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Sample records for peptides prediction methods

  1. 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.

  2. Improved methods for classification, prediction, and design of antimicrobial peptides.

    PubMed

    Wang, Guangshun

    2015-01-01

    Peptides with diverse amino acid sequences, structures, and functions are essential players in biological systems. The construction of well-annotated databases not only facilitates effective information management, search, and mining but also lays the foundation for developing and testing new peptide algorithms and machines. The antimicrobial peptide database (APD) is an original construction in terms of both database design and peptide entries. The host defense antimicrobial peptides (AMPs) registered in the APD cover the five kingdoms (bacteria, protists, fungi, plants, and animals) or three domains of life (bacteria, archaea, and eukaryota). This comprehensive database ( http://aps.unmc.edu/AP ) provides useful information on peptide discovery timeline, nomenclature, classification, glossary, calculation tools, and statistics. The APD enables effective search, prediction, and design of peptides with antibacterial, antiviral, antifungal, antiparasitic, insecticidal, spermicidal, anticancer activities, chemotactic, immune modulation, or antioxidative properties. A universal classification scheme is proposed herein to unify innate immunity peptides from a variety of biological sources. As an improvement, the upgraded APD makes predictions based on the database-defined parameter space and provides a list of the sequences most similar to natural AMPs. In addition, the powerful pipeline design of the database search engine laid a solid basis for designing novel antimicrobials to combat resistant superbugs, viruses, fungi, or parasites. This comprehensive AMP database is a useful tool for both research and education. PMID:25555720

  3. Improved Methods for Classification, Prediction and Design of Antimicrobial Peptides

    PubMed Central

    Wang, Guangshun

    2015-01-01

    Peptides with diverse amino acid sequences, structures and functions are essential players in biological systems. The construction of well-annotated databases not only facilitates effective information management, search and mining, but also lays the foundation for developing and testing new peptide algorithms and machines. The antimicrobial peptide database (APD) is an original construction in terms of both database design and peptide entries. The host defense antimicrobial peptides (AMPs) registered in the APD cover the five kingdoms (bacteria, protists, fungi, plants, and animals) or three domains of life (bacteria, archaea, and eukaryota). This comprehensive database (http://aps.unmc.edu/AP) provides useful information on peptide discovery timeline, nomenclature, classification, glossary, calculation tools, and statistics. The APD enables effective search, prediction, and design of peptides with antibacterial, antiviral, antifungal, antiparasitic, insecticidal, spermicidal, anticancer activities, chemotactic, immune modulation, or anti-oxidative properties. A universal classification scheme is proposed herein to unify innate immunity peptides from a variety of biological sources. As an improvement, the upgraded APD makes predictions based on the database-defined parameter space and provides a list of the sequences most similar to natural AMPs. In addition, the powerful pipeline design of the database search engine laid a solid basis for designing novel antimicrobials to combat resistant superbugs, viruses, fungi or parasites. This comprehensive AMP database is a useful tool for both research and education. PMID:25555720

  4. 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.

  5. A model-based method for the prediction of the isotopic distribution of peptides.

    PubMed

    Valkenborg, Dirk; Jansen, Ivy; Burzykowski, Tomasz

    2008-05-01

    The process of monoisotopic mass determination, i.e., nomination of the correct peak of an isotopically resolved group of peptide peaks as a monoisotopic peak, requires prior information about the isotopic distribution of the peptide. This points immediately to the difficulty of monoisotopic mass determination, whereas a single mass spectrum does not contain information about the atomic composition of a peptide and therefore the isotopic distribution of the peptide remains unknown. To solve this problem a technique is required, which is able to estimate the isotopic distribution given the information of a single mass spectrum. Senko et al. calculated the average isotopic distribution for any mass peptide via the multinomial expansion (Yergey 1983), using a scaled version of the average amino acid Averagine (Senko et al. 1995). Another method, introduced by Breen et al., approximates the result of the multinomial expansion by a Poisson model (Breen et al. 2000). Although both methods perform well, they have their specific limitations. In this manuscript, we propose an alternative method for the prediction of the isotopic distribution based on a model for consecutive ratios of peaks from the isotopic distribution, similar in spirit to the approach introduced by Gay et al. (1999). The presented method is computationally simple and accurate in predicting the expected isotopic distribution. Further, we extend our method to estimate the isotopic distribution of sulphur-containing peptides. This is important because the naturally occurring isotopes of sulphur have an impact on the isotopic distribution of a peptide. PMID:18325782

  6. A fast method for large-scale de novo peptide and miniprotein structure prediction.

    PubMed

    Maupetit, Julien; Derreumaux, Philippe; Tufféry, Pierre

    2010-03-01

    Although peptides have many biological and biomedical implications, an accurate method predicting their equilibrium structural ensembles from amino acid sequences and suitable for large-scale experiments is still missing. We introduce a new approach-PEP-FOLD-to the de novo prediction of peptides and miniproteins. It first predicts, in the terms of a Hidden Markov Model-derived structural alphabet, a limited number of local conformations at each position of the structure. It then performs their assembly using a greedy procedure driven by a coarse-grained energy score. On a benchmark of 52 peptides with 9-23 amino acids, PEP-FOLD generates lowest-energy conformations within 2.8 and 2.3 A Calpha root-mean-square deviation from the full nuclear magnetic resonance structures (NMR) and the NMR rigid cores, respectively, outperforming previous approaches. For 13 miniproteins with 27-49 amino acids, PEP-FOLD reaches an accuracy of 3.6 and 4.6 A Calpha root-mean-square deviation for the most-native and lowest-energy conformations, using the nonflexible regions identified by NMR. PEP-FOLD simulations are fast-a few minutes only-opening therefore, the door to in silico large-scale rational design of new bioactive peptides and miniproteins. PMID:19569182

  7. A Consensus Method for the Prediction of ‘Aggregation-Prone’ Peptides in Globular Proteins

    PubMed Central

    Tsolis, Antonios C.; Papandreou, Nikos C.; Iconomidou, Vassiliki A.; Hamodrakas, Stavros J.

    2013-01-01

    The purpose of this work was to construct a consensus prediction algorithm of ‘aggregation-prone’ peptides in globular proteins, combining existing tools. This allows comparison of the different algorithms and the production of more objective and accurate results. Eleven (11) individual methods are combined and produce AMYLPRED2, a publicly, freely available web tool to academic users (http://biophysics.biol.uoa.gr/AMYLPRED2), for the consensus prediction of amyloidogenic determinants/‘aggregation-prone’ peptides in proteins, from sequence alone. The performance of AMYLPRED2 indicates that it functions better than individual aggregation-prediction algorithms, as perhaps expected. AMYLPRED2 is a useful tool for identifying amyloid-forming regions in proteins that are associated with several conformational diseases, called amyloidoses, such as Altzheimer's, Parkinson's, prion diseases and type II diabetes. It may also be useful for understanding the properties of protein folding and misfolding and for helping to the control of protein aggregation/solubility in biotechnology (recombinant proteins forming bacterial inclusion bodies) and biotherapeutics (monoclonal antibodies and biopharmaceutical proteins). PMID:23326595

  8. 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

  9. 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

  10. Molecular dynamics methods to predict peptide locations in membranes: LAH4 as a stringent test case.

    PubMed

    Farrotti, A; Bocchinfuso, G; Palleschi, A; Rosato, N; Salnikov, E S; Voievoda, N; Bechinger, B; Stella, L

    2015-02-01

    Determining the structure of membrane-active peptides inside lipid bilayers is essential to understand their mechanism of action. Molecular dynamics simulations can easily provide atomistic details, but need experimental validation. We assessed the reliability of self-assembling (or "minimum-bias") and potential of mean force (PMF) approaches, using all-atom (AA) and coarse-grained (CG) force-fields. The LAH4 peptide was selected as a stringent test case, since it is known to attain different orientations depending on the protonation state of its four histidine residues. In all simulations the histidine side-chains inserted in the membrane when neutral, while they interacted with phospholipid headgroups in their charged state. This led to transmembrane orientations for neutral-His LAH4 in all minimum-bias AA simulations and in most CG trajectories. By contrast, the charged-His peptide stabilized membrane defects in AA simulations, whereas it was located at the membrane surface in some CG trajectories, and interacted with both lipid leaflets in others. This behavior is consistent with the higher antimicrobial activity and membrane-permeabilizing behavior of the charged-His LAH4. In addition, good agreement with solid-state NMR orientational data was observed in AA simulations. PMF calculations correctly predicted a higher membrane affinity for the neutral-His peptide. Interestingly, the structures and relative populations of PMF local free-energy minima corresponded to those determined in the less computationally demanding minimum-bias simulations. These data provide an indication about the possible membrane-perturbation mechanism of the charged-His LAH4 peptide: by interacting with lipid headgroups of both leaflets through its cationic side-chains, it could favor membrane defects and facilitate translocation across the bilayer. PMID:25445672

  11. 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).

  12. [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. PMID:23285964

  13. 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. PMID:12645903

  14. On the hydrophobicity of peptides: Comparing empirical predictions of peptide log P values.

    PubMed

    Thompson, Sarah J; Hattotuwagama, Channa K; Holliday, John D; Flower, Darren R

    2006-01-01

    Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient logP, is useful for the development of predictive Quantitative Structure-Activity Relationships (QSARs). We have investigated the accuracy of available programs for the prediction of logP values for peptides with known experimental values obtained from the literature. Eight prediction programs were tested, of which seven programs were fragment-based methods: XLogP, LogKow, PLogP, ACDLogP, AlogP, Interactive Analysis's LogP and MlogP; and one program used a whole molecule approach: QikProp. The predictive accuracy of the programs was assessed using r(2) values, with ALogP being the most effective (r( 2) = 0.822) and MLogP the least (r(2) = 0.090). We also examined three distinct types of peptide structure: blocked, unblocked, and cyclic. For each study (all peptides, blocked, unblocked and cyclic peptides) the performance of programs rated from best to worse is as follows: all peptides - ALogP, QikProp, PLogP, XLogP, IALogP, LogKow, ACDLogP, and MlogP; blocked peptides - PLogP, XLogP, ACDLogP, IALogP, LogKow, QikProp, ALogP, and MLogP; unblocked peptides - QikProp, IALogP, ALogP, ACDLogP, MLogP, XLogP, LogKow and PLogP; cyclic peptides - LogKow, ALogP, XLogP, MLogP, QikProp, ACDLogP, IALogP. In summary, all programs gave better predictions for blocked peptides, while, in general, logP values for cyclic peptides were under-predicted and those of unblocked peptides were over-predicted. PMID:17597897

  15. Prediction of cell-penetrating peptides with feature selection techniques.

    PubMed

    Tang, Hua; Su, Zhen-Dong; Wei, Huan-Huan; Chen, Wei; Lin, Hao

    2016-08-12

    Cell-penetrating peptides are a group of peptides which can transport different types of cargo molecules such as drugs across plasma membrane and have been applied in the treatment of various diseases. Thus, the accurate prediction of cell-penetrating peptides with bioinformatics methods will accelerate the development of drug delivery systems. The study aims to develop a powerful model to accurately identify cell-penetrating peptides. At first, the peptides were translated into a set of vectors with the same dimension by using dipeptide compositions. Secondly, the Analysis of Variance-based technique was used to reduce the dimension of the vector and explore the optimized features. Finally, the support vector machine was utilized to discriminate cell-penetrating peptides from non-cell-penetrating peptides. The five-fold cross-validated results showed that our proposed method could achieve an overall prediction accuracy of 83.6%. Based on the proposed model, we constructed a free webserver called C2Pred (http://lin.uestc.edu.cn/server/C2Pred). PMID:27291150

  16. Prediction of cell penetrating peptides by support vector machines.

    PubMed

    Sanders, William S; Johnston, C Ian; Bridges, Susan M; Burgess, Shane C; Willeford, Kenneth O

    2011-07-01

    Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating. PMID:21779156

  17. Prediction of Antibacterial Activity from Physicochemical Properties of Antimicrobial Peptides

    PubMed Central

    Melo, Manuel N.; Ferre, Rafael; Feliu, Lídia; Bardají, Eduard; Planas, Marta; Castanho, Miguel A. R. B.

    2011-01-01

    Consensus is gathering that antimicrobial peptides that exert their antibacterial action at the membrane level must reach a local concentration threshold to become active. Studies of peptide interaction with model membranes do identify such disruptive thresholds but demonstrations of the possible correlation of these with the in vivo onset of activity have only recently been proposed. In addition, such thresholds observed in model membranes occur at local peptide concentrations close to full membrane coverage. In this work we fully develop an interaction model of antimicrobial peptides with biological membranes; by exploring the consequences of the underlying partition formalism we arrive at a relationship that provides antibacterial activity prediction from two biophysical parameters: the affinity of the peptide to the membrane and the critical bound peptide to lipid ratio. A straightforward and robust method to implement this relationship, with potential application to high-throughput screening approaches, is presented and tested. In addition, disruptive thresholds in model membranes and the onset of antibacterial peptide activity are shown to occur over the same range of locally bound peptide concentrations (10 to 100 mM), which conciliates the two types of observations. PMID:22194847

  18. 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.

  19. Prediction of Biofilm Inhibiting Peptides: An In silico Approach

    PubMed Central

    Gupta, Sudheer; Sharma, Ashok K.; Jaiswal, Shubham K.; Sharma, Vineet K.

    2016-01-01

    Approximately 75% of microbial infections found in humans are caused by microbial biofilms. These biofilms are resistant to host immune system and most of the currently available antibiotics. Small peptides are extensively studied for their role as anti-microbial peptides, however, only a limited studies have shown their potential as inhibitors of biofilm. Therefore, to develop a unique computational method aimed at the prediction of biofilm inhibiting peptides, the experimentally validated biofilm inhibiting peptides sequences were used to extract sequence based features and to identify unique sequence motifs. Biofilm inhibiting peptides were observed to be abundant in positively charged and aromatic amino acids, and also showed selective abundance of some dipeptides and sequence motifs. These individual sequence based features were utilized to construct Support Vector Machine-based prediction models and additionally by including sequence motifs information, the hybrid models were constructed. Using 10-fold cross validation, the hybrid model displayed the accuracy and Matthews Correlation Coefficient (MCC) of 97.83% and 0.87, respectively. On the validation dataset, the hybrid model showed the accuracy and MCC value of 97.19% and 0.84, respectively. The validated model and other tools developed for the prediction of biofilm inhibiting peptides are available freely as web server at http://metagenomics.iiserb.ac.in/biofin/ and http://metabiosys.iiserb.ac.in/biofin/. PMID:27379078

  20. Epitope prediction algorithms for peptide-based vaccine design.

    PubMed

    Florea, Liliana; Halldórsson, Bjarni; Kohlbacher, Oliver; Schwartz, Russell; Hoffman, Stephen; Istrail, Sorin

    2003-01-01

    Peptide-based vaccines, in which small peptides derived from target proteins (eptiopes) are used to provoke an immune reaction, have attracted considerable attention recently as a potential means both of treating infectious diseases and promoting the destruction of cancerous cells by a patient's own immune system. With the availability of large sequence databases and computers fast enough for rapid processing of large numbers of peptides, computer aided design of peptide-based vaccines has emerged as a promising approach to screening among billions of possible immune-active peptides to find those likely to provoke an immune response to a particular cell type. In this paper, we describe the development of three novel classes of methods for the prediction problem. We present a quadratic programming approach that can be trained on quantitative as well as qualitative data. The second method uses linear programming to counteract the fact that our training data contains mostly positive examples. The third class of methods uses sequence profiles obtained by clustering known epitopes to score candidate peptides. By integrating these methods, using a simple voting heuristic, we achieve improved accuracy over the state of the art. PMID:16826643

  1. 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.

  2. 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.

  3. 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

  4. Predicting protein-ligand and protein-peptide interfaces

    NASA Astrophysics Data System (ADS)

    Bertolazzi, Paola; Guerra, Concettina; Liuzzi, Giampaolo

    2014-06-01

    The paper deals with the identification of binding sites and concentrates on interactions involving small interfaces. In particular we focus our attention on two major interface types, namely protein-ligand and protein-peptide interfaces. As concerns protein-ligand binding site prediction, we classify the most interesting methods and approaches into four main categories: (a) shape-based methods, (b) alignment-based methods, (c) graph-theoretic approaches and (d) machine learning methods. Class (a) encompasses those methods which employ, in some way, geometric information about the protein surface. Methods falling into class (b) address the prediction problem as an alignment problem, i.e. finding protein-ligand atom pairs that occupy spatially equivalent positions. Graph theoretic approaches, class (c), are mainly based on the definition of a particular graph, known as the protein contact graph, and then apply some sophisticated methods from graph theory to discover subgraphs or score similarities for uncovering functional sites. The last class (d) contains those methods that are based on the learn-from-examples paradigm and that are able to take advantage of the large amount of data available on known protein-ligand pairs. As for protein-peptide interfaces, due to the often disordered nature of the regions involved in binding, shape similarity is no longer a determining factor. Then, in geometry-based methods, geometry is accounted for by providing the relative position of the atoms surrounding the peptide residues in known structures. Finally, also for protein-peptide interfaces, we present a classification of some successful machine learning methods. Indeed, they can be categorized in the way adopted to construct the learning examples. In particular, we envisage three main methods: distance functions, structure and potentials and structure alignment.

  5. A computational model for predicting fusion peptide of retroviruses.

    PubMed

    Wu, Sijia; Han, Jiuqiang; Liu, Ruiling; Liu, Jun; Lv, Hongqiang

    2016-04-01

    As a pivotal domain within envelope protein, fusion peptide (FP) plays a crucial role in pathogenicity and therapeutic intervention. Taken into account the limited FP annotations in NCBI database and absence of FP prediction software, it is urgent and desirable to develop a bioinformatics tool to predict new putative FPs (np-FPs) in retroviruses. In this work, a sequence-based FP model was proposed by combining Hidden Markov Method with similarity comparison. The classification accuracies are 91.97% and 92.31% corresponding to 10-fold and leave-one-out cross-validation. After scanning sequences without FP annotations, this model discovered 53,946 np-FPs. The statistical results on FPs or np-FPs reveal that FP is a conserved and hydrophobic domain. The FP software programmed for windows environment is available at https://sourceforge.net/projects/fptool/files/?source=navbar. PMID:26963379

  6. 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. PMID:27025565

  7. 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.

  8. Prediction of MHC binding peptides and epitopes from alfalfa mosaic virus.

    PubMed

    Gomase, Virendra S; Kale, Karbhari V; Chikhale, Nandkishor J; Changbhale, Smruti S

    2007-08-01

    Peptide fragments from alfalfa mosaic virus involved multiple antigenic components directing and empowering the immune system to protect the host from infection. MHC molecules are cell surface proteins, which take active part in host immune reactions and involvement of MHC class-I & II in response to almost all antigens. Coat protein of alfalfa mosaic virus contains 221 aa residues. Analysis found five MHC ligands in coat protein as 64-LSSFNGLGV-72; 86- RILEEDLIY-94; 96-MVFSITPSY-104; 100- ITPSYAGTF-108; 110- LTDDVTTED-118; having rescaled binding affinity and c-terminal cleavage affinity more than 0.5. The predicted binding affinity is normalized by the 1% fractil. The MHC peptide binding is predicted using neural networks trained on c-terminals of known epitopes. In analysis predicted MHC/peptide binding is a log transformed value related to the IC50 values in nM units. Total numbers of peptides found are 213. Predicted MHC binding regions act like red flags for antigen specific and generate immune response against the parent antigen. So a small fragment of antigen can induce immune response against whole antigen. This theme is implemented in designing subunit and synthetic peptide vaccines. The sequence analysis method allows potential drug targets to identify active sites against plant diseases. The method integrates prediction of peptide MHC class I binding; proteosomal c-terminal cleavage and TAP transport efficiency. PMID:17691913

  9. Coupling in silico and in vitro analysis of peptide-MHC binding: a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes.

    PubMed

    Doytchinova, Irini A; Walshe, Valerie A; Jones, Nicola A; Gloster, Simone E; Borrow, Persephone; Flower, Darren R

    2004-06-15

    The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions. PMID:15187128

  10. Prediction of a stable associated liquid of short amyloidogenic peptides.

    PubMed

    Luiken, Jurriaan A; Bolhuis, Peter G

    2015-04-28

    Amyloid fibril formation is believed to be a nucleation-controlled process. Depending on the nature of peptide sequence, fibril nucleation can occur in one step, straight from a dilute solution, or in multiple steps via oligomers or disordered aggregates. What determines this process is poorly understood. Since the fibril formation kinetics is driven by thermodynamic forces, knowledge of the phase behavior is crucial. Here, we investigated the phase behavior of three short peptide sequences of varying side-chain hydrophobicity. Replica exchange molecular dynamics simulations of a mid-resolution model indicate that the weakly hydrophobic peptide forms fibrils directly from solution, whereas the most hydrophobic peptide forms a dense liquid phase before crystallizing into ordered fibrils at low temperatures. For the medium hydrophobic peptide we found evidence of a novel additional transition to a liquid phase consisting of clusters of aligned peptides, implying a three-step nucleation process. We tested the robustness of this prediction by applying Wertheim's theory and statistical associating fluid theory to a hard-sphere model dressed with isotropic and anisotropic attractions. We found that the ratio of interaction strengths strongly affects the phase behavior, and under certain conditions indeed gives rise to a stable polymerized liquid phase. The peptide clusters in the associated liquid tend to be slow and long-lived, which may give the oligomer droplet more time to act as a toxic oligomer, before turning into a fibril. PMID:25804723

  11. Membrane Protein Prediction Methods

    PubMed Central

    Punta, Marco; Forrest, Lucy R.; Bigelow, Henry; Kernytsky, Andrew; Liu, Jinfeng; Rost, Burkhard

    2007-01-01

    We survey computational approaches that tackle membrane protein structure and function prediction. While describing the main ideas that have led to the development of the most relevant and novel methods, we also discuss pitfalls, provide practical hints and highlight the challenges that remain. The methods covered include: sequence alignment, motif search, functional residue identification, transmembrane segment and protein topology predictions, homology and ab initio modeling. Overall, predictions of functional and structural features of membrane proteins are improving, although progress is hampered by the limited amount of high-resolution experimental information available. While predictions of transmembrane segments and protein topology rank among the most accurate methods in computational biology, more attention and effort will be required in the future to ameliorate database search, homology and ab initio modeling. PMID:17367718

  12. 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.

  13. 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.

