosa, A V; Martín-Alvarez, P J; Moreno-Arribas, V; Polo, M C

    2002-12-01

    The influence of five yeast strains on the nitrogen fractions, amino acids, peptides and proteins, during 12 months of aging of sparkling wines produced by the traditional or Champenoise method, was studied. High-performance liquid chromatography (HPLC) techniques were used for analysis of the amino acid and peptide fractions. Proteins plus polypeptides were determined by the colorimetric Bradford method. Four main stages were detected in the aging of wines with yeast. In the first stage, a second fermentation took place; amino acids and proteins plus polypeptides diminished, and peptides were liberated. In the second stage, there was a release of amino acids and proteins, and peptides were degraded. In the third stage, the release of proteins and peptides predominated. In the fourth stage, the amino acid concentration diminished. The yeast strain used influenced the content of free amino acids and peptides and the aging time in all the nitrogen fractions.

  9. Evaluation of ride quality prediction methods for operational military helicopters

    NASA Technical Reports Server (NTRS)

    Leatherwood, J. D.; Clevenson, S. A.; Hollenbaugh, D. D.

    1984-01-01

    The results of a simulator study conducted to compare and validate various ride quality prediction methods for use in assessing passenger/crew ride comfort within helicopters are presented. Included are results quantifying 35 helicopter pilots' discomfort responses to helicopter interior noise and vibration typical of routine flights, assessment of various ride quality metrics including the NASA ride comfort model, and examination of possible criteria approaches. Results of the study indicated that crew discomfort results from a complex interaction between vibration and interior noise. Overall measures such as weighted or unweighted root-mean-square acceleration level and A-weighted noise level were not good predictors of discomfort. Accurate prediction required a metric incorporating the interactive effects of both noise and vibration. The best metric for predicting crew comfort to the combined noise and vibration environment was the NASA discomfort index.

  10. Rational, computer-enabled peptide drug design: principles, methods, applications and future directions.

    PubMed

    Diller, David J; Swanson, Jon; Bayden, Alexander S; Jarosinski, Mark; Audie, Joseph

    2015-01-01

    Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.

  11. Peptides-staple method development and its application in cancer therapy.

    PubMed

    Zhang, Q Z; Tian, Y; Lao, Y Z; Li, Z-G

    2014-01-01

    α-Helixes are important structural motifs of protein three dimension structures and are largely involved in protein- protein interactions. This review covers the recent advances of the peptide stabilizing methodologies and introduces their applications in cancer research.

  12. Comparison of Predictive Modeling Methods of Aircraft Landing Speed

    NASA Technical Reports Server (NTRS)

    Diallo, Ousmane H.

    2012-01-01

    Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

  13. A Method for the Sample Handling and Analysis of Bio-Active Peptides

    DTIC Science & Technology

    1998-05-01

    two disulfide bridges of a-conotoxin GI would be expected to break and the side chain hydrogen on each cysteine would be replaced with an acetamide...peptides in a database. Disulfide bridge reductive alkylation is used to determine the number of cysteines in the peptide as well as the presence of...was 42 ppm. Reductive alkylation indicated the presence of four cysteines and two intramolecular disulfide bridges which was consistent with the

  14. Epidemiology and statistical methods in prediction of patient outcome.

    PubMed

    Bostwick, David G; Adolfsson, Jan; Burke, Harry B; Damber, Jan-Erik; Huland, Hartwig; Pavone-Macaluso, Michele; Waters, David J

    2005-05-01

    Substantial gaps exist in the data of the assessment of risk and prognosis that limit our understanding of the complex mechanisms that contribute to the greatest cancer epidemic, prostate cancer, of our time. This report was prepared by an international multidisciplinary committee of the World Health Organization to address contemporary issues of epidemiology and statistical methods in prostate cancer, including a summary of current risk assessment methods and prognostic factors. Emphasis was placed on the relative merits of each of the statistical methods available. We concluded that: 1. An international committee should be created to guide the assessment and validation of molecular biomarkers. The goal is to achieve more precise identification of those who would benefit from treatment. 2. Prostate cancer is a predictable disease despite its biologic heterogeneity. However, the accuracy of predicting it must be improved. We expect that more precise statistical methods will supplant the current staging system. The simplicity and intuitive ease of using the current staging system must be balanced against the serious compromise in accuracy for the individual patient. 3. The most useful new statistical approaches will integrate molecular biomarkers with existing prognostic factors to predict conditional life expectancy (i.e. the expected remaining years of a patient's life) and take into account all-cause mortality.

  15. Macromolecule Biosynthesis Assay and Fluorescence Spectroscopy Methods to Explore Antimicrobial Peptide Mode(s) of Action.

    PubMed

    Jana, Bimal; Baker, Kristin Renee; Guardabassi, Luca

    2017-01-01

    Antimicrobial peptides (AMPs) are viable alternatives to the currently available antimicrobials, and numerous studies have investigated their possible use as therapeutic agents for specific clinical applications. AMPs are a diverse class of antimicrobials that often act upon the bacterial cell membrane but may exhibit additional modes of action. Identification of the multiple modes of action requires a comprehensive study at subinhibitory concentrations and careful data analysis since additional modes of action can be eclipsed by AMP action on the cell membrane.Techniques that measure the biosynthesis rate of macromolecules (e.g., DNA, RNA, protein, and cell wall) and the cytoplasmic membrane proton motive force (PMF) energy can help to unravel the diverse modes of action of AMPs. Here, we present an overview of macromolecule biosynthesis rate measurement and fluorescence spectroscopy methods to identify AMP mode(s) of action. Detailed protocols designed to measure inhibition of DNA, RNA, protein, and cell wall synthesis or membrane de-energization are presented and discussed for optimal application of these two techniques as well as to enable accurate interpretation of the experimental findings.

  16. Researches on High Accuracy Prediction Methods of Earth Orientation Parameters

    NASA Astrophysics Data System (ADS)

    Xu, X. Q.

    2015-09-01

    The Earth rotation reflects the coupling process among the solid Earth, atmosphere, oceans, mantle, and core of the Earth on multiple spatial and temporal scales. The Earth rotation can be described by the Earth's orientation parameters, which are abbreviated as EOP (mainly including two polar motion components PM_X and PM_Y, and variation in the length of day ΔLOD). The EOP is crucial in the transformation between the terrestrial and celestial reference systems, and has important applications in many areas such as the deep space exploration, satellite precise orbit determination, and astrogeodynamics. However, the EOP products obtained by the space geodetic technologies generally delay by several days to two weeks. The growing demands for modern space navigation make high-accuracy EOP prediction be a worthy topic. This thesis is composed of the following three aspects, for the purpose of improving the EOP forecast accuracy. (1) We analyze the relation between the length of the basic data series and the EOP forecast accuracy, and compare the EOP prediction accuracy for the linear autoregressive (AR) model and the nonlinear artificial neural network (ANN) method by performing the least squares (LS) extrapolations. The results show that the high precision forecast of EOP can be realized by appropriate selection of the basic data series length according to the required time span of EOP prediction: for short-term prediction, the basic data series should be shorter, while for the long-term prediction, the series should be longer. The analysis also showed that the LS+AR model is more suitable for the short-term forecasts, while the LS+ANN model shows the advantages in the medium- and long-term forecasts. (2) We develop for the first time a new method which combines the autoregressive model and Kalman filter (AR+Kalman) in short-term EOP prediction. The equations of observation and state are established using the EOP series and the autoregressive coefficients

  17. Developing a Comparative Docking Protocol for the Prediction of Peptide Selectivity Profiles: Investigation of Potassium Channel Toxins

    PubMed Central

    Chen, Po-Chia; Kuyucak, Serdar

    2012-01-01

    During the development of selective peptides against highly homologous targets, a reliable tool is sought that can predict information on both mechanisms of binding and relative affinities. These tools must first be tested on known profiles before application on novel therapeutic candidates. We therefore present a comparative docking protocol in HADDOCK using critical motifs, and use it to “predict” the various selectivity profiles of several major αKTX scorpion toxin families versus Kv1.1, Kv1.2 and Kv1.3. By correlating results across toxins of similar profiles, a comprehensive set of functional residues can be identified. Reasonable models of channel-toxin interactions can be then drawn that are consistent with known affinity and mutagenesis. Without biological information on the interaction, HADDOCK reproduces mechanisms underlying the universal binding of αKTX-2 toxins, and Kv1.3 selectivity of αKTX-3 toxins. The addition of constraints encouraging the critical lysine insertion confirms these findings, and gives analogous explanations for other families, including models of partial pore-block in αKTX-6. While qualitatively informative, the HADDOCK scoring function is not yet sufficient for accurate affinity-ranking. False minima in low-affinity complexes often resemble true binding in high-affinity complexes, despite steric/conformational penalties apparent from visual inspection. This contamination significantly complicates energetic analysis, although it is usually possible to obtain correct ranking via careful interpretation of binding-well characteristics and elimination of false positives. Aside from adaptations to the broader potassium channel family, we suggest that this strategy of comparative docking can be extended to other channels of interest with known structure, especially in cases where a critical motif exists to improve docking effectiveness. PMID:22474570

  18. Humoral immune responses to EGFR-derived peptides predict progression-free and overall survival of non-small cell lung cancer patients receiving gefitinib.

    PubMed

    Azuma, Koichi; Komatsu, Nobukazu; Hattori, Satoshi; Matsueda, Satoko; Kawahara, Akihiko; Sasada, Tetsuro; Itoh, Kyogo; Hoshino, Tomoaki

    2014-01-01

    Somatic mutations in the epidermal growth factor receptor (EGFR) gene are associated with clinical response to EGFR tyrosine kinase inhibitors (TKIs), such as gefitinib, in patients with non-small cell lung cancer (NSCLC). However, humoral immune responses to EGFR in NSCLC patients have not been well studied. In this study, we investigated the clinical significance of immunoglobulin G (IgG) responses to EGFR-derived peptides in NSCLC patients receiving gefitinib. Plasma IgG titers to each of 60 different EGFR-derived 20-mer peptides were measured by the Luminex system in 42 NSCLC patients receiving gefitinib therapy. The relationships between the peptide-specific IgG titers and presence of EGFR mutations or patient survival were evaluated statistically. IgG titers against the egfr_481-500, egfr_721-740, and egfr_741-760 peptides were significantly higher in patients with exon 21 mutation than in those without it. On the other hand, IgG titers against the egfr_841-860 and egfr_1001-1020 peptides were significantly lower and higher, respectively, in patients with deletion in exon 19. Multivariate Cox regression analysis showed that IgG responses to egfr_41_ 60, egfr_61_80 and egfr_481_500 were significantly prognostic for progression-free survival independent of other clinicopathological characteristics, whereas those to the egfr_41_60 and egfr_481_500 peptides were significantly prognostic for overall survival. Detection of IgG responses to EGFR-derived peptides may be a promising method for prognostication of NSCLC patients receiving gefitinib. Our results may provide new insight for better understanding of humoral responses to EGFR in NSCLC patients.

  19. Linear reduction method for predictive and informative tag SNP selection.

    PubMed

    He, Jingwu; Westbrooks, Kelly; Zelikovsky, Alexander

    2005-01-01

    Constructing a complete human haplotype map is helpful when associating complex diseases with their related SNPs. Unfortunately, the number of SNPs is very large and it is costly to sequence many individuals. Therefore, it is desirable to reduce the number of SNPs that should be sequenced to a small number of informative representatives called tag SNPs. In this paper, we propose a new linear algebra-based method for selecting and using tag SNPs. We measure the quality of our tag SNP selection algorithm by comparing actual SNPs with SNPs predicted from selected linearly independent tag SNPs. Our experiments show that for sufficiently long haplotypes, knowing only 0.4% of all SNPs the proposed linear reduction method predicts an unknown haplotype with the error rate below 2% based on 10% of the population.

  20. Method to predict external store carriage characteristics at transonic speeds

    NASA Technical Reports Server (NTRS)

    Rosen, Bruce S.

    1988-01-01

    Development of a computational method for prediction of external store carriage characteristics at transonic speeds is described. The geometric flexibility required for treatment of pylon-mounted stores is achieved by computing finite difference solutions on a five-level embedded grid arrangement. A completely automated grid generation procedure facilitates applications. Store modeling capability consists of bodies of revolution with multiple fore and aft fins. A body-conforming grid improves the accuracy of the computed store body flow field. A nonlinear relaxation scheme developed specifically for modified transonic small disturbance flow equations enhances the method's numerical stability and accuracy. As a result, treatment of lower aspect ratio, more highly swept and tapered wings is possible. A limited supersonic freestream capability is also provided. Pressure, load distribution, and force/moment correlations show good agreement with experimental data for several test cases. A detailed computer program description for the Transonic Store Carriage Loads Prediction (TSCLP) Code is included.

  1. A review of statistical methods for prediction of proteolytic cleavage.

    PubMed

    duVerle, David A; Mamitsuka, Hiroshi

    2012-05-01

    A fundamental component of systems biology, proteolytic cleavage is involved in nearly all aspects of cellular activities: from gene regulation to cell lifecycle regulation. Current sequencing technologies have made it possible to compile large amount of cleavage data and brought greater understanding of the underlying protein interactions. However, the practical impossibility to exhaustively retrieve substrate sequences through experimentation alone has long highlighted the need for efficient computational prediction methods. Such methods must be able to quickly mark substrate candidates and putative cleavage sites for further analysis. Available methods and expected reliability depend heavily on the type and complexity of proteolytic action, as well as the availability of well-labelled experimental data sets: factors varying greatly across enzyme families. For this review, we chose to give a quick overview of the general issues and challenges in cleavage prediction methods followed by a more in-depth presentation of major techniques and implementations, with a focus on two particular families of cysteine proteases: caspases and calpains. Through their respective differences in proteolytic specificity (high for caspases, broader for calpains) and data availability (much lower for calpains), we aimed to illustrate the strengths and limitations of techniques ranging from position-based matrices and decision trees to more flexible machine-learning methods such as hidden Markov models and Support Vector Machines. In addition to a technical overview for each family of algorithms, we tried to provide elements of evaluation and performance comparison across methods.

  2. Epileptic seizure prediction by non-linear methods

    DOEpatents

    Hively, L.M.; Clapp, N.E.; Day, C.S.; Lawkins, W.F.

    1999-01-12

    This research discloses methods and apparatus for automatically predicting epileptic seizures monitor and analyze brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis tools; obtaining time serial trends in the nonlinear measures; comparison of the trend to known seizure predictors; and providing notification that a seizure is forthcoming. 76 figs.

  3. Epileptic seizure prediction by non-linear methods

    DOEpatents

    Hively, Lee M.; Clapp, Ned E.; Daw, C. Stuart; Lawkins, William F.

    1999-01-01

    Methods and apparatus for automatically predicting epileptic seizures monitor and analyze brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis tools; obtaining time serial trends in the nonlinear measures; comparison of the trend to known seizure predictors; and providing notification that a seizure is forthcoming.

  4. Sixth blind test of organic crystal-structure prediction methods.

    PubMed

    Groom, Colin R; Reilly, Anthony M

    2014-08-01

    Over the past 15 years progress in predicting crystal structures of small organic molecules has been charted by a series of blind tests hosted by the Cambridge Crystallographic Data Centre. This letter announces a sixth blind test to take place between September 2014 and August 2015, giving details of the target systems and the revised procedure. We hope that as many methods as possible will be assessed and benchmarked in this new blind test.

  5. B‐type Natriuretic Peptides for the Prediction of Cardiovascular Events in Patients With Stable Coronary Heart Disease: The Heart and Soul Study

    PubMed Central

    Mishra, Rakesh K.; Beatty, Alexis L.; Jaganath, Rajesh; Regan, Mathilda; Wu, Alan H.B.; Whooley, Mary A.

    2014-01-01

    Background Brain‐type natriuretic peptide (BNP) and the amino‐terminal fragment of its prohormone (NT‐proBNP) are known predictors of cardiovascular outcomes in patients with coronary heart disease; however, the relative prognostic value of these 2 biomarkers for secondary events remains unclear. Methods and Results In 983 participants with stable coronary heart disease, we evaluated the association of BNP and NT‐proBNP with time to hospitalization for heart failure, nonfatal myocardial infarction, stroke or transient ischemic attack, cardiovascular death, and combined major adverse cardiovascular events (MACE). During an average follow‐up of 6.5±3.3 years, both BNP and NT‐proBNP were associated with increased risk of MACE in a multivariable‐adjusted model (hazard ratio per standard deviation of log BNP: 1.58; 95% CI: 1.32 to 1.89; hazard ratio per standard deviation of log NT‐proBNP: 1.84; 95% CI: 1.52 to 2.24). When added to traditional risk factors, NT‐proBNP predicted MACE better than BNP (C statistic: 0.76 versus 0.72, P<0.001). Similarly, the addition of NT‐proBNP resulted in a greater net reclassification improvement for predicting MACE than the addition of BNP (65% for NT‐proBNP, 56% for BNP). Conclusions Both BNP and NT‐proBNP were significant predictors of MACE in stable coronary heart disease; however, NT‐proBNP was superior to BNP for net risk reclassification for MACE. PMID:25053234

  6. Application of hydrophilic interaction chromatography retention coefficients for predicting peptide elution with TFA and methanesulfonic acid ion-pairing reagents.

    PubMed

    Wujcik, Chad E; Tweed, Joseph; Kadar, Eugene P

    2010-03-01

    Hydrophilic retention coefficients for 17 peptides were calculated based on retention coefficients previously published for TSKgel silica-60 and were compared with the experimental elution profile on a Waters Atlantis HILIC silica column using TFA and methanesulfonic acid (MSA) as ion-pairing reagents. Relative peptide retention could be accurately determined with both counter-ions. Peptide retention and chromatographic behavior were influenced by the percent acid modifier used with increases in both retention and peak symmetry observed at increasing modifier concentrations. The enhancement of net peptide polarity through MSA pairing shifted retention out by nearly five-fold for the earliest eluting peptide, compared with TFA. Despite improvements in retention and efficiency (N(eff)) for MSA over TFA, a consistent reduction in calculated selectivity (alpha) was observed. This result is believed to be attributed to the stronger polar contribution of MSA masking and diminishing the underlying influence of the amino acid residues of each associated peptide. Finally, post-column infusion of propionic acid and acetic acid was evaluated for their potential to recover signal intensity for TFA and MSA counter-ions for LC-ESI-MS applications. Acetic acid generally yielded more substantial signal improvements over propionic acid on the TFA system while minimal benefits and some further reductions were noted with MSA.

  7. CREME96 and Related Error Rate Prediction Methods

    NASA Technical Reports Server (NTRS)

    Adams, James H., Jr.

    2012-01-01

    Predicting the rate of occurrence of single event effects (SEEs) in space requires knowledge of the radiation environment and the response of electronic devices to that environment. Several analytical models have been developed over the past 36 years to predict SEE rates. The first error rate calculations were performed by Binder, Smith and Holman. Bradford and Pickel and Blandford, in their CRIER (Cosmic-Ray-Induced-Error-Rate) analysis code introduced the basic Rectangular ParallelePiped (RPP) method for error rate calculations. For the radiation environment at the part, both made use of the Cosmic Ray LET (Linear Energy Transfer) spectra calculated by Heinrich for various absorber Depths. A more detailed model for the space radiation environment within spacecraft was developed by Adams and co-workers. This model, together with a reformulation of the RPP method published by Pickel and Blandford, was used to create the CR ME (Cosmic Ray Effects on Micro-Electronics) code. About the same time Shapiro wrote the CRUP (Cosmic Ray Upset Program) based on the RPP method published by Bradford. It was the first code to specifically take into account charge collection from outside the depletion region due to deformation of the electric field caused by the incident cosmic ray. Other early rate prediction methods and codes include the Single Event Figure of Merit, NOVICE, the Space Radiation code and the effective flux method of Binder which is the basis of the SEFA (Scott Effective Flux Approximation) model. By the early 1990s it was becoming clear that CREME and the other early models needed Revision. This revision, CREME96, was completed and released as a WWW-based tool, one of the first of its kind. The revisions in CREME96 included improved environmental models and improved models for calculating single event effects. The need for a revision of CREME also stimulated the development of the CHIME (CRRES/SPACERAD Heavy Ion Model of the Environment) and MACREE (Modeling and

  8. [Statistical prediction methods in violence risk assessment and its application].

    PubMed

    Liu, Yuan-Yuan; Hu, Jun-Mei; Yang, Min; Li, Xiao-Song

    2013-06-01

    It is an urgent global problem how to improve the violence risk assessment. As a necessary part of risk assessment, statistical methods have remarkable impacts and effects. In this study, the predicted methods in violence risk assessment from the point of statistics are reviewed. The application of Logistic regression as the sample of multivariate statistical model, decision tree model as the sample of data mining technique, and neural networks model as the sample of artificial intelligence technology are all reviewed. This study provides data in order to contribute the further research of violence risk assessment.

  9. Side-chain conformational space analysis (SCSA): a multi conformation-based QSAR approach for modeling and prediction of protein-peptide binding affinities.

    PubMed

    Zhou, Peng; Chen, Xiang; Shang, Zhicai

    2009-03-01

    In this article, the concept of multi conformation-based quantitative structure-activity relationship (MCB-QSAR) is proposed, and based upon that, we describe a new approach called the side-chain conformational space analysis (SCSA) to model and predict protein-peptide binding affinities. In SCSA, multi-conformations (rather than traditional single-conformation) have received much attention, and the statistical average information on multi-conformations of side chains is determined using self-consistent mean field theory based upon side chain rotamer library. Thereby, enthalpy contributions (including electrostatic, steric, hydrophobic interaction and hydrogen bond) and conformational entropy effects to the binding are investigated in terms of occurrence probability of residue rotamers. Then, SCSA was applied into the dataset of 419 HLA-A 0201 binding peptides, and nonbonding contributions of each position in peptide ligands are well determined. For the peptides, the hydrogen bond and electrostatic interactions of the two ends are essential to the binding specificity, van der Waals and hydrophobic interactions of all the positions ensure strong binding affinity, and the loss of conformational entropy at anchor positions partially counteracts other favorable nonbonding effects.

  10. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.

    PubMed

    Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Saini, Varsha; Rao, Atmakuri Ramakrishna

    2017-02-13

    Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Identification of AMPs through wet-lab experiment is expensive. Therefore, development of efficient computational tool is essential to identify the best candidate AMP prior to the in vitro experimentation. In this study, we made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy. Initially, compositional, physico-chemical and structural features of the peptides were generated that were subsequently used as input in SVM for prediction of AMPs. The proposed approach achieved higher accuracy than several existing approaches, while compared using benchmark dataset. Based on the proposed approach, an online prediction server iAMPpred has also been developed to help the scientific community in predicting AMPs, which is freely accessible at http://cabgrid.res.in:8080/amppred/. The proposed approach is believed to supplement the tools and techniques that have been developed in the past for prediction of AMPs.

  11. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC

    PubMed Central

    Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Saini, Varsha; Rao, Atmakuri Ramakrishna

    2017-01-01

    Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Identification of AMPs through wet-lab experiment is expensive. Therefore, development of efficient computational tool is essential to identify the best candidate AMP prior to the in vitro experimentation. In this study, we made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy. Initially, compositional, physico-chemical and structural features of the peptides were generated that were subsequently used as input in SVM for prediction of AMPs. The proposed approach achieved higher accuracy than several existing approaches, while compared using benchmark dataset. Based on the proposed approach, an online prediction server iAMPpred has also been developed to help the scientific community in predicting AMPs, which is freely accessible at http://cabgrid.res.in:8080/amppred/. The proposed approach is believed to supplement the tools and techniques that have been developed in the past for prediction of AMPs. PMID:28205576

  12. Discontinuous Galerkin method for predicting heat transfer in hypersonic environments

    NASA Astrophysics Data System (ADS)

    Ching, Eric; Lv, Yu; Ihme, Matthias

    2016-11-01

    This study is concerned with predicting surface heat transfer in hypersonic flows using high-order discontinuous Galerkin methods. A robust and accurate shock capturing method designed for steady calculations that uses smooth artificial viscosity for shock stabilization is developed. To eliminate parametric dependence, an optimization method is formulated that results in the least amount of artificial viscosity necessary to sufficiently suppress nonlinear instabilities and achieve steady-state convergence. Performance is evaluated in two canonical hypersonic tests, namely a flow over a circular half-cylinder and flow over a double cone. Results show this methodology to be significantly less sensitive than conventional finite-volume techniques to mesh topology and inviscid flux function. The method is benchmarked against state-of-the-art finite-volume solvers to quantify computational cost and accuracy. Financial support from a Stanford Graduate Fellowship and the NASA Early Career Faculty program are gratefully acknowledged.