  14. Computer-based prediction of mitochondria-targeting peptides.

    PubMed

    Martelli, Pier Luigi; Savojardo, Castrense; Fariselli, Piero; Tasco, Gianluca; Casadio, Rita

    2015-01-01

    Computational methods are invaluable when protein sequences, directly derived from genomic data, need functional and structural annotation. Subcellular localization is a feature necessary for understanding the protein role and the compartment where the mature protein is active and very difficult to characterize experimentally. Mitochondrial proteins encoded on the cytosolic ribosomes carry specific patterns in the precursor sequence from where it is possible to recognize a peptide targeting the protein to its final destination. Here we discuss to which extent it is feasible to develop computational methods for detecting mitochondrial targeting peptides in the precursor sequences and benchmark our and other methods on the human mitochondrial proteins endowed with experimentally characterized targeting peptides. Furthermore, we illustrate our newly implemented web server and its usage on the whole human proteome in order to infer mitochondrial targeting peptides, their cleavage sites, and whether the targeting peptide regions contain or not arginine-rich recurrent motifs. By this, we add some other 2,800 human proteins to the 124 ones already experimentally annotated with a mitochondrial targeting peptide. PMID:25631024

  15. 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. PMID:27141850

  16. 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. PMID:26476978

  17. Prediction of peptides binding to MHC class I and II alleles by temporal motif mining

    PubMed Central

    2013-01-01

    Background MHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently. Results The prediction method was tested on a benchmark set of 28 different alleles for MHC class I and 27 different alleles for MHC class II. The obtained results are comparable to the state of the art methods for both MHC classes, surpassing the published results for some alleles. The average prediction AUC values are 0.897 for class I, and 0.858 for class II. Conclusions Temporal motif mining using partial periodic patterns can capture information about the sequences well enough to predict the binding of the peptides and is comparable to state of the art methods in the literature. Unlike neural networks or matrix based predictors, our proposed method does not depend on peptide length and can work with both short and long fragments. This advantage allows better use of the available training data and the prediction of peptides of uncommon lengths. PMID:23368521

  18. 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.

  19. Quantitative prediction of peptide binding to HLA-DP1 protein.

    PubMed

    Ivanov, Stefan; Dimitrov, Ivan; Doytchinova, Irini

    2013-01-01

    The exogenous proteins are processed by the host antigen-processing cells. Peptidic fragments of them are presented on the cell surface bound to the major hystocompatibility complex (MHC) molecules class II and recognized by the CD4+ T lymphocytes. The MHC binding is considered as the crucial prerequisite for T-cell recognition. Only peptides able to form stable complexes with the MHC proteins are recognized by the T-cells. These peptides are known as T-cell epitopes. All T-cell epitopes are MHC binders, but not all MHC binders are T-cell epitopes. The T-cell epitope prediction is one of the main priorities of immunoinformatics. In the present study, three chemometric techniques are combined to derive a model for in silico prediction of peptide binding to the human MHC class II protein HLA-DP1. The structures of a set of known peptide binders are described by amino acid z-descriptors. Data are processed by an iterative self-consisted algorithm using the method of partial least squares, and a quantitative matrix (QM) for peptide binding prediction to HLA-DP1 is derived. The QM is validated by two sets of proteins and showed an average accuracy of 86 percent. PMID:24091413

  20. 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. PMID:27491034

  1. 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

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

    DOE PAGESBeta

    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.; et al

    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

  3. 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.

  4. Predictive chromatography of peptides and proteins as a complementary tool for proteomics.

    PubMed

    Tarasova, Irina A; Masselon, Christophe D; Gorshkov, Alexander V; Gorshkov, Mikhail V

    2016-08-01

    In the last couple of decades, considerable effort has been focused on developing methods for quantitative and qualitative proteome characterization. The method of choice in this characterization is mass spectrometry used in combination with sample separation. One of the most widely used separation techniques at the front end of a mass spectrometer is high performance liquid chromatography (HPLC). A unique feature of HPLC is its specificity to the amino acid sequence of separated peptides and proteins. This specificity may provide additional information about the peptides or proteins under study which is complementary to the mass spectrometry data. The value of this information for proteomics has been recognized in the past few decades, which has stimulated significant effort in the development and implementation of computational and theoretical models for the prediction of peptide retention time for a given sequence. Here we review the advances in this area and the utility of predicted retention times for proteomic applications. PMID:27419248

  5. Sequence-based prediction of protein-peptide binding sites using support vector machine.

    PubMed

    Taherzadeh, Ghazaleh; Yang, Yuedong; Zhang, Tuo; Liew, Alan Wee-Chung; Zhou, Yaoqi

    2016-05-15

    Protein-peptide interactions are essential for all cellular processes including DNA repair, replication, gene-expression, and metabolism. As most protein-peptide interactions are uncharacterized, it is cost effective to investigate them computationally as the first step. All existing approaches for predicting protein-peptide binding sites, however, are based on protein structures despite the fact that the structures for most proteins are not yet solved. This article proposes the first machine-learning method called SPRINT to make Sequence-based prediction of Protein-peptide Residue-level Interactions. SPRINT yields a robust and consistent performance for 10-fold cross validations and independent test. The most important feature is evolution-generated sequence profiles. For the test set (1056 binding and non-binding residues), it yields a Matthews' Correlation Coefficient of 0.326 with a sensitivity of 64% and a specificity of 68%. This sequence-based technique shows comparable or more accurate than structure-based methods for peptide-binding site prediction. SPRINT is available as an online server at: http://sparks-lab.org/. © 2016 Wiley Periodicals, Inc. PMID:26833816

  6. Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.

    PubMed

    Khan, Waqasuddin; Duffy, Fergal; Pollastri, Gianluca; Shields, Denis C; Mooney, Catherine

    2013-01-01

    Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions.We assembled a redundancy reduced dataset of SLiM (Short Linear Motif) containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58).Next, we trained a bidirectional recurrent neural network (BRNN) using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72) showing that multiple sources of information can be combined to produce results which are clearly superior to any single source.We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods) clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors. PMID:24019881

  7. Downstream prediction using a nonlinear prediction method

    NASA Astrophysics Data System (ADS)

    Adenan, N. H.; Noorani, M. S. M.

    2013-11-01

    The estimation of river flow is significantly related to the impact of urban hydrology, as this could provide information to solve important problems, such as flooding downstream. The nonlinear prediction method has been employed for analysis of four years of daily river flow data for the Langat River at Kajang, Malaysia, which is located in a downstream area. The nonlinear prediction method involves two steps; namely, the reconstruction of phase space and prediction. The reconstruction of phase space involves reconstruction from a single variable to the m-dimensional phase space in which the dimension m is based on optimal values from two methods: the correlation dimension method (Model I) and false nearest neighbour(s) (Model II). The selection of an appropriate method for selecting a combination of preliminary parameters, such as m, is important to provide an accurate prediction. From our investigation, we gather that via manipulation of the appropriate parameters for the reconstruction of the phase space, Model II provides better prediction results. In particular, we have used Model II together with the local linear prediction method to achieve the prediction results for the downstream area with a high correlation coefficient. In summary, the results show that Langat River in Kajang is chaotic, and, therefore, predictable using the nonlinear prediction method. Thus, the analysis and prediction of river flow in this area can provide river flow information to the proper authorities for the construction of flood control, particularly for the downstream area.

  8. Predictive spark timing method

    SciTech Connect

    Tang, D.L.; Chang, M.F.; Sultan, M.C.

    1990-01-09

    This patent describes a method of determining spark time in a spark timing system of an internal combustion engine having a plurality of cylinders and a spark period for each cylinder in which a spark occurs. It comprises: generating at least one crankshaft position reference pulse for each spark firing event, the reference pulse nearest the next spark being set to occur within a same cylinder event as the next spark; measuring at least two reference periods between recent reference pulses; calculating the spark timing synchronously with crankshaft position by performing the calculation upon receipt of the reference pulse nearest the next spark; predicting the engine speed for the next spark period from at least two reference periods including the most recent reference period; and based on the predicted speed, calculating a spark time measured from the the reference pulse nearest the next spark.

  9. Accurate Structure Prediction and Conformational Analysis of Cyclic Peptides with Residue-Specific Force Fields.

    PubMed

    Geng, Hao; Jiang, Fan; Wu, Yun-Dong

    2016-05-19

    Cyclic peptides (CPs) are promising candidates for drugs, chemical biology tools, and self-assembling nanomaterials. However, the development of reliable and accurate computational methods for their structure prediction has been challenging. Here, 20 all-trans CPs of 5-12 residues selected from Cambridge Structure Database have been simulated using replica-exchange molecular dynamics with four different force fields. Our recently developed residue-specific force fields RSFF1 and RSFF2 can correctly identify the crystal-like conformations of more than half CPs as the most populated conformation. The RSFF2 performs the best, which consistently predicts the crystal structures of 17 out of 20 CPs with rmsd < 1.1 Å. We also compared the backbone (ϕ, ψ) sampling of residues in CPs with those in short linear peptides and in globular proteins. In general, unlike linear peptides, CPs have local conformational free energies and entropies quite similar to globular proteins. PMID:27128113

  10. 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

  11. 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. PMID:25522009

  12. 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.

  13. 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

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

    PubMed

    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

  15. Quantitative online prediction of peptide binding to the major histocompatibility complex.

    PubMed

    Hattotuwagama, Channa K; Guan, Pingping; Doytchinova, Irini A; Zygouri, Christianna; Flower, Darren R

    2004-01-01

    With its implications for vaccine discovery, the accurate prediction of T cell epitopes is one of the key aspirations of computational vaccinology. We have developed a robust multivariate statistical method, based on partial least squares, for the quantitative prediction of peptide binding to major histocompatibility complexes (MHC), the principal checkpoint on the antigen presentation pathway. As a service to the immunobiology community, we have made a Perl implementation of the method available via a World Wide Web server. We call this server MHCPred. Access to the server is freely available from the URL: http://www.jenner.ac.uk/MHCPred. We have exemplified our method with a model for peptides binding to the common human MHC molecule HLA-B*3501. PMID:14629978

  16. Computational Framework for Prediction of Peptide Sequences That May Mediate Multiple Protein Interactions in Cancer-Associated Hub Proteins

    PubMed Central

    Sarkar, Debasree; Patra, Piya; Ghosh, Abhirupa; Saha, Sudipto

    2016-01-01

    A considerable proportion of protein-protein interactions (PPIs) in the cell are estimated to be mediated by very short peptide segments that approximately conform to specific sequence patterns known as linear motifs (LMs), often present in the disordered regions in the eukaryotic proteins. These peptides have been found to interact with low affinity and are able bind to multiple interactors, thus playing an important role in the PPI networks involving date hubs. In this work, PPI data and de novo motif identification based method (MEME) were used to identify such peptides in three cancer-associated hub proteins—MYC, APC and MDM2. The peptides corresponding to the significant LMs identified for each hub protein were aligned, the overlapping regions across these peptides being termed as overlapping linear peptides (OLPs). These OLPs were thus predicted to be responsible for multiple PPIs of the corresponding hub proteins and a scoring system was developed to rank them. We predicted six OLPs in MYC and five OLPs in MDM2 that scored higher than OLP predictions from randomly generated protein sets. Two OLP sequences from the C-terminal of MYC were predicted to bind with FBXW7, component of an E3 ubiquitin-protein ligase complex involved in proteasomal degradation of MYC. Similarly, we identified peptides in the C-terminal of MDM2 interacting with FKBP3, which has a specific role in auto-ubiquitinylation of MDM2. The peptide sequences predicted in MYC and MDM2 look promising for designing orthosteric inhibitors against possible disease-associated PPIs. Since these OLPs can interact with other proteins as well, these inhibitors should be specific to the targeted interactor to prevent undesired side-effects. This computational framework has been designed to predict and rank the peptide regions that may mediate multiple PPIs and can be applied to other disease-associated date hub proteins for prediction of novel therapeutic targets of small molecule PPI modulators. PMID

  17. How frequently are predicted peptides actually recognized by CD8 cells?

    PubMed

    Moldovan, Ioana; Targoni, Oleg; Zhang, Wenji; Sundararaman, Srividya; Lehmann, Paul V

    2016-07-01

    Detection of antigen-specific CD8 cells frequently relies on the use of peptides that are predicted to bind to HLA Class I molecules or have been shown to induce immune responses. There is extensive knowledge on individual HLA alleles' peptide-binding requirements, and immunogenic peptides for many antigens have been defined. The 32 individual peptides that comprise the CEF peptide pool represent such well-defined peptide determinants for Cytomegalo-, Epstein-barr-, and Influenza virus. We tested the accuracy of these peptide recognition predictions on 42 healthy human donors that have been high-resolution HLA-typed. According to the predictions, 241 recall responses should have been detected in these donors. Actual testing showed that 36 (15 %) of the predicted CD8 cell responses occurred in the high frequency range, 41 (17 %) in mid-frequencies, and 45 (19 %) were at the detection limit. In 119 instances (49 %), the predicted peptides were not targeted by CD8 cells detectably. The individual CEF peptides were recognized in an unpredicted fashion in 57 test cases. Moreover, the frequency of CD8 cells responding to a single peptide did not reflect on the number of CD8 cells targeting other determinants on the same antigen. Thus, reliance on one or a few predicted peptides provides a rather inaccurate assessment of antigen-specific CD8 cell immunity, strongly arguing for the use of peptide pools for immune monitoring. PMID:27108305

  18. Predicting Peptide Structures in Native Proteins from Physical Simulations of Fragments

    PubMed Central

    Voelz, Vincent A.; Shell, M. Scott; Dill, Ken A.

    2009-01-01

    It has long been proposed that much of the information encoding how a protein folds is contained locally in the peptide chain. Here we present a large-scale simulation study designed to examine the extent to which conformations of peptide fragments in water predict native conformations in proteins. We perform replica exchange molecular dynamics (REMD) simulations of 872 8-mer, 12-mer, and 16-mer peptide fragments from 13 proteins using the AMBER 96 force field and the OBC implicit solvent model. To analyze the simulations, we compute various contact-based metrics, such as contact probability, and then apply Bayesian classifier methods to infer which metastable contacts are likely to be native vs. non-native. We find that a simple measure, the observed contact probability, is largely more predictive of a peptide's native structure in the protein than combinations of metrics or multi-body components. Our best classification model is a logistic regression model that can achieve up to 63% correct classifications for 8-mers, 71% for 12-mers, and 76% for 16-mers. We validate these results on fragments of a protein outside our training set. We conclude that local structure provides information to solve some but not all of the conformational search problem. These results help improve our understanding of folding mechanisms, and have implications for improving physics-based conformational sampling and structure prediction using all-atom molecular simulations. PMID:19197352

  19. 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.

  20. 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.

  1. Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application.

    PubMed

    Fernandes, Fabiano C; Rigden, Daniel J; Franco, Octavio L

    2012-01-01

    Antimicrobial peptides (AMPs) are widely distributed defense molecules and represent a promising alternative for solving the problem of antibiotic resistance. Nevertheless, the experimental time required to screen putative AMPs makes computational simulations based on peptide sequence analysis and/or molecular modeling extremely attractive. Artificial intelligence methods acting as simulation and prediction tools are of great importance in helping to efficiently discover and design novel AMPs. In the present study, state-of-the-art published outcomes using different prediction methods and databases were compared to an adaptive neuro-fuzzy inference system (ANFIS) model. Data from our study showed that ANFIS obtained an accuracy of 96.7% and a Matthew's Correlation Coefficient (MCC) of0.936, which proved it to be an efficient model for pattern recognition in antimicrobial peptide prediction. Furthermore, a lower number of input parameters were needed for the ANFIS model, improving the speed and ease of prediction. In summary, due to the fuzzy nature ofAMP physicochemical properties, the ANFIS approach presented here can provide an efficient solution for screening putative AMP sequences and for exploration of properties characteristic of AMPs. PMID:23193592

  2. Analysis and prediction of affinity of TAP binding peptides using cascade SVM.

    PubMed

    Bhasin, Manoj; Raghava, G P S

    2004-03-01

    The generation of cytotoxic T lymphocyte (CTL) epitopes from an antigenic sequence involves number of intracellular processes, including production of peptide fragments by proteasome and transport of peptides to endoplasmic reticulum through transporter associated with antigen processing (TAP). In this study, 409 peptides that bind to human TAP transporter with varying affinity were analyzed to explore the selectivity and specificity of TAP transporter. The abundance of each amino acid from P1 to P9 positions in high-, intermediate-, and low-affinity TAP binders were examined. The rules for predicting TAP binding regions in an antigenic sequence were derived from the above analysis. The quantitative matrix was generated on the basis of contribution of each position and residue in binding affinity. The correlation of r = 0.65 was obtained between experimentally determined and predicted binding affinity by using a quantitative matrix. Further a support vector machine (SVM)-based method has been developed to model the TAP binding affinity of peptides. The correlation (r = 0.80) was obtained between the predicted and experimental measured values by using sequence-based SVM. The reliability of prediction was further improved by cascade SVM that uses features of amino acids along with sequence. An extremely good correlation (r = 0.88) was obtained between measured and predicted values, when the cascade SVM-based method was evaluated through jackknife testing. A Web service, TAPPred (http://www.imtech.res.in/raghava/tappred/ or http://bioinformatics.uams.edu/mirror/tappred/), has been developed based on this approach. PMID:14978300

  3. Optimization of reversed-phase chromatography methods for peptide analytics.

    PubMed

    Khalaf, Rushd; Baur, Daniel; Pfister, David

    2015-12-18

    The analytical description and quantification of peptide solutions is an essential part in the quality control of peptide production processes and in peptide mapping techniques. Traditionally, an important tool is analytical reversed phase liquid chromatography. In this work, we develop a model-based tool to find optimal analytical conditions in a clear, efficient and robust manner. The model, based on the Van't Hoff equation, the linear solvent strength correlation, and an analytical solution of the mass balance on a chromatographic column describing peptide retention in gradient conditions is used to optimize the analytical scale separation between components in a peptide mixture. The proposed tool is then applied in the design of analytical reversed phase liquid chromatography methods of five different peptide mixtures. PMID:26620597

  4. New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity.

    PubMed

    Hattotuwagama, Channa K; Guan, Pingping; Doytchinova, Irini A; Flower, Darren R

    2004-11-21

    Quantitative structure-activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide-protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2-D(b), H2-K(b) and H2-K(k). As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online ( http://www.jenner.ac.uk/MHCPred). PMID:15534705

  5. 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.

  6. CS-AMPPred: An Updated SVM Model for Antimicrobial Activity Prediction in Cysteine-Stabilized Peptides

    PubMed Central

    Porto, William F.; Pires, Állan S.; Franco, Octavio L.

    2012-01-01

    The antimicrobial peptides (AMP) have been proposed as an alternative to control resistant pathogens. However, due to multifunctional properties of several AMP classes, until now there has been no way to perform efficient AMP identification, except through in vitro and in vivo tests. Nevertheless, an indication of activity can be provided by prediction methods. In order to contribute to the AMP prediction field, the CS-AMPPred (Cysteine-Stabilized Antimicrobial Peptides Predictor) is presented here, consisting of an updated version of the Support Vector Machine (SVM) model for antimicrobial activity prediction in cysteine-stabilized peptides. The CS-AMPPred is based on five sequence descriptors: indexes of (i) α-helix and (ii) loop formation; and averages of (iii) net charge, (iv) hydrophobicity and (v) flexibility. CS-AMPPred was based on 310 cysteine-stabilized AMPs and 310 sequences extracted from PDB. The polynomial kernel achieves the best accuracy on 5-fold cross validation (85.81%), while the radial and linear kernels achieve 84.19%. Testing in a blind data set, the polynomial and radial kernels achieve an accuracy of 90.00%, while the linear model achieves 89.33%. The three models reach higher accuracies than previously described methods. A standalone version of CS-AMPPred is available for download at and runs on any Linux machine. PMID:23240023

  7. 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.

  8. Evaluating protocols and analytical methods for peptide adsorption experiments.

    PubMed

    Fears, Kenan P; Petrovykh, Dmitri Y; Clark, Thomas D

    2013-12-01

    This paper evaluates analytical techniques that are relevant for performing reliable quantitative analysis of peptide adsorption on surfaces. Two salient problems are addressed: determining the solution concentrations of model GG-X-GG, X5, and X10 oligopeptides (G = glycine, X = a natural amino acid), and quantitative analysis of these peptides following adsorption on surfaces. To establish a uniform methodology for measuring peptide concentrations in water across the entire GG-X-GG and X n series, three methods were assessed: UV spectroscopy of peptides having a C-terminal tyrosine, the bicinchoninic acid (BCA) protein assay, and amino acid (AA) analysis. Due to shortcomings or caveats associated with each of the different methods, none were effective at measuring concentrations across the entire range of representative model peptides. In general, reliable measurements were within 30% of the nominal concentration based on the weight of as-received lyophilized peptide. In quantitative analysis of model peptides adsorbed on surfaces, X-ray photoelectron spectroscopy (XPS) data for a series of lysine-based peptides (GGKGG, K5, and K10) on Au substrates, and for controls incubated in buffer in the absence of peptides, suggested a significant presence of aliphatic carbon species. Detailed analysis indicated that this carbonaceous contamination adsorbed from the atmosphere after the peptide deposition. The inferred adventitious nature of the observed aliphatic carbon was supported by control experiments in which substrates were sputter-cleaned by Ar(+) ions under ultra-high vacuum (UHV) then re-exposed to ambient air. In contrast to carbon contamination, no adventitious nitrogen species were detected on the controls; therefore, the relative surface densities of irreversibly-adsorbed peptides were calculated by normalizing the N/Au ratios by the average number of nitrogen atoms per residue. PMID:24706133

  9. Predicting Peptide-Mediated Interactions on a Genome-Wide Scale

    PubMed Central

    Chen, T. Scott; Petrey, Donald; Garzon, Jose Ignacio; Honig, Barry

    2015-01-01

    We describe a method to predict protein-protein interactions (PPIs) formed between structured domains and short peptide motifs. We take an integrative approach based on consensus patterns of known motifs in databases, structures of domain-motif complexes from the PDB and various sources of non-structural evidence. We combine this set of clues using a Bayesian classifier that reports the likelihood of an interaction and obtain significantly improved prediction performance when compared to individual sources of evidence and to previously reported algorithms. Our Bayesian approach was integrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to interactions formed between two structured domains. Around 80,000 new domain-motif mediated interactions were predicted, thus enhancing PrePPI’s coverage of the human protein interactome. PMID:25938916

  10. 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

  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. 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

  13. Prediction of calcite morphology from computational and experimental studies of mutations of a de novo-designed peptide.