  13. Method of predicting mechanical properties of decayed wood

    DOEpatents

    Kelley, Stephen S.

    2003-07-15

    A method for determining the mechanical properties of decayed wood that has been exposed to wood decay microorganisms, comprising: a) illuminating a surface of decayed wood that has been exposed to wood decay microorganisms with wavelengths from visible and near infrared (VIS-NIR) spectra; b) analyzing the surface of the decayed wood using a spectrometric method, the method generating a first spectral data of wavelengths in VIS-NIR spectra region; and c) using a multivariate analysis to predict mechanical properties of decayed wood by comparing the first spectral data with a calibration model, the calibration model comprising a second spectrometric method of spectral data of wavelengths in VIS-NIR spectra obtained from a reference decay wood, the second spectral data being correlated with a known mechanical property analytical result obtained from the reference decayed wood.

  14. A Fmoc-compatible Method for the Solid-Phase Synthesis of Peptide C-Terminal (alpha)-Thioesters based on the Safety-Catch Hydrazine Linker

    SciTech Connect

    Camarero, J A; Hackel, B J; de Yoreo, J J; Mitchell, A R

    2003-11-22

    C-terminal peptide thioesters are key intermediates for the synthesis/semisynthesis of proteins and for the production of cyclic peptides by native chemical ligation. They can be synthetically prepared by solid-phase peptide synthesis (SPPS) methods or biosynthetically by protein splicing techniques. Until recently, the chemical synthesis of C-terminal a-thioester peptides by SPPS was largely restricted to the Boc/Benzyl methodology because of the poor stability of the thioester bond to the basic conditions employed for the deprotection of the N{sup {alpha}}-Fmoc group. In the present work, we describe a new method for the SPPS of C-terminal thioesters by Fmoc/t-Bu chemistry. This method is based on the use of an aryl hydrazide linker, which is totally stable to the Fmoc-SPPS conditions. Once the peptide synthesis has been completed, activation of the linker can be achieved by mild oxidation. This step transforms the hydrazide group into a highly reactive diazene intermediate which can react with different H-AA-SEt to yield the corresponding {alpha}-thioester peptide in good yields. This method has been successfully used for the generation of different thioester peptides, circular peptides and a fully functional SH3 protein domain.

  15. Development of a capillary high performance liquid chromatography-ion trap-mass spectrometry method for the determination of VLIVP antihypertensive peptide in soybean crops.

    PubMed

    Puchalska, Patrycja; García, M Concepción; Marina, M Luisa

    2014-04-18

    Soybean peptide VLIVP presents a very high antihypertensive activity (IC50 value 1.69μM), even higher than extensively studied IPP and VPP peptides from milk. Nevertheless, no much attention has been paid to this peptide and there is no method enabling its determination in soybeans. The aim of this work was the development of an analytical methodology for this purpose. A methodology consisting of the extraction of soybean proteins, their digestion with Protease P enzyme, their chromatographic separation using capillary-HPLC, and IT-MS detection was optimized. Protein extraction was performed by the use of high intensity focused ultrasounds to obtain a reduced extraction time. Optimization of chromatographic and mass spectrometry parameters enabled the separation of VLIVP peptide within just 7min and its sensitive detection. The analytical characteristics of the capillary-HPLC-IT-MS method were evaluated through the study of linearity, LOD, LOQ, study of the presence of matrix interferences, precision, and recovery. The method enabled to detect as low as 3.6ng of peptide and to determine as low as 12ng of peptide in 1g of soybean (as dry basis). Finally, the developed method was applied to the determination of the antihypertensive peptide VLIVP in different soybean varieties. The results showed the highest yield of VLIVP peptide in variety Mazowiecka II from Poland.

  16. Efficient Unstructured Grid Adaptation Methods for Sonic Boom Prediction

    NASA Technical Reports Server (NTRS)

    Campbell, Richard L.; Carter, Melissa B.; Deere, Karen A.; Waithe, Kenrick A.

    2008-01-01

    This paper examines the use of two grid adaptation methods to improve the accuracy of the near-to-mid field pressure signature prediction of supersonic aircraft computed using the USM3D unstructured grid flow solver. The first method (ADV) is an interactive adaptation process that uses grid movement rather than enrichment to more accurately resolve the expansion and compression waves. The second method (SSGRID) uses an a priori adaptation approach to stretch and shear the original unstructured grid to align the grid with the pressure waves and reduce the cell count required to achieve an accurate signature prediction at a given distance from the vehicle. Both methods initially create negative volume cells that are repaired in a module in the ADV code. While both approaches provide significant improvements in the near field signature (< 3 body lengths) relative to a baseline grid without increasing the number of grid points, only the SSGRID approach allows the details of the signature to be accurately computed at mid-field distances (3-10 body lengths) for direct use with mid-field-to-ground boom propagation codes.

  17. Methods for exploring uncertainty in groundwater management predictions

    USGS Publications Warehouse

    Guillaume, Joseph H. A.; Hunt, Randall J.; Comunian, Alessandro; Fu, Baihua; Blakers, Rachel S; Jakeman, Anthony J; Barreteau, Olivier; Hunt, Randall J.; Rinaudo, Jean-Daniel; Ross, Andrew

    2016-01-01

    Models of groundwater systems help to integrate knowledge about the natural and human system covering different spatial and temporal scales, often from multiple disciplines, in order to address a range of issues of concern to various stakeholders. A model is simply a tool to express what we think we know. Uncertainty, due to lack of knowledge or natural variability, means that there are always alternative models that may need to be considered. This chapter provides an overview of uncertainty in models and in the definition of a problem to model, highlights approaches to communicating and using predictions of uncertain outcomes and summarises commonly used methods to explore uncertainty in groundwater management predictions. It is intended to raise awareness of how alternative models and hence uncertainty can be explored in order to facilitate the integration of these techniques with groundwater management.

  18. Predicting recreational water quality advisories: A comparison of statistical methods

    USGS Publications Warehouse

    Brooks, Wesley R.; Corsi, Steven R.; Fienen, Michael N.; Carvin, Rebecca B.

    2016-01-01

    Epidemiological studies indicate that fecal indicator bacteria (FIB) in beach water are associated with illnesses among people having contact with the water. In order to mitigate public health impacts, many beaches are posted with an advisory when the concentration of FIB exceeds a beach action value. The most commonly used method of measuring FIB concentration takes 18–24 h before returning a result. In order to avoid the 24 h lag, it has become common to ”nowcast” the FIB concentration using statistical regressions on environmental surrogate variables. Most commonly, nowcast models are estimated using ordinary least squares regression, but other regression methods from the statistical and machine learning literature are sometimes used. This study compares 14 regression methods across 7 Wisconsin beaches to identify which consistently produces the most accurate predictions. A random forest model is identified as the most accurate, followed by multiple regression fit using the adaptive LASSO.

  19. Flow resistance and its prediction methods in compound channels

    NASA Astrophysics Data System (ADS)

    Yang, Kejun; Cao, Shuyou; Liu, Xingnian

    2007-02-01

    A series of experiments was carried out in a large symmetric compound channel composed of a rough main channel and rough floodplains to investigate the resistance characteristics of inbank and overbank flows. The effective Manning, Darcy-Weisbach, Chezy coefficients and the relative Nikuradse roughness height were analyzed. Many different representative methods for predicting the composite roughness were systematically summarized. Besides the measured data, a vast number of laboratory data and field data for compound channels were collected and used to check the validity of these methods for different subsection divisions including the vertical, horizontal, diagonal and bisectional divisions. The computation showed that these methods resulted in big errors in assessing the composite roughness in compound channels, and the reasons were analyzed in detail. The error magnitude is related to the subsection divisions.

  20. PeakLink: a new peptide peak linking method in LC-MS/MS using wavelet and SVM

    PubMed Central

    Ghanat Bari, Mehrab; Ma, Xuepo; Zhang, Jianqiu

    2014-01-01

    Motivation: In liquid chromatography–mass spectrometry/tandem mass spectrometry (LC-MS/MS), it is necessary to link tandem MS-identified peptide peaks so that protein expression changes between the two runs can be tracked. However, only a small number of peptides can be identified and linked by tandem MS in two runs, and it becomes necessary to link peptide peaks with tandem identification in one run to their corresponding ones in another run without identification. In the past, peptide peaks are linked based on similarities in retention time (rt), mass or peak shape after rt alignment, which corrects mean rt shifts between runs. However, the accuracy in linking is still limited especially for complex samples collected from different conditions. Consequently, large-scale proteomics studies that require comparison of protein expression profiles of hundreds of patients can not be carried out effectively. Method: In this article, we consider the problem of linking peptides from a pair of LC-MS/MS runs and propose a new method, PeakLink (PL), which uses information in both the time and frequency domain as inputs to a non-linear support vector machine (SVM) classifier. The PL algorithm first uses a threshold on an rt likelihood ratio score to remove candidate corresponding peaks with excessively large elution time shifts, then PL calculates the correlation between a pair of candidate peaks after reducing noise through wavelet transformation. After converting rt and peak shape correlation to statistical scores, an SVM classifier is trained and applied for differentiating corresponding and non-corresponding peptide peaks. Results: PL is tested in multiple challenging cases, in which LC-MS/MS samples are collected from different disease states, different instruments and different laboratories. Testing results show significant improvement in linking accuracy compared with other algorithms. Availability and implementation: M files for the PL alignment method are available

  1. Biomarkers: Non-destructive Method for Predicting Meat Tenderization.

    PubMed

    Singh, Arashdeep; Ahluwalia, Preeti; Rafiq, Aasima; Sharma, Savita

    2015-07-06

    Meat tenderness is the primary and most important quality attribute for the consumers worldwide. Tenderness is the process of breakdown of collagen tissue in meat to make it palatable. The earlier methods of tenderness evaluation like taste panels and shear force methods are destructive, time consuming and ill suited as they requires removing a piece of steak from the carcass for performing the test. Therefore, a non-destructive method for predicting the tenderness would be more desirable. The development of a meat quality grading and guarantee system through muscle profiling research can help to meet this demand. Biomarkers have the ability to identify if an exposure has occurred. Biomarkers of the meat quality are of prime importance for meat industry, which has ability to satisfy consumers' expectations. The biomarkers so far identified have been then sorted and grouped according to their common biological functions. All of them refer to a series of biological pathways including glycolytic and oxidative energy production, cell detoxification, protease inhibition and production of Heat Shock Proteins. On this basis, a detailed analysis of these metabolic pathways helps in identifying tenderization of meat having some domains of interest. It was, therefore, stressed forward that biomarkers can be used to determine meat tenderness. This review article summarizes the uses of several biomarkers for predicting the meat tenderness.

  2. Unstructured CFD and Noise Prediction Methods for Propulsion Airframe Aeroacoustics

    NASA Technical Reports Server (NTRS)

    Pao, S. Paul; Abdol-Hamid, Khaled S.; Campbell, Richard L.; Hunter, Craig A.; Massey, Steven J.; Elmiligui, Alaa A.

    2006-01-01

    Using unstructured mesh CFD methods for Propulsion Airframe Aeroacoustics (PAA) analysis has the distinct advantage of precise and fast computational mesh generation for complex propulsion and airframe integration arrangements that include engine inlet, exhaust nozzles, pylon, wing, flaps, and flap deployment mechanical parts. However, accurate solution values of shear layer velocity, temperature and turbulence are extremely important for evaluating the usually small noise differentials of potential applications to commercial transport aircraft propulsion integration. This paper describes a set of calibration computations for an isolated separate flow bypass ratio five engine nozzle model and the same nozzle system with a pylon. These configurations have measured data along with prior CFD solutions and noise predictions using a proven structured mesh method, which can be used for comparison to the unstructured mesh solutions obtained in this investigation. This numerical investigation utilized the TetrUSS system that includes a Navier-Stokes solver, the associated unstructured mesh generation tools, post-processing utilities, plus some recently added enhancements to the system. New features necessary for this study include the addition of two equation turbulence models to the USM3D code, an h-refinement utility to enhance mesh density in the shear mixing region, and a flow adaptive mesh redistribution method. In addition, a computational procedure was developed to optimize both solution accuracy and mesh economy. Noise predictions were completed using an unstructured mesh version of the JeT3D code.

  3. Non-animal test methods for predicting skin sensitization potentials.

    PubMed

    Mehling, Annette; Eriksson, Tove; Eltze, Tobias; Kolle, Susanne; Ramirez, Tzutzuy; Teubner, Wera; van Ravenzwaay, Bennard; Landsiedel, Robert

    2012-08-01

    Contact allergies are complex diseases, and it is estimated that 15-20 % of the general population suffers from contact allergy, with increasing prevalence. Evaluation of the sensitization potential of a substance is usually carried out in animal models. Nowadays, there is much interest in reducing and ultimately replacing current animal tests. Furthermore, as of 2013, the EU has posed a ban on animal testing of cosmetic ingredients that includes skin sensitization. Therefore, predictive and robust in vitro tests are urgently needed. In order to establish alternatives to animal testing, the in vitro tests must mimic the very complex interactions between the sensitizing chemical and the different parts of the immune system. This review article summarizes recent efforts to develop in vitro tests for predicting skin sensitizers. Cell-based assays, in chemico methods and, to a lesser extent, in silico methods are presented together with a discussion of their current status. With considerable progress having been achieved during the last years, the rationale today is that data from different non-animal test methods will have to be combined in order to obtain reliable hazard and potency information on potential skin sensitizers.

  4. Prediction of protein-peptide interactions: application of the XPairIt API to anthrax lethal factor and substrates

    NASA Astrophysics Data System (ADS)

    Hurley, Margaret M.; Sellers, Michael S.

    2013-05-01

    As software and methodology develop, key aspects of molecular interactions such as detailed energetics and flexibility are continuously better represented in docking simulations. In the latest iteration of the XPairIt API and Docking Protocol, we perform a blind dock of a peptide into the cleavage site of the Anthrax lethal factor (LF) metalloprotein. Molecular structures are prepared from RCSB:1JKY and we demonstrate a reasonably accurate docked peptide through analysis of protein motion and, using NCI Plot, visualize and characterize the forces leading to binding. We compare our docked structure to the 1JKY crystal structure and the more recent 1PWV structure, and discuss both captured and overlooked interactions. Our results offer a more detailed look at secondary contact and show that both van der Waals and electrostatic interactions from peptide residues further from the enzyme's catalytic site are significant.

  5. Predicting mooring system fatigue life by probabilistic methods

    SciTech Connect

    Saders, D.R.; Dominguez, R.F.; Ho, K.C.; Lai, N.W.

    1983-05-01

    Failure of moored structures from accumulated fatigue damage in shackles, connecting links, chain and wire rope components is common. When systems will be deployed for long periods, it is especially important to determine at the design, inspection and maintenance stages the fatigue damage. Since slack moored structures behave in a highly nonlinear manner, commonly used fatigue analysis procedures are normally inadequate. This paper reviews present probablistic fatigue analysis methods, and provides a means for incorporating nonlinear mooring behavior into analysis and design to predict accumulated damage and remaining service life. The procedures presented are general, and they are also applicable to ship and buoy moorings, offshore terminals, and guyed and tension leg platforms.

  6. Predictions for rapid methods and automation in food microbiology.

    PubMed

    Fung, Daniel Y C

    2002-01-01

    A discussion is presented on the present status of rapid methods and automation in microbiology. Predictions are also presented for development in the following areas: viable cell counts; real-time monitoring of hygiene; polymerase chain reaction, ribotyping, and genetic tests in food laboratories; automated enzyme-linked immunosorbent assay and immunotests; rapid dipstick technology; biosensors for Hazard Analysis Critical Control Point programs; instant detection of target pathogens by computer-generated matrix; effective separation and concentration for rapid identification of target cells; microbiological alert systems in food packages; and rapid alert kits for detecting pathogens at home.

  7. Sparse Neural Network Models of Antimicrobial Peptide-Activity Relationships.

    PubMed

    Müller, Alex T; Kaymaz, Aral C; Gabernet, Gisela; Posselt, Gernot; Wessler, Silja; Hiss, Jan A; Schneider, Gisbert

    2016-12-01

    We present an adaptive neural network model for chemical data classification. The method uses an evolutionary algorithm for optimizing the network structure by seeking sparsely connected architectures. The number of hidden layers, the number of neurons in each layer and their connectivity are free variables of the system. We used the method for predicting antimicrobial peptide activity from the amino acid sequence. Visualization of the evolved sparse network structures suggested a high charge density and a low aggregation potential in solution as beneficial for antimicrobial activity. However, different training data sets and peptide representations resulted in greatly varying network structures. Overall, the sparse network models turned out to be less accurate than fully-connected networks. In a prospective application, we synthesized and tested 10 de novo generated peptides that were predicted to either possess antimicrobial activity, or to be inactive. Two of the predicted antibacterial peptides showed cosiderable bacteriostatic effects against both Staphylococcus aureus and Escherichia coli. None of the predicted inactive peptides possessed antibacterial properties. Molecular dynamics simulations of selected peptide structures in water and TFE suggest a pronounced peptide helicity in a hydrophobic environment. The results of this study underscore the applicability of neural networks for guiding the computer-assisted design of new peptides with desired properties.

  8. Virtual screening using combinatorial cyclic peptide libraries reveals protein interfaces readily targetable by cyclic peptides.

    PubMed

    Duffy, Fergal J; O'Donovan, Darragh; Devocelle, Marc; Moran, Niamh; O'Connell, David J; Shields, Denis C

    2015-03-23

    Protein-protein and protein-peptide interactions are responsible for the vast majority of biological functions in vivo, but targeting these interactions with small molecules has historically been difficult. What is required are efficient combined computational and experimental screening methods to choose among a number of potential protein interfaces worthy of targeting lead macrocyclic compounds for further investigation. To achieve this, we have generated combinatorial 3D virtual libraries of short disulfide-bonded peptides and compared them to pharmacophore models of important protein-protein and protein-peptide structures, including short linear motifs (SLiMs), protein-binding peptides, and turn structures at protein-protein interfaces, built from 3D models available in the Protein Data Bank. We prepared a total of 372 reference pharmacophores, which were matched against 108,659 multiconformer cyclic peptides. After normalization to exclude nonspecific cyclic peptides, the top hits notably are enriched for mimetics of turn structures, including a turn at the interaction surface of human α thrombin, and also feature several protein-binding peptides. The top cyclic peptide hits also cover the critical "hot spot" interaction sites predicted from the interaction crystal structure. We have validated our method by testing cyclic peptides predicted to inhibit thrombin, a key protein in the blood coagulation pathway of important therapeutic interest, identifying a cyclic peptide inhibitor with lead-like activity. We conclude that protein interfaces most readily targetable by cyclic peptides and related macrocyclic drugs may be identified computationally among a set of candidate interfaces, accelerating the choice of interfaces against which lead compounds may be screened.

  9. An experiment in hurricane track prediction using parallel computing methods

    NASA Technical Reports Server (NTRS)

    Song, Chang G.; Jwo, Jung-Sing; Lakshmivarahan, S.; Dhall, S. K.; Lewis, John M.; Velden, Christopher S.

    1994-01-01

    The barotropic model is used to explore the advantages of parallel processing in deterministic forecasting. We apply this model to the track forecasting of hurricane Elena (1985). In this particular application, solutions to systems of elliptic equations are the essence of the computational mechanics. One set of equations is associated with the decomposition of the wind into irrotational and nondivergent components - this determines the initial nondivergent state. Another set is associated with recovery of the streamfunction from the forecasted vorticity. We demonstrate that direct parallel methods based on accelerated block cyclic reduction (BCR) significantly reduce the computational time required to solve the elliptic equations germane to this decomposition and forecast problem. A 72-h track prediction was made using incremental time steps of 16 min on a network of 3000 grid points nominally separated by 100 km. The prediction took 30 sec on the 8-processor Alliant FX/8 computer. This was a speed-up of 3.7 when compared to the one-processor version. The 72-h prediction of Elena's track was made as the storm moved toward Florida's west coast. Approximately 200 km west of Tampa Bay, Elena executed a dramatic recurvature that ultimately changed its course toward the northwest. Although the barotropic track forecast was unable to capture the hurricane's tight cycloidal looping maneuver, the subsequent northwesterly movement was accurately forecasted as was the location and timing of landfall near Mobile Bay.

  10. A Primer In Advanced Fatigue Life Prediction Methods

    NASA Technical Reports Server (NTRS)

    Halford, Gary R.

    2000-01-01

    Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable more cost effective, and better performing products. In other words, as the envelop is expanded, components are then designed to operate just as close to the newly expanded envelop as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.

  11. Tiered analytics for purity assessment of macrocyclic peptides in drug discovery: Analytical consideration and method development.

    PubMed

    Qian Cutrone, Jingfang Jenny; Huang, Xiaohua Stella; Kozlowski, Edward S; Bao, Ye; Wang, Yingzi; Poronsky, Christopher S; Drexler, Dieter M; Tymiak, Adrienne A

    2017-05-10

    Synthetic macrocyclic peptides with natural and unnatural amino acids have gained considerable attention from a number of pharmaceutical/biopharmaceutical companies in recent years as a promising approach to drug discovery, particularly for targets involving protein-protein or protein-peptide interactions. Analytical scientists charged with characterizing these leads face multiple challenges including dealing with a class of complex molecules with the potential for multiple isomers and variable charge states and no established standards for acceptable analytical characterization of materials used in drug discovery. In addition, due to the lack of intermediate purification during solid phase peptide synthesis, the final products usually contain a complex profile of impurities. In this paper, practical analytical strategies and methodologies were developed to address these challenges, including a tiered approach to assessing the purity of macrocyclic peptides at different stages of drug discovery. Our results also showed that successful progression and characterization of a new drug discovery modality benefited from active analytical engagement, focusing on fit-for-purpose analyses and leveraging a broad palette of analytical technologies and resources.

  12. Jet Noise Diagnostics Supporting Statistical Noise Prediction Methods

    NASA Technical Reports Server (NTRS)

    Bridges, James E.

    2006-01-01

    compared against measurements of mean and rms velocity statistics over a range of jet speeds and temperatures. Models for flow parameters used in the acoustic analogy, most notably the space-time correlations of velocity, have been compared against direct measurements, and modified to better fit the observed data. These measurements have been extremely challenging for hot, high speed jets, and represent a sizeable investment in instrumentation development. As an intermediate check that the analysis is predicting the physics intended, phased arrays have been employed to measure source distributions for a wide range of jet cases. And finally, careful far-field spectral directivity measurements have been taken for final validation of the prediction code. Examples of each of these experimental efforts will be presented. The main result of these efforts is a noise prediction code, named JeNo, which is in middevelopment. JeNo is able to consistently predict spectral directivity, including aft angle directivity, for subsonic cold jets of most geometries. Current development on JeNo is focused on extending its capability to hot jets, requiring inclusion of a previously neglected second source associated with thermal fluctuations. A secondary result of the intensive experimentation is the archiving of various flow statistics applicable to other acoustic analogies and to development of time-resolved prediction methods. These will be of lasting value as we look ahead at future challenges to the aeroacoustic experimentalist.

  13. Decision tree methods: applications for classification and prediction

    PubMed Central

    SONG, Yan-yan; LU, Ying

    2015-01-01

    Summary Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure. PMID:26120265

  14. Mathematical analysis method for predicting temperature rise in BIPV

    NASA Astrophysics Data System (ADS)

    Ibarahim, Zahari; Ruslan, Mohd Hafidz; Ibrahim, Zamry

    2013-05-01

    In order to estimate the airflow rate and temperature in the channel of building an integrated photovoltaic (BIPV) system, a simplified mathematical method has been derived in which the buoyancy force balances the friction along the channel. The channel is represented as a closed duct and the pressure losses are calculated in a similar fashion to those in a pipe circuit. Pressure losses at ventilation inlets and outlets are calculated using pressure loss factors, Kf. The effect of wind pressure on mass flow rate and temperature in the channel can be estimated with the inclusion of the wind speed pressure into the formulation. The benefit of wind pressure applied normal to the inlet and tangential to the outlet of the channel can be seen. The procedure yields the mass flow rate and temperature directly by solution of a simple cubic equation. This method allows the engineers and scientist to predict optimum configuration of their PV cladding design.

  15. Osteoporosis risk prediction using machine learning and conventional methods.

    PubMed

    Kim, Sung Kean; Yoo, Tae Keun; Oh, Ein; Kim, Deok Won

    2013-01-01

    A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women, and compared with the ability of a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Surveys (KNHANES V-1). The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and logistic regression (LR) based on various predictors associated with low bone density. The learning models were compared with OST. SVM had significantly better area under the curve (AUC) of the receiver operating characteristic (ROC) than ANN, LR, and OST. Validation on the test set showed that SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0%. We were the first to perform comparisons of the performance of osteoporosis prediction between the machine learning and conventional methods using population-based epidemiological data. The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.

  16. Conditional Weather Resampling Method for Seasonal Ensemble Streamflow Prediction

    NASA Astrophysics Data System (ADS)

    Beckers, Joost; Weerts, Albrecht; Welles, Edwin

    2014-05-01

    Ensemble Streamflow Prediction (ESP) is a commonly used method for water resources planning on the seasonal time scale. The starting point for the ESP is the current state of the hydrological system, which is generated form a short historical simulation up to the time of forecast. Starting from this initial state, a hydrologic model is run to produce an ensemble of possible realizations of future streamflows, taking meteorological time series from historical years as input. It is assumed that these historical weather time series represent climatology. One disadvantage of the original ESP method is that an expected deviation from average climatology is not accounted for. Here, we propose a variation to the ESP, in which shorter periods from historical time years are resampled and assembled to generate additional possible realizations of future weather. The resampling is done in such a way as to incorporate statistical deviations from the average climate that are linked to climate modes, such as El Niño Southern Oscillation (ENSO) or Pacific Decadal Oscillation (PDO). These climate modes are known to affect the local weather in many regions around the world. The resampling of historical weather periods is conditioned on the climate mode indices, starting with the current climate index value and searching for historical years with similar climate indices. The resampled weather time series are used as input for the hydrological model, similar to the original ESP procedure. The method was implemented in the operational forecasting environment of Bonneville Power Administration (BPA), which based on Delft-FEWS. The method was run for 55 non-operational years of hindcasts (forecasts in retrospect) for the Columbia River in the North-West of the U.S. An increase in forecast skill up to 5% was found relative to the standard ESP for streamflow predictions at three test-locations.

  17. In silico peptide prediction for antibody generation to recognize 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) in genetically modified organisms.

    PubMed

    Marani, Mariela M; Costa, Joana; Mafra, Isabel; Oliveira, Maria Beatriz P P; Camperi, Silvia A; Leite, José Roberto de Souza Almeida

    2015-03-01

    For the prospective immunorecognition of 5-enolpyruvylshikimate-3-phosphate synthase (CP4-EPSPS) as a biomarker protein expressed by transgenic soybean, an extensive in silico evaluation of the referred protein was performed. The main objective of this study was the selection of a set of peptides that could function as potential immunogens for the production of novel antibodies against CP4-EPSPS protein. For this purpose, the protein was in silico cleaved with trypsin/chymotrypsin and the resultant peptides were extensively analyzed for further selection of the best candidates for antibody production. The analysis enabled the successful proposal of four peptides with potential immunogenicity for their future use as screening biomarkers of genetically modified organisms. To our knowledge, this is the first attempt to select and define potential linear epitopes for the immunization of animals and, subsequently, to generate adequate antibodies for CP4-EPSPS recognition. The present work will be followed by the synthesis of the candidate peptides to be incubated in animals for antibody generation and potential applicability for the development of an immunosensor for CP4-EPSPS detection.

  18. Prediction and In Silico Identification of Novel B-Cells and T-Cells Epitopes in the S1-Spike Glycoprotein of M41 and CR88 (793/B) Infectious Bronchitis Virus Serotypes for Application in Peptide Vaccines

    PubMed Central

    Hair Bejo, Mohd; Kadkhodaei, Saeid

    2016-01-01

    Bioinformatic analysis was used to predict antigenic B-cell and T-cell epitopes within the S1 glycoprotein of M41 and CR88 IBV strains. A conserved linear B-cell epitope peptide, YTSNETTDVTS175–185, was identified in M41 IBV strains while three such epitopes types namely, VSNASPNSGGVD279–290, HPKCNFRPENI328–338, and NETNNAGSVSDCTAGT54–69, were predicted in CR88 IBV strains. Analysis of MHCI binding peptides in M41 IBV strains revealed the presence of 15 antigenic peptides out of which 12 were highly conserved in 96–100% of the total M41 strains analysed. Interestingly three of these peptides, GGPITYKVM208, WFNSLSVSI356, and YLADAGLAI472, relatively had high antigenicity index (>1.0). On the other hand, 11 MHCI binding epitope peptides were identified in CR88 IBV strains. Of these, five peptides were found to be highly conserved with a range between 90% and 97%. However, WFNSLSVSL358, SYNISAASV88, and YNISAASVA89 peptides comparably showed high antigenicity scores (>1.0). Combination of antigenic B-cells and T-cells peptides that are conserved across many strains as approach to evoke humoral and CTL immune response will potentially lead to a broad-based vaccine that could reduce the challenges in using live attenuated vaccine technology in the control of IBV infection in poultry. PMID:27667997

  19. A GIS modeling method applied to predicting forest songbird habitat

    USGS Publications Warehouse

    Dettmers, Randy; Bart, Jonathan

    1999-01-01

    We have developed an approach for using a??presencea?? data to construct habitat models. Presence data are those that indicate locations where the target organism is observed to occur, but that cannot be used to define locations where the organism does not occur. Surveys of highly mobile vertebrates often yield these kinds of data. Models developed through our approach yield predictions of the amount and the spatial distribution of good-quality habitat for the target species. This approach was developed primarily for use in a GIS context; thus, the models are spatially explicit and have the potential to be applied over large areas. Our method consists of two primary steps. In the first step, we identify an optimal range of values for each habitat variable to be used as a predictor in the model. To find these ranges, we employ the concept of maximizing the difference between cumulative distribution functions of (1) the values of a habitat variable at the observed presence locations of the target organism, and (2) the values of that habitat variable for all locations across a study area. In the second step, multivariate models of good habitat are constructed by combining these ranges of values, using the Boolean operators a??anda?? and a??or.a?? We use an approach similar to forward stepwise regression to select the best overall model. We demonstrate the use of this method by developing species-specific habitat models for nine forest-breeding songbirds (e.g., Cerulean Warbler, Scarlet Tanager, Wood Thrush) studied in southern Ohio. These models are based on speciesa?? microhabitat preferences for moisture and vegetation characteristics that can be predicted primarily through the use of abiotic variables. We use slope, land surface morphology, land surface curvature, water flow accumulation downhill, and an integrated moisture index, in conjunction with a land-cover classification that identifies forest/nonforest, to develop these models. The performance of these

  20. Can simulations quantitatively predict peptide transfer free energies to urea solutions? Thermodynamic concepts and force field limitations.

    PubMed

    Horinek, Dominik; Netz, Roland R

    2011-06-16

    Many proteins denature when they are transferred to concentrated urea solutions. Three mechanisms for urea's denaturing ability have been proposed: (i) direct binding to polar parts of the protein surface, (ii) direct binding to nonpolar parts of the protein surface, and (iii) an indirect effect mediated by modifications of the bulk water properties. The disentanglement of these three processes has been the goal of many experimental and computational studies, yet there is no final agreement on the relative importance of the three contributions. The separation of the two direct mechanisms, albeit conceptually clear, is difficult in experimental studies and in simulations depends subtly on how the discrimination between polar and nonpolar groups is accomplished. Indirect effects, embodied in the change of solution activity as urea is added, are rarely monitored in urea/peptide simulations and thus have remained elusive in numerical studies. In this paper we establish a rigorous separation of all three contributions to the solvation thermodynamics of stretched peptide chains. We contrast this scenario with two commonly used model systems: the air/water interface and the interface between water and a hydrophobic alkane self-assembled monolayer. Together with bulk thermodynamic properties of urea/water mixed solvents, a complete thermodynamic description of the urea/water/peptide system is obtained: urea avoids the air/water interface but readily adsorbs at the oil-water interface and at hydrophobic as well as hydrophilic peptide chains, in accordance with experimental results. Simple thermodynamic arguments show that the indirect contribution to urea's denaturing capability is negligibly small, although urea strongly changes the water bulk properties as judged by the number of hydrogen bonds formed. Urea's tendency to bind to proteins is correctly reproduced with several force field combinations, but the quantitative binding strength as well as the relative importance

  1. SATPdb: a database of structurally annotated therapeutic peptides.

    PubMed

    Singh, Sandeep; Chaudhary, Kumardeep; Dhanda, Sandeep Kumar; Bhalla, Sherry; Usmani, Salman Sadullah; Gautam, Ankur; Tuknait, Abhishek; Agrawal, Piyush; Mathur, Deepika; Raghava, Gajendra P S

    2016-01-04

    SATPdb (http://crdd.osdd.net/raghava/satpdb/) is a database of structurally annotated therapeutic peptides, curated from 22 public domain peptide databases/datasets including 9 of our own. The current version holds 19192 unique experimentally validated therapeutic peptide sequences having length between 2 and 50 amino acids. It covers peptides having natural, non-natural and modified residues. These peptides were systematically grouped into 10 categories based on their major function or therapeutic property like 1099 anticancer, 10585 antimicrobial, 1642 drug delivery and 1698 antihypertensive peptides. We assigned or annotated structure of these therapeutic peptides using structural databases (Protein Data Bank) and state-of-the-art structure prediction methods like I-TASSER, HHsearch and PEPstrMOD. In addition, SATPdb facilitates users in performing various tasks that include: (i) structure and sequence similarity search, (ii) peptide browsing based on their function and properties, (iii) identification of moonlighting peptides and (iv) searching of peptides having desired structure and therapeutic activities. We hope this database will be useful for researchers working in the field of peptide-based therapeutics.

  2. Extremely Randomized Machine Learning Methods for Compound Activity Prediction.

    PubMed

    Czarnecki, Wojciech M; Podlewska, Sabina; Bojarski, Andrzej J

    2015-11-09

    Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called 'extremely randomized methods'-Extreme Entropy Machine and Extremely Randomized Trees-for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their 'non-extreme' competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

  3. Predicting human height by Victorian and genomic methods

    PubMed Central

    Aulchenko, Yurii S; Struchalin, Maksim V; Belonogova, Nadezhda M; Axenovich, Tatiana I; Weedon, Michael N; Hofman, Albert; Uitterlinden, Andre G; Kayser, Manfred; Oostra, Ben A; van Duijn, Cornelia M; Janssens, A Cecile J W; Borodin, Pavel M

    2009-01-01

    In the Victorian era, Sir Francis Galton showed that ‘when dealing with the transmission of stature from parents to children, the average height of the two parents, … is all we need care to know about them' (1886). One hundred and twenty-two years after Galton's work was published, 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4–6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people, as characterized by the area under the receiver-operating characteristic curve (AUC). In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. We have also explored how much variance a genomic profile should explain to reach certain AUC values. For highly heritable traits such as height, we conclude that in applications in which parental phenotypic information is available (eg, medicine), the Victorian Galton's method will long stay unsurpassed, in terms of both discriminative accuracy and costs. For less heritable traits, and in situations in which parental information is not available (eg, forensics), genomic methods may provide an alternative, given that the variants determining an essential proportion of the trait's variation can be identified. PMID:19223933

  4. Impact of Thermostats on Folding and Aggregation Properties of Peptides Using the Optimized Potential for Efficient Structure Prediction Coarse-Grained Model.

    PubMed

    Spill, Yannick G; Pasquali, Samuela; Derreumaux, Philippe

    2011-05-10

    The simulation of amyloid fibril formation is impossible if one takes into account all chemical details of the amino acids and their detailed interactions with the solvent. We investigate the folding and aggregation of two model peptides using the optimized potential for efficient structure prediction (OPEP) coarse-grained model and replica exchange molecular dynamics (REMD) simulations coupled with either the Langevin or the Berendsen thermostat. For both the monomer of blocked penta-alanine and the trimer of the 25-35 fragment of the Alzheimer's amyloid β protein, we find little variations in the equilibrium structures and heat capacity curves using the two thermostats. Despite this high similarity, we detect significant differences in the populations of the dominant conformations at low temperatures, whereas the configurational distributions remain the same in proximity of the melting temperature. Aβ25-35 trimers at 300 K have an averaged β-sheet content of 12% and are primarily characterized by fully disordered peptides or a small curved two-stranded β-sheet stabilized by a disordered peptide. In addition, OPEP molecular dynamics simulations of Aβ25-35 hexamers at 300 K with a small curved six-stranded antiparallel β-sheet do not show any extension of the β-sheet content. These data support the idea that the mechanism of Aβ25-35 amyloid formation does not result from a high fraction of extended β-sheet-rich trimers and hexamers.

  5. Distinguishing Aspartic and Isoaspartic Acids in Peptides by Several Mass Spectrometric Fragmentation Methods

    NASA Astrophysics Data System (ADS)

    DeGraan-Weber, Nick; Zhang, Jun; Reilly, James P.

    2016-12-01

    Six ion fragmentation techniques that can distinguish aspartic acid from its isomer, isoaspartic acid, were compared. MALDI post-source decay (PSD), MALDI 157 nm photodissociation, tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP) charge tagging in PSD and photodissociation, ESI collision-induced dissociation (CID), electron transfer dissociation (ETD), and free-radical initiated peptide sequencing (FRIPS) with CID were applied to peptides containing either aspartic or isoaspartic acid. Diagnostic ions, such as the y-46 and b+H2O, are present in PSD, photodissociation, and charge tagging. c•+57 and z-57 ions are observed in ETD and FRIPS experiments. For some molecules, aspartic and isoaspartic acid yield ion fragments with significantly different intensities. ETD and charge tagging appear to be most effective at distinguishing these residues.

  6. Comparing Three Data Mining Methods to Predict Kidney Transplant Survival

    PubMed Central

    Shahmoradi, Leila; Langarizadeh, Mostafa; Pourmand, Gholamreza; fard, Ziba Aghsaei; Borhani, Alireza

    2016-01-01

    Introduction: One of the most important complications of post-transplant is rejection. Analyzing survival is one of the areas of medical prognosis and data mining, as an effective approach, has the capacity of analyzing and estimating outcomes in advance through discovering appropriate models among data. The present study aims at comparing the effectiveness of C5.0 algorithms, neural network and C&RTree to predict kidney transplant survival before transplant. Method: To detect factors effective in predicting transplant survival, information needs analysis was performed via a researcher-made questionnaire. A checklist was prepared and data of 513 kidney disease patient files were extracted from Sina Urology Research Center. Following CRISP methodology for data mining, IBM SPSS Modeler 14.2, C5.0, C&RTree algorithms and neural network were used. Results: Body Mass Index (BMI), cause of renal dysfunction and duration of dialysis were evaluated in all three models as the most effective factors in transplant survival. C5.0 algorithm with the highest validity (96.77%) was the first in estimating kidney transplant survival in patients followed by C&RTree (83.7%) and neural network (79.5%) models. Conclusion: Among the three models, C5.0 algorithm was the top model with high validity that confirms its strength in predicting survival. The most effective kidney transplant survival factors were detected in this study; therefore, duration of transplant survival (year) can be determined considering the regulations set for a new sample with specific characteristics. PMID:28163356

  7. Plasma-Enhanced Chemical Vapor Deposition as a Method for the Deposition of Peptide Nanotubes

    DTIC Science & Technology

    2013-09-17

    45432, United States Distribution A: Approved for public release; distribution is unlimited. 2    Introduction: The unique ability of dipeptides ...Using physical vapor deposition (PVD) well-ordered assemblies of peptide nanotubes (PNTs) composed of dipeptide subunits are obtained on various...the PECVD deposition chamber with sublimation capability in the laboratory of Dr. Rajesh Naik (AFRL/RXAS) and conditions were modified for dipeptide

  8. A method for identification of the peptides that bind to a clone of thyroid-stimulating antibodies in the serum of Graves' disease patients.

    PubMed

    Na, Chan Hyun; Lee, Mi Hwa; Cho, Bo Youn; Chae, Chi-Bom

    2003-04-01

    A method was developed for identification of the peptide sequences that bind to thyroid-stimulating antibody (TSAb) clones from phage-displayed peptide library. Immunoglobulin G (IgG) was purified from the serum of a Graves' disease patient that stimulates the synthesis of cAMP in the cells that express TSH receptor (TSHR). The IgG that binds to TSHR was purified by an affinity column packed with the resin cross-linked with the extracellular domain of human TSHR. The receptor-binding IgG was then mixed with phages that display linear or cyclic peptides at the end of tail protein pIII. The bound phages were eluted with acidic glycine after extensive washing. From sequencing of the pIII gene of the bound phages, one can deduce the sequences of the peptides that bind to the receptor-binding IgG. Each peptide sequence was then tested for inhibition of the synthesis of cAMP from thyroid cells induced by the serum of a Graves' patient. In this way, one can obtain the peptides that bind to a clone of TSAb. We obtained a peptide sequence that inhibits the action of TSAb at an extremely low concentration (<10(-14) M). Such a peptide will be useful for various studies on TSAb.

  9. Gold nanoparticles-based electrochemical method for the detection of protein kinase with a peptide-like inhibitor as the bioreceptor

    PubMed Central

    Sun, Kai; Chang, Yong; Zhou, Binbin; Wang, Xiaojin; Liu, Lin

    2017-01-01

    This article presents a general method for the detection of protein kinase with a peptide-like kinase inhibitor as the bioreceptor, and it was done by converting gold nanoparticles (AuNPs)-based colorimetric assay into sensitive electrochemical analysis. In the colorimetric assay, the kinase-specific aptameric peptide triggered the aggregation of AuNPs in solution. However, the specific binding of peptide to the target protein (kinase) inhibited its ability to trigger the assembly of AuNPs. In the electrochemical analysis, peptides immobilized on a gold electrode and presented as solution triggered together the in situ formation of AuNPs-based network architecture on the electrode surface. Nevertheless, the formation of peptide–kinase complex on the electrode surface made the peptide-triggered AuNPs assembly difficult. Electrochemical impedance spectroscopy was used to measure the change in surface property in the binding events. When a ferrocene-labeled peptide (Fc-peptide) was used in this design, the network of AuNPs/Fc-peptide produced a good voltammetric signal. The competitive assay allowed for the detection of protein kinase A with a detection limit of 20 mU/mL. This work should be valuable for designing novel optical or electronic biosensors and likely lead to many detection applications. PMID:28331314

  10. Predicting lattice thermal conductivity with help from ab initio methods

    NASA Astrophysics Data System (ADS)

    Broido, David

    2015-03-01

    The lattice thermal conductivity is a fundamental transport parameter that determines the utility a material for specific thermal management applications. Materials with low thermal conductivity find applicability in thermoelectric cooling and energy harvesting. High thermal conductivity materials are urgently needed to help address the ever-growing heat dissipation problem in microelectronic devices. Predictive computational approaches can provide critical guidance in the search and development of new materials for such applications. Ab initio methods for calculating lattice thermal conductivity have demonstrated predictive capability, but while they are becoming increasingly efficient, they are still computationally expensive particularly for complex crystals with large unit cells . In this talk, I will review our work on first principles phonon transport for which the intrinsic lattice thermal conductivity is limited only by phonon-phonon scattering arising from anharmonicity. I will examine use of the phase space for anharmonic phonon scattering and the Grüneisen parameters as measures of the thermal conductivities for a range of materials and compare these to the widely used guidelines stemming from the theory of Liebfried and Schölmann. This research was supported primarily by the NSF under Grant CBET-1402949, and by the S3TEC, an Energy Frontier Research Center funded by the US DOE, office of Basic Energy Sciences under Award No. DE-SC0001299.

  11. Methods and Techniques for Risk Prediction of Space Shuttle Upgrades

    NASA Technical Reports Server (NTRS)

    Hoffman, Chad R.; Pugh, Rich; Safie, Fayssal

    1998-01-01

    Since the Space Shuttle Accident in 1986, NASA has been trying to incorporate probabilistic risk assessment (PRA) in decisions concerning the Space Shuttle and other NASA projects. One major study NASA is currently conducting is in the PRA area in establishing an overall risk model for the Space Shuttle System. The model is intended to provide a tool to predict the Shuttle risk and to perform sensitivity analyses and trade studies including evaluation of upgrades. Marshall Space Flight Center (MSFC) and its prime contractors including Pratt and Whitney (P&W) are part of the NASA team conducting the PRA study. MSFC responsibility involves modeling the External Tank (ET), the Solid Rocket Booster (SRB), the Reusable Solid Rocket Motor (RSRM), and the Space Shuttle Main Engine (SSME). A major challenge that faced the PRA team is modeling the shuttle upgrades. This mainly includes the P&W High Pressure Fuel Turbopump (HPFTP) and the High Pressure Oxidizer Turbopump (HPOTP). The purpose of this paper is to discuss the various methods and techniques used for predicting the risk of the P&W redesigned HPFTP and HPOTP.

  12. Predictive Sampling of Rare Conformational Events in Aqueous Solution: Designing a Generalized Orthogonal Space Tempering Method.

    PubMed

    Lu, Chao; Li, Xubin; Wu, Dongsheng; Zheng, Lianqing; Yang, Wei

    2016-01-12

    In aqueous solution, solute conformational transitions are governed by intimate interplays of the fluctuations of solute-solute, solute-water, and water-water interactions. To promote molecular fluctuations to enhance sampling of essential conformational changes, a common strategy is to construct an expanded Hamiltonian through a series of Hamiltonian perturbations and thereby broaden the distribution of certain interactions of focus. Due to a lack of active sampling of configuration response to Hamiltonian transitions, it is challenging for common expanded Hamiltonian methods to robustly explore solvent mediated rare conformational events. The orthogonal space sampling (OSS) scheme, as exemplified by the orthogonal space random walk and orthogonal space tempering methods, provides a general framework for synchronous acceleration of slow configuration responses. To more effectively sample conformational transitions in aqueous solution, in this work, we devised a generalized orthogonal space tempering (gOST) algorithm. Specifically, in the Hamiltonian perturbation part, a solvent-accessible-surface-area-dependent term is introduced to implicitly perturb near-solute water-water fluctuations; more importantly in the orthogonal space response part, the generalized force order parameter is generalized as a two-dimension order parameter set, in which essential solute-solvent and solute-solute components are separately treated. The gOST algorithm is evaluated through a molecular dynamics simulation study on the explicitly solvated deca-alanine (Ala10) peptide. On the basis of a fully automated sampling protocol, the gOST simulation enabled repetitive folding and unfolding of the solvated peptide within a single continuous trajectory and allowed for detailed constructions of Ala10 folding/unfolding free energy surfaces. The gOST result reveals that solvent cooperative fluctuations play a pivotal role in Ala10 folding/unfolding transitions. In addition, our assessment

  13. Differential T-cell responses of semi-immune and susceptible malaria subjects to in silico predicted and synthetic peptides of Plasmodium falciparum.