    PubMed

    Schrier, Sarah B; Sayeg, Marianna K; Gray, Jeffrey J

    2011-09-20

    Many organisms use macromolecules, often proteins or peptides, to control the growth of inorganic crystals into complex materials. The ability to model peptide-mineral interactions accurately could allow for the design of novel peptides to produce materials with desired properties. Here, we tested a computational algorithm developed to predict the structure of peptides on mineral surfaces. Using this algorithm, we analyzed energetic and structural differences between a 16-residue peptide (bap4) designed to interact with a calcite growth plane and single- and double-point mutations of the charged residues. Currently, no experimental method is available to resolve the structures of proteins on solid surfaces, which precludes benchmarking for computational models. Therefore, to test the models, we chemically synthesized each peptide and analyzed its effects on calcite crystal growth. Whereas bap4 affected the crystal growth by producing heavily stepped corners and edges, point mutants had variable influences on morphology. Calculated residue-specific binding energies correlated with experimental observations; point mutations of residues predicted to be crucial to surface interactions produced morphologies most similar to unmodified calcite. These results suggest that peptide conformation plays a role in mineral interactions and that the computational model supplies valid energetic and structural data that can provide information about expected crystal morphology. PMID:21797243

  14. Predicting most probable conformations of a given peptide sequence in the random coil state.

    PubMed

    Bayrak, Cigdem Sevim; Erman, Burak

    2012-11-01

    In this work, we present a computational scheme for finding high probability conformations of peptides. The scheme calculates the probability of a given conformation of the given peptide sequence using the probability distribution of torsion states. Dependence of the states of a residue on the states of its first neighbors along the chain is considered. Prior probabilities of torsion states are obtained from a coil library. Posterior probabilities are calculated by the matrix multiplication Rotational Isomeric States Model of polymer theory. The conformation of a peptide with highest probability is determined by using a hidden Markov model Viterbi algorithm. First, the probability distribution of the torsion states of the residues is obtained. Using the highest probability torsion state, one can generate, step by step, states with lower probabilities. To validate the method, the highest probability state of residues in a given sequence is calculated and compared with probabilities obtained from the Coil Databank. Predictions based on the method are 32% better than predictions based on the most probable states of residues. The ensemble of "n" high probability conformations of a given protein is also determined using the Viterbi algorithm with multistep backtracking. PMID:22955874

  15. Pan-Specific Prediction of Peptide-MHC Class I Complex Stability, a Correlate of T Cell Immunogenicity.

    PubMed

    Rasmussen, Michael; Fenoy, Emilio; Harndahl, Mikkel; Kristensen, Anne Bregnballe; Nielsen, Ida Kallehauge; Nielsen, Morten; Buus, Søren

    2016-08-15

    Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of Ags to CTL, and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. Peptide-MHC-I interactions have traditionally been quantified by the strength of the interaction, that is, the binding affinity, yet it has been shown that the stability of the peptide-MHC-I complex is a better correlate of immunogenicity compared with binding affinity. In this study, we have experimentally analyzed peptide-MHC-I complex stability of a large panel of human MHC-I allotypes and generated a body of data sufficient to develop a neural network-based pan-specific predictor of peptide-MHC-I complex stability. Integrating the neural network predictors of peptide-MHC-I complex stability with state-of-the-art predictors of peptide-MHC-I binding is shown to significantly improve the prediction of CTL epitopes. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCstabpan. PMID:27402703

  16. Signal-3L: A 3-layer approach for predicting signal peptides.

    PubMed

    Shen, Hong-Bin; Chou, Kuo-Chen

    2007-11-16

    Functioning as an "address tag" that directs nascent proteins to their proper cellular and extracellular locations, signal peptides have become a crucial tool in finding new drugs or reprogramming cells for gene therapy. To effectively and timely use such a tool, however, the first important thing is to develop an automated method for rapidly and accurately identifying the signal peptide for a given nascent protein. With the avalanche of new protein sequences generated in the post-genomic era, the challenge has become even more urgent and critical. In this paper, we have developed a novel method for predicting signal peptide sequences and their cleavage sites in human, plant, animal, eukaryotic, Gram-positive, and Gram-negative protein sequences, respectively. The new predictor is called Signal-3L that consists of three prediction engines working, respectively, for the following three progressively deepening layers: (1) identifying a query protein as secretory or non-secretory by an ensemble classifier formed by fusing many individual OET-KNN (optimized evidence-theoretic K nearest neighbor) classifiers operated in various dimensions of PseAA (pseudo amino acid) composition spaces; (2) selecting a set of candidates for the possible signal peptide cleavage sites of a query secretory protein by a subsite-coupled discrimination algorithm; (3) determining the final cleavage site by fusing the global sequence alignment outcome for each of the aforementioned candidates through a voting system. Signal-3L is featured by high success prediction rates with short computational time, and hence is particularly useful for the analysis of large-scale datasets. Signal-3L is freely available as a web-server at http://chou.med.harvard.edu/bioinf/Signal-3L/ or http://202.120.37.186/bioinf/Signal-3L, where, to further support the demand of the related areas, the signal peptides identified by Signal-3L for all the protein entries in Swiss-Prot databank that do not have signal peptide

  17. Virtual Screening for Dipeptide Aggregation: Toward Predictive Tools for Peptide Self-Assembly.

    PubMed

    Frederix, Pim W J M; Ulijn, Rein V; Hunt, Neil T; Tuttle, Tell

    2011-10-01

    Several short peptide sequences are known to self-assemble into supramolecular nanostructures with interesting properties. In this study, coarse-grained molecular dynamics is employed to rapidly screen all 400 dipeptide combinations and predict their ability to aggregate as a potential precursor to their self-assembly. The simulation protocol and scoring method proposed allows a rapid determination of whether a given peptide sequence is likely to aggregate (an indicator for the ability to self-assemble) under aqueous conditions. Systems that show strong aggregation tendencies in the initial screening are selected for longer simulations, which result in good agreement with the known self-assembly or aggregation of dipeptides reported in the literature. Our extended simulations of the diphenylalanine system show that the coarse-grain model is able to reproduce salient features of nanoscale systems and provide insight into the self-assembly process for this system. PMID:23795243

  18. Virtual Screening for Dipeptide Aggregation: Toward Predictive Tools for Peptide Self-Assembly

    PubMed Central

    2011-01-01

    Several short peptide sequences are known to self-assemble into supramolecular nanostructures with interesting properties. In this study, coarse-grained molecular dynamics is employed to rapidly screen all 400 dipeptide combinations and predict their ability to aggregate as a potential precursor to their self-assembly. The simulation protocol and scoring method proposed allows a rapid determination of whether a given peptide sequence is likely to aggregate (an indicator for the ability to self-assemble) under aqueous conditions. Systems that show strong aggregation tendencies in the initial screening are selected for longer simulations, which result in good agreement with the known self-assembly or aggregation of dipeptides reported in the literature. Our extended simulations of the diphenylalanine system show that the coarse-grain model is able to reproduce salient features of nanoscale systems and provide insight into the self-assembly process for this system. PMID:23795243

  19. 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. PMID:26369913

  20. The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides

    PubMed Central

    Tsirigos, Konstantinos D.; Peters, Christoph; Shu, Nanjiang; Käll, Lukas; Elofsson, Arne

    2015-01-01

    TOPCONS (http://topcons.net/) is a widely used web server for consensus prediction of membrane protein topology. We hereby present a major update to the server, with some substantial improvements, including the following: (i) TOPCONS can now efficiently separate signal peptides from transmembrane regions. (ii) The server can now differentiate more successfully between globular and membrane proteins. (iii) The server now is even slightly faster, although a much larger database is used to generate the multiple sequence alignments. For most proteins, the final prediction is produced in a matter of seconds. (iv) The user-friendly interface is retained, with the additional feature of submitting batch files and accessing the server programmatically using standard interfaces, making it thus ideal for proteome-wide analyses. Indicatively, the user can now scan the entire human proteome in a few days. (v) For proteins with homology to a known 3D structure, the homology-inferred topology is also displayed. (vi) Finally, the combination of methods currently implemented achieves an overall increase in performance by 4% as compared to the currently available best-scoring methods and TOPCONS is the only method that can identify signal peptides and still maintain a state-of-the-art performance in topology predictions. PMID:25969446

  1. New method of peptide cleavage based on Edman degradation.

    PubMed

    Bąchor, Remigiusz; Kluczyk, Alicja; Stefanowicz, Piotr; Szewczuk, Zbigniew

    2013-08-01

    A straightforward cleavage method for N- acylated peptides based on the phenylthiohydantoin (PTH) formation is presented. The procedure could be applied to acid-stable resins, such as TentaGel HL-NH[Formula: see text]. We designed a cleavable linker that consists of a lysine residue with the [Formula: see text]-amino group blocked by Boc, whereas the [Formula: see text]-amino group is used for peptide synthesis. After the peptide assembly is completed, the protecting groups in peptide side chains are removed using trifluoroacetic acid, thus liberating also the [Formula: see text]-amino group of the lysine in the linker. Then the reaction with phenyl isothiocyanate followed by acidolysis causes an efficient peptide release from the resin as a stable PTH derivative. Furthermore, the application of a fixed charge tag in the form of 2-(4-aza-1-azoniabicyclo[2.2.2]octylammonium)acetyl group increases ionization efficiency and reduces the detection limit, allowing ESI-MS/MS sequencing of peptides in the subfemtomolar range. The proposed strategy is compatible with standard conditions during one-bead-one-compound peptide library synthesis. The applicability of the developed strategy in combinatorial chemistry was confirmed using a small training library of [Formula: see text]-chymotrypsin substrates. PMID:23690169

  2. 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

  3. SMALL CYSTEINE-RICH PEPTIDES RESEMBLING ANTIMICROBIAL PEPTIDES HAVE BEEN UNDER-PREDICTED IN PLANTS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Multicellular organisms produce small cysteine-rich anti-microbial peptides as an innate defense against pathogens. While defensins, a well-known class of such peptides, are common among eukaryotes, there are classes restricted to the plant kingdom. These include thionins, lipid transfer proteins,...

  4. 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. PMID:25587840

  5. 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 . PMID:27076335

  6. SABinder: A Web Service for Predicting Streptavidin-Binding Peptides

    PubMed Central

    Kang, Juanjuan; Ru, Beibei; Zhou, Peng

    2016-01-01

    Streptavidin is sometimes used as the intended target to screen phage-displayed combinatorial peptide libraries for streptavidin-binding peptides (SBPs). More often in the biopanning system, however, streptavidin is just a commonly used anchoring molecule that can efficiently capture the biotinylated target. In this case, SBPs creeping into the biopanning results are not desired binders but target-unrelated peptides (TUP). Taking them as intended binders may mislead subsequent studies. Therefore, it is important to find if a peptide is likely to be an SBP when streptavidin is either the intended target or just the anchoring molecule. In this paper, we describe an SVM-based ensemble predictor called SABinder. It is the first predictor for SBP. The model was built with the feature of optimized dipeptide composition. It was observed that 89.20% (MCC = 0.78; AUC = 0.93; permutation test, p < 0.001) of peptides were correctly classified. As a web server, SABinder is freely accessible. The tool provides a highly efficient way to exclude potential SBP when they are TUP or to facilitate identification of possibly new SBP when they are the desired binders. In either case, it will be helpful and can benefit related scientific community. PMID:27610387

  7. SABinder: A Web Service for Predicting Streptavidin-Binding Peptides.

    PubMed

    He, Bifang; Kang, Juanjuan; Ru, Beibei; Ding, Hui; Zhou, Peng; Huang, Jian

    2016-01-01

    Streptavidin is sometimes used as the intended target to screen phage-displayed combinatorial peptide libraries for streptavidin-binding peptides (SBPs). More often in the biopanning system, however, streptavidin is just a commonly used anchoring molecule that can efficiently capture the biotinylated target. In this case, SBPs creeping into the biopanning results are not desired binders but target-unrelated peptides (TUP). Taking them as intended binders may mislead subsequent studies. Therefore, it is important to find if a peptide is likely to be an SBP when streptavidin is either the intended target or just the anchoring molecule. In this paper, we describe an SVM-based ensemble predictor called SABinder. It is the first predictor for SBP. The model was built with the feature of optimized dipeptide composition. It was observed that 89.20% (MCC = 0.78; AUC = 0.93; permutation test, p < 0.001) of peptides were correctly classified. As a web server, SABinder is freely accessible. The tool provides a highly efficient way to exclude potential SBP when they are TUP or to facilitate identification of possibly new SBP when they are the desired binders. In either case, it will be helpful and can benefit related scientific community. PMID:27610387

  8. A Novel Peptide Binding Prediction Approach for HLA-DR Molecule Based on Sequence and Structural Information

    PubMed Central

    Li, Zhao; Zhao, Yilei; Pan, Gaofeng; Tang, Jijun; Guo, Fei

    2016-01-01

    MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHC II molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides. In this paper, we aim to propose a novel prediction method for MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC II molecules. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameter α in the weight formula. By changing α value, we can observe different performances of each scoring function. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules. PMID:27340658

  9. 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

  10. Activity Prediction and Molecular Mechanism of Bovine Blood Derived Angiotensin I-Converting Enzyme Inhibitory Peptides

    PubMed Central

    Zhang, Ting; Nie, Shaoping; Liu, Boqun; Yu, Yiding; Zhang, Yan; Liu, Jingbo

    2015-01-01

    Development of angiotensin I-converting enzyme (ACE, EC 3.4.15.1) inhibitory peptides from food protein is under extensive research as alternative for the prevention of hypertension. However, it is difficult to identify peptides released from food sources. To accelerate the progress of peptide identification, a three layer back propagation neural network model was established to predict the ACE-inhibitory activity of pentapeptides derived from bovine hemoglobin by simulated enzyme digestion. The pentapeptide WTQRF has the best predicted value with experimental IC50 23.93 μM. The potential molecular mechanism of the WTQRF / ACE interaction was investigated by flexible docking. PMID:25768442

  11. 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. PMID:26965954

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

    PubMed Central

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

    2014-01-01

    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 Rosetta. 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 Rosetta 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. PMID:24305904

  13. Prediction of Antimicrobial Activity of Synthetic Peptides by a Decision Tree Model

    PubMed Central

    Lira, Felipe; Perez, Pedro S.; Baranauskas, José A.

    2013-01-01

    Antimicrobial resistance is a persistent problem in the public health sphere. However, recent attempts to find effective substitutes to combat infections have been directed at identifying natural antimicrobial peptides in order to circumvent resistance to commercial antibiotics. This study describes the development of synthetic peptides with antimicrobial activity, created in silico by site-directed mutation modeling using wild-type peptides as scaffolds for these mutations. Fragments of antimicrobial peptides were used for modeling with molecular modeling computational tools. To analyze these peptides, a decision tree model, which indicated the action range of peptides on the types of microorganisms on which they can exercise biological activity, was created. The decision tree model was processed using physicochemistry properties from known antimicrobial peptides available at the Antimicrobial Peptide Database (APD). The two most promising peptides were synthesized, and antimicrobial assays showed inhibitory activity against Gram-positive and Gram-negative bacteria. Colossomin C and colossomin D were the most inhibitory peptides at 5 μg/ml against Staphylococcus aureus and Escherichia coli. The methods described in this work and the results obtained are useful for the identification and development of new compounds with antimicrobial activity through the use of computational tools. PMID:23455341

  14. Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide-HLA Interactions.

    PubMed

    Bassani-Sternberg, Michal; Gfeller, David

    2016-09-15

    Ag presentation on HLA molecules plays a central role in infectious diseases and tumor immunology. To date, large-scale identification of (neo-)Ags from DNA sequencing data has mainly relied on predictions. In parallel, mass spectrometry analysis of HLA peptidome is increasingly performed to directly detect peptides presented on HLA molecules. In this study, we use a novel unsupervised approach to assign mass spectrometry-based HLA peptidomics data to their cognate HLA molecules. We show that incorporation of deconvoluted HLA peptidomics data in ligand prediction algorithms can improve their accuracy for HLA alleles with few ligands in existing databases. The results of our computational analysis of large datasets of naturally processed HLA peptides, together with experimental validation and protein structure analysis, further reveal how HLA-binding motifs change with peptide length and predict new cooperative effects between distant residues in HLA-B07:02 ligands. PMID:27511729

  15. NetMHCstab - predicting stability of peptide-MHC-I complexes; impacts for cytotoxic T lymphocyte epitope discovery.

    PubMed

    Jørgensen, Kasper W; Rasmussen, Michael; Buus, Søren; Nielsen, Morten

    2014-01-01

    Major histocompatibility complex class I (MHC-I) molecules play an essential role in the cellular immune response, presenting peptides to cytotoxic T lymphocytes (CTLs) allowing the immune system to scrutinize ongoing intracellular production of proteins. In the early 1990s, immunogenicity and stability of the peptide-MHC-I (pMHC-I) complex were shown to be correlated. At that time, measuring stability was cumbersome and time consuming and only small data sets were analysed. Here, we investigate this fairly unexplored area on a large scale compared with earlier studies. A recent small-scale study demonstrated that pMHC-I complex stability was a better correlate of CTL immunogenicity than peptide-MHC-I affinity. We here extended this study and analysed a total of 5509 distinct peptide stability measurements covering 10 different HLA class I molecules. Artificial neural networks were used to construct stability predictors capable of predicting the half-life of the pMHC-I complex. These predictors were shown to predict T-cell epitopes and MHC ligands from SYFPEITHI and IEDB to form significantly more stable MHC-I complexes compared with affinity-matched non-epitopes. Combining the stability predictions with a state-of-the-art affinity predictions NetMHCcons significantly improved the performance for identification of T-cell epitopes and ligands. For the HLA alleles included in the study, we could identify distinct sub-motifs that differentiate between stable and unstable peptide binders and demonstrate that anchor positions in the N-terminal of the binding motif (primarily P2 and P3) play a critical role for the formation of stable pMHC-I complexes. A webserver implementing the method is available at www.cbs.dtu.dk/services/NetMHCstab. PMID:23927693

  16. Discovery of a latent calcineurin inhibitory peptide from its autoinhibitory domain by docking, dynamic simulation, and in vitro methods.

    PubMed

    Harish, B M; Saraswathi, R; Vinod, D; Devaraju, K S

    2016-05-01

    Autoinhibitory domain (AID) of calcineurin (CN) was discovered two decades ago. Fewer investigations are reported to find out shortest possible peptide from the AID for CN inhibition. Hence, this study has focused on screening of nearly 150 peptide fragments derived from the AID using in silico method. Therefore, we have employed docking studies, aiming to analyze the best pose of AID-derived peptides on CN active site. We also analyzed binding free energy (ΔG) of docked complex using molecular mechanics/generalized Born surface area (MM/GBSA). MM/GBSA predicts two short peptides P1 and P2 found to be lowest binding free energy. Two peptides exhibit better binding affinity with CN, suggests that the possible candidates for potential CN inhibition. Further, the stability of the docked complex was analyzed using molecular dynamic (MD) simulation. MD study shows that CNA:P2 is the most stable complex than CN A:P1 and CN A:AID. Besides, we have synthesized and purified P1 and P2 peptides over high performance liquid chromatography (HPLC) found to be 90.31% and 98.93% of purity, respectively. In addition, AID peptides were characterized over mass spectral analysis. Peptides were subjected to CN inhibitory assay using malachite green method. Where, P1 and P2 exhibit CN inhibition better than AID. In particular, shortest peptide P2 shows highest inhibitory activity than AID. Enzyme assay reveals CN inhibitory activity of P2 peptide is consistent within silico results. In silico and in vitro, results corroborated each other to confirm short peptide P2 can be used as a potential CN inhibitor. PMID:26111023

  17. 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. PMID:27094284

  18. dPABBs: A Novel in silico Approach for Predicting and Designing Anti-biofilm Peptides

    PubMed Central

    Sharma, Arun; Gupta, Pooja; Kumar, Rakesh; Bhardwaj, Anshu

    2016-01-01

    Increasingly, biofilms are being recognised for their causative role in persistent infections (like cystic fibrosis, otitis media, diabetic foot ulcers) and nosocomial diseases (biofilm-infected vascular catheters, implants and prosthetics). Given the clinical relevance of biofilms and their recalcitrance to conventional antibiotics, it is imperative that alternative therapeutics are proactively sought. We have developed dPABBs, a web server that facilitates the prediction and design of anti-biofilm peptides. The six SVM and Weka models implemented on dPABBs were observed to identify anti-biofilm peptides on the basis of their whole amino acid composition, selected residue features and the positional preference of the residues (maximum accuracy, sensitivity, specificity and MCC of 95.24%, 92.50%, 97.73% and 0.91, respectively, on the training datasets). On the N-terminus, it was seen that either of the cationic polar residues, R and K, is present at all five positions in case of the anti-biofilm peptides, whereas in the QS peptides, the uncharged polar residue S is preponderant at the first (also anionic polar residues D, E), third and fifth positions. Positive predictions were also obtained for 29 FDA-approved peptide drugs and ten antimicrobial peptides in clinical development, indicating at their possible repurposing for anti-biofilm therapy. dPABBs is freely accessible on: http://ab-openlab.csir.res.in/abp/antibiofilm/. PMID:26912180

  19. dPABBs: A Novel in silico Approach for Predicting and Designing Anti-biofilm Peptides.

    PubMed

    Sharma, Arun; Gupta, Pooja; Kumar, Rakesh; Bhardwaj, Anshu

    2016-01-01

    Increasingly, biofilms are being recognised for their causative role in persistent infections (like cystic fibrosis, otitis media, diabetic foot ulcers) and nosocomial diseases (biofilm-infected vascular catheters, implants and prosthetics). Given the clinical relevance of biofilms and their recalcitrance to conventional antibiotics, it is imperative that alternative therapeutics are proactively sought. We have developed dPABBs, a web server that facilitates the prediction and design of anti-biofilm peptides. The six SVM and Weka models implemented on dPABBs were observed to identify anti-biofilm peptides on the basis of their whole amino acid composition, selected residue features and the positional preference of the residues (maximum accuracy, sensitivity, specificity and MCC of 95.24%, 92.50%, 97.73% and 0.91, respectively, on the training datasets). On the N-terminus, it was seen that either of the cationic polar residues, R and K, is present at all five positions in case of the anti-biofilm peptides, whereas in the QS peptides, the uncharged polar residue S is preponderant at the first (also anionic polar residues D, E), third and fifth positions. Positive predictions were also obtained for 29 FDA-approved peptide drugs and ten antimicrobial peptides in clinical development, indicating at their possible repurposing for anti-biofilm therapy. dPABBs is freely accessible on: http://ab-openlab.csir.res.in/abp/antibiofilm/. PMID:26912180

  20. 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

  1. MULTIPRED2: a computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles.

    PubMed

    Zhang, Guang Lan; DeLuca, David S; Keskin, Derin B; Chitkushev, Lou; Zlateva, Tanya; Lund, Ole; Reinherz, Ellis L; Brusic, Vladimir

    2011-11-30

    MULTIPRED2 is a computational system for facile prediction of peptide binding to multiple alleles belonging to human leukocyte antigen (HLA) class I and class II DR molecules. It enables prediction of peptide binding to products of individual HLA alleles, combination of alleles, or HLA supertypes. NetMHCpan and NetMHCIIpan are used as prediction engines. The 13 HLA Class I supertypes are A1, A2, A3, A24, B7, B8, B27, B44, B58, B62, C1, and C4. The 13 HLA Class II DR supertypes are DR1, DR3, DR4, DR6, DR7, DR8, DR9, DR11, DR12, DR13, DR14, DR15, and DR16. In total, MULTIPRED2 enables prediction of peptide binding to 1077 variants representing 26 HLA supertypes. MULTIPRED2 has visualization modules for mapping promiscuous T-cell epitopes as well as those regions of high target concentration - referred to as T-cell epitope hotspots. Novel graphic representations are employed to display the predicted binding peptides and immunological hotspots in an intuitive manner and also to provide a global view of results as heat maps. Another function of MULTIPRED2, which has direct relevance to vaccine design, is the calculation of population coverage. Currently it calculates population coverage in five major groups in North America. MULTIPRED2 is an important tool to complement wet-lab experimental methods for identification of T-cell epitopes. It is available at http://cvc.dfci.harvard.edu/multipred2/. PMID:21130094

  2. 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…

  3. Utility and limitations of a peptide reactivity assay to predict fragrance allergens in vitro.

    PubMed

    Natsch, A; Gfeller, H; Rothaupt, M; Ellis, G

    2007-10-01

    A key step in the skin sensitization process is the formation of a covalent adduct between the skin sensitizer and endogenous proteins and/or peptides in the skin. A published peptide depletion assay was used to relate the in vitro reactivity of fragrance molecules to LLNA data. Using the classical assay, 22 of 28 tested moderate to strong sensitizers were positive. The prediction of weak sensitizers proved to be more difficult with only 50% of weak sensitizers giving a positive response, but for some compounds this could also be due to false-positive results from the LLNA. LC-MS analysis yielded the expected mass of the peptide adducts in several cases, whereas in other cases putative oxidation reactions led to adducts of unexpected molecular weight. Several moderately sensitizing aldehydes were correctly predicted by the depletion assay, but no adducts were found and the depletion appears to be due to an oxidation of the parent peptide catalyzed by the test compound. Finally, alternative test peptides derived from a physiological reactive protein with enhanced sensitivity for weak Michael acceptors were found, further increasing the sensitivity of the assay. PMID:17513083

  4. Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.