    PubMed

    Dinga, Jerome Nyhalah; Kimbung Mbandi, Stanley; Cho-Ngwa, Fidelis; Fon, Nde Peter; Moliki, Johnson; Efeti, Rose Mary; Nyasa, Babila Raymond; Anong, Damian Nota; Jojic, Nebojsa; Heckerman, David; Wang, Ruobing; Titanji, Vincent P K

    2014-07-01

    Malaria remains a public health hazard in tropical countries as a consequence of the rise and spread of drug and insecticide resistances; hence the need for a vaccine with widespread application. Protective immunity to malaria is known to be mediated by both antibody and cellular immune responses, though characterization of the latter has been less extensive. The aim of the present investigation was to identify novel T-cell epitopes that may contribute to naturally acquired immune responses against malaria. Using the Microsoft software, Epitome™ T-cell peptide epitopes on 19 Plasmodium falciparum proteins in the Plasmodium Database (www.plasmodb.org.PlasmoDB 9.0) were predicted in-silico. The peptides were synthesized and used to stimulate peripheral blood mononuclear cells (PBMCs) in 14 semi-immune and 21 malaria susceptible subjects for interferon-gamma (IFN-γ) production ex-vivo. The level of IFN-γ production, a marker of T-cell responses, was measured by ELISPOT assay in semi-immune subjects (SIS) and frequently sick subjects (FSS) from an endemic zone with perennial malaria transmission. Of the 19 proteins studied, 17 yielded 27 pools (189 peptides), which were reactive with the subjects' PBMCs when tested for IFN-γ production, taking a stimulation index (SI) of ≥2 as a cutoff point for a positive response. There were 10 reactive peptide pools (constituting eight protein loci) with an SI of 10 or greater. Of the 19 proteins studied, two were known vaccine candidates (MSP-8 and SSP2/TRAP), which reacted both with SIS and FSS. Similarly the hypothetical proteins (PFF1030w, PFE0795c, PFD0880w, PFC0065c and PF10_0052) also reacted strongly with both SIS and FSS making them attractive for further characterization as mediators of protective immunity and/or pathogenesis.

  14. Detection of trans–cis flips and peptide-plane flips in protein structures

    SciTech Connect

    Touw, Wouter G.; Joosten, Robbie P.; Vriend, Gert

    2015-07-28

    A method is presented to detect peptide bonds that need either a trans–cis flip or a peptide-plane flip. A coordinate-based method is presented to detect peptide bonds that need correction either by a peptide-plane flip or by a trans–cis inversion of the peptide bond. When applied to the whole Protein Data Bank, the method predicts 4617 trans–cis flips and many thousands of hitherto unknown peptide-plane flips. A few examples are highlighted for which a correction of the peptide-plane geometry leads to a correction of the understanding of the structure–function relation. All data, including 1088 manually validated cases, are freely available and the method is available from a web server, a web-service interface and through WHAT-CHECK.

  15. Methods for evaluating the predictive accuracy of structural dynamic models

    NASA Technical Reports Server (NTRS)

    Hasselman, T. K.; Chrostowski, Jon D.

    1990-01-01

    Uncertainty of frequency response using the fuzzy set method and on-orbit response prediction using laboratory test data to refine an analytical model are emphasized with respect to large space structures. Two aspects of the fuzzy set approach were investigated relative to its application to large structural dynamics problems: (1) minimizing the number of parameters involved in computing possible intervals; and (2) the treatment of extrema which may occur in the parameter space enclosed by all possible combinations of the important parameters of the model. Extensive printer graphics were added to the SSID code to help facilitate model verification, and an application of this code to the LaRC Ten Bay Truss is included in the appendix to illustrate this graphics capability.

  16. Development of methods to predict agglomeration and disposition in FBCs

    SciTech Connect

    Mann, M.D.; Henderson, A.K.; Swanson, M.K.; Erickson, T.A.

    1995-11-01

    This 3-year, multiclient program is providing the information needed to determine the behavior of inorganic components in FBC units using advanced methods of analysis coupled with bench-scale combustion experiments. The major objectives of the program are as follows: (1) To develop further our advanced ash and deposit characterization techniques to quantify the effects of the liquid-phase components in terms of agglomerate formation and ash deposits, (2) To determine the mechanisms of inorganic transformations that lead to bed agglomeration and ash deposition in FBC systems, and (3) To develop a better means to predict the behavior of inorganic components as a function of coal composition, bed material characteristics, and combustion conditions.

  17. Computational Prediction of the Protonation Sites of Ac-Lys-(Ala)n-Lys-NH2 Peptides through Conceptual DFT Descriptors.

    PubMed

    Sastre, Sebastián; Frau, Juan; Glossman-Mitnik, Daniel

    2017-03-13

    Six density functionals (M11, M11L, MN12L, MN12SX, N12, and N12SX) in connection with the Def2TZVP basis set and the SMD solvation model (water as a solvent) have been assessed for the calculation of the molecular structure and properties of several peptides with the general formulaAc-Lys-(Ala)n-Lys-NH2,withn=0to5  [...].

  18. An analytical method for predicting postwildfire peak discharges

    USGS Publications Warehouse

    Moody, John A.

    2012-01-01

    An analytical method presented here that predicts postwildfire peak discharge was developed from analysis of paired rainfall and runoff measurements collected from selected burned basins. Data were collected from 19 mountainous basins burned by eight wildfires in different hydroclimatic regimes in the western United States (California, Colorado, Nevada, New Mexico, and South Dakota). Most of the data were collected for the year of the wildfire and for 3 to 4 years after the wildfire. These data provide some estimate of the changes with time of postwildfire peak discharges, which are known to be transient but have received little documentation. The only required inputs for the analytical method are the burned area and a quantitative measure of soil burn severity (change in the normalized burn ratio), which is derived from Landsat reflectance data and is available from either the U.S. Department of Agriculture Forest Service or the U.S. Geological Survey. The method predicts the postwildfire peak discharge per unit burned area for the year of a wildfire, the first year after a wildfire, and the second year after a wildfire. It can be used at three levels of information depending on the data available to the user; each subsequent level requires either more data or more processing of the data. Level 1 requires only the burned area. Level 2 requires the burned area and the basin average value of the change in the normalized burn ratio. Level 3 requires the burned area and the calculation of the hydraulic functional connectivity, which is a variable that incorporates the sequence of soil burn severity along hillslope flow paths within the burned basin. Measurements indicate that the unit peak discharge response increases abruptly when the 30-minute maximum rainfall intensity is greater than about 5 millimeters per hour (0.2 inches per hour). This threshold may relate to a change in runoff generation from saturated-excess to infiltration-excess overland flow. The

  19. Numerical modelling methods for predicting antenna performance on aircraft

    NASA Astrophysics Data System (ADS)

    Kubina, S. J.

    1983-09-01

    Typical case studies that involve the application of Moment Methods to the prediction of the radiation characteristics of antennas in the HF frequency band are examined. The examples consist of the analysis of a shorted transmission line HF antenna on a CHSS-2/Sea King helicopter, wire antennas on the CP-140/Aurora patrol aircraft and a long dipole antenna on the Space Shuttle Orbiter spacecraft. In each of these cases the guidelines for antenna modeling by the use of the program called the Numerical Electromagnetic Code are progressively applied and results are compared to measurements made by the use of scale-model techniques. In complex examples of this type comparisons based on individual radiation patterns are insufficient for the validation of computer models. A volumetric method of radiation pattern comparison is used based on criteria that result from pattern integration and that are related to communication system performance. This is supplemented by hidden-surface displays of an entire set of conical radiation patterns resulting from measurements and computations. Antenna coupling considerations are discussed for the case of the dual HF installation on the CP-140/Aurora aircraft.

  20. Study of improved methods for predicting chemical equilibria

    SciTech Connect

    Lenz, T.G.; Vaughan, J.D.

    1992-06-01

    The objective of our research has been to develop computational methods that have the capability of accurately predicting equilibrium constants of typical organic reactions in gas and liquid solution phases. We have chosen Diels-Alder reactions as prototypic systems for the investigation, chiefly because there are an adequate number of reported equilibrium constants for the candidate reactions in both gas and solution phases, which data provides a suitable basis for tests of the developed computational methods. Our approach has been to calculate the standard enthalpies of formation ({Delta}H{sub f}{sup 0}) at 298.15K and the standard thermodynamic functions (S{sup 0}, Cp{sup 0}, and (H{sup 0}-H{sub 0}{sup 0})/T) for a range of temperatures for reactants and products, and from these properties to calculate standard enthalpies, entropies, Gibbs free energies, and equilibrium constants ({Delta}H{sub T}{sup 0}, {Delta}S{sub T}{sup 0}, and K{sub a}) at various temperatures for the chosen reaction.

  1. StructBoost: Boosting Methods for Predicting Structured Output Variables.

    PubMed

    Chunhua Shen; Guosheng Lin; van den Hengel, Anton

    2014-10-01

    Boosting is a method for learning a single accurate predictor by linearly combining a set of less accurate weak learners. Recently, structured learning has found many applications in computer vision. Inspired by structured support vector machines (SSVM), here we propose a new boosting algorithm for structured output prediction, which we refer to as StructBoost. StructBoost supports nonlinear structured learning by combining a set of weak structured learners. As SSVM generalizes SVM, our StructBoost generalizes standard boosting approaches such as AdaBoost, or LPBoost to structured learning. The resulting optimization problem of StructBoost is more challenging than SSVM in the sense that it may involve exponentially many variables and constraints. In contrast, for SSVM one usually has an exponential number of constraints and a cutting-plane method is used. In order to efficiently solve StructBoost, we formulate an equivalent 1-slack formulation and solve it using a combination of cutting planes and column generation. We show the versatility and usefulness of StructBoost on a range of problems such as optimizing the tree loss for hierarchical multi-class classification, optimizing the Pascal overlap criterion for robust visual tracking and learning conditional random field parameters for image segmentation.

  2. Water accessibility in a membrane-inserting peptide comparing Overhauser DNP and pulse EPR methods

    NASA Astrophysics Data System (ADS)

    Segawa, Takuya F.; Doppelbauer, Maximilian; Garbuio, Luca; Doll, Andrin; Polyhach, Yevhen O.; Jeschke, Gunnar

    2016-05-01

    Water accessibility is a key parameter for the understanding of the structure of biomolecules, especially membrane proteins. Several experimental techniques based on the combination of electron paramagnetic resonance (EPR) spectroscopy with site-directed spin labeling are currently available. Among those, we compare relaxation time measurements and electron spin echo envelope modulation (ESEEM) experiments using pulse EPR with Overhauser dynamic nuclear polarization (DNP) at X-band frequency and a magnetic field of 0.33 T. Overhauser DNP transfers the electron spin polarization to nuclear spins via cross-relaxation. The change in the intensity of the 1H NMR spectrum of H2O at a Larmor frequency of 14 MHz under a continuous-wave microwave irradiation of the nitroxide spin label contains information on the water accessibility of the labeled site. As a model system for a membrane protein, we use the hydrophobic α-helical peptide WALP23 in unilamellar liposomes of DOPC. Water accessibility measurements with all techniques are conducted for eight peptides with different spin label positions and low radical concentrations (10-20 μM). Consistently in all experiments, the water accessibility appears to be very low, even for labels positioned near the end of the helix. The best profile is obtained by Overhauser DNP, which is the only technique that succeeds in discriminating neighboring positions in WALP23. Since the concentration of the spin-labeled peptides varied, we normalized the DNP parameter ɛ, being the relative change of the NMR intensity, by the electron spin concentration, which was determined from a continuous-wave EPR spectrum.

  3. Prediction of plantar shear stress distribution by artificial intelligence methods.

    PubMed

    Yavuz, Metin; Ocak, Hasan; Hetherington, Vincent J; Davis, Brian L

    2009-09-01

    Shear forces under the human foot are thought to be responsible for various foot pathologies such as diabetic plantar ulcers and athletic blisters. Frictional shear forces might also play a role in the metatarsalgia observed among hallux valgus (HaV) and rheumatoid arthritis (RA) patients. Due to the absence of commercial devices capable of measuring shear stress distribution, a number of linear models were developed. All of these have met with limited success. This study used nonlinear methods, specifically neural network and fuzzy logic schemes, to predict the distribution of plantar shear forces based on vertical loading parameters. In total, 73 subjects were recruited; 17 had diabetic neuropathy, 14 had HaV, 9 had RA, 11 had frequent foot blisters, and 22 were healthy. A feed-forward neural network (NN) and adaptive neurofuzzy inference system (NFIS) were built. These systems were then applied to a custom-built platform, which collected plantar pressure and shear stress data as subjects walked over the device. The inputs to both models were peak pressure, peak pressure-time integral, and time to peak pressure, and the output was peak resultant shear. Root-mean-square error (RMSE) values were calculated to test the models' accuracy. RMSE/actual shear ratio varied between 0.27 and 0.40 for NN predictions. Similarly, NFIS estimations resulted in a 0.28-0.37 ratio for local peak values in all subject groups. On the other hand, error percentages for global peak shear values were found to be in the range 11.4-44.1. These results indicate that there is no direct relationship between pressure and shear magnitudes. Future research should aim to decrease error levels by introducing shear stress dependent variables into the models.

  4. Antimicrobial Peptides in Reptiles

    PubMed Central

    van Hoek, Monique L.

    2014-01-01

    Reptiles are among the oldest known amniotes and are highly diverse in their morphology and ecological niches. These animals have an evolutionarily ancient innate-immune system that is of great interest to scientists trying to identify new and useful antimicrobial peptides. Significant work in the last decade in the fields of biochemistry, proteomics and genomics has begun to reveal the complexity of reptilian antimicrobial peptides. Here, the current knowledge about antimicrobial peptides in reptiles is reviewed, with specific examples in each of the four orders: Testudines (turtles and tortosises), Sphenodontia (tuataras), Squamata (snakes and lizards), and Crocodilia (crocodilans). Examples are presented of the major classes of antimicrobial peptides expressed by reptiles including defensins, cathelicidins, liver-expressed peptides (hepcidin and LEAP-2), lysozyme, crotamine, and others. Some of these peptides have been identified and tested for their antibacterial or antiviral activity; others are only predicted as possible genes from genomic sequencing. Bioinformatic analysis of the reptile genomes is presented, revealing many predicted candidate antimicrobial peptides genes across this diverse class. The study of how these ancient creatures use antimicrobial peptides within their innate immune systems may reveal new understandings of our mammalian innate immune system and may also provide new and powerful antimicrobial peptides as scaffolds for potential therapeutic development. PMID:24918867

  5. Channel Current Analysis for Pore-forming Properties of an Antimicrobial Peptide, Magainin 1, Using the Droplet Contact Method.

    PubMed

    Watanabe, Hirokazu; Kawano, Ryuji

    2016-01-01

    This study describes the pore-forming properties of magainin 1 in planar lipid bilayers. These bilayers were prepared by the droplet contact method, which was executed on a microfabricated device for a high-throughput study. We arrayed four droplet chambers parallelly in the single device, and the current measurements were carried out simultaneously. Using this system, we measured the channel current conductance of magainin 1. We determined the pore size and the number of assembling monomers in magainin pores in mammalian and bacterial model membranes. This system is a powerful tool for analyzing transmembrane peptides and their antimicrobial activities.

  6. MapReduce Implementation of a Hybrid Spectral Library-Database Search Method for Large-Scale Peptide Identification

    SciTech Connect

    Kalyanaraman, Anantharaman; Cannon, William R.; Latt, Benjamin K.; Baxter, Douglas J.

    2011-11-01

    A MapReduce-based implementation called MR- MSPolygraph for parallelizing peptide identification from mass spectrometry data is presented. The underlying serial method, MSPolygraph, uses a novel hybrid approach to match an experimental spectrum against a combination of a protein sequence database and a spectral library. Our MapReduce implementation can run on any Hadoop cluster environment. Experimental results demonstrate that, relative to the serial version, MR-MSPolygraph reduces the time to solution from weeks to hours, for processing tens of thousands of experimental spectra. Speedup and other related performance studies are also reported on a 400-core Hadoop cluster using spectral datasets from environmental microbial communities as inputs.

  7. Numerical Weather Predictions Evaluation Using Spatial Verification Methods

    NASA Astrophysics Data System (ADS)

    Tegoulias, I.; Pytharoulis, I.; Kotsopoulos, S.; Kartsios, S.; Bampzelis, D.; Karacostas, T.

    2014-12-01

    During the last years high-resolution numerical weather prediction simulations have been used to examine meteorological events with increased convective activity. Traditional verification methods do not provide the desired level of information to evaluate those high-resolution simulations. To assess those limitations new spatial verification methods have been proposed. In the present study an attempt is made to estimate the ability of the WRF model (WRF -ARW ver3.5.1) to reproduce selected days with high convective activity during the year 2010 using those feature-based verification methods. Three model domains, covering Europe, the Mediterranean Sea and northern Africa (d01), the wider area of Greece (d02) and central Greece - Thessaly region (d03) are used at horizontal grid-spacings of 15km, 5km and 1km respectively. By alternating microphysics (Ferrier, WSM6, Goddard), boundary layer (YSU, MYJ) and cumulus convection (Kain-­-Fritsch, BMJ) schemes, a set of twelve model setups is obtained. The results of those simulations are evaluated against data obtained using a C-Band (5cm) radar located at the centre of the innermost domain. Spatial characteristics are well captured but with a variable time lag between simulation results and radar data. Acknowledgements: This research is co­financed by the European Union (European Regional Development Fund) and Greek national funds, through the action "COOPERATION 2011: Partnerships of Production and Research Institutions in Focused Research and Technology Sectors" (contract number 11SYN_8_1088 - DAPHNE) in the framework of the operational programme "Competitiveness and Entrepreneurship" and Regions in Transition (OPC II, NSRF 2007-­-2013).

  8. Effective Design of Multifunctional Peptides by Combining Compatible Functions

    PubMed Central

    Diener, Christian; Garza Ramos Martínez, Georgina; Moreno Blas, Daniel; Castillo González, David A.; Corzo, Gerardo; Castro-Obregon, Susana; Del Rio, Gabriel

    2016-01-01

    Multifunctionality is a common trait of many natural proteins and peptides, yet the rules to generate such multifunctionality remain unclear. We propose that the rules defining some protein/peptide functions are compatible. To explore this hypothesis, we trained a computational method to predict cell-penetrating peptides at the sequence level and learned that antimicrobial peptides and DNA-binding proteins are compatible with the rules of our predictor. Based on this finding, we expected that designing peptides for CPP activity may render AMP and DNA-binding activities. To test this prediction, we designed peptides that embedded two independent functional domains (nuclear localization and yeast pheromone activity), linked by optimizing their composition to fit the rules characterizing cell-penetrating peptides. These peptides presented effective cell penetration, DNA-binding, pheromone and antimicrobial activities, thus confirming the effectiveness of our computational approach to design multifunctional peptides with potential therapeutic uses. Our computational implementation is available at http://bis.ifc.unam.mx/en/software/dcf. PMID:27096600

  9. Efficient Methods to Generate Reproducible Mass Spectra in Matrix-Assisted Laser Desorption Ionization of Peptides

    NASA Astrophysics Data System (ADS)

    Ahn, Sung Hee; Park, Kyung Man; Bae, Yong Jin; Kim, Myung Soo

    2013-06-01

    In our previous matrix-assisted laser desorption ionization (MALDI) studies of peptides, we found that their mass spectra were virtually determined by the effective temperature in the early matrix plume, Tearly, when samples were rather homogeneous. This empirical rule allowed acquisition of quantitatively reproducible spectra. A difficulty in utilizing this rule was the complicated spectral treatment needed to get Tearly. In this work, we found another empirical rule that the total number of particles hitting the detector, or TIC, was a good measure of the spectral temperature and, hence, selection of spectra with the same TIC resulted in reproducible spectra. We also succeeded in obtaining reproducible spectra throughout a measurement by controlling TIC near a preset value through feedback adjustment of laser pulse energy. Both TIC selection and TIC control substantially reduced the shot-to-shot spectral variation in a spot, spot-to-spot variation in a sample, and even sample-to-sample variation in MALDI using α-cyano-4-hydroxycinnamic acid or 2,5-dihydroxybenzoic acid as matrix. Based on the utilization of acquired data, TIC control was more efficient than TIC selection by an order of magnitude. Both techniques produced calibration curves with excellent linearity, suggesting their utility in quantification of peptides.

  10. IRIS: Towards an Accurate and Fast Stage Weight Prediction Method

    NASA Astrophysics Data System (ADS)

    Taponier, V.; Balu, A.

    2002-01-01

    The knowledge of the structural mass fraction (or the mass ratio) of a given stage, which affects the performance of a rocket, is essential for the analysis of new or upgraded launchers or stages, whose need is increased by the quick evolution of the space programs and by the necessity of their adaptation to the market needs. The availability of this highly scattered variable, ranging between 0.05 and 0.15, is of primary importance at the early steps of the preliminary design studies. At the start of the staging and performance studies, the lack of frozen weight data (to be obtained later on from propulsion, trajectory and sizing studies) leads to rely on rough estimates, generally derived from printed sources and adapted. When needed, a consolidation can be acquired trough a specific analysis activity involving several techniques and implying additional effort and time. The present empirical approach allows thus to get approximated values (i.e. not necessarily accurate or consistent), inducing some result inaccuracy as well as, consequently, difficulties of performance ranking for a multiple option analysis, and an increase of the processing duration. This forms a classical harsh fact of the preliminary design system studies, insufficiently discussed to date. It appears therefore highly desirable to have, for all the evaluation activities, a reliable, fast and easy-to-use weight or mass fraction prediction method. Additionally, the latter should allow for a pre selection of the alternative preliminary configurations, making possible a global system approach. For that purpose, an attempt at modeling has been undertaken, whose objective was the determination of a parametric formulation of the mass fraction, to be expressed from a limited number of parameters available at the early steps of the project. It is based on the innovative use of a statistical method applicable to a variable as a function of several independent parameters. A specific polynomial generator

  11. A facile method for expression and purification of 15N isotope-labeled human Alzheimer's β-amyloid peptides from E. coli for NMR-based structural analysis

    PubMed Central

    Armand, Tara; Ball, K. Aurelia; Chen, Anna; Pelton, Jeffrey G.; Wemmer, David E.; Head-Gordon, Teresa

    2016-01-01

    Alzheimer's disease (AD) is a progressive neurodegenerative disease affecting millions of people worldwide. AD is characterized by the presence of extracellular plaques composed of aggregated/oligomerized β-amyloid peptides with Aβ42 peptide representing a major isoform in the senile plaques. Given the pathological significance of Aβ42 in the progression of AD, there is considerable interest in understanding the structural ensembles for soluble monomer and oligomeric forms of Aβ42. This report describes an efficient method to express and purify high quality 15N isotope-labeled Aβ42 for structural studies by NMR. The protocol involves utilization of an auto induction system with 15N isotope labeled medium, for high-level expression of Aβ42 as a fusion with IFABP. After the over-expression of the 15N isotope-labeled IFABP-Aβ42 fusion protein in the inclusion bodies, pure 15N isotope-labeled Aβ42 peptide is obtained following a purification method that is streamlined and improved from the method originally developed for the isolation of unlabeled Aβ42 peptide (Garai et al., 2009). We obtain a final yield of ∼6 mg/L culture for 15N isotope-labeled Aβ42 peptide. Mass spectrometry and 1H–15N HSQC spectra of monomeric Aβ42 peptide validate the uniform incorporation of the isotopic label. The method described here is equally applicable for the uniform isotope labeling with 15N and 13C in Aβ42 peptide as well as its other variants including any Aβ42 peptide mutants. PMID:26231074

  12. Topology prediction of Brucella abortus Omp2b and Omp2a porins after critical assessment of transmembrane beta strands prediction by several secondary structure prediction methods.