    PubMed

    Kundu, Kousik; Costa, Fabrizio; Huber, Michael; Reth, Michael; Backofen, Rolf

    2013-01-01

    Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2, in-silico ligand peptide prediction is of great interest. Currently however, only a few approaches have been published for the prediction of SH2-peptide interactions. Their main shortcomings range from limited coverage, to restrictive modeling assumptions (they are mainly based on position specific scoring matrices and do not take into consideration complex amino acids inter-dependencies) and high computational complexity. We propose a simple yet effective machine learning approach for a large set of known human SH2 domains. We used comprehensive data from micro-array and peptide-array experiments on 51 human SH2 domains. In order to deal with the high data imbalance problem and the high signal-to-noise ration, we casted the problem in a semi-supervised setting. We report competitive predictive performance w.r.t. state-of-the-art. Specifically we obtain 0.83 AUC ROC and 0.93 AUC PR in comparison to 0.71 AUC ROC and 0.87 AUC PR previously achieved by the position specific scoring matrices (PSSMs) based SMALI approach. Our work provides three main contributions. First, we showed that better models can be obtained when the information on the non-interacting peptides (negative examples) is also used. Second, we improve performance when considering high order correlations between the ligand positions employing regularization techniques to effectively avoid overfitting issues. Third, we developed an approach to tackle the data imbalance problem using a semi-supervised strategy. Finally, we performed a genome-wide prediction of human SH2-peptide binding, uncovering several findings of biological relevance. We make our models and genome-wide predictions, for all the 51 SH2

  5. Machine learning based prediction for peptide drift times in ion mobility spectrometry

    PubMed Central

    Shah, Anuj R.; Agarwal, Khushbu; Baker, Erin S.; Singhal, Mudita; Mayampurath, Anoop M.; Ibrahim, Yehia M.; Kangas, Lars J.; Monroe, Matthew E.; Zhao, Rui; Belov, Mikhail E.; Anderson, Gordon A.; Smith, Richard D.

    2010-01-01

    Motivation: Ion mobility spectrometry (IMS) has gained significant traction over the past few years for rapid, high-resolution separations of analytes based upon gas-phase ion structure, with significant potential impacts in the field of proteomic analysis. IMS coupled with mass spectrometry (MS) affords multiple improvements over traditional proteomics techniques, such as in the elucidation of secondary structure information, identification of post-translational modifications, as well as higher identification rates with reduced experiment times. The high throughput nature of this technique benefits from accurate calculation of cross sections, mobilities and associated drift times of peptides, thereby enhancing downstream data analysis. Here, we present a model that uses physicochemical properties of peptides to accurately predict a peptide's drift time directly from its amino acid sequence. This model is used in conjunction with two mathematical techniques, a partial least squares regression and a support vector regression setting. Results: When tested on an experimentally created high confidence database of 8675 peptide sequences with measured drift times, both techniques statistically significantly outperform the intrinsic size parameters-based calculations, the currently held practice in the field, on all charge states (+2, +3 and +4). Availability: The software executable, imPredict, is available for download from http:/omics.pnl.gov/software/imPredict.php Contact: rds@pnl.gov Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20495001

  6. Orientational landscapes of peptides in membranes: prediction of (2)H NMR couplings in a dynamic context.

    PubMed

    Esteban-Martín, Santi; Giménez, Diana; Fuertes, Gustavo; Salgado, Jesús

    2009-12-01

    Unlike soluble proteins, membrane polypeptides face an anisotropic milieu. This imposes restraints on their orientation and provides a reference that makes structure prediction tractable by minimalistic thermodynamic models. Here we use this framework to build orientational distributions of monomeric membrane-bound peptides and to predict their expected solid-state (2)H NMR quadrupolar couplings when labeled at specific side chain positions. Using a complete rigid-body sampling of configurations relative to an implicit lipid membrane, peptide free energy landscapes are calculated. This allows us to obtain probability distributions of the peptide tilt, azimuthal rotation, and depth of membrane insertion. The orientational distributions are broad and originate from an interplay among the three relevant rigid-body degrees of freedom, which allows population of multiple states in shallow free energy minima. Remarkably, only when the orientational distributions are taken into account do we obtain a close correlation between predicted (2)H NMR splittings and values measured in experiments. Such a good correlation is not seen with splittings calculated from single configurations, being either the averaged or the lowest free energy state, showing there are distributions, rather than single structures, that best define the peptide-membrane systems. Moreover, we propose that these distributions contribute to the understanding of the rigid-body dynamics of the system. PMID:19860438

  7. 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

  8. The pH dependence of predictive models relating electrophoretic mobility to peptide chemico-physical properties in capillary zone electrophoresis.

    PubMed

    Castagnola, M; Rossetti, D V; Corda, M; Pellegrini, M; Misiti, F; Olianas, A; Giardina, B; Messana, I

    1998-10-01

    We applied best fitting procedures to capillary electrophoresis (CE) mobility values, measured at varying acidic pH, of a set of 21 peptides with a molecular mass ranging from about 350 to 1850 Da. This method allowed the contemporary measurements of C-terminus and carboxylic group of the side-chain of aspartic and glutamic acid dissociation constants and of peptide Stokes radius at different protonation stages. Stokes radius was related to peptide molecular mass M at the power of a fractional coefficient, and best correlation was found at pH 2.25, the fractional coefficient being equal to 0.68. This value is close to that proposed by R. E. Offord (Nature 1966, 211, 591-593), who suggested a proportionality between the polymer Stokes radius and M(2/3). The coefficient value decreases at higher pH, reaching a value of 0.58 at pH 4.25, corresponding to a mean peptide conformational transition towards more compact structures as a consequence of C-terminus dissociation. The measurement of the dissociation constants of each peptide allowed us to determine the percentage error on peptide charge predictions performed utilizing mean dissociation constants. Even for the charge, the best predictive performance is obtained at the most acidic edge of the range of the pH studied, mainly at pH 2.25. Conclusively, this study shows that the best performance of predictive models for peptide CE mobility is obtainable in the very acidic pH range (2.25-2.50) and in the absence of electroosmotic flow, and that a satisfactory predictive equation of peptide electrophoretic mobility (m2V(-1)s(-1) is given by mu = 85.4(Z/M(0.68))10(-8). PMID:9788308

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

    SciTech Connect

    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.

  10. Prediction of Antimicrobial Peptides Based on Sequence Alignment and Support Vector Machine-Pairwise Algorithm Utilizing LZ-Complexity

    PubMed Central

    Shahrudin, Shahriza

    2015-01-01

    This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs) which are important to the immune system. Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics. However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time. Therefore, a computational tool for AMPs prediction is needed to resolve this problem. In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM-) LZ complexity pairwise algorithm. It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used. Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity. PMID:25802839

  11. 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

    2016-08-01

    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. PMID:27280735

  12. Incremental predictive value of natriuretic peptides for prognosis in the chronic stable heart failure population: a systematic review.

    PubMed

    Don-Wauchope, Andrew C; Santaguida, Pasqualina L; Oremus, Mark; McKelvie, Robert; Ali, Usman; Brown, Judy A; Bustamam, Amy; Sohel, Nazmul; Hill, Stephen A; Booth, Ronald A; Balion, Cynthia; Raina, Parminder

    2014-08-01

    The aim of this study was to determine whether measurement of natriuretic peptides independently adds incremental predictive value for mortality and morbidity in patients with chronic stable heart failure (CSHF). We electronically searched Medline®, Embase™, AMED, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and CINAHL from 1989 to June 2012. We also searched reference lists of included articles, systematic reviews, and the gray literature. Studies were screened for eligibility criteria and assessed for methodological quality. Data were extracted on study design, population demographics, assay cutpoints, prognostic risk prediction model covariates, statistical methods, outcomes, and results. One hundred and eighty-three studies were identified as prognostic in the systematic review. From these, 15 studies (all NT-proBNP) considered incremental predictive value in CSHF subjects. Follow-up varied from 12 to 37 months. All studies presented at least one estimate of incremental predictive value of NT-proBNP relative to the base prognostic model. Using discrimination or likelihood statistics, these studies consistently showed that NT-proBNP increased model performance. Three studies used re-classification and model validation computations to establish incremental predictive value; these studies showed less consistency with respect to added value. Although there were differences in the base risk prediction models, assay cutpoints, and lengths of follow-up, there was consistency in NT-proBNP adding incremental predictive value for prognostic models in chronic stable CSHF patients. The limitations in the literature suggest that studies designed to evaluate prognostic models should be undertaken to evaluate the incremental value of natriuretic peptide as a predictor of mortality and morbidity in CSHF. PMID:25120174

  13. Empirical methods for identifying specific peptide-protein interactions for smart reagent development

    NASA Astrophysics Data System (ADS)

    Kogot, Joshua M.; Sarkes, Deborah A.; Stratis-Cullum, Dimitra N.; Pellegrino, Paul M.

    2012-06-01

    The current state of the art in the development of antibody alternatives is fraught with difficulties including mass production, robustness, and overall cost of production. The isolation of synthetic alternatives using peptide libraries offers great potential for recognition elements that are more stable and have improved binding affinity and target specificity. Although recent advances in rapid and automated discovery and synthetic library engineering continue to show promise for this emerging science, there remains a critical need for an improved fundamental understanding of the mechanisms of recognition. To better understand the fundamental mechanisms of binding, it is critical to be able to accurately assess binding between peptide reagents and protein targets. The development of empirical methods to analyze peptide-protein interactions is often overlooked, since it is often assumed that peptides can easily substitute for antibodies in antibody-derived immunoassays. The physico-chemical difference between peptides and antibodies represents a major challenge for developing peptides in standard immunoassays as capture or detection reagents. Analysis of peptide presents a unique challenge since the peptide has to be soluble, must be capable of target recognition, and capable of ELISA plate or SPR chip binding. Incorporating a plate-binding, hydrophilic peptide fusion (PS-tag) improves both the solubility and plate binding capability in a direct peptide ELISA format. Secondly, a solution based methods, affinity capillary electrophoresis (ACE) method is presented as a solution-based, affinity determination method that can be used for determining both the association constants and binding kinetics.

  14. Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides

    PubMed Central

    Schanstra, Joost P.; Alkhalaf, Alaa; Argiles, Angel; Bakker, Stephan J.L.; Beige, Joachim; Bilo, Henk J.G.; Chatzikyrkou, Christos; Dakna, Mohammed; Dawson, Jesse; Delles, Christian; Haller, Hermann; Haubitz, Marion; Husi, Holger; Jankowski, Joachim; Jerums, George; Kleefstra, Nanne; Kuznetsova, Tatiana; Maahs, David M.; Menne, Jan; Mullen, William; Ortiz, Alberto; Persson, Frederik; Rossing, Peter; Ruggenenti, Piero; Rychlik, Ivan; Serra, Andreas L.; Siwy, Justyna; Snell-Bergeon, Janet; Spasovski, Goce; Staessen, Jan A.; Vlahou, Antonia; Mischak, Harald; Vanholder, Raymond

    2015-01-01

    Progressive CKD is generally detected at a late stage by a sustained decline in eGFR and/or the presence of significant albuminuria. With the aim of early and improved risk stratification of patients with CKD, we studied urinary peptides in a large cross-sectional multicenter cohort of 1990 individuals, including 522 with follow-up data, using proteome analysis. We validated that a previously established multipeptide urinary biomarker classifier performed significantly better in detecting and predicting progression of CKD than the current clinical standard, urinary albumin. The classifier was also more sensitive for identifying patients with rapidly progressing CKD. Compared with the combination of baseline eGFR and albuminuria (area under the curve [AUC]=0.758), the addition of the multipeptide biomarker classifier significantly improved CKD risk prediction (AUC=0.831) as assessed by the net reclassification index (0.303±−0.065; P<0.001) and integrated discrimination improvement (0.058±0.014; P<0.001). Correlation of individual urinary peptides with CKD stage and progression showed that the peptides that associated with CKD, irrespective of CKD stage or CKD progression, were either fragments of the major circulating proteins, suggesting failure of the glomerular filtration barrier sieving properties, or different collagen fragments, suggesting accumulation of intrarenal extracellular matrix. Furthermore, protein fragments associated with progression of CKD originated mostly from proteins related to inflammation and tissue repair. Results of this study suggest that urinary proteome analysis might significantly improve the current state of the art of CKD detection and outcome prediction and that identification of the urinary peptides allows insight into various ongoing pathophysiologic processes in CKD. PMID:25589610

  15. Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides.

    PubMed

    Schanstra, Joost P; Zürbig, Petra; Alkhalaf, Alaa; Argiles, Angel; Bakker, Stephan J L; Beige, Joachim; Bilo, Henk J G; Chatzikyrkou, Christos; Dakna, Mohammed; Dawson, Jesse; Delles, Christian; Haller, Hermann; Haubitz, Marion; Husi, Holger; Jankowski, Joachim; Jerums, George; Kleefstra, Nanne; Kuznetsova, Tatiana; Maahs, David M; Menne, Jan; Mullen, William; Ortiz, Alberto; Persson, Frederik; Rossing, Peter; Ruggenenti, Piero; Rychlik, Ivan; Serra, Andreas L; Siwy, Justyna; Snell-Bergeon, Janet; Spasovski, Goce; Staessen, Jan A; Vlahou, Antonia; Mischak, Harald; Vanholder, Raymond

    2015-08-01

    Progressive CKD is generally detected at a late stage by a sustained decline in eGFR and/or the presence of significant albuminuria. With the aim of early and improved risk stratification of patients with CKD, we studied urinary peptides in a large cross-sectional multicenter cohort of 1990 individuals, including 522 with follow-up data, using proteome analysis. We validated that a previously established multipeptide urinary biomarker classifier performed significantly better in detecting and predicting progression of CKD than the current clinical standard, urinary albumin. The classifier was also more sensitive for identifying patients with rapidly progressing CKD. Compared with the combination of baseline eGFR and albuminuria (area under the curve [AUC]=0.758), the addition of the multipeptide biomarker classifier significantly improved CKD risk prediction (AUC=0.831) as assessed by the net reclassification index (0.303±-0.065; P<0.001) and integrated discrimination improvement (0.058±0.014; P<0.001). Correlation of individual urinary peptides with CKD stage and progression showed that the peptides that associated with CKD, irrespective of CKD stage or CKD progression, were either fragments of the major circulating proteins, suggesting failure of the glomerular filtration barrier sieving properties, or different collagen fragments, suggesting accumulation of intrarenal extracellular matrix. Furthermore, protein fragments associated with progression of CKD originated mostly from proteins related to inflammation and tissue repair. Results of this study suggest that urinary proteome analysis might significantly improve the current state of the art of CKD detection and outcome prediction and that identification of the urinary peptides allows insight into various ongoing pathophysiologic processes in CKD. PMID:25589610

  16. 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.

  17. 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

  18. Protein Residue Contacts and Prediction Methods

    PubMed Central

    Adhikari, Badri

    2016-01-01

    In the field of computational structural proteomics, contact predictions have shown new prospects of solving the longstanding problem of ab initio protein structure prediction. In the last few years, application of deep learning algorithms and availability of large protein sequence databases, combined with improvement in methods that derive contacts from multiple sequence alignments, have shown a huge increase in the precision of contact prediction. In addition, these predicted contacts have also been used to build three-dimensional models from scratch. In this chapter, we briefly discuss many elements of protein residue–residue contacts and the methods available for prediction, focusing on a state-of-the-art contact prediction tool, DNcon. Illustrating with a case study, we describe how DNcon can be used to make ab initio contact predictions for a given protein sequence and discuss how the predicted contacts may be analyzed and evaluated. PMID:27115648

  19. Protein Residue Contacts and Prediction Methods.

    PubMed

    Adhikari, Badri; Cheng, Jianlin

    2016-01-01

    In the field of computational structural proteomics, contact predictions have shown new prospects of solving the longstanding problem of ab initio protein structure prediction. In the last few years, application of deep learning algorithms and availability of large protein sequence databases, combined with improvement in methods that derive contacts from multiple sequence alignments, have shown a huge increase in the precision of contact prediction. In addition, these predicted contacts have also been used to build three-dimensional models from scratch.In this chapter, we briefly discuss many elements of protein residue-residue contacts and the methods available for prediction, focusing on a state-of-the-art contact prediction tool, DNcon. Illustrating with a case study, we describe how DNcon can be used to make ab initio contact predictions for a given protein sequence and discuss how the predicted contacts may be analyzed and evaluated. PMID:27115648

  20. 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. PMID:26460120

  1. 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.

  2. Oriented Circular Dichroism: A Method to Characterize Membrane-Active Peptides in Oriented Lipid Bilayers.

    PubMed

    Bürck, Jochen; Wadhwani, Parvesh; Fanghänel, Susanne; Ulrich, Anne S

    2016-02-16

    The structures of membrane-bound polypeptides are intimately related to their functions and may change dramatically with the lipid environment. Circular dichroism (CD) is a rapid analytical method that requires relatively low amounts of material and no labeling. Conventional CD is routinely used to monitor the secondary structure of peptides and proteins in solution, for example, in the presence of ligands and other binding partners. In the case of membrane-active peptides and transmembrane proteins, these measurements can be applied to, and remain limited to, samples containing detergent micelles or small sonicated lipid vesicles. Such traditional CD analysis reveals only secondary structures. With the help of an oriented circular dichroism (OCD) setup, however, based on the preparation of macroscopically oriented lipid bilayers, it is possible to address the membrane alignment of a peptide in addition to its conformation. This approach has been mostly used for α-helical peptides so far, but other structural elements are conceivable as well. OCD analysis relies on Moffitt's theory, which predicts that the electronic transition dipole moments of the backbone amide bonds in helical polypeptides are polarized either parallel or perpendicular to the helix axis. The interaction of the electric field vector of the circularly polarized light with these transitions results in an OCD spectrum of a membrane-bound α-helical peptide, which exhibits a characteristic line shape and reflects the angle between the helix axis and the bilayer normal. For parallel alignment of a peptide helix with respect to the membrane surface (S-state), the corresponding "fingerprint" CD band around 208 nm will exhibit maximum negative amplitude. If the helix changes its alignment via an obliquely tilted (T-state) to a fully inserted transmembrane orientation (I-state), the ellipticity at 208 nm decreases and the value approaches zero due to the decreased interactions between the field and the

  3. Prediction of anticancer peptides against MCF-7 breast cancer cells from the peptidomes of Achatina fulica mucus fractions

    PubMed Central

    E-kobon, Teerasak; Thongararm, Pennapa; Roytrakul, Sittiruk; Meesuk, Ladda; Chumnanpuen, Pramote

    2015-01-01

    Several reports have shown antimicrobial and anticancer activities of mucous glycoproteins extracted from the giant African snail Achatina fulica. Anticancer properties of the snail mucous peptides remain incompletely revealed. The aim of this study was to predict anticancer peptides from A. fulica mucus. Two of HPLC-separated mucous fractions (F2 and F5) showed in vitro cytotoxicity against the breast cancer cell line (MCF-7) and normal epithelium cell line (Vero). According to the mass spectrometric analysis, 404 and 424 peptides from the F2 and F5 fractions were identified. Our comprehensive bioinformatics workflow predicted 16 putative cationic and amphipathic anticancer peptides with diverse structures from these two peptidome data. These peptides would be promising molecules for new anti-breast cancer drug development. PMID:26862373

  4. An effective and effecient peptide binding prediction approach for a broad set of HLA-DR molecules based on ordered weighted averaging of binding pocket profiles

    PubMed Central

    2013-01-01

    Background The immune system must detect a wide variety of microbial pathogens, such as viruses, bacteria, fungi and parasitic worms, to protect the host against disease. Antigenic peptides displayed by MHC II (class II Major Histocompatibility Complex) molecules is a pivotal process to activate CD4+ TH cells (Helper T cells). The activated TH cells can differentiate into effector cells which assist various cells in activating against pathogen invasion. Each MHC locus encodes a great number of allele variants. Yet this limited number of MHC molecules are required to display enormous number of antigenic peptides. Since the peptide binding measurements of MHC molecules by biochemical experiments are expensive, only a few of the MHC molecules have suffecient measured peptides. To perform accurate binding prediction for those MHC alleles without suffecient measured peptides, a number of computational algorithms were proposed in the last decades. Results Here, we propose a new MHC II binding prediction approach, OWA-PSSM, which is a significantly extended version of a well known method called TEPITOPE. The TEPITOPE method is able to perform prediction for only 50 MHC alleles, while OWA-PSSM is able to perform prediction for much more, up to 879 HLA-DR molecules. We evaluate the method on five benchmark datasets. The method is demonstrated to be the best one in identifying binding cores compared with several other popular state-of-the-art approaches. Meanwhile, the method performs comparably to the TEPITOPE and NetMHCIIpan2.0 approaches in identifying HLA-DR epitopes and ligands, and it performs significantly better than TEPITOPEpan in the identification of HLA-DR ligands and MultiRTA in identifying HLA-DR T cell epitopes. Conclusions The proposed approach OWA-PSSM is fast and robust in identifying ligands, epitopes and binding cores for up to 879 MHC II molecules. PMID:24565049

  5. Design and Evaluation of Antimalarial Peptides Derived from Prediction of Short Linear Motifs in Proteins Related to Erythrocyte Invasion

    PubMed Central

    Bianchin, Alessandra; Bell, Angus; Chubb, Anthony J.; Doolan, Nathalie; Leneghan, Darren; Stavropoulos, Ilias; Shields, Denis C.; Mooney, Catherine

    2015-01-01

    The purpose of this study was to investigate the blood stage of the malaria causing parasite, Plasmodium falciparum, to predict potential protein interactions between the parasite merozoite and the host erythrocyte and design peptides that could interrupt these predicted interactions. We screened the P. falciparum and human proteomes for computationally predicted short linear motifs (SLiMs) in cytoplasmic portions of transmembrane proteins that could play roles in the invasion of the erythrocyte by the merozoite, an essential step in malarial pathogenesis. We tested thirteen peptides predicted to contain SLiMs, twelve of them palmitoylated to enhance membrane targeting, and found three that blocked parasite growth in culture by inhibiting the initiation of new infections in erythrocytes. Scrambled peptides for two of the most promising peptides suggested that their activity may be reflective of amino acid properties, in particular, positive charge. However, one peptide showed effects which were stronger than those of scrambled peptides. This was derived from human red blood cell glycophorin-B. We concluded that proteome-wide computational screening of the intracellular regions of both host and pathogen adhesion proteins provides potential lead peptides for the development of anti-malarial compounds. PMID:26039561

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

    PubMed

    Akagawa, Kengo; Sakai, Nobutaka; Kudo, Kazuaki

    2015-02-01

    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. PMID:25521645

  7. RAId_DbS: Method for Peptide ID using Database Search with Accurate Statistics

    NASA Astrophysics Data System (ADS)

    Alves, Gelio; Ogurtsov, Aleksey; Yu, Yi-Kuo

    2007-03-01

    The key to proteomics studies, essential in systems biology, is peptide identification. Under tandem mass spectrometry, each spectrum generated consists of a list of mass/charge peaks along with their intensities. Software analysis is then required to identify from the spectrum peptide candidates that best interpret the spectrum. The library search, which compares the spectral peaks against theoretical peaks generated by each peptide in a library, is among the most popular methods. This method, although robust, lacks good quantitative statistical underpinning. As we show, many library search algorithms suffer from statistical instability. The need for a better statistical basis prompted us to develop RAId_DbS. Taking into account the skewness in the peak intensity distribution while scoring peptides, RAId_DbS provides an accurate statistical significance assignment to each peptide candidate. RAId_DbS will be a valuable tool especially when one intends to identify proteins through peptide identifications.

  8. In silico prediction of peptide binding affinity to class I mouse major histocompatibility complexes: a comparative molecular similarity index analysis (CoMSIA) study.

    PubMed

    Hattotuwagama, Channa K; Doytchinova, Irini A; Flower, Darren R

    2005-01-01

    Current methods for the in silico identification of T cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate prediction of peptide-major histocompatibility complex (MHC) affinity. A three-dimensional quantitative structure-activity relationship (3D-QSAR) for the prediction of peptide binding to class I MHC molecules was established using the comparative molecular similarity index analysis (CoMSIA) method. Three MHC alleles were studied: H2-D(b), H2-K(b), and H2-K(k). Models were produced for each allele. Each model consisted of five physicochemical descriptors-steric bulk, electrostatic potentials, hydrophobic interactions, and hydrogen-bond donor and hydrogen-bond acceptor abilities. The models have an acceptable level of predictivity: cross-validation leave-one-out statistical terms q2 and SEP (standard error of prediction) ranged between 0.490 and 0.679 and between 0.525 and 0.889, respectively. The non-cross-validated statistical terms r2 and SEE (standard error of estimate) ranged between 0.913 and 0.979 and between 0.167 and 0.248, respectively. The use of coefficient contour maps, which indicate favored and disfavored areas for each position of the MHC-bound peptides, allowed the binding specificity of each allele to be identified, visualized, and understood. The present study demonstrates the effectiveness of CoMSIA as a method for studying peptide-MHC interactions. The peptides used in this study are available on the Internet (http://www.jenner.ac.uk/AntiJen). The partial least-squares method is available commercially in the SYBYL molecular modeling software package. PMID:16180918

  9. 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.

  10. 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. PMID:21074533

  11. 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

  12. A continuous peptide epitope reacting with pandemic influenza AH1N1 predicted by bioinformatic approaches.

    PubMed

    Carrillo-Vazquez, Jonathan P; Correa-Basurto, José; García-Machorro, Jazmin; Campos-Rodríguez, Rafael; Moreau, Violaine; Rosas-Trigueros, Jorge L; Reyes-López, Cesar A; Rojas-López, Marlon; Zamorano-Carrillo, Absalom

    2015-09-01

    Computational identification of potential epitopes with an immunogenic capacity challenges immunological research. Several methods show considerable success, and together with experimental studies, the efficiency of the algorithms to identify potential peptides with biological activity has improved. Herein, an epitope was designed by combining bioinformatics, docking, and molecular dynamics simulations. The hemagglutinin protein of the H1N1 influenza pandemic strain served as a template, owing to the interest of obtaining a scheme of immunization. Afterward, we performed enzyme-linked immunosorbent assay (ELISA) using the epitope to analyze if any antibodies in human sera before and after the influenza outbreak in 2009 recognize this peptide. Also, a plaque reduction neutralization test induced by virus-neutralizing antibodies and the IgG determination showed the biological activity of this computationally designed peptide. The results of the ELISAs demonstrated that the serum of both prepandemic and pandemic recognized the epitope. Moreover, the plaque reduction neutralization test evidenced the capacity of the designed peptide to neutralize influenza virus in Madin-Darby canine cells. PMID:25788327

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

    PubMed

    Wong, Yue Him; Yu, Li; 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

  14. Analysis and Prediction of the Critical Regions of Antimicrobial Peptides Based on Conditional Random Fields

    PubMed Central

    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. PMID:25803302

  15. Prediction and preliminary screening of HLA-A*0201-restricted epitope peptides of human GPC3.

    PubMed

    Hu, P; Wei, Z; Li, R; Wu, D; Meng, Z

    2016-06-01

    In response to the limited therapeutic option for hepatocellular carcinoma (HCC), immunotherapy as a promising approach points out a new direction to the cure of tumours through specific recognition and elimination of tumour cells by the immunity-enhanced autologous immunocytes of patients. Few effective tumour antigens, however, are alternative in addition to alpha fetoprotein or tumour cell lysates. Recent studies have demonstrated that glypican-3 (GPC3) is not only a promising diagnostic marker, but also ideal therapeutic target to HCC. In this study, potential HLA-A*0201 GPC3 peptides were screened with three epitope prediction software, the binding affinity of 13 predicted epitopes with high scores was determined by T2 cells binding assay and four optimal epitopes were identified. This is the first study in which the optimal HLA-A*0201 GPC3 epitopes were screened from a large number of candidates predicted by three software. The optimized HLA-A*0201 GPC3 peptides will provide new epitope candidates for HCC immunotherapy. PMID:27102087

  16. 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

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

    PubMed Central

    Antipas, Georgios S. E.; Germenis, Anastasios E.

    2015-01-01

    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. PMID:25713797

  18. Computational Prediction of Immunodominant Epitopes on Outer Membrane Protein (Omp) H of Pasteurella multocida Toward Designing of a Peptide Vaccine.

    PubMed

    Ganguly, Bhaskar

    2016-01-01

    Contemporary vaccine design necessitates discrimination between the immunogenic and non-immunogenic components within a pathogen. To successfully target a humoral immune response, the vaccine antigen should contain not only B-cell epitopes but abounding Th-cell agretopes and MHC-II binding regions as well. No single computational method is available that allows the identification of such regions on antigens with good reliability. A consensus approach based on several prediction methods can be adopted to overcome this problem.Targeting the outer membrane protein (Omp) H as a candidate, a comprehensive work flow is described for the computational identification of immunodominant epitopes toward the designing of a peptide vaccine against Pasteurella multocida. PMID:27076289

  19. 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. PMID:24447175

  20. MHCPred: A server for quantitative prediction of peptide-MHC binding.

    PubMed

    Guan, Pingping; Doytchinova, Irini A; Zygouri, Christianna; Flower, Darren R

    2003-07-01

    Accurate T-cell epitope prediction is a principal objective of computational vaccinology. As a service to the immunology and vaccinology communities at large, we have implemented, as a server on the World Wide Web, a partial least squares-based multivariate statistical approach to the quantitative prediction of peptide binding to major histocom- patibility complexes (MHC), the key checkpoint on the antigen presentation pathway within adaptive cellular immunity. MHCPred implements robust statistical models for both Class I alleles (HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3301, HLA-A*6801, HLA-A*6802 and HLA-B*3501) and Class II alleles (HLA-DRB*0401, HLA-DRB*0401 and HLA-DRB*0701). MHCPred is available from the URL: http://www.jenner.ac.uk/MHCPred. PMID:12824380

  1. 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

  2. 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

  3. Structural load prediction methods for space payloads

    NASA Technical Reports Server (NTRS)

    Wada, B. K.

    1982-01-01

    The state of the art in structural loads prediction procedures for spacecraft is summarized. Three categories of prediction techniques delineated by cost, complexity, comprehensiveness, accuracy, and applications are outlined. The lowest cost method has been used for earth resources, communications, and weather satellites, the medium cost method for sun-synchronous orbits and the large space telescope, and the most expensive for planetary missions, comet rendezvous, and out-of-ecliptic orbits, all assuming Shuttle launch. The lowest cost method involves a mass-acceleration curve. A shock spectra technique predicts a least upper bound for loads. A recovered transient method analyzes the interface acceleration of two connected launch vehicles. The most accurate method devised thus far is a transient analysis of the total launch vehicle/payload dynamic system.

  4. B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: the CHARGE-AF Consortium of community-based cohort studies

    PubMed Central

    Sinner, Moritz F.; Stepas, Katherine A.; Moser, Carlee B.; Krijthe, Bouwe P.; Aspelund, Thor; Sotoodehnia, Nona; Fontes, João D.; Janssens, A. Cecile J.W.; Kronmal, Richard A.; Magnani, Jared W.; Witteman, Jacqueline C.; Chamberlain, Alanna M.; Lubitz, Steven A.; Schnabel, Renate B.; Vasan, Ramachandran S.; Wang, Thomas J.; Agarwal, Sunil K.; McManus, David D.; Franco, Oscar H.; Yin, Xiaoyan; Larson, Martin G.; Burke, Gregory L.; Launer, Lenore J.; Hofman, Albert; Levy, Daniel; Gottdiener, John S.; Kääb, Stefan; Couper, David; Harris, Tamara B.; Astor, Brad C.; Ballantyne, Christie M.; Hoogeveen, Ron C.; Arai, Andrew E.; Soliman, Elsayed Z.; Ellinor, Patrick T.; Stricker, Bruno H.C.; Gudnason, Vilmundur; Heckbert, Susan R.; Pencina, Michael J.; Benjamin, Emelia J.; Alonso, Alvaro

    2014-01-01

    Aims B-type natriuretic peptide (BNP) and C-reactive protein (CRP) predict atrial fibrillation (AF) risk. However, their risk stratification abilities in the broad community remain uncertain. We sought to improve risk stratification for AF using biomarker information. Methods and results We ascertained AF incidence in 18 556 Whites and African Americans from the Atherosclerosis Risk in Communities Study (ARIC, n=10 675), Cardiovascular Health Study (CHS, n = 5043), and Framingham Heart Study (FHS, n = 2838), followed for 5 years (prediction horizon). We added BNP (ARIC/CHS: N-terminal pro-B-type natriuretic peptide; FHS: BNP), CRP, or both to a previously reported AF risk score, and assessed model calibration and predictive ability [C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI)]. We replicated models in two independent European cohorts: Age, Gene/Environment Susceptibility Reykjavik Study (AGES), n = 4467; Rotterdam Study (RS), n = 3203. B-type natriuretic peptide and CRP were significantly associated with AF incidence (n = 1186): hazard ratio per 1-SD ln-transformed biomarker 1.66 [95% confidence interval (CI), 1.56–1.76], P < 0.0001 and 1.18 (95% CI, 1.11–1.25), P < 0.0001, respectively. Model calibration was sufficient (BNP, χ2 = 17.0; CRP, χ2 = 10.5; BNP and CRP, χ2 = 13.1). B-type natriuretic peptide improved the C-statistic from 0.765 to 0.790, yielded an IDI of 0.027 (95% CI, 0.022–0.032), a relative IDI of 41.5%, and a continuous NRI of 0.389 (95% CI, 0.322–0.455). The predictive ability of CRP was limited (C-statistic increment 0.003). B-type natriuretic peptide consistently improved prediction in AGES and RS. Conclusion B-type natriuretic peptide, not CRP, substantially improved AF risk prediction beyond clinical factors in an independently replicated, heterogeneous population. B-type natriuretic peptide may serve as a benchmark to evaluate novel putative AF risk biomarkers. PMID:25037055

  5. A graph kernel approach for alignment-free domain–peptide interaction prediction with an application to human SH3 domains

    PubMed Central

    Kundu, Kousik; Costa, Fabrizio; Backofen, Rolf

    2013-01-01

    Motivation: State-of-the-art experimental data for determining binding specificities of peptide recognition modules (PRMs) is obtained by high-throughput approaches like peptide arrays. Most prediction tools applicable to this kind of data are based on an initial multiple alignment of the peptide ligands. Building an initial alignment can be error-prone, especially in the case of the proline-rich peptides bound by the SH3 domains. Results: Here, we present a machine-learning approach based on an efficient graph-kernel technique to predict the specificity of a large set of 70 human SH3 domains, which are an important class of PRMs. The graph-kernel strategy allows us to (i) integrate several types of physico-chemical information for each amino acid, (ii) consider high-order correlations between these features and (iii) eliminate the need for an initial peptide alignment. We build specialized models for each human SH3 domain and achieve competitive predictive performance of 0.73 area under precision-recall curve, compared with 0.27 area under precision-recall curve for state-of-the-art methods based on position weight matrices. We show that better models can be obtained when we use information on the noninteracting peptides (negative examples), which is currently not used by the state-of-the art approaches based on position weight matrices. To this end, we analyze two strategies to identify subsets of high confidence negative data. The techniques introduced here are more general and hence can also be used for any other protein domains, which interact with short peptides (i.e. other PRMs). Availability: The program with the predictive models can be found at http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/SH3PepInt.tar.gz. We also provide a genome-wide prediction for all 70 human SH3 domains, which can be found under http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/Genome-Wide-Predictions.tar.gz. Contact: backofen@informatik.uni-freiburg.de Supplementary

  6. Novel method to identify the optimal antimicrobial peptide in a combination matrix, using anoplin as an example.

    PubMed

    Munk, J K; Ritz, C; Fliedner, F P; Frimodt-Møller, N; Hansen, P R

    2014-01-01

    Microbial resistance is an increasing health concern and a true danger to human well-being. A worldwide search for new compounds is ongoing, and antimicrobial peptides are promising lead candidates for tomorrow's antibiotics. The decapeptide anoplin (GLLKRIKTLL-NH2) is an especially interesting candidate because of its small size as well as its antimicrobial and nonhemolytic properties. Optimization of the properties of an antimicrobial peptide such as anoplin requires multidimensional searching in a complex chemical space. Typically, such optimization is performed by labor-intensive and costly trial-and-error methods. In this study, we show the benefit of fractional factorial design for identification of the optimal antimicrobial peptide in a combination matrix. We synthesized and analyzed a training set of 12 anoplin analogs, representative of 64 analogs in total. Using MIC, hemolysis, and high-performance liquid chromatography retention time data, we constructed analysis-of-variance models that describe the relationship between these properties and the structural characteristics of the analogs. We show that the mathematical models derived from the training set data can be used to predict the properties of other analogs in the chemical space. Hence, this method provides an efficient means of identification of the optimal peptide in the searched chemical space. PMID:24277042

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

    SciTech Connect

    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.

  8. 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.

  9. 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.

    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.

  10. 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

  11. Predicting antitumor activity of peptides by consensus of regression models trained on a small data sample.

    PubMed

    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

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

    PubMed Central

    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

    2015-01-01

    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. PMID:25959593

  13. A T-EOF Based Prediction Method.

    NASA Astrophysics Data System (ADS)

    Lee, Yung-An

    2002-01-01

    A new statistical time series prediction method based on temporal empirical orthogonal function (T-EOF) is introduced in this study. This method first applies singular spectrum analysis (SSA) to extract dominant T-EOFs from historical data. Then, the most recent data are projected onto an optimal subset of the T-EOFs to estimate the corresponding temporal principal components (T-PCs). Finally, a forecast is constructed from these T-EOFs and T-PCs. Results from forecast experiments on the El Niño sea surface temperature (SST) indices from 1993 to 2000 showed that this method consistently yielded better correlation skill than autoregressive models for a lead time longer than 6 months. Furthermore, the correlation skills of this method in predicting Niño-3 index remained above 0.5 for a lead time up to 36 months during this period. However, this method still encountered the `spring barrier' problem. Because the 1990s exhibited relatively weak spring barrier, these results indicate that the T-EOF based prediction method has certain extended forecasting capability in the period when the spring barrier is weak. They also suggest that the potential predictability of ENSO in a certain period may be longer than previously thought.

  14. 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.

  15. 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 Central

    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. PMID:24741624

  16. Predicting the Structure-Activity Relationship of Hydroxyapatite-Binding Peptides by Enhanced-Sampling Molecular Simulation.

    PubMed

    Zhao, Weilong; Xu, Zhijun; Cui, Qiang; Sahai, Nita

    2016-07-12

    Understanding the molecular structural and energetic basis of the interactions between peptides and inorganic surfaces is critical to their applications in tissue engineering and biomimetic material synthesis. Despite recent experimental progresses in the identification and functionalization of hydroxyapatite (HAP)-binding peptides, the molecular mechanisms of their interactions with HAP surfaces are yet to be explored. In particular, the traditional method of molecular dynamics (MD) simulation suffers from insufficient sampling at the peptide-inorganic interface that renders the molecular-level observation dubious. Here we demonstrate that an integrated approach combining bioinformatics, MD, and metadynamics provides a powerful tool for investigating the structure-activity relationship of HAP-binding peptides. Four low charge density peptides, previously identified by phage display, have been considered. As revealed by bioinformatics and MD, the binding conformation of the peptides is controlled by both the sequence and the amino acid composition. It was found that formation of hydrogen bonds between lysine residue and phosphate ions on the surface dictates the binding of positively charged peptide to HAP. The binding affinities of the peptides to the surface are estimated by free energy calculation using parallel-tempering metadynamics, and the results compare favorably to measurements reported in previous experimental studies. The calculation suggests that the charge density of the peptide primarily controls the binding affinity to the surface, while the backbone secondary structure that may restrain side chain orientation toward the surface plays a minor role. We also report that the application of enhanced-sampling metadynamics effects a major advantage over the steered MD method by significantly improving the reliability of binding free energy calculation. In general, our novel integration of diverse sampling techniques should contribute to the rational

  17. GECluster: a novel protein complex prediction method

    PubMed Central

    Su, Lingtao; Liu, Guixia; Wang, Han; Tian, Yuan; Zhou, Zhihui; Han, Liang; Yan, Lun

    2014-01-01

    Identification of protein complexes is of great importance in the understanding of cellular organization and functions. Traditional computational protein complex prediction methods mainly rely on the topology of protein–protein interaction (PPI) networks but seldom take biological information of proteins (such as Gene Ontology (GO)) into consideration. Meanwhile, the environment relevant analysis of protein complex evolution has been poorly studied, partly due to the lack of high-precision protein complex datasets. In this paper, a combined PPI network is introduced to predict protein complexes which integrate both GO and expression value of relevant protein-coding genes. A novel protein complex prediction method GECluster (Gene Expression Cluster) was proposed based on a seed node expansion strategy, in which a combined PPI network was utilized. GECluster was applied to a training combined PPI network and it predicted more credible complexes than peer methods. The results indicate that using a combined PPI network can efficiently improve protein complex prediction accuracy. In order to study protein complex evolution within cells due to changes in the living environment surrounding cells, GECluster was applied to seven combined PPI networks constructed using the data of a test set including yeast response to stress throughout a wine fermentation process. Our results showed that with the rise of alcohol concentration, protein complexes within yeast cells gradually evolve from one state to another. Besides this, the number of core and attachment proteins within a protein complex both changed significantly. PMID:26019559

  18. 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.

  19. 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

    2015-01-01

    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. PMID:26007178

  20. Prediction of the effect on antihyperglycaemic action of sitagliptin by plasma active form glucagon-like peptide-1

    PubMed Central

    Kushiyama, Akifumi; Kikuchi, Takako; Tanaka, Kentaro; Tahara, Tazu; Takao, Toshiko; Onishi, Yukiko; Yoshida, Yoko; Kawazu, Shoji; Iwamoto, Yasuhiko

    2016-01-01

    AIM: To investigate whether active glucagon-like peptide-1 (GLP-1) is a prediction Factor of Effect of sitagliptin on patients with type 2 diabetes mellitus (GLP-1 FEST:UMIN000010645). METHODS: Seventy-six patients with type 2 diabetes, who had insufficient glycemic control [Hemoglobin A1c (HbA1c) ≥ 7%] in spite of treatment with metformin and/or sulfonylurea, were included in the investigation. Patients were divided into three groups by tertiles of fasting plasma active GLP-1 level, before the administration of 50 mg sitagliptin. RESULTS: At baseline, body mass index, serum UA, insulin and HOMA-IR were higher in the high active GLP-1 group than in the other two groups. The high active GLP-1 group did not show any decline of HbA1c (7.6% ± 1.4% to 7.5% ± 1.5%), whereas the middle and low groups indicated significant decline of HbA1c (7.4 ± 0.7 to 6.8 ± 0.6 and 7.4 ± 1.2 to 6.9 ± 1.3, respectively) during six months. Only the low and middle groups showed a significant increment of active GLP-1, C-peptide level, a decreased log and proinsulin/insulin ratio after administration. In logistic analysis, the low or middle group is a significant explanatory variable for an HbA1c decrease of ≥ 0.5%, and its odds ratio is 4.5 (1.40-17.6) (P = 0.01) against the high active GLP-1 group. This remains independent when adjusted for HbA1c level before administration, patients’ medical history, medications, insulin secretion and insulin resistance. CONCLUSION: Plasma fasting active GLP-1 is an independent predictive marker for the efficacy of dipeptidyl peptidase 4 inhibitor sitagliptin. PMID:27326345

  1. 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.

  2. Quantitative Evaluation of Peptide-Material Interactions by a Force Mapping Method: Guidelines for Surface Modification.

    PubMed

    Mochizuki, Masahito; Oguchi, Masahiro; Kim, Seong-Oh; Jackman, Joshua A; Ogawa, Tetsu; Lkhamsuren, Ganchimeg; Cho, Nam-Joon; Hayashi, Tomohiro

    2015-07-28

    Peptide coatings on material surfaces have demonstrated wide application across materials science and biotechnology, facilitating the development of nanobio interfaces through surface modification. A guiding motivation in the field is to engineer peptides with a high and selective binding affinity to target materials. Herein, we introduce a quantitative force mapping method in order to evaluate the binding affinity of peptides to various hydrophilic oxide materials by atomic force microscopy (AFM). Statistical analysis of adhesion forces and probabilities obtained on substrates with a materials contrast enabled us to simultaneously compare the peptide binding affinity to different materials. On the basis of the experimental results and corresponding theoretical analysis, we discuss the role of various interfacial forces in modulating the strength of peptide attachment to hydrophilic oxide solid supports as well as to gold. The results emphasize the precision and robustness of our approach to evaluating the adhesion strength of peptides to solid supports, thereby offering guidelines to improve the design and fabrication of peptide-coated materials. PMID:26125092

  3. 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.