    PubMed

    Paquet, J Y; Vinals, C; Wouters, J; Letesson, J J; Depiereux, E

    2000-02-01

    In order to propose a reliable model for Brucella porin topology, several structure prediction methods were evaluated in their ability to predict porin topology. Four porins of known structure were selected as test-cases and their secondary structure delineated. The specificity and sensitivity of 11 methods were separately evaluated. Our critical assessment shows that some secondary structure prediction methods (PHD, Dsc, Sopma) originally designed to predict globular protein structure are useful on porin topology prediction. The overall best prediction is obtained by combining these three "generalist" methods with a transmembrane beta strand prediction technique. This "consensus" method was applied to Brucella porins Omp2b and Omp2a, sharing no sequence homology with any other porin. The predicted topology is a 16-stranded antiparallel beta barrel with Omp2a showing a higher number of negatively charged residue in the exposed loops than Omp2b. Experiments are in progress to validate the proposed topology and the functional hypotheses. The ability of the proposed consensus method to predict topology of complex outer membrane protein is briefly discussed.

  13. Predicting juvenile offending: a comparison of data mining methods.

    PubMed

    Ang, Rebecca P; Goh, Dion H

    2013-02-01

    In this study, the authors compared logistic regression and predictive data mining techniques such as decision trees (DTs), artificial neural networks (ANNs), and support vector machines (SVMs), and examined these methods on whether they could discriminate between adolescents who were charged or not charged for initial juvenile offending in a large Asian sample. Results were validated and tested in independent samples with logistic regression and DT, ANN, and SVM classifiers achieving accuracy rates of 95% and above. Findings from receiver operating characteristic analyses also supported these results. In addition, the authors examined distinct patterns of occurrences within and across classifiers. Proactive aggression and teacher-rated conflict consistently emerged as risk factors across validation and testing data sets of DT and ANN classifiers, and logistic regression. Reactive aggression, narcissistic exploitativeness, being male, and coming from a nonintact family were risk factors that emerged in one or more of these data sets across classifiers, while anxiety and poor peer relationships failed to emerge as predictors.

  14. Peptide dynamics by molecular dynamics simulation and diffusion theory method with improved basis sets

    NASA Astrophysics Data System (ADS)

    Hsu, Po Jen; Lai, S. K.; Rapallo, Arnaldo

    2014-03-01

    Improved basis sets for the study of polymer dynamics by means of the diffusion theory, and tests on a melt of cis-1,4-polyisoprene decamers, and a toluene solution of a 71-mer syndiotactic trans-1,2-polypentadiene were presented recently [R. Gaspari and A. Rapallo, J. Chem. Phys. 128, 244109 (2008)]. The proposed hybrid basis approach (HBA) combined two techniques, the long time sorting procedure and the maximum correlation approximation. The HBA takes advantage of the strength of these two techniques, and its basis sets proved to be very effective and computationally convenient in describing both local and global dynamics in cases of flexible synthetic polymers where the repeating unit is a unique type of monomer. The question then arises if the same efficacy continues when the HBA is applied to polymers of different monomers, variable local stiffness along the chain and with longer persistence length, which have different local and global dynamical properties against the above-mentioned systems. Important examples of this kind of molecular chains are the proteins, so that a fragment of the protein transthyretin is chosen as the system of the present study. This peptide corresponds to a sequence that is structured in β-sheets of the protein and is located on the surface of the channel with thyroxin. The protein transthyretin forms amyloid fibrils in vivo, whereas the peptide fragment has been shown [C. P. Jaroniec, C. E. MacPhee, N. S. Astrof, C. M. Dobson, and R. G. Griffin, Proc. Natl. Acad. Sci. U.S.A. 99, 16748 (2002)] to form amyloid fibrils in vitro in extended β-sheet conformations. For these reasons the latter is given considerable attention in the literature and studied also as an isolated fragment in water solution where both experimental and theoretical efforts have indicated the propensity of the system to form β turns or α helices, but is otherwise predominantly unstructured. Differing from previous computational studies that employed implicit

  15. Peptide dynamics by molecular dynamics simulation and diffusion theory method with improved basis sets

    SciTech Connect

    Hsu, Po Jen; Lai, S. K.; Rapallo, Arnaldo

    2014-03-14

    Improved basis sets for the study of polymer dynamics by means of the diffusion theory, and tests on a melt of cis-1,4-polyisoprene decamers, and a toluene solution of a 71-mer syndiotactic trans-1,2-polypentadiene were presented recently [R. Gaspari and A. Rapallo, J. Chem. Phys. 128, 244109 (2008)]. The proposed hybrid basis approach (HBA) combined two techniques, the long time sorting procedure and the maximum correlation approximation. The HBA takes advantage of the strength of these two techniques, and its basis sets proved to be very effective and computationally convenient in describing both local and global dynamics in cases of flexible synthetic polymers where the repeating unit is a unique type of monomer. The question then arises if the same efficacy continues when the HBA is applied to polymers of different monomers, variable local stiffness along the chain and with longer persistence length, which have different local and global dynamical properties against the above-mentioned systems. Important examples of this kind of molecular chains are the proteins, so that a fragment of the protein transthyretin is chosen as the system of the present study. This peptide corresponds to a sequence that is structured in β-sheets of the protein and is located on the surface of the channel with thyroxin. The protein transthyretin forms amyloid fibrils in vivo, whereas the peptide fragment has been shown [C. P. Jaroniec, C. E. MacPhee, N. S. Astrof, C. M. Dobson, and R. G. Griffin, Proc. Natl. Acad. Sci. U.S.A. 99, 16748 (2002)] to form amyloid fibrils in vitro in extended β-sheet conformations. For these reasons the latter is given considerable attention in the literature and studied also as an isolated fragment in water solution where both experimental and theoretical efforts have indicated the propensity of the system to form β turns or α helices, but is otherwise predominantly unstructured. Differing from previous computational studies that employed implicit

  16. Peptide dynamics by molecular dynamics simulation and diffusion theory method with improved basis sets.

    PubMed

    Hsu, Po Jen; Lai, S K; Rapallo, Arnaldo

    2014-03-14

    Improved basis sets for the study of polymer dynamics by means of the diffusion theory, and tests on a melt of cis-1,4-polyisoprene decamers, and a toluene solution of a 71-mer syndiotactic trans-1,2-polypentadiene were presented recently [R. Gaspari and A. Rapallo, J. Chem. Phys. 128, 244109 (2008)]. The proposed hybrid basis approach (HBA) combined two techniques, the long time sorting procedure and the maximum correlation approximation. The HBA takes advantage of the strength of these two techniques, and its basis sets proved to be very effective and computationally convenient in describing both local and global dynamics in cases of flexible synthetic polymers where the repeating unit is a unique type of monomer. The question then arises if the same efficacy continues when the HBA is applied to polymers of different monomers, variable local stiffness along the chain and with longer persistence length, which have different local and global dynamical properties against the above-mentioned systems. Important examples of this kind of molecular chains are the proteins, so that a fragment of the protein transthyretin is chosen as the system of the present study. This peptide corresponds to a sequence that is structured in β-sheets of the protein and is located on the surface of the channel with thyroxin. The protein transthyretin forms amyloid fibrils in vivo, whereas the peptide fragment has been shown [C. P. Jaroniec, C. E. MacPhee, N. S. Astrof, C. M. Dobson, and R. G. Griffin, Proc. Natl. Acad. Sci. U.S.A. 99, 16748 (2002)] to form amyloid fibrils in vitro in extended β-sheet conformations. For these reasons the latter is given considerable attention in the literature and studied also as an isolated fragment in water solution where both experimental and theoretical efforts have indicated the propensity of the system to form β turns or α helices, but is otherwise predominantly unstructured. Differing from previous computational studies that employed implicit

  17. Relevance of biophysical interactions of nanoparticles with a model membrane in predicting cellular uptake: study with TAT peptide-conjugated nanoparticles

    PubMed Central

    Peetla, Chiranjeevi; Rao, Kavitha S.; Labhasetwar, Vinod

    2009-01-01

    The aim of the study was to test the hypothesis that the biophysical interactions of the trans-activating transcriptor (TAT) peptide-conjugated nanoparticles (NPs) with a model cell membrane could predict the cellular uptake of the encapsulated therapeutic agent. To test the above hypothesis, the biophysical interactions of ritonavir-loaded poly (L-lactide) nanoparticles (RNPs), either conjugated to a TAT peptide (TAT-RNPs) or scrambled TAT peptide (sc-TAT-RNPs), were studied with an endothelial cell model membrane (EMM) using a Langmuir film balance, and the corresponding human vascular endothelial cells (HUVECs) were used to study the uptake of the encapsulated therapeutic. Biophysical interactions were determined from the changes in surface pressure (SP) of the EMM as a function of time following interaction with NPs, and the compression isotherm (π–A) of the EMM lipid mixture in the presence of NPs. In addition, the EMMs were transferred onto a silicon substrate following interactions with NPs using the Langmuir–Schaeffer (LS) technique. The transferred LS films were imaged by atomic force microscopy (AFM) to determine the changes in lipid morphology and to characterize the NP–membrane interactions. TAT-RNPs showed an increase in SP of the EMM, which was dependent upon the amount of the peptide bound to NPs and the concentration of NPs, whereas sc-TAT-RNPs and RNPs did not show any significant change in SP. The isotherm experiment showed a shift towards higher mean molecular area (mmA) in the presence of TAT-RNPs, indicating their interactions with the lipids of the EMM, whereas sc-TAT-RNPs and RNPs did not show any significant change. The AFM images showed condensation of the lipids following interaction with TAT-RNPs, indicating their penetration into the EMM, whereas RNPs did not cause any change. Surface analysis and 3-D AFM images of the EMM further confirmed penetration of TAT-RNPs into the EMM whereas RNPs were seen anchored loosely to the

  18. A Novel Method for Direct site-specific Radiolabeling of Peptides Using [18F]FDG

    PubMed Central

    Namavari, Mohammad; Cheng, Zhen; Zhang, Rong; De, Abhijit; Levi, Jelena; Hoerner, Joshua K.; Yaghoubi, Shahriar S.; Syud, Faisal A.; Gambhir, Sanjiv S.

    2009-01-01

    We have used the well-accepted and easily available 2-[18F]Fluoro-2-deoxyglucose ([18F]FDG) positron emission tomography (PET) tracer as a prosthetic group for synthesis of 18F-labeled peptides. We herein report the synthesis of [18F]FDG-RGD (18F labeled linear RGD) and [18F]FDG-cyclo(RGDDYK) (18F labeled cyclic RGD) as examples of the use of [18F]FDG. We have successfully prepared [18F]FDG-RGD and [18F]FDG-cyclo(RGDDYK) in 27.5% and 41% radiochemical yields (decay corrected) respectively. The receptor binding affinity study of FDG-cyclo(RGDDYK) for integrin αvβ3 , using αvβ3 positive U87MG cells confirmed a competitive displacement with 125I-echistatin as a radioligand. The IC50 value for FDG-cyclo(RGDDYK) was determined to be 0.67 ± 0.19µM. High contrast small animal PET images with relatively moderate tumor uptake were observed for [18F]FDG-RGD and [18F]FDG-cyclo(RGDDYK) as PET probes in xenografts models expressing αvβ3 integrin. In conclusion, we have successfully used [18F]FDG as a prosthetic group to prepare 18F]FDG-RGD and [18F]FDG-cyclic[RGDDYK] based on a simple one step radiosynthesis. The one step radiosynthesis methodology consists of chemoselective oxime formation between an aminooxy functionalized peptide and [18F]FDG. The results have implications for radiolabeling of other macromolecules and would lead to a very simple strategy for routine pre-clinical and clinical use. PMID:19226160

  19. The Immunogenicity of HLA Class II Mismatches: The Predicted Presentation of Nonself Allo-HLA-Derived Peptide by the HLA-DR Phenotype of the Recipient Is Associated with the Formation of DSA

    PubMed Central

    2017-01-01

    The identification of permissible HLA class II mismatches can prevent DSA in mismatched transplantation. The HLA-DR phenotype of recipients contributes to DSA formation by presenting allo-HLA-derived peptides to T-helper cells, which induces the differentiation of B cells into plasma cells. Comparing the binding affinity of self and nonself allo-HLA-derived peptides for recipients' HLA class II antigens may distinguish immunogenic HLA mismatches from nonimmunogenic ones. The binding affinities of allo-HLA-derived peptides to recipients' HLA-DR and HLA-DQ antigens were predicted using the NetMHCIIpan 3.1 server. HLA class II mismatches were classified based on whether they induced DSA and whether self or nonself peptide was predicted to bind with highest affinity to recipients' HLA-DR and HLA-DQ. Other mismatch characteristics (eplet, hydrophobic, electrostatic, and amino acid mismatch scores and PIRCHE-II) were evaluated. A significant association occurred between DSA formation and the predicted HLA-DR presentation of nonself peptides (P = 0.0169; accuracy = 80%; sensitivity = 88%; specificity = 63%). In contrast, mismatch characteristics did not differ significantly between mismatches that induced DSA and the ones that did not, except for PIRCHE-II (P = 0.0094). This methodology predicts DSA formation based on HLA mismatches and recipients' HLA-DR phenotype and may identify permissible HLA mismatches to help optimize HLA matching and guide donor selection. PMID:28331856

  20. Does the Current Minimum Validate (or Invalidate) Cycle Prediction Methods?

    NASA Technical Reports Server (NTRS)

    Hathaway, David H.

    2010-01-01

    This deep, extended solar minimum and the slow start to Cycle 24 strongly suggest that Cycle 24 will be a small cycle. A wide array of solar cycle prediction techniques have been applied to predicting the amplitude of Cycle 24 with widely different results. Current conditions and new observations indicate that some highly regarded techniques now appear to have doubtful utility. Geomagnetic precursors have been reliable in the past and can be tested with 12 cycles of data. Of the three primary geomagnetic precursors only one (the minimum level of geomagnetic activity) suggests a small cycle. The Sun's polar field strength has also been used to successfully predict the last three cycles. The current weak polar fields are indicative of a small cycle. For the first time, dynamo models have been used to predict the size of a solar cycle but with opposite predictions depending on the model and the data assimilation. However, new measurements of the surface meridional flow indicate that the flow was substantially faster on the approach to Cycle 24 minimum than at Cycle 23 minimum. In both dynamo predictions a faster meridional flow should have given a shorter cycle 23 with stronger polar fields. This suggests that these dynamo models are not yet ready for solar cycle prediction.

  1. A low computation cost method for seizure prediction.

    PubMed

    Zhang, Yanli; Zhou, Weidong; Yuan, Qi; Wu, Qi

    2014-10-01

    The dynamic changes of electroencephalograph (EEG) signals in the period prior to epileptic seizures play a major role in the seizure prediction. This paper proposes a low computation seizure prediction algorithm that combines a fractal dimension with a machine learning algorithm. The presented seizure prediction algorithm extracts the Higuchi fractal dimension (HFD) of EEG signals as features to classify the patient's preictal or interictal state with Bayesian linear discriminant analysis (BLDA) as a classifier. The outputs of BLDA are smoothed by a Kalman filter for reducing possible sporadic and isolated false alarms and then the final prediction results are produced using a thresholding procedure. The algorithm was evaluated on the intracranial EEG recordings of 21 patients in the Freiburg EEG database. For seizure occurrence period of 30 min and 50 min, our algorithm obtained an average sensitivity of 86.95% and 89.33%, an average false prediction rate of 0.20/h, and an average prediction time of 24.47 min and 39.39 min, respectively. The results confirm that the changes of HFD can serve as a precursor of ictal activities and be used for distinguishing between interictal and preictal epochs. Both HFD and BLDA classifier have a low computational complexity. All of these make the proposed algorithm suitable for real-time seizure prediction.

  2. An empirical method for prediction of cheese yield.

    PubMed

    Melilli, C; Lynch, J M; Carpino, S; Barbano, D M; Licitra, G; Cappa, A

    2002-10-01

    Theoretical cheese yield can be estimated from the milk fat and casein or protein content of milk using classical formulae, such as the VanSlyke formula. These equations are reliable predictors of theoretical or actual yield based on accurately measured milk fat and casein content. Many cheese makers desire to base payment for milk to dairy farmers on the yield of cheese. In small factories, however, accurate measurement of fat and casein content of milk by either chemical methods or infrared milk analysis is too time consuming and expensive. Therefore, an empirical test to predict cheese yield was developed which uses simple equipment (i.e., clinical centrifuge, analytical balance, and forced air oven) to carry out a miniature cheese making, followed by a gravimetric measurement of dry weight yield. A linear regression of calculated theoretical versus dry weight yields for milks of known fat and casein content was calculated. A regression equation of y = 1.275x + 1.528, where y is theoretical yield and x is measured dry solids yield (r2 = 0.981), for Cheddar cheese was developed using milks with a range of theoretical yield from 7 to 11.8%. The standard deviation of the difference (SDD) between theoretical cheese yield and dry solids yield was 0.194 and the coefficient of variation (SDD/mean x 100) was 1.95% upon cross validation. For cheeses without a well-established theoretical cheese yield equation, the measured dry weight yields could be directly correlated to the observed yields in the factory; this would more accurately reflect the expected yield performance. Payments for milk based on these measurements would more accurately reflect quality and composition of the milk and the actual average recovery of fat and casein achieved under practical cheese making conditions.

  3. Method for predicting cracking in waste glass canisters

    SciTech Connect

    Faletti, D.W.; Ethridge, L.J.

    1986-08-01

    A correlation has been developed that predicts the surface area created by cracking to within the accuracy of the existing data. The correlation is a simple linear equation; the surface area can be computed from a knowledge of the steady-state radial temperature difference and the radial temperature difference when the glass centerline temperature was at 500/sup 0/C. This correlation should be easy to use for waste glass canister applications since, in many cases, a two-dimensional heat transfer analysis can be used to determine the radial temperature differences. Although the correlation is useful for scoping purposes, there is a need to validate the correlation against additional canister cracking data, particularly in the case of stainless steel canisters. The use of Fiberfrax liners deserves serious consideration for use in stainless steel waste glass canisters. The amount of cracking is reduced because the liner eliminates the metal-glass interactions that produce significant stresses in the glass. Another less obvious, but very important, advantage of using Fiberfrax is that thermal shocking during decontamination and post-fill operations is reduced because of the liner's insulating capacity. More extensive studies to verify these results are recommended; canisters should be produced, under identical cooling conditions, that differ only in the use of liners. The data for any canister type are extremely sparse, and there is considerable uncertainty about the accuracy of the different methods that have been used to obtain surface area estimates. The comparative roles played by batch and continuous filling of the canisters also need to be clarified. There is a need for accurate thermal data to validate computer codes for determining the temperature histories of canisters. Suggestions for future cracking studies are given.

  4. Accelerating ab initio molecular dynamics simulations by linear prediction methods

    NASA Astrophysics Data System (ADS)

    Herr, Jonathan D.; Steele, Ryan P.

    2016-09-01

    Acceleration of ab initio molecular dynamics (AIMD) simulations can be reliably achieved by extrapolation of electronic data from previous timesteps. Existing techniques utilize polynomial least-squares regression to fit previous steps' Fock or density matrix elements. In this work, the recursive Burg 'linear prediction' technique is shown to be a viable alternative to polynomial regression, and the extrapolation-predicted Fock matrix elements were three orders of magnitude closer to converged elements. Accelerations of 1.8-3.4× were observed in test systems, and in all cases, linear prediction outperformed polynomial extrapolation. Importantly, these accelerations were achieved without reducing the MD integration timestep.

  5. C-Peptide Level in Fasting Plasma and Pooled Urine Predicts HbA1c after Hospitalization in Patients with Type 2 Diabetes Mellitus.

    PubMed

    Sonoda, Remi; Tanaka, Kentaro; Kikuchi, Takako; Onishi, Yukiko; Takao, Toshiko; Tahara, Tazu; Yoshida, Yoko; Suzawa, Naoki; Kawazu, Shoji; Iwamoto, Yasuhiko; Kushiyama, Akifumi

    2016-01-01

    In this study, we investigate how measures of insulin secretion and other clinical information affect long-term glycemic control in patients with type 2 diabetes mellitus. Between October 2012 and June 2014, we monitored 202 diabetes patients who were admitted to the hospital of Asahi Life Foundation for glycemic control, as well as for training and education in diabetes management. We measured glycated hemoglobin (HbA1c) six months after discharge to assess disease management. In univariate analysis, fasting plasma C-peptide immunoreactivity (F-CPR) and pooled urine CPR (U-CPR) were significantly associated with HbA1c, in contrast to ΔCPR and C-peptide index (CPI). This association was strongly independent of most other patient variables. In exploratory factor analysis, five underlying factors, namely insulin resistance, aging, sex differences, insulin secretion, and glycemic control, represented patient characteristics. In particular, insulin secretion and resistance strongly influenced F-CPR, while insulin secretion affected U-CPR. In conclusion, the data indicate that among patients with type 2 diabetes mellitus, F-CPR and U-CPR may predict improved glycemic control six months after hospitalization.

  6. N-terminal pro b-type natriuretic peptide (NT-pro-BNP) –based score can predict in-hospital mortality in patients with heart failure

    PubMed Central

    Huang, Ya-Ting; Tseng, Yuan-Teng; Chu, Tung-Wei; Chen, John; Lai, Min-Yu; Tang, Woung-Ru; Shiao, Chih-Chung

    2016-01-01

    Serum N-terminal pro b-type natriuretic peptide (NT-pro-BNP) testing is recommended in the patients with heart failure (HF). We hypothesized that NT-pro-BNP, in combination with other clinical factors in terms of a novel NT-pro BNP-based score, may provide even better predictive power for in-hospital mortality among patients with HF. A retrospective study enrolled adult patients with hospitalization-requiring HF who fulfilled the predefined criteria during the period from January 2011 to December 2013. We proposed a novel scoring system consisting of several independent predictors including NT-pro-BNP for predicting in-hospital mortality, and then compared the prognosis-predictive power of the novel NT-pro BNP-based score with other prognosis-predictive scores. A total of 269 patients were enrolled in the current study. Factors such as “serum NT-pro-BNP level above 8100 mg/dl,” “age above 79 years,” “without taking angiotensin converting enzyme inhibitors/angiotensin receptor blocker,” “without taking beta-blocker,” “without taking loop diuretics,” “with mechanical ventilator support,” “with non-invasive ventilator support,” “with vasopressors use,” and “experience of cardio-pulmonary resuscitation” were found as independent predictors. A novel NT-pro BNP-based score composed of these risk factors was proposed with excellent predictability for in-hospital mortality. The proposed novel NT-pro BNP-based score was extremely effective in predicting in-hospital mortality in HF patients. PMID:27411951

  7. Predicting Critical Speeds in Rotordynamics: A New Method

    NASA Astrophysics Data System (ADS)

    Knight, J. D.; Virgin, L. N.; Plaut, R. H.

    2016-09-01

    In rotordynamics, it is often important to be able to predict critical speeds. The passage through resonance is generally difficult to model. Rotating shafts with a disk are analyzed in this study, and experiments are conducted with one and two disks on a shaft. The approach presented here involves the use of a relatively simple prediction technique, and since it is a black-box data-based approach, it is suitable for in-situ applications.

  8. Comparison of predictive control methods for high consumption industrial furnace.

    PubMed

    Stojanovski, Goran; Stankovski, Mile

    2013-01-01

    We describe several predictive control approaches for high consumption industrial furnace control. These furnaces are major consumers in production industries, and reducing their fuel consumption and optimizing the quality of the products is one of the most important engineer tasks. In order to demonstrate the benefits from implementation of the advanced predictive control algorithms, we have compared several major criteria for furnace control. On the basis of the analysis, some important conclusions have been drawn.

  9. Development of a novel efficient method to construct an adenovirus library displaying random peptides on the fiber knob

    PubMed Central

    Yamamoto, Yuki; Goto, Naoko; Miura, Kazuki; Narumi, Kenta; Ohnami, Shumpei; Uchida, Hiroaki; Miura, Yoshiaki; Yamamoto, Masato; Aoki, Kazunori

    2014-01-01

    Redirection of adenovirus vectors by engineering the capsid-coding region has shown limited success because proper targeting ligands are generally unknown. To overcome this limitation, we constructed an adenovirus library displaying random peptides on the fiber knob, and its screening led to successful selections of several particular targeted vectors. In the previous library construction method, the full length of an adenoviral genome was generated by a Cre-lox mediated in vitro recombination between a fiber-modified plasmid library and the enzyme-digested adenoviral DNA/terminal protein complex (DNA-TPC) before transfection to the producer cells. In this system, the procedures were complicated and time-consuming, and approximately 30% of the vectors in the library were defective with no displaying peptide. These may hinder further extensive exploration of cancer-targeting vectors. To resolve these problems, in this study, we developed a novel method with the transfection of a fiber-modified plasmid library and a fiberless adenoviral DNA-TPC in Cre-expressing 293 cells. The use of in-cell Cre recombination and fiberless adenovirus greatly simplified the library-making steps. The fiberless adenovirus was useful in suppressing the expansion of unnecessary adenovirus vectors. In addition, the complexity of the library was more than a 104 level in one well in a 6-well dish, which was 10-fold higher than that of the original method. The results demonstrated that this novel method is useful in producing a high quality live adenovirus library, which could facilitate the development of targeted adenovirus vectors for a variety of applications in medicine. PMID:24380399

  10. Low Levels of IgM Antibodies against an Advanced Glycation Endproduct-Modified Apolipoprotein B100 Peptide Predict Cardiovascular Events in Nondiabetic Subjects.

    PubMed

    Engelbertsen, Daniel; Vallejo, Jenifer; Quách, Tâm Dan; Fredrikson, Gunilla Nordin; Alm, Ragnar; Hedblad, Bo; Björkbacka, Harry; Rothstein, Thomas L; Nilsson, Jan; Bengtsson, Eva

    2015-10-01

    Increased glucose levels are associated with the generation of advanced glycation endproduct (AGE) modifications. Interaction between AGE-modified plaque components and immune cells is believed to have an important role in the development of vascular complications in diabetes. Methylglyoxal (MGO) is one type of reactive aldehyde that gives rise to AGE modification. The present study analyzed whether autoantibodies against MGO-modified epitopes of the low-density lipoprotein apolipoprotein B (apoB) 100 predict cardiovascular events. A library consisting of 302 peptides comprising the complete apoB100 molecule was screened to identify peptides targeted by MGO-specific autoantibodies. Peptide (p) 220 (apoB amino acids 3286-3305) was identified as a major target. Baseline IgM and IgG against MGO-peptide 220 (p220) were measured in 700 individuals from the Malmö Diet and Cancer Cohort. A total of 139 cardiovascular events were registered during the 15-y follow-up period. Controlling for major cardiovascular risk factors demonstrated that subjects in the lowest tertile of MGO-p220 IgM had an increased risk for cardiovascular events (hazard ratio [95% confidence interval]: 2.07 [1.22-3.50]; p(trend) = 0.004). Interestingly, the association between MGO-p220 IgM and cardiovascular events remained and even tended to become stronger when subjects with prevalent diabetes were excluded from the analysis (2.51 [1.37-4.61]; p(trend) = 0.002). MGO-p220 IgM was inversely associated with blood glucose, but not with oxidized low-density lipoprotein. Finally, we demonstrate that anti-MGO-p220 IgM is produced by B1 cells. These data show that subjects with low levels of IgM recognizing MGO-modified p220 in apoB have an increased risk to develop cardiovascular events and that this association is present in nondiabetic subjects.

  11. VitAL: Viterbi Algorithm for de novo Peptide Design

    PubMed Central

    Unal, E. Besray; Gursoy, Attila; Erman, Burak

    2010-01-01

    Background Drug design against proteins to cure various diseases has been studied for several years. Numerous design techniques were discovered for small organic molecules for specific protein targets. The specificity, toxicity and selectivity of small molecules are hard problems to solve. The use of peptide drugs enables a partial solution to the toxicity problem. There has been a wide interest in peptide design, but the design techniques of a specific and selective peptide inhibitor against a protein target have not yet been established. Methodology/Principal Findings A novel de novo peptide design approach is developed to block activities of disease related protein targets. No prior training, based on known peptides, is necessary. The method sequentially generates the peptide by docking its residues pair by pair along a chosen path on a protein. The binding site on the protein is determined via the coarse grained Gaussian Network Model. A binding path is determined. The best fitting peptide is constructed by generating all possible peptide pairs at each point along the path and determining the binding energies between these pairs and the specific location on the protein using AutoDock. The Markov based partition function for all possible choices of the peptides along the path is generated by a matrix multiplication scheme. The best fitting peptide for the given surface is obtained by a Hidden Markov model using Viterbi decoding. The suitability of the conformations of the peptides that result upon binding on the surface are included in the algorithm by considering the intrinsic Ramachandran potentials. Conclusions/Significance The model is tested on known protein-peptide inhibitor complexes. The present algorithm predicts peptides that have better binding energies than those of the existing ones. Finally, a heptapeptide is designed for a protein that has excellent binding affinity according to AutoDock results. PMID:20532195

  12. Selective enrichment and desalting of hydrophilic peptides using graphene oxide.

    PubMed

    Jiang, Miao; Qi, Linyu; Liu, Peiru; Wang, Zijun; Duan, Zhigui; Wang, Ying; Liu, Zhonghua; Chen, Ping

    2016-08-01

    The wide variety and low abundance of peptides in tissue brought great difficulties to the separation and identification of peptides, which is not in favor of the development of peptidomics. RP-HPLC, which could purify small molecules based on their hydrophobicity, has been widely used in the separation and enrichment of peptide due to its fast, good reproducibility and high resolution. However, RP-HPLC requires the instrument and expensive C18 column and its sample capacity is also limited. Recently, graphene oxide has been applied to the adsorption of amino acids. However, the enrichment efficiency and selectivity of graphene oxide for peptides remain unclear. In this study, the adsorption efficiency and selectivity of graphene oxide and RP-C18 matrix were compared on trypsinized α-actin and also on tissue extracts from pituitary gland and hippocampus. For α-actin, there exhibit similar elution peaks for total trypsinized products and those adsorpted by GO and C18 matrix. But peptides adsorbed by GO showed the higher hydrophilic peaks than which adsorbed by C18 matrix. The resulted RP-HPLC profile showed that most of peptides enriched by graphene oxide were eluted at low concentration of organic solvent, while peptides adsorbed by RP-C18 matrix were mostly eluted at relatively high concentration. Moreover, mass spectrometry analysis suggested that, in pituitary sample, there were 495 peptides enriched by graphene oxide, 447 peptides enriched by RP-C18 matrix while in hippocampus sample 333 and 243 peptides respectively. The GRAVY value analysis suggested that the graphene oxide has a stronger adsorption for highly hydrophilic peptides compared to the RP-C18 matrix. Furthermore, the combination of these two methods could notably increase the number of identification peptides but also the number of predicted protein precursors. Our study provided a new thought to the role of graphene oxide during the enrichment of peptides from tissue which should be useful for

  13. The single GUV method for revealing the functions of antimicrobial, pore-forming toxin, and cell-penetrating peptides or proteins.

    PubMed

    Islam, Md Zahidul; Alam, Jahangir Md; Tamba, Yukihiro; Karal, Mohammad Abu Sayem; Yamazaki, Masahito

    2014-08-14

    We recently developed the single giant unilamellar vesicle (GUV) method for investigating the functions and dynamics of biomembranes. The single GUV method can provide detailed information on the elementary processes of physiological phenomena in biomembranes, such as their rate constants. Here we describe the process of pore formation induced by the antimicrobial peptide (AMP), magainin 2, and the pore-forming toxin (PFT), lysenin, as revealed by the single GUV method. We obtained the rate constants of several elementary steps, such as peptide/protein-induced pore formation in lipid membranes and the membrane permeation of fluorescent probes through the pores. Information on the entry of the cell-penetrating peptide (CPP), transportan 10 (TP10), into a single GUV and its induced pore formation in lipid membranes was also obtained. We compare the single GUV method with other methods for investigating the interaction of peptides/proteins with lipid membranes (i.e., the large unilamellar vesicle (LUV) suspension method, the GUV suspension method, and single channel recording), and discuss the pros and cons of the single GUV method. On the basis of these data, we discuss the advantages of the single GUV method.

  14. Screening Method for the Discovery of Potential Bioactive Cysteine-Containing Peptides Using 3D Mass Mapping

    NASA Astrophysics Data System (ADS)

    van Oosten, Luuk N.; Pieterse, Mervin; Pinkse, Martijn W. H.; Verhaert, Peter D. E. M.

    2015-12-01

    Animal venoms and toxins are a valuable source of bioactive peptides with pharmacologic relevance as potential drug leads. A large subset of biologically active peptides discovered up till now contain disulfide bridges that enhance stability and activity. To discover new members of this class of peptides, we developed a workflow screening specifically for those peptides that contain inter- and intra-molecular disulfide bonds by means of three-dimensional (3D) mass mapping. Two intrinsic properties of the sulfur atom, (1) its relatively large negative mass defect, and (2) its isotopic composition, allow for differentiation between cysteine-containing peptides and peptides lacking sulfur. High sulfur content in a peptide decreases the normalized nominal mass defect (NMD) and increases the normalized isotopic shift (NIS). Hence in a 3D plot of mass, NIS, and NMD, peptides with sulfur appear in this plot with a distinct spatial localization compared with peptides that lack sulfur. In this study we investigated the skin secretion of two frog species; Odorrana schmackeri and Bombina variegata. Peptides from the crude skin secretions were separated by nanoflow LC, and of all eluting peptides high resolution zoom scans were acquired in order to accurately determine both monoisotopic mass and average mass. Both the NMD and the NIS were calculated from the experimental data using an in-house developed MATLAB script. Candidate peptides exhibiting a low NMD and high NIS values were selected for targeted de novo sequencing, and this resulted in the identification of several novel inter- and intra-molecular disulfide bond containing peptides.

  15. Methods for evaluating the predictive accuracy of structural dynamic models

    NASA Technical Reports Server (NTRS)

    Hasselman, Timothy K.; Chrostowski, Jon D.

    1991-01-01

    Modeling uncertainty is defined in terms of the difference between predicted and measured eigenvalues and eigenvectors. Data compiled from 22 sets of analysis/test results was used to create statistical databases for large truss-type space structures and both pretest and posttest models of conventional satellite-type space structures. Modeling uncertainty is propagated through the model to produce intervals of uncertainty on frequency response functions, both amplitude and phase. This methodology was used successfully to evaluate the predictive accuracy of several structures, including the NASA CSI Evolutionary Structure tested at Langley Research Center. Test measurements for this structure were within + one-sigma intervals of predicted accuracy for the most part, demonstrating the validity of the methodology and computer code.

  16. CancerPPD: a database of anticancer peptides and proteins.

    PubMed

    Tyagi, Atul; Tuknait, Abhishek; Anand, Priya; Gupta, Sudheer; Sharma, Minakshi; Mathur, Deepika; Joshi, Anshika; Singh, Sandeep; Gautam, Ankur; Raghava, Gajendra P S

    2015-01-01

    CancerPPD (http://crdd.osdd.net/raghava/cancerppd/) is a repository of experimentally verified anticancer peptides (ACPs) and anticancer proteins. Data were manually collected from published research articles, patents and from other databases. The current release of CancerPPD consists of 3491 ACP and 121 anticancer protein entries. Each entry provides comprehensive information related to a peptide like its source of origin, nature of the peptide, anticancer activity, N- and C-terminal modifications, conformation, etc. Additionally, CancerPPD provides the information of around 249 types of cancer cell lines and 16 different assays used for testing the ACPs. In addition to natural peptides, CancerPPD contains peptides having non-natural, chemically modified residues and D-amino acids. Besides this primary information, CancerPPD stores predicted tertiary structures as well as peptide sequences in SMILES format. Tertiary structures of peptides were predicted using the state-of-art method, PEPstr and secondary structural states were assigned using DSSP. In order to assist users, a number of web-based tools have been integrated, these include keyword search, data browsing, sequence and structural similarity search. We believe that CancerPPD will be very useful in designing peptide-based anticancer therapeutics.

  17. Alignment-Free Methods for the Detection and Specificity Prediction of Adenylation Domains.

    PubMed

    Agüero-Chapin, Guillermin; Pérez-Machado, Gisselle; Sánchez-Rodríguez, Aminael; Santos, Miguel Machado; Antunes, Agostinho

    2016-01-01

    Identifying adenylation domains (A-domains) and their substrate specificity can aid the detection of nonribosomal peptide synthetases (NRPS) at genome/proteome level and allow inferring the structure of oligopeptides with relevant biological activities. However, that is challenging task due to the high sequence diversity of A-domains (~10-40 % of amino acid identity) and their selectivity for 50 different natural/unnatural amino acids. Altogether these characteristics make their detection and the prediction of their substrate specificity a real challenge when using traditional sequence alignment methods, e.g., BLAST searches. In this chapter we describe two workflows based on alignment-free methods intended for the identification and substrate specificity prediction of A-domains. To identify A-domains we introduce a graphical-numerical method, implemented in TI2BioP version 2.0 (topological indices to biopolymers), which in a first step uses protein four-color maps to represent A-domains. In a second step, simple topological indices (TIs), called spectral moments, are derived from the graphical representations of known A-domains (positive dataset) and of unrelated but well-characterized sequences (negative set). Spectral moments are then used as input predictors for statistical classification techniques to build alignment-free models. Finally, the resulting alignment-free models can be used to explore entire proteomes for unannotated A-domains. In addition, this graphical-numerical methodology works as a sequence-search method that can be ensemble with homology-based tools to deeply explore the A-domain signature and cope with the diversity of this class (Aguero-Chapin et al., PLoS One 8(7):e65926, 2013). The second workflow for the prediction of A-domain's substrate specificity is based on alignment-free models constructed by transductive support vector machines (TSVMs) that incorporate information of uncharacterized A-domains. The construction of the models was

  18. What Predicts Method Effects in Child Behavior Ratings

    ERIC Educational Resources Information Center

    Low, Justin A.; Keith, Timothy Z.; Jensen, Megan

    2015-01-01

    The purpose of this research was to determine whether child, parent, and teacher characteristics such as sex, socioeconomic status (SES), parental depressive symptoms, the number of years of teaching experience, number of children in the classroom, and teachers' disciplinary self-efficacy predict deviations from maternal ratings in a…

  19. A modern approach for epitope prediction: identification of foot-and-mouth disease virus peptides binding bovine leukocyte antigen (BoLA) class I molecules

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Major histocompatibility complex (MHC) class I molecules regulate adaptive immune responses through the presentation of antigenic peptides to CD8positive T-cells. Polymorphisms in the peptide binding region of class I molecules determine peptide binding affinity and stability during antigen presenta...

  20. Easy and Rapid Binding Assay for Functional Analysis of Disulfide-Containing Peptides by a Pull-Down Method Using a Puromycin-Linker and a Cell-Free Translation System

    PubMed Central

    Tanemura, Yutaro; Mochizuki, Yuki; Kumachi, Shigefumi; Nemoto, Naoto

    2015-01-01

    Constrained peptides are an attractive class as affinity reagents or drug leads owing to their excellent binding properties. Many kinds of these peptides, such as cyclic peptides containing disulfide bridges, are found in nature or designed artificially by directed evolution. However, confirming the binding properties of the disulfide-rich peptides can be generally difficult, because of oxidative folding problems in the preparation steps. Therefore, a method for evaluating the binding properties of such peptides rapidly and easily is required. Here, we report an easy and rapid method for preparing biotin-attached peptides containing disulfide bridges or a chemical cross-linker using a cell-free translation system and a puromycin-linker, which is applicable to pull-down assays for protein (or peptide) molecular interaction analysis. PMID:25738808

  1. Qualification of a Quantitative Method for Monitoring Aspartate Isomerization of a Monoclonal Antibody by Focused Peptide Mapping.

    PubMed

    Cao, Mingyan; Mo, Wenjun David; Shannon, Anthony; Wei, Ziping; Washabaugh, Michael; Cash, Patricia

    Aspartate (Asp) isomerization is a common post-translational modification of recombinant therapeutic proteins that can occur during manufacturing, storage, or administration. Asp isomerization in the complementarity-determining regions of a monoclonal antibody may affect the target binding and thus a sufficiently robust quality control method for routine monitoring is desirable. In this work, we utilized a liquid chromatography-mass spectrometry (LC/MS)-based approach to identify the Asp isomerization in the complementarity-determining regions of a therapeutic monoclonal antibody. To quantitate the site-specific Asp isomerization of the monoclonal antibody, a UV detection-based quantitation assay utilizing the same LC platform was developed. The assay was qualified and implemented for routine monitoring of this product-specific modification. Compared with existing methods, this analytical paradigm is applicable to identify Asp isomerization (or other modifications) and subsequently develop a rapid, sufficiently robust quality control method for routine site-specific monitoring and quantitation to ensure product quality. This approach first identifies and locates a product-related impurity (a critical quality attribute) caused by isomerization, deamidation, oxidation, or other post-translational modifications, and then utilizes synthetic peptides and MS to assist the development of a LC-UV-based chromatographic method that separates and quantifies the product-related impurities by UV peaks. The established LC-UV method has acceptable peak specificity, precision, linearity, and accuracy; it can be validated and used in a good manufacturing practice environment for lot release and stability testing.

  2. Methods and apparatus for measurement of a dimensional characteristic and methods of predictive modeling related thereto

    DOEpatents

    Robertson, Eric P; Christiansen, Richard L.

    2007-05-29

    A method of optically determining a change in magnitude of at least one dimensional characteristic of a sample in response to a selected chamber environment. A magnitude of at least one dimension of the at least one sample may be optically determined subsequent to altering the at least one environmental condition within the chamber. A maximum change in dimension of the at least one sample may be predicted. A dimensional measurement apparatus for indicating a change in at least one dimension of at least one sample. The dimensional measurement apparatus may include a housing with a chamber configured for accommodating pressure changes and an optical perception device for measuring a dimension of at least one sample disposed in the chamber. Methods of simulating injection of a gas into a subterranean formation, injecting gas into a subterranean formation, and producing methane from a coal bed are also disclosed.

  3. Methods for measurement of a dimensional characteristic and methods of predictive modeling related thereto

    DOEpatents

    Robertson, Eric P; Christiansen, Richard L.

    2007-10-23

    A method of optically determining a change in magnitude of at least one dimensional characteristic of a sample in response to a selected chamber environment. A magnitude of at least one dimension of the at least one sample may be optically determined subsequent to altering the at least one environmental condition within the chamber. A maximum change in dimension of the at least one sample may be predicted. A dimensional measurement apparatus for indicating a change in at least one dimension of at least one sample. The dimensional measurement apparatus may include a housing with a chamber configured for accommodating pressure changes and an optical perception device for measuring a dimension of at least one sample disposed in the chamber. Methods of simulating injection of a gas into a subterranean formation, injecting gas into a subterranean formation, and producing methane from a coal bed are also disclosed.

  4. A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods

    PubMed Central

    Jung, Jaehee; Lee, Heung Ki

    2014-01-01

    Automated protein function prediction defines the designation of functions of unknown protein functions by using computational methods. This technique is useful to automatically assign gene functional annotations for undefined sequences in next generation genome analysis (NGS). NGS is a popular research method since high-throughput technologies such as DNA sequencing and microarrays have created large sets of genes. These huge sequences have greatly increased the need for analysis. Previous research has been based on the similarities of sequences as this is strongly related to the functional homology. However, this study aimed to designate protein functions by automatically predicting the function of the genome by utilizing InterPro (IPR), which can represent the properties of the protein family and groups of the protein function. Moreover, we used gene ontology (GO), which is the controlled vocabulary used to comprehensively describe the protein function. To define the relationship between IPR and GO terms, three pattern recognition techniques have been employed under different conditions, such as feature selection and weighted value, instead of a binary one. PMID:25133242

  5. Correlation of Puma airloads: Evaluation of CFD prediction methods

    NASA Technical Reports Server (NTRS)

    Strawn, Roger C.; Desopper, Andre; Miller, Judith; Jones, Alan

    1989-01-01

    A cooperative program was undertaken by research organizations in England, France, Australia and the U.S. to study the capabilities of computational fluid dynamics codes (CFD) to predict the aerodynamic loading on helicopter rotor blades. The program goal is to compare predictions with experimental data for flight tests of a research Puma helicopter with rectangular and swept tip blades. Two topics are studied. First, computed results from three CFD codes are compared for flight test cases where all three codes use the same partial inflow-angle boundary conditions. Second, one of the CFD codes (FPR) is iteratively coupled with the CAMRAD/JA helicopter performance code. These results are compared with experimental data and with an uncoupled CAMRAD/JA solution. The influence of flow field unsteadiness is found to play an important role in the blade aerodynamics. Alternate boundary conditions are suggested in order to properly model this unsteadiness in the CFD codes.

  6. Correlation of Puma airfoils - Evaluation of CFD prediction methods

    NASA Technical Reports Server (NTRS)

    Strawn, Roger C.; Desopper, Andre; Miller, Judith; Jones, Alan

    1989-01-01

    A cooperative program was undertaken by research organizations in England, France, Australia and the U.S. to study the capabilities of computational fluid dynamics codes (CFD) to predict the aerodynamic loading on helicopter rotor blades. The program goal is to compare predictions with experimental data for flight tests of a research Puma helicopter with rectangular and swept tip blades. Two topics are studied. First, computed results from three CFD codes are compared for flight test cases where all three codes use the same partial inflow-angle boundary conditions. Second, one of the CFD codes (FPR) is iteratively coupled with the CAMRAD/JA heilcopter performance code. These results are compared with experimental data and with an uncoupled CAMRAD/JA solution. The influence of flow field unsteadiness is found to play an important role in the blade aerodynamics. Alternate boundary conditions are suggested in order to properly model this unsteadiness in the CFD codes.

  7. Development of Statistical Methods Using Predictive Inference and Entropy.

    DTIC Science & Technology

    1986-03-01

    Inference and Entopy APPENDIX B: Achieab Accuracy in Parametric Estimation of B-I Multivariate spectra ii LWl OF MIUMU AND TABLES FIGURES PAGE Figre1...1986e). "Achievable Accuracy in Parametric Estimation of Multivariate Spec- tra’. Draft. Larimore, WE. (1983a). ’Predictive inference, sufficiency... PARAMETRIC ESTIMATION OF MULTIVARIATE SPECTRA By Wallace E. Larimore Scientific Systems Inc., Cambridge, Massachusetts, U.SA. Research Sponsored by the

  8. DO TIE LABORATORY BASED ASSESSMENT METHODS REALLY PREDICT FIELD EFFECTS?

    EPA Science Inventory

    Sediment Toxicity Identification and Evaluation (TIE) methods have been developed for both porewaters and whole sediments. These relatively simple laboratory methods are designed to identify specific toxicants or classes of toxicants in sediments; however, the question of whethe...

  9. Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

    EPA Science Inventory

    Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...

  10. Ceramic Matrix Composites (CMC) Life Prediction Method Development

    NASA Technical Reports Server (NTRS)

    Levine, Stanley R.; Calomino, Anthony M.; Ellis, John R.; Halbig, Michael C.; Mital, Subodh K.; Murthy, Pappu L.; Opila, Elizabeth J.; Thomas, David J.; Thomas-Ogbuji, Linus U.; Verrilli, Michael J.

    2000-01-01

    Advanced launch systems (e.g., Reusable Launch Vehicle and other Shuttle Class concepts, Rocket-Based Combine Cycle, etc.), and interplanetary vehicles will very likely incorporate fiber reinforced ceramic matrix composites (CMC) in critical propulsion components. The use of CMC is highly desirable to save weight, to improve reuse capability, and to increase performance. CMC candidate applications are mission and cycle dependent and may include turbopump rotors, housings, combustors, nozzle injectors, exit cones or ramps, and throats. For reusable and single mission uses, accurate prediction of life is critical to mission success. The tools to accomplish life prediction are very immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for a variety of space propulsion applications. This paper describes an approach to satisfy the need to develop an integrated life prediction system for CMC that addresses mechanical durability due to cyclic and steady thermomechanical loads, and takes into account the impact of environmental degradation.