  4. Predicting functional regulatory SNPs in the human antimicrobial peptide genes DEFB1 and CAMP in tuberculosis and HIV/AIDS.

    PubMed

    Flores Saiffe Farías, Adolfo; Jaime Herrera López, Enrique; Moreno Vázquez, Cristopher Jorge; Li, Wentian; Prado Montes de Oca, Ernesto

    2015-12-01

    Single nucleotide polymorphisms (SNPs) in transcription factor binding sites (TFBSs) within gene promoter region or enhancers can modify the transcription rate of genes related to complex diseases. These SNPs can be called regulatory SNPs (rSNPs). Data compiled from recent projects, such as the 1000 Genomes Project and ENCODE, has revealed essential information used to perform in silico prediction of the molecular and biological repercussions of SNPs within TFBS. However, most of these studies are very limited, as they only analyze SNPs in coding regions or when applied to promoters, and do not integrate essential biological data like TFBSs, expression profiles, pathway analysis, homotypic redundancy (number of TFBSs for the same TF in a region), chromatin accessibility and others, which could lead to a more accurate prediction. Our aim was to integrate different data in a biologically coherent method to analyze the proximal promoter regions of two antimicrobial peptide genes, DEFB1 and CAMP, that are associated with tuberculosis (TB) and HIV/AIDS. We predicted SNPs within the promoter regions that are more likely to interact with transcription factors (TFs). We also assessed the impact of homotypic redundancy using a novel approach called the homotypic redundancy weight factor (HWF). Our results identified 10 SNPs, which putatively modify the binding affinity of 24 TFs previously identified as related to TB and HIV/AIDS expression profiles (e.g. KLF5, CEBPA and NFKB1 for TB; FOXP2, BRCA1, CEBPB, CREB1, EBF1 and ZNF354C for HIV/AIDS; and RUNX2, HIF1A, JUN/AP-1, NR4A2, EGR1 for both diseases). Validating with the OregAnno database and cell-specific functional/non functional SNPs from additional 13 genes, our algorithm performed 53% sensitivity and 84.6% specificity to detect functional rSNPs using the DNAseI-HUP database. We are proposing our algorithm as a novel in silico method to detect true functional rSNPs in antimicrobial peptide genes. With further

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

    PubMed

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

    2016-07-01

    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

  6. BactPepDB: a database of predicted peptides from a exhaustive survey of complete prokaryote genomes

    PubMed Central

    Rey, Julien; Deschavanne, Patrick; Tuffery, Pierre

    2014-01-01

    With the recent progress in complete genome sequencing, mining the increasing amount of genomic information available should in theory provide the means to discover new classes of peptides. However, annotation pipelines often do not consider small reading frames likely to be expressed. BactPepDB, available online at http://bactpepdb.rpbs.univ-paris-diderot.fr, is a database that aims at providing an exhaustive re-annotation of all complete prokaryotic genomes—chromosomal and plasmid DNA—available in RefSeq for coding sequences ranging between 10 and 80 amino acids. The identified peptides are classified as (i) previously identified in RefSeq, (ii) entity-overlapping (intragenic) or intergenic, and (iii) potential pseudogenes—intergenic sequences corresponding to a portion of a previously annotated larger gene. Additional information is related to homologs within order, predicted signal sequence, transmembrane segments, disulfide bonds, secondary structure, and the existence of a related 3D structure in the Protein Databank. As a result, BactPepDB provides insights about candidate peptides, and provides information about their conservation, together with some of their expected biological/structural features. The BactPepDB interface allows to search for candidate peptides in the database, or to search for peptides similar to a query, according to the multiple properties predicted or related to genomic localization. Database URL: http://www.yeastgenome.org/ PMID:25377257

  7. 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. PMID:25864448

  8. 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

  9. 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

  10. Machine learning methods for predictive proteomics.

    PubMed

    Barla, Annalisa; Jurman, Giuseppe; Riccadonna, Samantha; Merler, Stefano; Chierici, Marco; Furlanello, Cesare

    2008-03-01

    The search for predictive biomarkers of disease from high-throughput mass spectrometry (MS) data requires a complex analysis path. Preprocessing and machine-learning modules are pipelined, starting from raw spectra, to set up a predictive classifier based on a shortlist of candidate features. As a machine-learning problem, proteomic profiling on MS data needs caution like the microarray case. The risk of overfitting and of selection bias effects is pervasive: not only potential features easily outnumber samples by 10(3) times, but it is easy to neglect information-leakage effects during preprocessing from spectra to peaks. The aim of this review is to explain how to build a general purpose design analysis protocol (DAP) for predictive proteomic profiling: we show how to limit leakage due to parameter tuning and how to organize classification and ranking on large numbers of replicate versions of the original data to avoid selection bias. The DAP can be used with alternative components, i.e. with different preprocessing methods (peak clustering or wavelet based), classifiers e.g. Support Vector Machine (SVM) or feature ranking methods (recursive feature elimination or I-Relief). A procedure for assessing stability and predictive value of the resulting biomarkers' list is also provided. The approach is exemplified with experiments on synthetic datasets (from the Cromwell MS simulator) and with publicly available datasets from cancer studies. PMID:18310105

  11. Method for the synthesis of highly pure vaccines using the lipid core peptide system.

    PubMed

    Moyle, Peter M; Olive, Colleen; Good, Michael F; Toth, Istvan

    2006-12-01

    Traditional vaccines consisting of whole attenuated microorganisms, killed microorganisms, or microbial components, administered with an adjuvant (e.g. alum), have been proved to be extremely successful. However, to develop new vaccines, or to improve upon current vaccines, new vaccine development techniques are required. Peptide vaccines offer the capacity to administer only the minimal microbial components necessary to elicit appropriate immune responses, minimizing the risk of vaccination associated adverse effects, and focusing the immune response toward important antigens. Peptide vaccines, however, are generally poorly immunogenic, necessitating administration with powerful, and potentially toxic adjuvants. The attachment of lipids to peptide antigens has been demonstrated as a potentially safe method for adjuvanting peptide epitopes. The lipid core peptide (LCP) system, which incorporates a lipidic adjuvant, carrier, and peptide epitopes into a single molecular entity, has been demonstrated to boost immunogenicity of attached peptide epitopes without the need for additional adjuvants. The synthesis of LCP systems normally yields a product that cannot be purified to homogeneity. The current study describes the development of methods for the synthesis of highly pure LCP analogs using native chemical ligation. Because of the highly lipophilic nature of the LCP lipid adjuvant, difficulties (e.g. poor solubility) were experienced with the ligation reactions. The addition of organic solvents to the ligation buffer solubilized lipidic species, but did not result in successful ligation reactions. In comparison, the addition of approximately 1% (w/v) sodium dodecyl sulfate (SDS) proved successful, enabling the synthesis of two highly pure, tri-epitopic Streptococcus pyogenes LCP analogs. Subcutaneous immunization of B10.BR (H-2(k)) mice with one of these vaccines, without the addition of any adjuvant, elicited high levels of systemic IgG antibodies against each of

  12. Secondary structure of short β-peptides as the chiral expression of monomeric building units: a rational and predictive model.

    PubMed

    Gorrea, Esther; Pohl, Gábor; Nolis, Pau; Celis, Sergio; Burusco, Kepa K; Branchadell, Vicenç; Perczel, András; Ortuño, Rosa M

    2012-11-01

    Chirality of the monomeric residues controls and determines the prevalent folding of small oligopeptides (from di- to tetramers) composed of 2-aminocyclobutane-1-carboxylic acid (ACBA) derivatives with the same or different absolute and relative configuration. The cis-form of the monomeric ACBA gives rise to two conformers, namely, Z6 and Z8, while the trans-form manifests uniquely as an H8 structure. By combining these subunits in oligo- and polypeptides, their local structural preference remains, thus allowing the rational design of new short foldamers. A lego-type molecular architecture evolves; the overall look depends only on the conformational properties of the structural building units. A versatile and efficient method to predict the backbone folds of designed cyclobutane β-peptides is based on QM calculations. Predictions are corroborated by high-resolution NMR studies on selected stereoisomers, most of them being new foldamers that have been synthesized and characterized for the first time. Thus, the chiral expression of monomeric building units results in the defined secondary structures of small oligomers. As a result of this study, a new set of chirality controlled foldamers is provided to probe as biocompatible biopolymers. PMID:23030251

  13. 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.

  14. NetMHCcons: a consensus method for the major histocompatibility complex class I predictions.

    PubMed

    Karosiene, Edita; Lundegaard, Claus; Lund, Ole; Nielsen, Morten

    2012-03-01

    A key role in cell-mediated immunity is dedicated to the major histocompatibility complex (MHC) molecules that bind peptides for presentation on the cell surface. Several in silico methods capable of predicting peptide binding to MHC class I have been developed. The accuracy of these methods depends on the data available characterizing the binding specificity of the MHC molecules. It has, moreover, been demonstrated that consensus methods defined as combinations of two or more different methods led to improved prediction accuracy. This plethora of methods makes it very difficult for the non-expert user to choose the most suitable method for predicting binding to a given MHC molecule. In this study, we have therefore made an in-depth analysis of combinations of three state-of-the-art MHC-peptide binding prediction methods (NetMHC, NetMHCpan and PickPocket). We demonstrate that a simple combination of NetMHC and NetMHCpan gives the highest performance when the allele in question is included in the training and is characterized by at least 50 data points with at least ten binders. Otherwise, NetMHCpan is the best predictor. When an allele has not been characterized, the performance depends on the distance to the training data. NetMHCpan has the highest performance when close neighbours are present in the training set, while the combination of NetMHCpan and PickPocket outperforms either of the two methods for alleles with more remote neighbours. The final method, NetMHCcons, is publicly available at www.cbs.dtu.dk/services/NetMHCcons , and allows the user in an automatic manner to obtain the most accurate predictions for any given MHC molecule. PMID:22009319

  15. 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.

  16. PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides

    PubMed Central

    Thévenet, Pierre; Shen, Yimin; Maupetit, Julien; Guyon, Frédéric; Derreumaux, Philippe; Tufféry, Pierre

    2012-01-01

    In the context of the renewed interest of peptides as therapeutics, it is important to have an on-line resource for 3D structure prediction of peptides with well-defined structures in aqueous solution. We present an updated version of PEP-FOLD allowing the treatment of both linear and disulphide bonded cyclic peptides with 9–36 amino acids. The server makes possible to define disulphide bonds and any residue–residue proximity under the guidance of the biologists. Using a benchmark of 34 cyclic peptides with one, two and three disulphide bonds, the best PEP-FOLD models deviate by an average RMS of 2.75 Å from the full NMR structures. Using a benchmark of 37 linear peptides, PEP-FOLD locates lowest-energy conformations deviating by 3 Å RMS from the NMR rigid cores. The evolution of PEP-FOLD comes as a new on-line service to supersede the previous server. The server is available at: http://bioserv.rpbs.univ-paris-diderot.fr/PEP-FOLD. PMID:22581768

  17. 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.

  18. 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.

  19. Prediction and Analysis of Quorum Sensing Peptides Based on Sequence Features

    PubMed Central

    Rajput, Akanksha; Gupta, Amit Kumar; Kumar, Manoj

    2015-01-01

    Quorum sensing peptides (QSPs) are the signaling molecules used by the Gram-positive bacteria in orchestrating cell-to-cell communication. In spite of their enormous importance in signaling process, their detailed bioinformatics analysis is lacking. In this study, QSPs and non-QSPs were examined according to their amino acid composition, residues position, motifs and physicochemical properties. Compositional analysis concludes that QSPs are enriched with aromatic residues like Trp, Tyr and Phe. At the N-terminal, Ser was a dominant residue at maximum positions, namely, first, second, third and fifth while Phe was a preferred residue at first, third and fifth positions from the C-terminal. A few motifs from QSPs were also extracted. Physicochemical properties like aromaticity, molecular weight and secondary structure were found to be distinguishing features of QSPs. Exploiting above properties, we have developed a Support Vector Machine (SVM) based predictive model. During 10-fold cross-validation, SVM achieves maximum accuracy of 93.00%, Mathew’s correlation coefficient (MCC) of 0.86 and Receiver operating characteristic (ROC) of 0.98 on the training/testing dataset (T200p+200n). Developed models performed equally well on the validation dataset (V20p+20n). The server also integrates several useful analysis tools like “QSMotifScan”, “ProtFrag”, “MutGen” and “PhysicoProp”. Our analysis reveals important characteristics of QSPs and on the basis of these unique features, we have developed a prediction algorithm “QSPpred” (freely available at: http://crdd.osdd.net/servers/qsppred). PMID:25781990

  20. Trailing edge noise prediction using Amiet's method

    NASA Technical Reports Server (NTRS)

    Brooks, T. F.

    1981-01-01

    Amiet's (1976, 1978) solution to the problem of airfoil trailing edge noise prediction is discussed in light of the results of evanescent wave theory's application to the measured surface pressure behavior near the trailing edge of an airfoil with a turbulent boundary layer. The method employed by Amiet has the advantage of incorporating the effect of finite chord in its solution. The assumed form of the pressure distribution is examined as well as the constant turbulent boundary layer convection assumption, which is found to be unnecessarily restrictive.

  1. 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.

  2. A novel conformational B-cell epitope prediction method based on mimotope and patch analysis.

    PubMed

    Sun, Pingping; Qi, Jialiang; Zhao, Yizhu; Huang, Yanxin; Yang, Guifu; Ma, Zhiqiang; Li, Yuxin

    2016-04-01

    A B-cell epitope is a group of residues on the surface of an antigen that stimulates humoral immune responses. Identifying B-cell epitopes is important for effective vaccine design. Predicting epitopes by experimental methods is expensive in terms of time, cost and effort; therefore, computational methods that have a low cost and high speed are widely used to predict B-cell epitopes. Recently, epitope prediction based on random peptide library screening has been viewed as a promising method. Some novel software and web-based servers have been proposed that have succeeded in some test cases. Herein, we propose a novel epitope prediction method based on amino acid pairs and patch analysis. The method first divides antigen surfaces into overlapping patches based on both radius (R) and number (N), then predict epitopes based on Amino Acid Pairs (AAPs) from mimotopes and the surface patch. The proposed method yields a mean sensitivity of 0.53, specificity of 0.77, ACC of 0.75 and F-measure of 0.45 for 39 test cases. Compared with mimotope-based methods, patch-based methods and two other prediction methods, the sensitivity of the new method offers a certain improvement. Our findings demonstrate that this proposed method was successful for patch and AAPs analysis and allowed for conformational B-cell epitope prediction. PMID:26804644

  3. Antibody Production with Synthetic Peptides.

    PubMed

    Lee, Bao-Shiang; Huang, Jin-Sheng; Jayathilaka, Lasanthi P; Lee, Jenny; Gupta, Shalini

    2016-01-01

    Peptides (usually 10-20 amino acid residues in length) can be used as effectively as proteins in raising antibodies producing both polyclonal and monoclonal antibodies routinely with titers higher than 20,000. Peptide antigens do not function as immunogens unless they are conjugated to proteins. Production of high quality antipeptide antibodies is dependent upon peptide sequence selection, the success of peptide synthesis, peptide-carrier protein conjugation, the humoral immune response in the host animal, the adjuvant used, the peptide dose administered, the injection method, and the purification of the antibody. Peptide sequence selection is probably the most critical step in the production of antipeptide antibodies. Although the process for designing peptide antigens is not exact, several guidelines and computational B-cell epitope prediction methods can help maximize the likelihood of producing antipeptide antibodies that recognize the protein. Antibodies raised by peptides have become essential tools in life science research. Virtually all phospho-specific antibodies are now produced using phosphopeptides as antigens. Typically, 5-20 mg of peptide is enough for antipeptide antibody production. It takes 3 months to produce a polyclonal antipeptide antibody in rabbits that yields ~100 mL of serum which corresponds to ~8-10 mg of the specific antibody after affinity purification using a peptide column. PMID:27515072

  4. The depsipeptide method for solid-phase synthesis of difficult peptides.

    PubMed

    Coin, Irene

    2010-05-01

    After about one century of peptide chemistry, the main limitation to the accessibility of peptides and proteins via chemosynthesis is the arising of folding and aggregation phenomena. This is true not only for sequences above a critical length but also for several biologically relevant substrates that are relatively short yet form either highly folded structures (e.g. WW domains) or fibrils and aggregates after final deprotection (beta-amyloid peptide). Such so-called difficult sequences may be more easily obtained via their corresponding depsipeptides (O-acyl isopeptides), ester isomers that are often easier to assemble and purify, and are smoothly converted to the parent amides under mild conditions. The depsipeptide method is the most recent technique to improve the outcome of difficult syntheses, applicable to sequences containing residues of serine or threonine. A brief overview is presented about chemical aspects of the method, the steps that have been undertaken for its optimization, and the evaluation of its efficiency. Further applications of analogous principles to other critical topics in peptide synthesis such as condensation of peptide segments and solid-phase synthesis of naturally occurring cyclodepsipeptides are addressed as well. PMID:20401924

  5. MALDI immunoscreening (MiSCREEN): a method for selection of anti-peptide monoclonal antibodies for use in immunoproteomics.

    PubMed

    Razavi, Morteza; Pope, Matthew E; Soste, Martin V; Eyford, Brett A; Jackson, Angela M; Anderson, N Leigh; Pearson, Terry W

    2011-02-01

    A scalable method for screening and selection of peptide-specific monoclonal antibodies (mAbs) is described. To identify high affinity anti-peptide mAbs in hybridoma supernatants, antibodies were captured by magnetic affinity beads followed by binding of specific peptides from solution. After timed washing steps, the remaining bound peptides were eluted from the beads and detected by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS). This allowed measurement of monovalent interactions of peptides with single antigen binding sites on the antibodies, thus reflecting antibody affinity rather than avidity. Antibodies that were able to bind target peptides from solution phase and retain them during washing for a minimum of 10 min were identified by the strength of the appropriate m/z peptide MS signals obtained. This wash time reflects the minimum peptide dissociation time required for use of these antibodies in several current immuno-mass spectrometry assays. Kinetic analysis of antibody-peptide binding by surface plasmon resonance (SPR) showed that the selected antibodies were of high affinity and, most importantly, had low dissociation constants. This method, called MALDI immunoscreening (MiSCREEN), thus enables rapid screening and selection of high affinity anti-peptide antibodies that are useful for a variety of immunoproteomics applications. To demonstrate their functional utility in immuno-mass spectrometry assays, we used the selected, purified RabMAbs to enrich natural (tryptic) peptides from digested human plasma. PMID:21078325

  6. Minimizing Postsampling Degradation of Peptides by a Thermal Benchtop Tissue Stabilization Method.

    PubMed

    Segerström, Lova; Gustavsson, Jenny; Nylander, Ingrid

    2016-04-01

    Enzymatic degradation is a major concern in peptide analysis. Postmortem metabolism in biological samples entails considerable risk for measurements misrepresentative of true in vivo concentrations. It is therefore vital to find reliable, reproducible, and easy-to-use procedures to inhibit enzymatic activity in fresh tissues before subjecting them to qualitative and quantitative analyses. The aim of this study was to test a benchtop thermal stabilization method to optimize measurement of endogenous opioids in brain tissue. Endogenous opioid peptides are generated from precursor proteins through multiple enzymatic steps that include conversion of one bioactive peptide to another, often with a different function. Ex vivo metabolism may, therefore, lead to erroneous functional interpretations. The efficacy of heat stabilization was systematically evaluated in a number of postmortem handling procedures. Dynorphin B (DYNB), Leu-enkephalin-Arg(6) (LARG), and Met-enkephalin-Arg(6)-Phe(7) (MEAP) were measured by radioimmunoassay in rat hypothalamus, striatum (STR), and cingulate cortex (CCX). Also, simplified extraction protocols for stabilized tissue were tested. Stabilization affected all peptide levels to varying degrees compared to those prepared by standard dissection and tissue handling procedures. Stabilization increased DYNB in hypothalamus, but not STR or CCX, whereas LARG generally decreased. MEAP increased in hypothalamus after all stabilization procedures, whereas for STR and CCX, the effect was dependent on the time point for stabilization. The efficacy of stabilization allowed samples to be left for 2 hours in room temperature (20°C) without changes in peptide levels. This study shows that conductive heat transfer is an easy-to-use and efficient procedure for the preservation of the molecular composition in biological samples. Region- and peptide-specific critical steps were identified and stabilization enabled the optimization of tissue handling and opioid

  7. Minimizing Postsampling Degradation of Peptides by a Thermal Benchtop Tissue Stabilization Method

    PubMed Central

    Segerström, Lova; Gustavsson, Jenny

    2016-01-01

    Enzymatic degradation is a major concern in peptide analysis. Postmortem metabolism in biological samples entails considerable risk for measurements misrepresentative of true in vivo concentrations. It is therefore vital to find reliable, reproducible, and easy-to-use procedures to inhibit enzymatic activity in fresh tissues before subjecting them to qualitative and quantitative analyses. The aim of this study was to test a benchtop thermal stabilization method to optimize measurement of endogenous opioids in brain tissue. Endogenous opioid peptides are generated from precursor proteins through multiple enzymatic steps that include conversion of one bioactive peptide to another, often with a different function. Ex vivo metabolism may, therefore, lead to erroneous functional interpretations. The efficacy of heat stabilization was systematically evaluated in a number of postmortem handling procedures. Dynorphin B (DYNB), Leu-enkephalin-Arg6 (LARG), and Met-enkephalin-Arg6-Phe7 (MEAP) were measured by radioimmunoassay in rat hypothalamus, striatum (STR), and cingulate cortex (CCX). Also, simplified extraction protocols for stabilized tissue were tested. Stabilization affected all peptide levels to varying degrees compared to those prepared by standard dissection and tissue handling procedures. Stabilization increased DYNB in hypothalamus, but not STR or CCX, whereas LARG generally decreased. MEAP increased in hypothalamus after all stabilization procedures, whereas for STR and CCX, the effect was dependent on the time point for stabilization. The efficacy of stabilization allowed samples to be left for 2 hours in room temperature (20°C) without changes in peptide levels. This study shows that conductive heat transfer is an easy-to-use and efficient procedure for the preservation of the molecular composition in biological samples. Region- and peptide-specific critical steps were identified and stabilization enabled the optimization of tissue handling and opioid

  8. Improved Sequence Tag Generation Method for Peptide Identification in Tandem Mass Spectrometry

    PubMed Central

    Cao, Xia; Nesvizhskii, Alexey I.

    2013-01-01

    The sequence tag-based peptide identification methods are a promising alternative to the traditional database search approach. However, a more comprehensive analysis, optimization, and comparison with established methods are necessary before these methods can gain widespread use in the proteomics community. Using the InsPecT open source code base (Tanner et al., Anal Chem. 2005, 77:4626–39), we present an improved sequence tag generation method that directly incorporates multi-charged fragment ion peaks present in many tandem mass spectra of higher charge states. We also investigate the performance of sequence tagging under different settings using control datasets generated on five different types of mass spectrometers, as well as using a complex phosphopeptide-enriched sample. We also demonstrate that additional modeling of InsPecT search scores using a semi-parametric approach incorporating the accuracy of the precursor ion mass measurement provides additional improvement in the ability to discriminate between correct and incorrect peptide identifications. The overall superior performance of the sequence tag-based peptide identification method is demonstrated by comparison with a commonly used SEQUEST/PeptideProphet approach. PMID:18785767

  9. 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

  10. 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.

  11. An Efficient Method for the In Vitro Production of Azol(in)e-Based Cyclic Peptides**

    PubMed Central

    Houssen, Wael E; Bent, Andrew F; McEwan, Andrew R; Pieiller, Nathalie; Tabudravu, Jioji; Koehnke, Jesko; Mann, Greg; Adaba, Rosemary I; Thomas, Louise; Hawas, Usama W; Liu, Huanting; Schwarz-Linek, Ulrich; Smith, Margaret C M; Naismith, James H; Jaspars, Marcel

    2014-01-01

    Heterocycle-containing cyclic peptides are promising scaffolds for the pharmaceutical industry but their chemical synthesis is very challenging. A new universal method has been devised to prepare these compounds by using a set of engineered marine-derived enzymes and substrates obtained from a family of ribosomally produced and post-translationally modified peptides called the cyanobactins. The substrate precursor peptide is engineered to have a non-native protease cleavage site that can be rapidly cleaved. The other enzymes used are heterocyclases that convert Cys or Cys/Ser/Thr into their corresponding azolines. A macrocycle is formed using a macrocyclase enzyme, followed by oxidation of the azolines to azoles with a specific oxidase. The work is exemplified by the production of 17 macrocycles containing 6–9 residues representing 11 out of the 20 canonical amino acids. PMID:25331823

  12. 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. PMID:27271502

  13. A nozzle internal performance prediction method

    NASA Technical Reports Server (NTRS)

    Carlson, John R.

    1992-01-01

    A prediction method was written and incorporated into a three-dimensional Navier-Stokes code (PAB3D) for the calculation of nozzle internal performance. The following quantities are calculated: (1) discharge coefficient; (2) normal, side, and axial thrust ratios; (3) rolling, pitching, and yawing moments; and (4) effective pitch and yaw vector angles. Four different case studies are presented to confirm the applicability of the methodology. Internal and, in most situations, external flow-field regions are required to be modeled. The computed nozzle discharge coefficient matches both the level and the trend of the experimental data within quoted experimental data accuracy (0.5 percent). Moment and force ratios are generally within 1 to 2 percent of the absolute level of experimental data, with the trends of data matched accurately.

  14. 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.

  15. Mode Decomposition Methods for Soil Moisture Prediction

    NASA Astrophysics Data System (ADS)

    Jana, R. B.; Efendiev, Y. R.; Mohanty, B.

    2014-12-01

    Lack of reliable, well-distributed, long-term datasets for model validation is a bottle-neck for most exercises in soil moisture analysis and prediction. Understanding what factors drive soil hydrological processes at different scales and their variability is very critical to further our ability to model the various components of the hydrologic cycle more accurately. For this, a comprehensive dataset with measurements across scales is very necessary. Intensive fine-resolution sampling of soil moisture over extended periods of time is financially and logistically prohibitive. Installation of a few long term monitoring stations is also expensive, and needs to be situated at critical locations. The concept of Time Stable Locations has been in use for some time now to find locations that reflect the mean values for the soil moisture across the watershed under all wetness conditions. However, the soil moisture variability across the watershed is lost when measuring at only time stable locations. We present here a study using techniques such as Dynamic Mode Decomposition (DMD) and Discrete Empirical Interpolation Method (DEIM) that extends the concept of time stable locations to arrive at locations that provide not simply the average soil moisture values for the watershed, but also those that can help re-capture the dynamics across all locations in the watershed. As with the time stability, the initial analysis is dependent on an intensive sampling history. The DMD/DEIM method is an application of model reduction techniques for non-linearly related measurements. Using this technique, we are able to determine the number of sampling points that would be required for a given accuracy of prediction across the watershed, and the location of those points. Locations with higher energetics in the basis domain are chosen first. We present case studies across watersheds in the US and India. The technique can be applied to other hydro-climates easily.

  16. Prediction of optimal peptide mixtures to induce broadly neutralizing antibodies to human immunodeficiency virus type 1.

    PubMed Central

    Holley, L H; Goudsmit, J; Karplus, M

    1991-01-01

    Sequences of the principal neutralizing determinant (PND) of the external envelope protein, gp120, from 245 isolates of human immunodeficiency virus type 1 are analyzed. The minimal set of peptides that would elicit antibodies to neutralize a majority of U.S. and European isolates of human immunodeficiency virus type 1 is determined with the assumption that peptides of a given length including the central Gly-Pro-Gly triad are required. In spite of the hypervariability of the PND, 90% of these 245 sequences include peptides from a set of 7 pentapeptides, 13 hexapeptides, or 17 heptapeptides. Tests of these peptide sets on 78 additional PND sequences show that 95% are covered by the 7 pentapeptides, 94% by the 13 hexapeptides, and 86% by the 17 heptapeptides. To anticipate variants not yet observed, single amino acid mutation frequencies from the 245 isolates are used to calculate an expanded set of the 10,000 most probable PND sequences. These sequences cover 86% of the total distribution expected for the central portion of the PND. Peptide lists derived from this expanded set when tested on the 78 additional sequences show that 7 pentapeptides cover 95%, 13 hexapeptides cover 94%, and 17 heptapeptides cover 94%. These results suggest that peptide cocktails of limited size with the potential to cover a large fraction of PND sequence variation may be feasible vaccine candidates. PMID:1862103

  17. 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

  18. 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

  19. Method For Determining And Modifying Protein/Peptide Solubilty

    DOEpatents

    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.

  20. An equation to estimate the difference between theoretically predicted and SDS PAGE-displayed molecular weights for an acidic peptide.

    PubMed

    Guan, Yihong; Zhu, Qinfang; Huang, Delai; Zhao, Shuyi; Jan Lo, Li; Peng, Jinrong

    2015-01-01

    The molecular weight (MW) of a protein can be predicted based on its amino acids (AA) composition. However, in many cases a non-chemically modified protein shows an SDS PAGE-displayed MW larger than its predicted size. Some reports linked this fact to high content of acidic AA in the protein. However, the exact relationship between the acidic AA composition and the SDS PAGE-displayed MW is not established. Zebrafish nucleolar protein Def is composed of 753 AA and shows an SDS PAGE-displayed MW approximately 13 kDa larger than its predicted MW. The first 188 AA in Def is defined by a glutamate-rich region containing ~35.6% of acidic AA. In this report, we analyzed the relationship between the SDS PAGE-displayed MW of thirteen peptides derived from Def and the AA composition in each peptide. We found that the difference between the predicted and SDS PAGE-displayed MW showed a linear correlation with the percentage of acidic AA that fits the equation y = 276.5x - 31.33 (x represents the percentage of acidic AA, 11.4% ≤ x ≤ 51.1%; y represents the average ΔMW per AA). We demonstrated that this equation could be applied to predict the SDS PAGE-displayed MW for thirteen different natural acidic proteins. PMID:26311515

  1. Methods of Predicting Solid Waste Characteristics.

    ERIC Educational Resources Information Center

    Boyd, Gail B.; Hawkins, Myron B.

    The project summarized by this report involved a preliminary design of a model for estimating and predicting the quantity and composition of solid waste and a determination of its feasibility. The novelty of the prediction model is that it estimates and predicts on the basis of knowledge of materials and quantities before they become a part of the…

  2. 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. PMID:26523666

  3. A Statistical Method for Assessing Peptide Identification Confidence in Accurate Mass and Time Tag Proteomics

    SciTech Connect

    Stanley, Jeffrey R.; Adkins, Joshua N.; Slysz, Gordon W.; Monroe, Matthew E.; Purvine, Samuel O.; Karpievitch, Yuliya V.; Anderson, Gordon A.; Smith, Richard D.; Dabney, Alan R.

    2011-07-15

    High-throughput proteomics is rapidly evolving to require high mass measurement accuracy for a variety of different applications. Increased mass measurement accuracy in bottom-up proteomics specifically allows for an improved ability to distinguish and characterize detected MS features, which may in turn be identified by, e.g., matching to entries in a database for both precursor and fragmentation mass identification methods. Many tools exist with which to score the identification of peptides from LC-MS/MS measurements or to assess matches to an accurate mass and time (AMT) tag database, but these two calculations remain distinctly unrelated. Here we present a statistical method, Statistical Tools for AMT tag Confidence (STAC), which extends our previous work incorporating prior probabilities of correct sequence identification from LC-MS/MS, as well as the quality with which LC-MS features match AMT tags, to evaluate peptide identification confidence. Compared to existing tools, we are able to obtain significantly more high-confidence peptide identifications at a given false discovery rate and additionally assign confidence estimates to individual peptide identifications. Freely available software implementations of STAC are available in both command line and as a Windows graphical application.

  4. A simple three-step method for design and affinity testing of new antisense peptides: an example of erythropoietin.

    PubMed

    Štambuk, Nikola; Manojlović, Zoran; Turčić, Petra; Martinić, Roko; Konjevoda, Paško; Weitner, Tin; Wardega, Piotr; Gabričević, Mario

    2014-01-01

    Antisense peptide technology is a valuable tool for deriving new biologically active molecules and performing peptide-receptor modulation. It is based on the fact that peptides specified by the complementary (antisense) nucleotide sequences often bind to each other with a higher specificity and efficacy. We tested the validity of this concept on the example of human erythropoietin, a well-characterized and pharmacologically relevant hematopoietic growth factor. The purpose of the work was to present and test simple and efficient three-step procedure for the design of an antisense peptide targeting receptor-binding site of human erythropoietin. Firstly, we selected the carboxyl-terminal receptor binding region of the molecule (epitope) as a template for the antisense peptide modeling; Secondly, we designed an antisense peptide using mRNA transcription of the epitope sequence in the 3'→5' direction and computational screening of potential paratope structures with BLAST; Thirdly, we evaluated sense-antisense (epitope-paratope) peptide binding and affinity by means of fluorescence spectroscopy and microscale thermophoresis. Both methods showed similar Kd values of 850 and 816 µM, respectively. The advantages of the methods were: fast screening with a small quantity of the sample needed, and measurements done within the range of physicochemical parameters resembling physiological conditions. Antisense peptides targeting specific erythropoietin region(s) could be used for the development of new immunochemical methods. Selected antisense peptides with optimal affinity are potential lead compounds for the development of novel diagnostic substances, biopharmaceuticals and vaccines. PMID:24865486

  5. 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.

  6. A Potent and Selective Peptide Blocker of the Kv1.3 Channel: Prediction from Free-Energy Simulations and Experimental Confirmation

    PubMed Central

    Rashid, M. Harunur; Heinzelmann, Germano; Huq, Redwan; Tajhya, Rajeev B.; Chang, Shih Chieh; Chhabra, Sandeep; Pennington, Michael W.; Beeton, Christine; Norton, Raymond S.; Kuyucak, Serdar

    2013-01-01

    The voltage-gated potassium channel Kv1.3 is a well-established target for treatment of autoimmune diseases. ShK peptide from a sea anemone is one of the most potent blockers of Kv1.3 but its application as a therapeutic agent for autoimmune diseases is limited by its lack of selectivity against other Kv channels, in particular Kv1.1. Accurate models of Kv1.x-ShK complexes suggest that specific charge mutations on ShK could considerably enhance its specificity for Kv1.3. Here we evaluate the K18A mutation on ShK, and calculate the change in binding free energy associated with this mutation using the path-independent free energy perturbation and thermodynamic integration methods, with a novel implementation that avoids convergence problems. To check the accuracy of the results, the binding free energy differences were also determined from path-dependent potential of mean force calculations. The two methods yield consistent results for the K18A mutation in ShK and predict a 2 kcal/mol gain in Kv1.3/Kv1.1 selectivity free energy relative to wild-type peptide. Functional assays confirm the predicted selectivity gain for ShK[K18A] and suggest that it will be a valuable lead in the development of therapeutics for autoimmune diseases. PMID:24244345

  7. Prediction Methods in Solar Sunspots Cycles

    NASA Astrophysics Data System (ADS)

    Ng, Kim Kwee

    2016-02-01

    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.

  8. 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

  9. Prediction Methods in Solar Sunspots Cycles.

    PubMed

    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

  10. Prediction of clinical outcomes using B-type natriuretic peptides in the general population: a systematic review.

    PubMed

    Don-Wauchope, Andrew C; Santaguida, Pasqualina L; McKelvie, Robert; Brown, Judy A; Oremus, Mark; Ali, Usman; Bustamam, Amy; Sohel, Nazmul; Hill, Stephen A; Booth, Ronald A; Balion, Cynthia; Raina, Parminder

    2014-08-01

    The use of B-type natriuretic peptides to predict outcomes in general populations has been investigated in a number of primary studies. A previous systematic review considering natriuretic peptides in cardiovascular disease included a subgroup of general population studies, which suggested an association with a number of clinical outcomes. We electronically searched Medline, Embase, AMED, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and CINAHL for English-language articles published between 1989 and mid-2012. We utilized trained reviewers and standardized forms to screen articles for inclusion and extract data from included articles. All included studies (n = 7) were summarized in narrative and tabular form. A general population was defined as one that was randomly selected from a community setting where no specific inclusion or exclusion criteria were specified. The seven included studies all used FDA approved assays for NT-proBNP. The range of clinical outcomes and heterogeneity did not allow for meta-analysis. The hazard ratios for predicting outcomes in the included studies ranged from 1.0 to 4.1 (all p values <0.05). The discrimination statistics reported in four studies all demonstrated statistically significant improvements in predicting outcomes. NT-proBNP is associated with heart failure, all-cause and cardiovascular mortality, and other combined cardiovascular events in a general unselected population. The discrimination statistics suggest modest improvements in risk stratification. No prospective studies exist to demonstrate the clinical utility of using B-type natriuretic peptides to predict clinical outcomes in a general population. PMID:25052419

  11. 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. PMID:27432793

  12. Fibpredictor: a computational method for rapid prediction of amyloid fibril structures.

    PubMed

    Tabatabaei Ghomi, Hamed; Topp, Elizabeth M; Lill, Markus A

    2016-09-01

    Amyloid fibrils are important in diseases such as Alzheimer's disease and Parkinson's disease, and are also a common instability in peptide and protein drug products. Despite their importance, experimental structures of amyloid fibrils in atomistic detail are rare. To address this limitation, we have developed a novel, rapid computational method to predict amyloid fibril structures (Fibpredictor). The method combines β-sheet model building, β-sheet replication, and symmetry operations with side-chain prediction and statistical scoring functions. When applied to nine amyloid fibrils with experimentally determined structures, the method predicted the correct structures of amyloid fibrils and enriched those among the top-ranked structures. These models can be used as the initial heuristic structures for more complicated computational studies. Fibpredictor is available at http://nanohub.org/resources/fibpredictor . PMID:27502172

  13. 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.

  14. Predictive Role of Intraoperative Serum Brain Natriuretic Peptide for Early Allograft Dysfunction in Living Donor Liver Transplantation.

    PubMed

    Chae, Min Suk; Koo, Jung Min; Park, Chul Soo

    2016-01-01

    BACKGROUND Early allograft dysfunction (EAD) is considered an important complication in liver transplantation. Serum brain natriuretic peptide (BNP) is a marker of cardiac dysfunction related to end-stage liver disease. We investigated the intraoperative change in the serum BNP level and its contribution to EAD after living donor liver transplantation (LDLT). MATERIAL AND METHODS The perioperative data of 104 patients who underwent LDLT were retrospectively reviewed and compared between patients with and without EAD. Serum BNPs were obtained at each phase, and potentially significant factors (P<0.1) were measured by univariate analysis. The intraoperative mean serum BNP level was compared with other predictors using the AUC, and was analyzed for its relationship with EAD by multivariate logistic regression. RESULTS A total of 31 patients (29.8%) developed EAD after LDLT. In all phases, the EAD group showed higher serum BNP levels than the non-EAD group. The serum BNP level at each phase was less accurate than the mean serum BNP level for EAD. The intraoperative mean serum BNP level showed higher predictive accuracy than the Child-Pugh-Turcotte, model for end-stage liver disease (MELD), and D-MELD (donor age × recipient MELD) scores (p<0.05 for all). After multivariate adjustment, intraoperative mean serum BNP level ≥100 pg/mL was identified as an independent risk factor for EAD, along with kidney disease and graft ischemic time. CONCLUSIONS During LDLT, the EAD group showed higher serum BNP levels than the non-EAD group. An intraoperative mean serum BNP level ≥100 pg/mL is independently associated with EAD after LDLT. PMID:27572618

  15. A Simple Three-Step Method for Design and Affinity Testing of New Antisense Peptides: An Example of Erythropoietin

    PubMed Central

    Štambuk, Nikola; Manojlović, Zoran; Turčić, Petra; Martinić, Roko; Konjevoda, Paško; Weitner, Tin; Wardega, Piotr; Gabričević, Mario

    2014-01-01

    Antisense peptide technology is a valuable tool for deriving new biologically active molecules and performing peptide–receptor modulation. It is based on the fact that peptides specified by the complementary (antisense) nucleotide sequences often bind to each other with a higher specificity and efficacy. We tested the validity of this concept on the example of human erythropoietin, a well-characterized and pharmacologically relevant hematopoietic growth factor. The purpose of the work was to present and test simple and efficient three-step procedure for the design of an antisense peptide targeting receptor-binding site of human erythropoietin. Firstly, we selected the carboxyl-terminal receptor binding region of the molecule (epitope) as a template for the antisense peptide modeling; Secondly, we designed an antisense peptide using mRNA transcription of the epitope sequence in the 3'→5' direction and computational screening of potential paratope structures with BLAST; Thirdly, we evaluated sense–antisense (epitope–paratope) peptide binding and affinity by means of fluorescence spectroscopy and microscale thermophoresis. Both methods showed similar Kd values of 850 and 816 µM, respectively. The advantages of the methods were: fast screening with a small quantity of the sample needed, and measurements done within the range of physicochemical parameters resembling physiological conditions. Antisense peptides targeting specific erythropoietin region(s) could be used for the development of new immunochemical methods. Selected antisense peptides with optimal affinity are potential lead compounds for the development of novel diagnostic substances, biopharmaceuticals and vaccines. PMID:24865486

  16. Many overlapping peptides for protein hydrogen exchange experiments by the fragment separation-mass spectrometry method.

    PubMed

    Mayne, Leland; Kan, Zhong-Yuan; Chetty, Palaniappan Sevugan; Ricciuti, Alec; Walters, Benjamin T; Englander, S Walter

    2011-11-01

    Measurement of the naturally occurring hydrogen exchange (HX) behavior of proteins can in principle provide highly resolved thermodynamic and kinetic information on protein structure, dynamics, and interactions. The HX fragment separation-mass spectrometry method (HX-MS) is able to measure hydrogen exchange in biologically important protein systems that are not accessible to NMR methods. In order to achieve high structural resolution in HX-MS experiments, it will be necessary to obtain many sequentially overlapping peptide fragments and be able to identify and analyze them efficiently and accurately by mass spectrometry. This paper describes operations which, when applied to four different proteins ranging in size from 140 to 908 residues, routinely provides hundreds of useful unique peptides, covering the entire protein length many times over. Coverage in terms of the average number of peptide fragments that span each amino acid exceeds 10. The ability to achieve these results required the integrated application of experimental methods that are described here and a computer analysis program, called ExMS, described in a following paper. PMID:21952777

  17. Assay of Protein and Peptide Adducts of Cholesterol Ozonolysis Products by Hydrophobic and Click Enrichment Methods

    PubMed Central

    2015-01-01

    Cholesterol undergoes ozonolysis to afford a variety of oxysterol products, including cholesterol-5,6-epoxide (CholEp) and the isomeric aldehydes secosterol A (seco A) and secosterol B (seco B). These oxysterols display numerous important biological activities, including protein adduction; however, much remains to be learned about the identity of the reactive species and the range of proteins modified by these oxysterols. Here, we synthesized alkynyl derivatives of cholesterol-derived oxysterols and employed a straightforward detection method to establish secosterols A and B as the most protein-reactive of the oxysterols tested. Model adduction studies with an amino acid, peptides, and proteins provide evidence for the potential role of secosterol dehydration products in protein adduction. Hydrophobic separation methods—Folch extraction and solid phase extraction (SPE)—were successfully applied to enrich oxysterol-adducted peptide species, and LC-MS/MS analysis of a model peptide–seco adduct revealed a unique fragmentation pattern (neutral loss of 390 Da) for that species. Coupling a hydrophobic enrichment method with proteomic analysis utilizing characteristic fragmentation patterns facilitates the identification of secosterol-modified peptides and proteins in an adducted protein. More broadly, these improved enrichment methods may give insight into the role of oxysterols and ozone exposure in the pathogenesis of a variety of diseases, including atherosclerosis, Alzheimer’s disease, Parkinson’s disease, and asthma. PMID:25185119

  18. 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

  19. 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

  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. 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. PMID:16279801

  3. Peptide motif analysis predicts lymphocytic choriomeningitis virus as trigger for multiple sclerosis.

    PubMed

    Hogeboom, Charissa

    2015-10-01

    The etiology of multiple sclerosis (MS) involves both genetic and environmental factors. Genetically, the strongest link is with HLA DRB1*1501, but the environmental trigger, probably a virus, remains uncertain. This investigation scans a panel of proteins from encephalitogenic viruses for peptides homologous to the primary autoantigen from myelin basic protein (MBP), then evaluates candidate peptides against a motif required for T cell cross-reactivity and compares viral prevalence patterns to epidemiological characteristics of MS. The only peptide meeting criteria for cross-reactivity with MBP was one from lymphocytic choriomeningitis virus (LCMV), a zoonotic agent. In contrast to current candidates such as Epstein-Barr virus, the distribution of LCMV is consistent with epidemiological features of MS, including concentration in the temperate zone, higher prevalence farther from the equator, and increased prevalence in proximity to regions of peak MS incidence, while lack of person-to-person transmission is consistent with low MS concordance across monozygotic twins. Further, LCMV blocks induction of type I interferon (IFN). Hypothetically this would dysregulate immune processes in favor of proinflammatory pathways as well as upregulating HLA class II and providing more binding sites for autoantigen. The combination of molecular mimicry with virally-induced immune dysregulation has the potential to explain aspects of autoimmunity not addressed by either mechanism alone. PMID:26319106

  4. 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

  5. A survey of aftbody flow prediction methods

    NASA Technical Reports Server (NTRS)

    Putnam, L. E.; Mace, J.