  11. Non-linear Equation using Plasma Brain Natriuretic Peptide Levels to Predict Cardiovascular Outcomes in Patients with Heart Failure

    PubMed Central

    Fukuda, Hiroki; Suwa, Hideaki; Nakano, Atsushi; Sakamoto, Mari; Imazu, Miki; Hasegawa, Takuya; Takahama, Hiroyuki; Amaki, Makoto; Kanzaki, Hideaki; Anzai, Toshihisa; Mochizuki, Naoki; Ishii, Akira; Asanuma, Hiroshi; Asakura, Masanori; Washio, Takashi; Kitakaze, Masafumi

    2016-01-01

    Brain natriuretic peptide (BNP) is the most effective predictor of outcomes in chronic heart failure (CHF). This study sought to determine the qualitative relationship between the BNP levels at discharge and on the day of cardiovascular events in CHF patients. We devised a mathematical probabilistic model between the BNP levels at discharge (y) and on the day (t) of cardiovascular events after discharge for 113 CHF patients (Protocol I). We then prospectively evaluated this model on another set of 60 CHF patients who were readmitted (Protocol II). P(t|y) was the probability of cardiovascular events occurring after >t, the probability on t was given as p(t|y) = −dP(t|y)/dt, and p(t|y) = pP(t|y) = αyβP(t|y), along with p = αyβ (α and β were constant); the solution was p(t|y) = αyβ exp(−αyβt). We fitted this equation to the data set of Protocol I using the maximum likelihood principle, and we obtained the model p(t|y) = 0.000485y0.24788 exp(−0.000485y0.24788t). The cardiovascular event-free rate was computed as P(t) = 1/60Σi=1,…,60 exp(−0.000485yi0.24788t), based on this model and the BNP levels yi in a data set of Protocol II. We confirmed no difference between this model-based result and the actual event-free rate. In conclusion, the BNP levels showed a non-linear relationship with the day of occurrence of cardiovascular events in CHF patients. PMID:27845390

  12. Non-linear Equation using Plasma Brain Natriuretic Peptide Levels to Predict Cardiovascular Outcomes in Patients with Heart Failure

    NASA Astrophysics Data System (ADS)

    Fukuda, Hiroki; Suwa, Hideaki; Nakano, Atsushi; Sakamoto, Mari; Imazu, Miki; Hasegawa, Takuya; Takahama, Hiroyuki; Amaki, Makoto; Kanzaki, Hideaki; Anzai, Toshihisa; Mochizuki, Naoki; Ishii, Akira; Asanuma, Hiroshi; Asakura, Masanori; Washio, Takashi; Kitakaze, Masafumi

    2016-11-01

    Brain natriuretic peptide (BNP) is the most effective predictor of outcomes in chronic heart failure (CHF). This study sought to determine the qualitative relationship between the BNP levels at discharge and on the day of cardiovascular events in CHF patients. We devised a mathematical probabilistic model between the BNP levels at discharge (y) and on the day (t) of cardiovascular events after discharge for 113 CHF patients (Protocol I). We then prospectively evaluated this model on another set of 60 CHF patients who were readmitted (Protocol II). P(t|y) was the probability of cardiovascular events occurring after >t, the probability on t was given as p(t|y) = ‑dP(t|y)/dt, and p(t|y) = pP(t|y) = αyβP(t|y), along with p = αyβ (α and β were constant); the solution was p(t|y) = αyβ exp(‑αyβt). We fitted this equation to the data set of Protocol I using the maximum likelihood principle, and we obtained the model p(t|y) = 0.000485y0.24788 exp(‑0.000485y0.24788t). The cardiovascular event-free rate was computed as P(t) = 1/60Σi=1,…,60 exp(‑0.000485yi0.24788t), based on this model and the BNP levels yi in a data set of Protocol II. We confirmed no difference between this model-based result and the actual event-free rate. In conclusion, the BNP levels showed a non-linear relationship with the day of occurrence of cardiovascular events in CHF patients.

  13. Online prediction model based on the SVD-KPCA method.

    PubMed

    Elaissi, Ilyes; Jaffel, Ines; Taouali, Okba; Messaoud, Hassani

    2013-01-01

    This paper proposes a new method for online identification of a nonlinear system modelled on Reproducing Kernel Hilbert Space (RKHS). The proposed SVD-KPCA method uses the Singular Value Decomposition (SVD) technique to update the principal components. Then we use the Reduced Kernel Principal Component Analysis (RKPCA) to approach the principal components which represent the observations selected by the KPCA method.

  14. Analysis and prediction of myristoylation sites using the mRMR method, the IFS method and an extreme learning machine algorithm.

    PubMed

    Wang, ShaoPeng; Zhang, Yu-Hang; Huang, Guohua; Chen, Lei; Cai, Yu-Dong

    2016-12-20

    Myristoylation is an important hydrophobic post-translational modificationthat is covalently bound tothe amino group of Gly residueson the N-terminus of proteins. The many diversefunctions of myristoylation on proteins,such as membrane targeting, signal pathway regulation and apoptosis,are largely due to the lipid modification,whereasabnormal or irregular myristoylation on proteins can lead to several pathological changes in the cell. To better understand the function o fmyristoylated sites and to correctly identify them in protein sequences, this study conducted a novel investigation of myristoylation sites. Four types of features derived from the peptide segments following the myristoylation sites were used to specify myristoylated sites. Then, feature selection methods including maximum relevance and minimum redundancy, incremental feature selection, and a machine learning algorithm (extreme learning machine method) were adopted to analyze these features. As a result, 41 key features were extracted and used to build an optimal prediction model. The effectiveness of the optimal prediction model was further validated by its performance on a test dataset. Furthermore, detailed analyses were also performed on the extracted 41 features to gain insight into the mechanism of myristoylation modification.

  15. Hierarchy of simulation models in predicting molecular recognition mechanisms from the binding energy landscapes: structural analysis of the peptide complexes with SH2 domains.

    PubMed

    Verkhivker, G M; Bouzida, D; Gehlhaar, D K; Rejto, P A; Schaffer, L; Arthurs, S; Colson, A B; Freer, S T; Larson, V; Luty, B A; Marrone, T; Rose, P W

    2001-12-01

    Computer simulations using the simplified energy function and simulated tempering dynamics have accurately determined the native structure of the pYVPML, SVLpYTAVQPNE, and SPGEpYVNIEF peptides in the complexes with SH2 domains. Structural and equilibrium aspects of the peptide binding with SH2 domains have been studied by generating temperature-dependent binding free energy landscapes. Once some native peptide-SH2 domain contacts are constrained, the underlying binding free energy profile has the funnel-like shape that leads to a rapid and consistent acquisition of the native structure. The dominant native topology of the peptide-SH2 domain complexes represents an extended peptide conformation with strong specific interactions in the phosphotyrosine pocket and hydrophobic interactions of the peptide residues C-terminal to the pTyr group. The topological features of the peptide-protein interface are primarily determined by the thermodynamically stable phosphotyrosyl group. A diversity of structurally different binding orientations has been observed for the amino-terminal residues to the phosphotyrosine. The dominant native topology for the peptide residues carboxy-terminal to the phosphotyrosine is tolerant to flexibility in this region of the peptide-SH2 domain interface observed in equilibrium simulations. The energy landscape analysis has revealed a broad, entropically favorable topology of the native binding mode for the bound peptides, which is robust to structural perturbations. This could provide an additional positive mechanism underlying tolerance of the SH2 domains to hydrophobic conservative substitutions in the peptide specificity region.

  16. Development of an integrated method for long-term water quality prediction using seasonal climate forecast

    NASA Astrophysics Data System (ADS)

    Cho, Jaepil; Shin, Chang-Min; Choi, Hwan-Kyu; Kim, Kyong-Hyeon; Choi, Ji-Yong

    2016-10-01

    The APEC Climate Center (APCC) produces climate prediction information utilizing a multi-climate model ensemble (MME) technique. In this study, four different downscaling methods, in accordance with the degree of utilizing the seasonal climate prediction information, were developed in order to improve predictability and to refine the spatial scale. These methods include: (1) the Simple Bias Correction (SBC) method, which directly uses APCC's dynamic prediction data with a 3 to 6 month lead time; (2) the Moving Window Regression (MWR) method, which indirectly utilizes dynamic prediction data; (3) the Climate Index Regression (CIR) method, which predominantly uses observation-based climate indices; and (4) the Integrated Time Regression (ITR) method, which uses predictors selected from both CIR and MWR. Then, a sampling-based temporal downscaling was conducted using the Mahalanobis distance method in order to create daily weather inputs to the Soil and Water Assessment Tool (SWAT) model. Long-term predictability of water quality within the Wecheon watershed of the Nakdong River Basin was evaluated. According to the Korean Ministry of Environment's Provisions of Water Quality Prediction and Response Measures, modeling-based predictability was evaluated by using 3-month lead prediction data issued in February, May, August, and November as model input of SWAT. Finally, an integrated approach, which takes into account various climate information and downscaling methods for water quality prediction, was presented. This integrated approach can be used to prevent potential problems caused by extreme climate in advance.

  17. Underwater Sound Propagation Modeling Methods for Predicting Marine Animal Exposure.

    PubMed

    Hamm, Craig A; McCammon, Diana F; Taillefer, Martin L

    2016-01-01

    The offshore exploration and production (E&P) industry requires comprehensive and accurate ocean acoustic models for determining the exposure of marine life to the high levels of sound used in seismic surveys and other E&P activities. This paper reviews the types of acoustic models most useful for predicting the propagation of undersea noise sources and describes current exposure models. The severe problems caused by model sensitivity to the uncertainty in the environment are highlighted to support the conclusion that it is vital that risk assessments include transmission loss estimates with statistical measures of confidence.

  18. Method of predicting a change in an economy

    DOEpatents

    Pryor, Richard J; Basu, Nipa

    2006-01-10

    An economy whose activity is to be predicted comprises a plurality of decision makers. Decision makers include, for example, households, government, industry, and banks. The decision makers are represented by agents, where an agent can represent one or more decision makers. Each agent has decision rules that determine the agent's actions. Each agent can affect the economy by affecting variable conditions characteristic of the economy or the internal state of other agents. Agents can communicate actions through messages. On a multiprocessor computer, the agents can be assigned to processing elements.

  19. Quantitative Analysis of Single Amino Acid Variant Peptides Associated with Pancreatic Cancer in Serum by an Isobaric Labeling Quantitative Method

    PubMed Central

    2015-01-01

    Single amino acid variations are highly associated with many human diseases. The direct detection of peptides containing single amino acid variants (SAAVs) derived from nonsynonymous single nucleotide polymorphisms (SNPs) in serum can provide unique opportunities for SAAV associated biomarker discovery. In the present study, an isobaric labeling quantitative strategy was applied to identify and quantify variant peptides in serum samples of pancreatic cancer patients and other benign controls. The largest number of SAAV peptides to date in serum including 96 unique variant peptides were quantified in this quantitative analysis, of which five variant peptides showed a statistically significant difference between pancreatic cancer and other controls (p-value < 0.05). Significant differences in the variant peptide SDNCEDTPEAGYFAVAVVK from serotransferrin were detected between pancreatic cancer and controls, which was further validated by selected reaction monitoring (SRM) analysis. The novel biomarker panel obtained by combining α-1-antichymotrypsin (AACT), Thrombospondin-1 (THBS1) and this variant peptide showed an excellent diagnostic performance in discriminating pancreatic cancer from healthy controls (AUC = 0.98) and chronic pancreatitis (AUC = 0.90). These results suggest that large-scale analysis of SAAV peptides in serum may provide a new direction for biomarker discovery research. PMID:25393578

  20. Optimization of the Use of Consensus Methods for the Detection and Putative Identification of Peptides via Mass Spectrometry Using Protein Standard Mixtures

    PubMed Central

    Sultana, Tamanna; Jordan, Rick; Lyons-Weiler, James

    2009-01-01

    Correct identification of peptides and proteins in complex biological samples from proteomic mass-spectra is a challenging problem in bioinformatics. The sensitivity and specificity of identification algorithms depend on underlying scoring methods, some being more sensitive, and others more specific. For high-throughput, automated peptide identification, control over the algorithms’ performance in terms of trade-off between sensitivity and specificity is desirable. Combinations of algorithms, called ‘consensus methods’, have been shown to provide more accurate results than individual algorithms. However, due to the proliferation of algorithms and their varied internal settings, a systematic understanding of relative performance of individual and consensus methods are lacking. We performed an in-depth analysis of various approaches to consensus scoring using known protein mixtures, and evaluated the performance of 2310 settings generated from consensus of three different search algorithms: Mascot, Sequest, and X!Tandem. Our findings indicate that the union of Mascot, Sequest, and X!Tandem performed well (considering overall accuracy), and methods using 80–99.9% protein probability and/or minimum 2 peptides and/or 0–50% minimum peptide probability for protein identification performed better (on average) among all consensus methods tested in terms of overall accuracy. The results also suggest method selection strategies to provide direct control over sensitivity and specificity. PMID:19779596

  1. Simple numerical method for predicting steady compressible flows

    NASA Technical Reports Server (NTRS)

    Vonlavante, Ernst; Nelson, N. Duane

    1986-01-01

    A numerical method for solving the isenthalpic form of the governing equations for compressible viscous and inviscid flows was developed. The method was based on the concept of flux vector splitting in its implicit form. The method was tested on several demanding inviscid and viscous configurations. Two different forms of the implicit operator were investigated. The time marching to steady state was accelerated by the implementation of the multigrid procedure. Its various forms very effectively increased the rate of convergence of the present scheme. High quality steady state results were obtained in most of the test cases; these required only short computational times due to the relative efficiency of the basic method.

  2. Specification and prediction of nickel mobilization using artificial intelligence methods

    NASA Astrophysics Data System (ADS)

    Gholami, Raoof; Ziaii, Mansour; Ardejani, Faramarz; Maleki, Shahoo

    2011-12-01

    Groundwater and soil pollution from pyrite oxidation, acid mine drainage generation, and release and transport of toxic metals are common environmental problems associated with the mining industry. Nickel is one toxic metal considered to be a key pollutant in some mining setting; to date, its formation mechanism has not yet been fully evaluated. The goals of this study are 1) to describe the process of nickel mobilization in waste dumps by introducing a novel conceptual model, and 2) to predict nickel concentration using two algorithms, namely the support vector machine (SVM) and the general regression neural network (GRNN). The results obtained from this study have shown that considerable amount of nickel concentration can be arrived into the water flow system during the oxidation of pyrite and subsequent Acid Drainage (AMD) generation. It was concluded that pyrite, water, and oxygen are the most important factors for nickel pollution generation while pH condition, SO4, HCO3, TDS, EC, Mg, Fe, Zn, and Cu are measured quantities playing significant role in nickel mobilization. SVM and GRNN have predicted nickel concentration with a high degree of accuracy. Hence, SVM and GRNN can be considered as appropriate tools for environmental risk assessment.

  3. Verifying a computational method for predicting extreme ground motion

    USGS Publications Warehouse

    Harris, R.A.; Barall, M.; Andrews, D.J.; Duan, B.; Ma, S.; Dunham, E.M.; Gabriel, A.-A.; Kaneko, Y.; Kase, Y.; Aagaard, B.T.; Oglesby, D.D.; Ampuero, J.-P.; Hanks, T.C.; Abrahamson, N.

    2011-01-01

    In situations where seismological data is rare or nonexistent, computer simulations may be used to predict ground motions caused by future earthquakes. This is particularly practical in the case of extreme ground motions, where engineers of special buildings may need to design for an event that has not been historically observed but which may occur in the far-distant future. Once the simulations have been performed, however, they still need to be tested. The SCEC-USGS dynamic rupture code verification exercise provides a testing mechanism for simulations that involve spontaneous earthquake rupture. We have performed this examination for the specific computer code that was used to predict maximum possible ground motion near Yucca Mountain. Our SCEC-USGS group exercises have demonstrated that the specific computer code that was used for the Yucca Mountain simulations produces similar results to those produced by other computer codes when tackling the same science problem. We also found that the 3D ground motion simulations produced smaller ground motions than the 2D simulations.

  4. A novel method for the rapid detection of microbes in blood using pleurocidin antimicrobial peptide functionalized piezoelectric sensor.

    PubMed

    Shi, Xiaohong; Zhang, Xiaoqing; Yao, Qiongqiong; He, Fengjiao

    2017-02-01

    The rapid detection of microbes is critical in clinical diagnosis and food safety. Culture-dependent assays are the most widely used microbial detection methods, but these assays are time-consuming. In this study, a rapid microbial detection method was proposed using a pleurocidin/single-walled carbon nanotubes/interdigital electrode-multichannel series piezoelectric quartz crystal (pleurocidin/SWCNT/IDE-MSPQC) sensor. The selected pleurocidin antimicrobial peptide served as a recognition probe that exhibits broad-spectrum antimicrobial activity and the SWCNT acted as the electronic transducer and cross-linker for the immobilization of pleurocidin on the IDE. The response mechanism of the sensor was based on the specific interaction between pleurocidin and the microbe causing pleurocidin to detach from the SWCNT modified IDE, resulting in a sensitive frequency shift response of the MSPQC. Microbes that may be clinically present in the bloodstream during an infection were successfully detected by the proposed method within 15min. The developed strategy provides a new universal platform for the rapid detection of microbes.

  5. Predicting Academic Library Circulations: A Forecasting Methods Competition.

    ERIC Educational Resources Information Center

    Brooks, Terrence A.; Forys, John W., Jr.

    Based on sample data representing five years of monthly circulation totals from 50 academic libraries in Illinois, Iowa, Michigan, Minnesota, Missouri, and Ohio, a study was conducted to determine the most efficient smoothing forecasting methods for academic libraries. Smoothing forecasting methods were chosen because they have been characterized…

  6. Model for predicting thermal conductivity using transient hot wire method

    NASA Astrophysics Data System (ADS)

    Kumar, Sublania Harish; Singh K., J.; Somani A., K.

    2016-05-01

    The use of the hot wire method for estimating the thermal conductivity measurement has recently known a significant increase. However, this method is theoretically not applicable to materials. Thermal conductivity values are necessary whenever a heat transfer problem is to be evaluated.

  7. In silico calculated affinity of FVIII-derived peptides for HLA class II alleles predicts inhibitor development in haemophilia A patients with missense mutations in the F8 gene.

    PubMed

    Pashov, A D; Calvez, T; Gilardin, L; Maillère, B; Repessé, Y; Oldenburg, J; Pavlova, A; Kaveri, S V; Lacroix-Desmazes, S

    2014-03-01

    Forty per cent of haemophilia A (HA) patients have missense mutations in the F8 gene. Yet, all patients with identical mutations are not at the same risk of developing factor VIII (FVIII) inhibitors. In severe HA patients, human leucocyte antigen (HLA) haplotype was identified as a risk factor for onset of FVIII inhibitors. We hypothesized that missense mutations in endogenous FVIII alter the affinity of the mutated peptides for HLA class II, thus skewing FVIII-specific T-cell tolerance and increasing the risk that the corresponding wild-type FVIII-derived peptides induce an anti-FVIII immune response during replacement therapy. Here, we investigated whether affinity for HLA class II of wild-type FVIII-derived peptides that correspond to missense mutations described in the Haemophilia A Mutation, Structure, Test and Resource database is associated with inhibitor development. We predicted the mean affinity for 10 major HLA class II alleles of wild-type FVIII-derived peptides that corresponded to 1456 reported cases of missense mutations. Linear regression analysis confirmed a significant association between the predicted mean peptide affinity and the mutation inhibitory status (P = 0.006). Significance was lost after adjustment on mutation position on FVIII domains. Although analysis of the A1-A2-A3-C1 domains yielded a positive correlation between predicted HLA-binding affinity and inhibitory status (OR = 0.29 [95% CI: 0.14-0.60] for the high affinity tertile, P = 0.002), the C2 domain-restricted analysis indicated an inverse correlation (OR = 3.56 [1.10-11.52], P = 0.03). Our data validate the importance of the affinity of FVIII peptides for HLA alleles to the immunogenicity of therapeutic FVIII in patients with missense mutations.

  8. Predicting permeability tensors of foams using vector kinetic method

    NASA Astrophysics Data System (ADS)

    Jobic, Y.; Kumar, P.; Topin, F.; Occelli, R.

    2016-09-01

    Light cellular materials are increasingly used in many engineering applications due to several attractive properties including heat and mass transfer enhancement, low pressure drop compared to packed bed of spheres. It is therefore important to simulate the complex and unsteady flows by reliable numerical methods to determine intrinsic macroscopic hydraulic properties on actual foam structures. The approach of numerical simulations at pore scale has become popular criterion with the development of high performance computational power. Numerical studies based on a type of Lattice Boltzmann Method (LBM) were performed in the present work. Another kinetic method than LBM has been explored. A vector kinetic method is proposed which has the advantage of being non-diffusive, explicit, parallel, and use only physical variables instead of discrete velocity. The proposed numerical method is validated against experimental and numerical permeability data obtained on idealized isotropic idealized as well as real foam samples.

  9. Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design.

    PubMed

    Foight, Glenna Wink; Chen, T Scott; Richman, Daniel; Keating, Amy E

    2017-01-01

    Peptide reagents with high affinity or specificity for their target protein interaction partner are of utility for many important applications. Optimization of peptide binding by screening large libraries is a proven and powerful approach. Libraries designed to be enriched in peptide sequences that are predicted to have desired affinity or specificity characteristics are more likely to yield success than random mutagenesis. We present a library optimization method in which the choice of amino acids to encode at each peptide position can be guided by available experimental data or structure-based predictions. We discuss how to use analysis of predicted library performance to inform rounds of library design. Finally, we include protocols for more complex library design procedures that consider the chemical diversity of the amino acids at each peptide position and optimize a library score based on a user-specified input model.

  10. Predicting the conformations of peptides and proteins in early evolution. A review article submitted to Biology Direct

    PubMed Central

    Milner-White, E James; Russell, Michael J

    2008-01-01

    Considering that short, mainly heterochiral, polypeptides with a high glycine content are expected to have played a prominent role in evolution at the earliest stage of life before nucleic acids were available, we review recent knowledge about polypeptide three-dimensional structure to predict the types of conformations they would have adopted. The possible existence of such structures at this time leads to a consideration of their functional significance, and the consequences for the course of evolution. This article was reviewed by Bill Martin, Eugene Koonin and Nick Grishin. PMID:18226248

  11. Evaluation of Dimensionality-reduction Methods from Peptide Folding-unfolding Simulations.

    PubMed

    Duan, Mojie; Fan, Jue; Li, Minghai; Han, Li; Huo, Shuanghong

    2013-05-14

    Dimensionality reduction methods have been widely used to study the free energy landscapes and low-free energy pathways of molecular systems. It was shown that the non-linear dimensionality-reduction methods gave better embedding results than the linear methods, such as principal component analysis, in some simple systems. In this study, we have evaluated several non linear methods, locally linear embedding, Isomap, and diffusion maps, as well as principal component analysis from the equilibrium folding/unfolding trajectory of the second β-hairpin of the B1 domain of streptococcal protein G. The CHARMM parm19 polar hydrogen potential function was used. A series of criteria which reflects different aspects of the embedding qualities were employed in the evaluation. Our results show that principal component analysis is not worse than the non-linear ones on this complex system. There is no clear winner in all aspects of the evaluation. Each dimensionality-reduction method has its limitations in a certain aspect. We emphasize that a fair, informative assessment of an embedding result requires a combination of multiple evaluation criteria rather than any single one. Caution should be used when dimensionality-reduction methods are employed, especially when only a few of top embedding dimensions are used to describe the free energy landscape.

  12. Test battery with the human cell line activation test, direct peptide reactivity assay and DEREK based on a 139 chemical data set for predicting skin sensitizing potential and potency of chemicals.

    PubMed

    Takenouchi, Osamu; Fukui, Shiho; Okamoto, Kenji; Kurotani, Satoru; Imai, Noriyasu; Fujishiro, Miyuki; Kyotani, Daiki; Kato, Yoshinao; Kasahara, Toshihiko; Fujita, Masaharu; Toyoda, Akemi; Sekiya, Daisuke; Watanabe, Shinichi; Seto, Hirokazu; Hirota, Morihiko; Ashikaga, Takao; Miyazawa, Masaaki

    2015-11-01

    To develop a testing strategy incorporating the human cell line activation test (h-CLAT), direct peptide reactivity assay (DPRA) and DEREK, we created an expanded data set of 139 chemicals (102 sensitizers and 37 non-sensitizers) by combining the existing data set of 101 chemicals through the collaborative projects of Japan Cosmetic Industry Association. Of the additional 38 chemicals, 15 chemicals with relatively low water solubility (log Kow > 3.5) were selected to clarify the limitation of testing strategies regarding the lipophilic chemicals. Predictivities of the h-CLAT, DPRA and DEREK, and the combinations thereof were evaluated by comparison to results of the local lymph node assay. When evaluating 139 chemicals using combinations of three methods based on integrated testing strategy (ITS) concept (ITS-based test battery) and a sequential testing strategy (STS) weighing the predictive performance of the h-CLAT and DPRA, overall similar predictivities were found as before on the 101 chemical data set. An analysis of false negative chemicals suggested a major limitation of our strategies was the testing of low water-soluble chemicals. When excluded the negative results for chemicals with log Kow > 3.5, the sensitivity and accuracy of ITS improved to 97% (91 of 94 chemicals) and 89% (114 of 128). Likewise, the sensitivity and accuracy of STS to 98% (92 of 94) and 85% (111 of 129). Moreover, the ITS and STS also showed good correlation with local lymph node assay on three potency classifications, yielding accuracies of 74% (ITS) and 73% (STS). Thus, the inclusion of log Kow in analysis could give both strategies a higher predictive performance.

  13. Improving the identification rate of endogenous peptides using electron transfer dissociation and collision-induced dissociation.

    PubMed

    Hayakawa, Eisuke; Menschaert, Gerben; De Bock, Pieter-Jan; Luyten, Walter; Gevaert, Kris; Baggerman, Geert; Schoofs, Liliane

    2013-12-06

    Tandem mass spectrometry (MS/MS) combined with bioinformatics tools have enabled fast and systematic protein identification based on peptide-to-spectrum matches. However, it remains challenging to obtain accurate identification of endogenous peptides, such as neuropeptides, peptide hormones, peptide pheromones, venom peptides, and antimicrobial peptides. Since these peptides are processed at sites that are difficult to predict reliably, the search of their MS/MS spectra in sequence databases needs to be done without any protease setting. In addition, many endogenous peptides carry various post-translational modifications, making it essential to take these into account in the database search. These characteristics of endogenous peptides result in a huge search space, frequently leading to poor confidence of the peptide characterizations in peptidomics studies. We have developed a new MS/MS spectrum search tool for highly accurate and confident identification of endogenous peptides by combining two different fragmentation methods. Our approach takes advantage of the combination of two independent fragmentation methods (collision-induced dissociation and electron transfer dissociation). Their peptide spectral matching is carried out separately in both methods, and the final score is built as a combination of the two separate scores. We demonstrate that this approach is very effective in discriminating correct peptide identifications from false hits. We applied this approach to a spectral data set of neuropeptides extracted from mouse pituitary tumor cells. Compared to conventional MS-based identification, i.e., using a single fragmentation method, our approach significantly increased the peptide identification rate. It proved also highly effective for scanning spectra against a very large search space, enabling more accurate genome-wide searches and searches including multiple potential post-translational modifications.

  14. Extensive Peptide Fractionation and y1 Ion-Based Interference Detection Method for Enabling Accurate Quantification by Isobaric Labeling and Mass Spectrometry.

    PubMed

    Niu, Mingming; Cho, Ji-Hoon; Kodali, Kiran; Pagala, Vishwajeeth; High, Anthony A; Wang, Hong; Wu, Zhiping; Li, Yuxin; Bi, Wenjian; Zhang, Hui; Wang, Xusheng; Zou, Wei; Peng, Junmin

    2017-02-22

    Isobaric labeling quantification by mass spectrometry (MS) has emerged as a powerful technology for multiplexed large-scale protein profiling, but measurement accuracy in complex mixtures is confounded by the interference from coisolated ions, resulting in ratio compression. Here we report that the ratio compression can be essentially resolved by the combination of pre-MS peptide fractionation, MS2-based interference detection, and post-MS computational interference correction. To recapitulate the complexity of biological samples, we pooled tandem mass tag (TMT)-labeled Escherichia coli peptides at 1:3:10 ratios and added in ∼20-fold more rat peptides as background, followed by the analysis of two-dimensional liquid chromatography (LC)-MS/MS. Systematic investigation shows that quantitative interference was impacted by LC fractionation depth, MS isolation window, and peptide loading amount. Exhaustive fractionation (320 × 4 h) can nearly eliminate the interference and achieve results comparable to the MS3-based method. Importantly, the interference in MS2 scans can be estimated by the intensity of contaminated y1 product ions, and we thus developed an algorithm to correct reporter ion ratios of tryptic peptides. Our data indicate that intermediate fractionation (40 × 2 h) and y1 ion-based correction allow accurate and deep TMT profiling of more than 10 000 proteins, which represents a straightforward and affordable strategy in isobaric labeling proteomics.

  15. A Nonlinear Reduced Order Method for Prediction of Acoustic Fatigue

    NASA Technical Reports Server (NTRS)

    Przekop, Adam; Rizzi, Stephen A.

    2006-01-01

    The goal of this investigation is to assess the quality of high-cycle-fatigue life estimation via a reduced order method, for structures undergoing geometrically nonlinear random vibrations. Modal reduction is performed with several different suites of basis functions. After numerically solving the reduced order system equations of motion, the physical displacement time history is obtained by an inverse transformation and stresses are recovered. Stress ranges obtained through the rainflow counting procedure are used in a linear damage accumulation method to yield fatigue estimates. Fatigue life estimates obtained using various basis functions in the reduced order method are compared with those obtained from numerical simulation in physical degrees-of-freedom.

  16. Protein fold class prediction: new methods of statistical classification.

    PubMed

    Grassmann, J; Reczko, M; Suhai, S; Edler, L

    1999-01-01

    Feed forward neural networks are compared with standard and new statistical classification procedures for the classification of proteins. We applied logistic regression, an additive model and projection pursuit regression from the methods based on a posterior probabilities; linear, quadratic and a flexible discriminant analysis from the methods based on class conditional probabilities, and the K-nearest-neighbors classification rule. Both, the apparent error rate obtained with the training sample (n = 143) and the test error rate obtained with the test sample (n = 125) and the 10-fold cross validation error were calculated. We conclude that some of the standard statistical methods are potent competitors to the more flexible tools of machine learning.

  17. Improving predictability of time series using maximum entropy methods

    NASA Astrophysics Data System (ADS)

    Chliamovitch, G.; Dupuis, A.; Golub, A.; Chopard, B.

    2015-04-01

    We discuss how maximum entropy methods may be applied to the reconstruction of Markov processes underlying empirical time series and compare this approach to usual frequency sampling. It is shown that, in low dimension, there exists a subset of the space of stochastic matrices for which the MaxEnt method is more efficient than sampling, in the sense that shorter historical samples have to be considered to reach the same accuracy. Considering short samples is of particular interest when modelling smoothly non-stationary processes, which provides, under some conditions, a powerful forecasting tool. The method is illustrated for a discretized empirical series of exchange rates.

  18. Satellite attitude prediction by multiple time scales method

    NASA Technical Reports Server (NTRS)

    Tao, Y. C.; Ramnath, R.

    1975-01-01

    An investigation is made of the problem of predicting the attitude of satellites under the influence of external disturbing torques. The attitude dynamics are first expressed in a perturbation formulation which is then solved by the multiple scales approach. The independent variable, time, is extended into new scales, fast, slow, etc., and the integration is carried out separately in the new variables. The theory is applied to two different satellite configurations, rigid body and dual spin, each of which may have an asymmetric mass distribution. The disturbing torques considered are gravity gradient and geomagnetic. Finally, as multiple time scales approach separates slow and fast behaviors of satellite attitude motion, this property is used for the design of an attitude control device. A nutation damping control loop, using the geomagnetic torque for an earth pointing dual spin satellite, is designed in terms of the slow equation.

  19. Method for Predicting and Optimizing System Parameters for Electrospinning System

    NASA Technical Reports Server (NTRS)

    Wincheski, Russell A. (Inventor)

    2011-01-01

    An electrospinning system using a spinneret and a counter electrode is first operated for a fixed amount of time at known system and operational parameters to generate a fiber mat having a measured fiber mat width associated therewith. Next, acceleration of the fiberizable material at the spinneret is modeled to determine values of mass, drag, and surface tension associated with the fiberizable material at the spinneret output. The model is then applied in an inversion process to generate predicted values of an electric charge at the spinneret output and an electric field between the spinneret and electrode required to fabricate a selected fiber mat design. The electric charge and electric field are indicative of design values for system and operational parameters needed to fabricate the selected fiber mat design.

  20. Small Engine Technology (Set) Task 8 Aeroelastic Prediction Methods

    NASA Technical Reports Server (NTRS)

    Eick, Chris D.; Liu, Jong-Shang

    1998-01-01

    AlliedSignal Engines, in cooperation with NASA LeRC, completed an evaluation of recently developed aeroelastic computer codes using test cases from the AlliedSignal Engines fan blisk database. Test data for this task includes strain gage, light probe, performance, and steady-state pressure information obtained for conditions where synchronous or flutter vibratory conditions were found to occur. Aeroelastic codes evaluated include the quasi 3-D UNSFLO (developed at MIT and modified to include blade motion by AlliedSignal), the 2-D FREPS (developed by NASA LeRC), and the 3-D TURBO-AE (under development at NASA LeRC). Six test cases each where flutter and synchronous vibrations were found to occur were used for evaluation of UNSFLO and FREPS. In addition, one of the flutter cases was evaluated using TURBO-AE. The UNSFLO flutter evaluations were completed for 75 percent radial span and provided good agreement with the experimental test data. Synchronous evaluations were completed for UNSFLO but further enhancement needs to be added to the code before the unsteady pressures can be used to predict forced response vibratory stresses. The FREPS evaluations were hindered as the steady flow solver (SFLOW) was unable to converge to a solution for the transonic flow conditions in the fan blisk. This situation resulted in all FREPS test cases being attempted but no results were obtained during the present program. Currently, AlliedSignal is evaluating integrating FREPS with our existing steady flow solvers to bypass the SFLOW difficulties. ne TURBO-AE steady flow solution provided an excellent match with the AlliedSignal Engines calibrated DAWES 3-D viscous solver. Finally, the TURBO-AE unsteady analyses also matched experimental observations by predicting flutter for the single test case evaluated.

  1. Prediction of Dynamic Stall Characteristics Using Advanced Nonlinear Panel Methods,

    DTIC Science & Technology

    This paper presents preliminary results of work in which a surface singularity panel method is being extended for modelling the dynamic interaction...between a separated wake and a surface undergoing an unsteady motion. The method combines the capabilities of an unsteady time-stepping code and a... technique for modelling extensive separation using free vortex sheets. Routines are developed for treating the dynamic interaction between the separated

  2. De Novo Design of Potent Antimicrobial Peptides

    PubMed Central

    Frecer, V.; Ho, B.; Ding, J. L.

    2004-01-01

    Lipopolysaccharide (LPS), shed by gram-negative bacteria during infection and antimicrobial therapy, may lead to lethal endotoxic shock syndrome. A rational design strategy based on the presumed mechanism of antibacterial effect was adopted to design cationic antimicrobial peptides capable of binding to LPS through tandemly repeated sequences of alternating cationic and nonpolar residues. The peptides were designed to achieve enhanced antimicrobial potency due to initial bacterial membrane binding with a reduced risk of endotoxic shock. The peptides designed displayed binding affinities to LPS and lipid A (LA) in the low micromolar range and by molecular modeling were predicted to form amphipathic β-hairpin-like structures when they bind to LPS or LA. They also exhibited strong effects against gram-negative bacteria, with MICs in the nanomolar range, and low cytotoxic and hemolytic activities at concentrations significantly exceeding their MICs. Quantitative structure-activity relationship (QSAR) analysis of peptide sequences and their antimicrobial, cytotoxic, and hemolytic activities revealed that site-directed substitutions of residues in the hydrophobic face of the amphipathic peptides with less lipophilic residues selectively decrease the hemolytic effect without significantly affecting the antimicrobial or cytotoxic activity. On the other hand, the antimicrobial effect can be enhanced by substitutions in the polar face with more polar residues, which increase the amphipathicity of the peptide. On the basis of the QSARs, new analogs that have strong antimicrobial effects but that lack hemolytic activity can be proposed. The findings highlight the importance of peptide amphipathicity and allow a rational method that can be used to dissociate the antimicrobial and hemolytic effects of cationic peptides, which have potent antimicrobial properties, to be proposed. PMID:15328096

  3. Validation of finite element and boundary element methods for predicting structural vibration and radiated noise

    NASA Technical Reports Server (NTRS)

    Seybert, A. F.; Wu, X. F.; Oswald, Fred B.

    1992-01-01

    Analytical and experimental validation of methods to predict structural vibration and radiated noise are presented. A rectangular box excited by a mechanical shaker was used as a vibrating structure. Combined finite element method (FEM) and boundary element method (BEM) models of the apparatus were used to predict the noise radiated from the box. The FEM was used to predict the vibration, and the surface vibration was used as input to the BEM to predict the sound intensity and sound power. Vibration predicted by the FEM model was validated by experimental modal analysis. Noise predicted by the BEM was validated by sound intensity measurements. Three types of results are presented for the total radiated sound power: (1) sound power predicted by the BEM modeling using vibration data measured on the surface of the box; (2) sound power predicted by the FEM/BEM model; and (3) sound power measured by a sound intensity scan. The sound power predicted from the BEM model using measured vibration data yields an excellent prediction of radiated noise. The sound power predicted by the combined FEM/BEM model also gives a good prediction of radiated noise except for a shift of the natural frequencies that are due to limitations in the FEM model.

  4. Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments.

    PubMed

    Searle, Brian C; Egertson, Jarrett D; Bollinger, James G; Stergachis, Andrew B; MacCoss, Michael J

    2015-09-01

    Targeted mass spectrometry is an essential tool for detecting quantitative changes in low abundant proteins throughout the proteome. Although selected reaction monitoring (SRM) is the preferred method for quantifying peptides in complex samples, the process of designing SRM assays is laborious. Peptides have widely varying signal responses dictated by sequence-specific physiochemical properties; one major challenge is in selecting representative peptides to target as a proxy for protein abundance. Here we present PREGO, a software tool that predicts high-responding peptides for SRM experiments. PREGO predicts peptide responses with an artificial neural network trained using 11 minimally redundant, maximally relevant properties. Crucial to its success, PREGO is trained using fragment ion intensities of equimolar synthetic peptides extracted from data independent acquisition experiments. Because of similarities in instrumentation and the nature of data collection, relative peptide responses from data independent acquisition experiments are a suitable substitute for SRM experiments because they both make quantitative measurements from integrated fragment ion chromatograms. Using an SRM experiment containing 12,973 peptides from 724 synthetic proteins, PREGO exhibits a 40-85% improvement over previously published approaches at selecting high-responding peptides. These results also represent a dramatic improvement over the rules-based peptide selection approaches commonly used in the literature.

  5. A method for concentrating lipid peptide DNA and siRNA nanocomplexes that retains their structure and transfection efficiency

    PubMed Central

    Tagalakis, Aristides D; Castellaro, Sara; Zhou, Haiyan; Bienemann, Alison; Munye, Mustafa M; McCarthy, David; White, Edward A; Hart, Stephen L

    2015-01-01

    Nonviral gene and small interfering RNA (siRNA) delivery formulations are extensively used for biological and therapeutic research in cell culture experiments, but less so in in vivo and clinical research. Difficulties with formulating the nanoparticles for uniformity and stability at concentrations required for in vivo and clinical use are limiting their progression in these areas. Here, we report a simple but effective method of formulating monodisperse nanocomplexes from a ternary formulation of lipids, targeting peptides, and nucleic acids at a low starting concentration of 0.2 mg/mL of DNA, and we then increase their concentration up to 4.5 mg/mL by reverse dialysis against a concentrated polymer solution at room temperature. The nanocomplexes did not aggregate and they had maintained their biophysical properties, but, importantly, they also mediated DNA transfection and siRNA silencing in cultured cells. Moreover, concentrated anionic nanocomplexes administered by convection-enhanced delivery in the striatum showed efficient silencing of the β-secretase gene BACE1. This method of preparing nanocomplexes could probably be used to concentrate other nonviral formulations and may enable more widespread use of nanoparticles in vivo. PMID:25878500

  6. Reduced Order Methods for Prediction of Thermal-Acoustic Fatigue

    NASA Technical Reports Server (NTRS)

    Przekop, A.; Rizzi, S. A.

    2004-01-01

    The goal of this investigation is to assess the quality of high-cycle-fatigue life estimation via a reduced order method, for structures undergoing random nonlinear vibrations in a presence of thermal loading. Modal reduction is performed with several different suites of basis functions. After numerically solving the reduced order system equations of motion, the physical displacement time history is obtained by an inverse transformation and stresses are recovered. Stress ranges obtained through the rainflow counting procedure are used in a linear damage accumulation method to yield fatigue estimates. Fatigue life estimates obtained using various basis functions in the reduced order method are compared with those obtained from numerical simulation in physical degrees-of-freedom.

  7. A Novel Method for Prediction of Nonlinear Aeroelastic Responses

    DTIC Science & Technology

    2010-01-01

    time, (3.59) is integrated using a fourth-order Runge - Kutta method (Gerald and Wheatley, 2004): Yn+1 = Yn + 1 6 ∆t (k1 + 2k2 + 2k3 + k4) , (3.61...Orthogonal De- composition 10 2.1 Acceleration Techniques for the Proper Orthogonal Decomposition Method . . . . . 10 2.1.1 Database Splitting...a Runge - Kutta -Fehlberg routine (Fehlberg, 1969). Further details of the implementation are given in (Brenner et al., 2009). For this test case, the

  8. Accuracy assessment of the ERP prediction method based on analysis of 100-year ERP series

    NASA Astrophysics Data System (ADS)

    Malkin, Z.; Tissen, V. M.

    2012-12-01

    A new method has been developed at the Siberian Research Institute of Metrology (SNIIM) for highly accurate prediction of UT1 and Pole motion (PM). In this study, a detailed comparison was made of real-time UT1 predictions made in 2006-2011 and PMpredictions made in 2009-2011making use of the SNIIM method with simultaneous predictions computed at the International Earth Rotation and Reference Systems Service (IERS), USNO. Obtained results have shown that proposed method provides better accuracy at different prediction lengths.

  9. Wing flutter boundary prediction using unsteady Euler aerodynamic method

    NASA Technical Reports Server (NTRS)

    Lee-Rausch, Elizabeth M.; Batina, John T.

    1993-01-01

    Modifications to an existing 3D implicit upwind Euler/Navier-Stokes code for the aeroelastic analysis of wings are described. These modifications include the incorporation of a deforming mesh algorithm and the addition of the structural equations of motion for their simultaneous time-integration with the governing flow equations. The paper gives a brief description of these modifications and presents unsteady calculations which check the modifications to the code. Euler flutter results for an isolated 45 deg swept-back wing are compared with experimental data for seven freestream Mach numbers which define the flutter boundary over a range of Mach number from 0.499 to 1.14. These comparisons show good agreement in flutter characteristics for freestream Mach numbers below unity. For freestream Mach numbers above unity, the computed aeroelastic results predict a premature rise in the flutter boundary as compared with the experimental boundary. Steady and unsteady contours of surface Mach number and pressure are included to illustrate the basic flow characteristics of the time-marching flutter calculations and to aid in identifying possible causes for the premature rise in the computational flutter boundary.

  10. Wing flutter boundary prediction using an unsteady Euler aerodynamic method

    NASA Technical Reports Server (NTRS)

    Lee-Rausch, Elizabeth M.; Batina, John T.

    1993-01-01

    Modifications to an existing three-dimensional, implicit, upwind Euler/Navier-Stokes code (CFL3D Version 2.1) for the aeroelastic analysis of wings are described. These modifications, which were previously added to CFL3D Version 1.0, include the incorporation of a deforming mesh algorithm and the addition of the structural equations of motion for their simultaneous time-integration with the government flow equations. The paper gives a brief description of these modifications and presents unsteady calculations which check the modifications to the code. Euler flutter results for an isolated 45 degree swept-back wing are compared with experimental data for seven freestream Mach numbers which define the flutter boundary over a range of Mach number from 0.499 to 1.14. These comparisons show good agreement in flutter characteristics for freestream Mach numbers below unity. For freestream Mach numbers above unity, the computed aeroelastic results predict a premature rise in the flutter boundary as compared with the experimental boundary. Steady and unsteady contours of surface Mach number and pressure are included to illustrate the basic flow characteristics of the time-marching flutter calculations and to aid in identifying possible causes for the premature rise in the computational flutter boundary.

  11. Peptide composition as antigen for detection of antibodies to HTLV-I, as a vaccine for ATL and methods therefore

    SciTech Connect

    Wang, C.Y.; Wang, J.J.G.; Walters, D.W

    1989-05-23

    This patent describes a peptide composition having specific immunoreactivity to antibodies to HTLV-1 comprising a peptide selected from the group consisting of: (I) GLDLLFWEQGGLCKALQEQC-X; (II) QNRRGLDLLFWEQGGLCKALQEQC-X; (III) NRRGLDLLFWEQGGLC-X, wherein X is -OH or -NH/sub 2/, analogues therefore wherein the amino acids in the sequence are substituted as long as the immunoreactivity to antibodies to HTLV-I derived from the three dimensional conformation to the sequences are substantially preserved; and mixtures and polymers of the peptides.

  12. Correction factory techniques for improving aerodynamic prediction methods

    NASA Technical Reports Server (NTRS)

    Giesing, J. P.; Kalman, T. P.; Rodden, W. P.

    1976-01-01

    A method for correcting discrete element lifting surface theory to reflect given experimental data is presented. Theoretical pressures are modified such that imposed constraints are satisfied while minimizing the changes to the pressures. Several types of correction procedures are presented and correlated; (1) scaling of pressures; (2) scaling of downwash values; and (3) addition of an increment to the downwash that is proportioned to pressure. Some special features are included in these methods and they include: (1) consideration of experimental data from multiple deflection modes, (2) limitation of the amplitudes of the correction factors, and (3) the use of correction factor mode shapes. These methods are correlated for cases involving all three Mach Number ranges using a FORTRAN IV computer program. Subsonically, a wing with an oscillating partial span control surface and a wing with a leading edge droop are presented. Transonically a two-dimensional airfoil with an oscillating flap is considered. Supersonically an arrow wing with and without camber is analyzed. In addition to correction factor methods an investigation is presented dealing with a new simplified transonic modification of the two-dimensional subsonic lifting surface theory. Correlations are presented for an airfoil with an oscillating flap.

  13. EVALUATION OF TWO METHODS FOR PREDICTION OF BIOACCUMULATION FACTORS

    EPA Science Inventory

    Two methods for deriving bioaccumulation factors (BAFs) used by the U.S. Environmental Protection Agency (EPA) in development of water quality criteria were evaluated using polychlorinated biphenyls (PCB) data from the Hudson River and Green Bay ecosystems. Greater than 90% of th...

  14. Four Methods of Handling Missing Data in Predicting Educational Achievement.

    ERIC Educational Resources Information Center

    Witta, E. Lea

    Four methods of handling missing data were applied to missing values for variables selected from the National Education Longitudinal Study of 1988. Variables used were those selected by K. Singh and M. Ozturk (1999) for a study concerning high school students' academic achievement and work. Samples