    1981-01-01

    A survey of computational methods used in the calculation of nozzle aftbody flows is presented. One class of methods reviewed are those which patch together solutions for the inviscid, boundary layer, and plume flow regions. The second class of methods reviewed are those which computationally solve the Navier Stokes equations over nozzle aftbodies with jet exhaust flow. Computed results from the methods are compared with experiment. Advantages and disadvantages of the various methods are discussed along with opportunities for further development of these methods.

  6. IUS solid rocket motor contamination prediction methods

    NASA Technical Reports Server (NTRS)

    Mullen, C. R.; Kearnes, J. H.

    1980-01-01

    A series of computer codes were developed to predict solid rocket motor produced contamination to spacecraft sensitive surfaces. Subscale and flight test data have confirmed some of the analytical results. Application of the analysis tools to a typical spacecraft has provided early identification of potential spacecraft contamination problems and provided insight into their solution; e.g., flight plan modifications, plume or outgassing shields and/or contamination covers.

  7. Evaluation of a generalized use of the log Sum(k+1)AA descriptor in a QSRR model to predict peptide retention on RPLC systems.

    PubMed

    Bodzioch, Karolina; Dejaegher, Bieke; Baczek, Tomasz; Kaliszan, Roman; Vander Heyden, Yvan

    2009-06-01

    At the current state of knowledge, the rational optimization of the chromatographic separation of peptides, as well as the identification of proteins in proteomics are challenges for analytical chemists. In this paper the generalized applicability of a recently derived descriptor log Sum(k+1)AA in a QSRR equation to model peptide retention in RP-LC systems was evaluated. For that purpose, two sets of peptides analyzed on dissimilar RP-LC systems were considered. A first set of 28 peptides was measured on 17 columns/systems, while a second of 70 peptides was eluted on four. The aim of this work was to confirm the usefulness of the partly experimental log Sum(k+1)AA descriptor for the prediction of peptides retention compared to the initially applied, fully experimental log SumAA descriptor. The verification of the predictive abilities of both QSRR models, applying either the initial or the alternative descriptor, was done by using the leave-one-out and leave-three-out cross-validation procedures. The results seem to demonstrate that the QSRR model with log Sum(k+1)AA, for which the retention measurement of only seven out of 20 existing amino acids is necessary, possesses similar or in some cases even better predictive abilities than that containing log SumAA. PMID:19479750

  8. Echo state network prediction method and its application in flue gas turbine condition prediction

    NASA Astrophysics Data System (ADS)

    Wang, Shaohong; Chen, Tao; Xu, Xiaoli

    2010-12-01

    On the background of the complex production process of fluid catalytic cracking energy recovery system in large-scale petrochemical refineries, this paper introduced an improved echo state network (ESN) model prediction method which is used to address the condition trend prediction problem of the key power equipment--flue gas turbine. Singular value decomposition method was used to obtain the ESN output weight. Through selecting the appropriate parameters and discarding small singular value, this method overcame the defective solution problem in the prediction by using the linear regression algorithm, improved the prediction performance of echo state network, and gave the network prediction process. In order to solve the problem of noise contained in production data, the translation-invariant wavelet transform analysis method is combined to denoise the noisy time series before prediction. Condition trend prediction results show the effectiveness of the proposed method.

  9. Peptide-based methods for assembling and controlling the morphologies, metrics, and properties of gold nanoparticle superstructures

    NASA Astrophysics Data System (ADS)

    Zhang, Chen

    This dissertation describes new peptide-based methods for assembling and controlling the morphologies, metrics, and properties of gold nanoparticle superstructures. The aim of this research is to develop the peptide-based method by modifying the peptide sequences and controlling the reaction conditions for the synthesis and assembly of gold nanoparticle superstructures to achieve reliable control over their morphology and metrics, and furthermore study their properties and applications. With this goal in mind, the C-terminus of a gold-binding peptide was modified with different numbers of hydrophobic phenylalanines to affect peptide assembly and ultimately nanoparticle assembly. The addition of hydrophobic phenylalanines to the C-terminus of peptide conjugates promoted fiber bundling which in turn lead to the formation of thick or intertwined 1-D nanoparticle superstructures. Furthermore, I prepared spherical gold nanoparticle superstructures with varied diameters (˜40nm, ˜75nm, and ˜150nm) and visible to near-infrared optical properties by using a single peptide conjugate molecule yet varied reaction conditions. Theoretical simulation and experiment were coupled to further understand their optical properties. Finally, I studied and demonstrated the drug storage and release properties of hollow spherical gold nanoparticle superstructures; this was the first demonstrated application of this class of nanoparticle superstructure.

  10. 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.

  11. Application of Hybrid Method for Aerodynamic Noise Prediction

    NASA Astrophysics Data System (ADS)

    Yu, L.; Song, W. P.

    2011-09-01

    A hybrid prediction method for aerodynamic noise is performed using high order accuracy method in this paper. The method combines a two-dimensional Unsteady Reynolds-Averaged Navier-Stokes(URANS) solver with the acoustic analogy method using Ffowcs Williams-Hawkings equation with penetrable data surface (FW-Hpds). Tandem cylinders are chosen to validate the prediction method. The computations are conducted at a Reynolds number of 1.66 × 105 based on the cylinder diameter. Both the aerodynamic and acoustic results show good agreement with the experimental data, showing a successful application of the hybrid prediction method using two-dimensional URANS simulation.

  12. 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.

  13. 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.

  14. 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.

  15. 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. PMID:25034430

  16. Simulation of peptide folding with explicit water--a mean solvation method.

    PubMed

    Wu, X W; Sung, S S

    1999-02-15

    A new approach to efficiently calculate solvent effect in computer simulation of macromolecular systems has been developed. Explicit solvent molecules are included in the simulation to provide a mean solvation force for the solute conformational search. Simulations of an alanine dipeptide in aqueous solution showed that the new approach is significantly more efficient than conventional molecular dynamics method in conformational search, mainly because the mean solvation force reduced the solvent damping effect. This approach allows the solute and solvent to be simulated separately with different methods. For the macromolecule, the rigid fragment constraint dynamics method we developed previously allows large time-steps. For the solvent, a combination of a modified force-bias Monte Carlo method and a preferential sampling can efficiently sample the conformational space. A folding simulation of a 16-residue peptide in water showed high efficiency of the new approach. PMID:10024017

  17. 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.

  18. 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 ...

  19. Peptide arrays for screening cancer specific peptides.

    PubMed

    Ahmed, Sahar; Mathews, Anu Stella; Byeon, Nara; Lavasanifar, Afsaneh; Kaur, Kamaljit

    2010-09-15

    In this paper, we describe a novel method to screen peptides for specific recognition by cancer cells. Seventy peptides were synthesized on a cellulose membrane in an array format, and a direct method to study the peptide-whole cell interaction was developed. The relative binding affinity of the cells for different peptides with respect to a lead 12-mer p160 peptide, identified by phage display, was evaluated using the CyQUANT fluorescence of the bound cells. Screening allowed identification of at least five new peptides that displayed higher affinity (up to 3-fold) for MDA-MB-435 and MCF-7 human cancer cells compared to the p160 peptide. These peptides showed very little binding to the control (noncancerous) human umbilical vein endothelial cells (HUVECs). Three of these peptides were synthesized separately and labeled with fluorescein isothiocyanate (FITC) to study their uptake and interaction with the cancer and control cells using confocal laser scanning microscopy and flow cytometry. The results confirmed the high and specific affinity of an 11-mer peptide 11 (RGDPAYQGRFL) and a 10-mer peptide 18 (WXEAAYQRFL) for the cancer cells versus HUVECs. Peptide 11 binds different receptors on target cancer cells as its sequence contains multiple recognition motifs, whereas peptide 18 binds mainly to the putative p160 receptor. The peptide array-whole cell binding assay reported here is a complementary method to phage display for further screening and optimization of cancer targeting peptides for cancer therapy and diagnosis. PMID:20799711

  20. 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.

  1. 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.

  2. In vitro susceptibility tests for cationic peptides: comparison of broth microdilution methods for bacteria that grow aerobically.

    PubMed

    Giacometti, A; Cirioni, O; Barchiesi, F; Del Prete, M S; Fortuna, M; Caselli, F; Scalise, G

    2000-06-01

    The in vitro susceptibilities of 90 clinical isolates of gram-positive and gram-negative aerobic bacteria to six cationic peptides, buforin II, cecropin P1, indolicidin, magainin II, nisin, and ranalexin, were evaluated by two broth microdilution methods. The first method was performed according to the procedures outlined by the National Committee for Clinical Laboratory Standards for bacteria that grow aerobically, while the second was performed according to the procedures recently proposed by the R. E. W. Hancock laboratory for testing antimicrobial peptides. Overall, the first method produced MICs two- and fourfold higher than the second method. PMID:10817731

  3. The predicted N-terminal signal sequence of the human α₂C-adrenoceptor does not act as a functional cleavable signal peptide.

    PubMed

    Jahnsen, Jan Anker; Uhlén, Staffan

    2012-06-01

    The N-terminal region of the human α(2C)-adrenoceptor has a 22 amino acid sequence MASPALAAALAVAAAAGPNASG. This stretch is predicted to be a cleavable signal peptide. Signal peptides facilitate the translocation of membrane proteins from ribosomes into the endoplasmatic reticulum (ER) for further transport to the plasma membrane. However, recently it has been suggested that the hydrophobic stretch ALAAALAAAAA in the N-tail of the rat α(2C)-adrenoceptor, rather than being part of a signal peptide, is an ER retention signal (Angelotti, 2010). Here, we have investigated the functionality of the N-terminal region of the human α(2C)-adrenoceptor further. The predicted signal peptide was found to be non-cleavable, as shown for a modified α(2C)-adrenoceptor construct equipped with a FLAG epitope. The influence of the N-terminal region on receptor translocation to the plasma membrane was investigated by rebuilding the N-tail and then by analyzing the expression level of binding-competent receptors in transfected COS-7 cell membranes. Truncated α(2C)-adrenoceptor constructs showed decreased expression levels as compared to the wild type α(2C)-adrenoceptor. Addition of, or exchange for, the influenza virus hemagglutinin signal peptide to the α(2C)-adrenoceptor had no effect, respectively decreased, the expression level of binding-competent receptor in the membranes. Our analysis supports the conclusions that the predicted signal peptide in the N-terminal tail of the α(2C)-adrenoceptor does not act as a cleavable signal peptide. In addition, the results indicate that the presence of an intact N-tail is augmenting the amount of binding-competent α(2C)-adrenoceptors at the cell surface. PMID:22503931

  4. Computational methods in sequence and structure prediction

    NASA Astrophysics Data System (ADS)

    Lang, Caiyi

    This dissertation is organized into two parts. In the first part, we will discuss three computational methods for cis-regulatory element recognition in three different gene regulatory networks as the following: (a) Using a comprehensive "Phylogenetic Footprinting Comparison" method, we will investigate the promoter sequence structures of three enzymes (PAL, CHS and DFR) that catalyze sequential steps in the pathway from phenylalanine to anthocyanins in plants. Our result shows there exists a putative cis-regulatory element "AC(C/G)TAC(C)" in the upstream of these enzyme genes. We propose this cis-regulatory element to be responsible for the genetic regulation of these three enzymes and this element, might also be the binding site for MYB class transcription factor PAP1. (b) We will investigate the role of the Arabidopsis gene glutamate receptor 1.1 (AtGLR1.1) in C and N metabolism by utilizing the microarray data we obtained from AtGLR1.1 deficient lines (antiAtGLR1.1). We focus our investigation on the putatively co-regulated transcript profile of 876 genes we have collected in antiAtGLR1.1 lines. By (a) scanning the occurrence of several groups of known abscisic acid (ABA) related cisregulatory elements in the upstream regions of 876 Arabidopsis genes; and (b) exhaustive scanning of all possible 6-10 bps motif occurrence in the upstream regions of the same set of genes, we are able to make a quantative estimation on the enrichment level of each of the cis-regulatory element candidates. We finally conclude that one specific cis-regulatory element group, called "ABRE" elements, are statistically highly enriched within the 876-gene group as compared to their occurrence within the genome. (c) We will introduce a new general purpose algorithm, called "fuzzy REDUCE1", which we have developed recently for automated cis-regulatory element identification. In the second part, we will discuss our newly devised protein design framework. With this framework we have developed

  5. A general method for making peptide therapeutics resistant to serine protease degradation: application to dipeptidyl peptidase IV substrates.

    PubMed

    Heard, Kathryn R; Wu, Wengen; Li, Youhua; Zhao, Peng; Woznica, Iwona; Lai, Jack H; Beinborn, Martin; Sanford, David G; Dimare, Matthew T; Chiluwal, Amrita K; Peters, Diane E; Whicher, Danielle; Sudmeier, James L; Bachovchin, William W

    2013-11-14

    Bioactive peptides have evolved to optimally fulfill specific biological functions, a fact which has long attracted attention for their use as therapeutic agents. While there have been some recent commercial successes fostered in part by advances in large-scale peptide synthesis, development of peptides as therapeutic agents has been significantly impeded by their inherent susceptibility to protease degradation in the bloodstream. Here we report that incorporation of specially designed amino acid analogues at the P1' position, directly C-terminal of the enzyme cleavage site, renders peptides, including glucagon-like peptide-1 (7-36) amide (GLP-1) and six other examples, highly resistant to serine protease degradation without significant alteration of their biological activity. We demonstrate the applicability of the method to a variety of proteases, including dipeptidyl peptidase IV (DPP IV), dipeptidyl peptidase 8 (DPP8), fibroblast activation protein α (FAPα), α-lytic protease (αLP), trypsin, and chymotrypsin. In summary, the "P1' modification" represents a simple, general, and highly adaptable method of generating enzymatically stable peptide-based therapeutics. PMID:24044354

  6. 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

  7. Identification of measles virus epitopes using an ultra-fast method of panning phage-displayed random peptide libraries

    PubMed Central

    Yu, Xiaoli; Barmina, Olga; Burgoon, Mark; Gilden, Don

    2010-01-01

    Phage-displayed random peptide libraries, in which high affinity phage peptides are enriched by repetitive selection (panning) on target antibody, provide a unique tool for identifying antigen specificity. This paper describes a new panning method that enables selection of peptides in 1 day as compared to about 6 days required in traditional panning to identify virus-specific epitopes. The method, termed ultra-fast selection of peptide (UFSP), utilizes phage produced by bacterial infection (phage amplification) directly for subsequent panning. Phage amplified in less than 1 h of infection in Escherichia coli are used for binding to target antibody pre-coated in the same wells of an ELISA plate, obviating the need for traditional large-scale amplification and purification. Importantly, phage elution at 37 °C was superior to that at room temperature, and phage amplification in a 150-μl volume of E. coli cells was superior to that in 250-μl volume. Application of UFSP to two monoclonal antibodies generated from clonally expanded plasma cells in subacute sclerosing panencephalitis (SSPE) brain identified high-affinity measles virus-specific-peptide epitopes. The UFSP panning methodology will expedite identification of peptides reacting with antibodies generated in other diseases of unknown antigenic specificity such as multiple sclerosis (MS), sarcoidosis and Behcet’s disease. PMID:19095007

  8. Identification of measles virus epitopes using an ultra-fast method of panning phage-displayed random peptide libraries.

    PubMed

    Yu, Xiaoli; Barmina, Olga; Burgoon, Mark; Gilden, Don

    2009-03-01

    Phage-displayed random peptide libraries, in which high affinity phage peptides are enriched by repetitive selection (panning) on target antibody, provide a unique tool for identifying antigen specificity. This paper describes a new panning method that enables selection of peptides in 1 day as compared to about 6 days required in traditional panning to identify virus-specific epitopes. The method, termed ultra-fast selection of peptide (UFSP), utilizes phage produced by bacterial infection (phage amplification) directly for subsequent panning. Phage amplified in less than 1h of infection in Escherichia coli are used for binding to target antibody pre-coated in the same wells of an ELISA plate, obviating the need for traditional large-scale amplification and purification. Importantly, phage elution at 37 degrees C was superior to that at room temperature, and phage amplification in a 150-microl volume of E. coli cells was superior to that in 250-microl volume. Application of UFSP to two monoclonal antibodies generated from clonally expanded plasma cells in subacute sclerosing panencephalitis (SSPE) brain identified high-affinity measles virus-specific-peptide epitopes. The UFSP panning methodology will expedite identification of peptides reacting with antibodies generated in other diseases of unknown antigenic specificity such as multiple sclerosis (MS), sarcoidosis and Behcet's disease. PMID:19095007

  9. Preliminary thoughts on helicopter cabin noise prediction methods

    NASA Astrophysics Data System (ADS)

    Pollard, J. S.

    The problems of predicting helicopter cabin noise are discussed with particular reference to the Lynx helicopter. Available methods such as modal analysis adopted for propeller noise prediction do not cope with the higher frequency discrete tone content of helicopter gear noise, with the airborne and structureborne noise contributions. Statistical energy analysis methods may be the answer but until these are developed, one has to rely on classical noise transmission analysis and transfer function methods.

  10. 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. PMID:26177815

  11. 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.

  12. Mass-spectrometry-based method for screening of new peptide ligands for G-protein-coupled receptors.

    PubMed

    Cologna, Camila T; Gilles, Nicolas; Echterbille, Julien; Degueldre, Michel; Servent, Denis; de Pauw, Edwin; Quinton, Loïc

    2015-07-01

    G-protein-coupled receptors (GPCRs) constitute the largest family of transmembrane proteins. Although implicated in almost all physiological processes in the human body, most of them remain unexploited, mostly because of the lack of specific ligands. The objective of this work is to develop a new mass-spectrometry-based technique capable of identifying new peptide ligands for GPCRs. The strategy is based on the incubation of cellular membranes overexpressing GPCRs with a mixture of peptides that contains potential ligands. Peptide ligands bind to the receptors, whereas other peptides remain in the binding buffer. Bound peptides are eluted from membranes and directly detected, identified, and characterized by MALDI TOF-TOF. The results reveal the efficacy of the procedure for selecting a specific ligand of GPCRs in both simple and complex mixtures of peptides. This new approach may offer direct purification, identification, and characterization of the new ligand in a single workflow. The proposed method is labeling-free and, unlike radio-binding and other techniques, it does not require a previously known labeled ligand of the studied GPCR. All these properties greatly reduce the experimental constraints. Moreover, because it is not based on the principle of a competitive specific binding, this technique constitutes a new tool to discover new ligands not only for known GPCRs, but also for orphan GPCRs. PMID:25935673

  13. 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. PMID:23653217

  14. A peptide ELISA to detect antibodies against Pythium insidiosum based on predicted antigenic determinants of exo-1,3-beta-glucanase.

    PubMed

    Keeratijarut, Angsana; Lohnoo, Tassanee; Yingyong, Wanta; Sriwanichrak, Kanchana; Krajaejun, Theerapong

    2013-07-01

    Human pythiosis is a life-threatening infectious disease caused by the oomycete Pythium insidiosum. Diagnosis of pythiosis relies on culture identification, serodiagnosis, and molecular-based assay. Preparation of a serodiagnostic test requires culture filtrate antigen (CFA) extracted from the live pathogen. A 74-kDa immunoreactive protein of P. insidiosum, is encoded by the exo-1,3-beta-glucanase gene (PinsEXO1). PinsEXO1 protein is recognized by sera from pythiosis patients but not by sera from uninfected patients; therefore, this protein could be used to detect anti-P. insidiosum antibodies. In this study we aimed to: identify, synthesize, and evaluate an antigenic determinant (epitope) of PinsEXO1 to be used to serodiagnose pythiosis based on peptide ELISA, and to compare the diagnostic performance of that test with the current CFA-based ELISA. Two antigenic determinants of PinsEXO1 (Peptide-A and -B) were predicted using the PREDITOP program. The sera from 34 pythiosis patients and 92 control subjects were evaluated. Peptide-A, Peptide-B, and CFA-based ELISAs all had a specificity of 100%. Peptide-B ELISA had a sensitivity of 91% and an accuracy of 98% and both Peptide-A and CFA-based ELISAs had a sensitivity of 100% and an accuracy of 100%. Peptide-A is a more efficient epitope than Peptide-B, and can be used as an alternative antigen to develop a serodiagnostic assay for pythiosis. PMID:24050102

  15. 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…

  16. 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.

  17. Optimization of Preparation of Antioxidative Peptides from Pumpkin Seeds Using Response Surface Method

    PubMed Central

    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%. PMID:24637721

  18. Assessment of a method for the prediction of mandibular rotation.

    PubMed

    Lee, R S; Daniel, F J; Swartz, M; Baumrind, S; Korn, E L

    1987-05-01

    A new method to predict mandibular rotation developed by Skieller and co-workers on a sample of 21 implant subjects with extreme growth patterns has been tested against an alternative sample of 25 implant patients with generally similar mean values, but with less extreme facial patterns. The method, which had been highly successful in retrospectively predicting changes in the sample of extreme subjects, was much less successful in predicting individual patterns of mandibular rotation in the new, less extreme sample. The observation of a large difference in the strength of the predictions for these two samples, even though their mean values were quite similar, should serve to increase our awareness of the complexity of the problem of predicting growth patterns in individual cases. PMID:3472458

  19. 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.

  20. Usefulness of B-type Natriuretic Peptides to Predict Cardiovascular Events in Women (from the Women's Health Study).

    PubMed

    Everett, Brendan M; Ridker, Paul M; Cook, Nancy R; Pradhan, Aruna D

    2015-08-15

    Natriuretic peptides are positively associated with incident cardiovascular disease (CVD), but data in women, particularly with regard to improvements in risk prediction, are sparse. We measured the N-terminal prohormone form of B-type natriuretic peptide (NT-proBNP) in 480 cases of incident CVD (myocardial infarction, stroke, and cardiovascular death) and a reference subcohort of 564 women from the Women's Health Study who were followed for a median of 12.0 (interquartile range 7.6 to 13.4) years. Median (interquartile range) NT-proBNP concentrations were greater in women who developed CVD (81 ng/l [50 to 147]) than those who did not (64 ng/l [38 to 117]; p <0.0001). For women in the highest compared to the lowest quartile, NT-proBNP was 65% greater after adjusting for established cardiovascular risk factors and kidney function (adjusted hazard ratio [aHR] 1.65, 95% confidence interval [CI] 1.03 to 2.64, p trend = 0.03). When analyzed as a continuous variable, the aHR per 1 - SD difference in Ln(NT-proBNP) was 1.22 (1.03 to 1.44; p = 0.02). The per 1 - SD change in Ln(NT-proBNP) appeared stronger for cardiovascular death (aHR 1.43, 95% CI 1.05 to 1.94, p = 0.02) and stroke (aHR 1.24, 95% CI 1.03 to 1.50, p = 0.03) than myocardial infarction (aHR 1.09, 95% CI 0.87 to 1.37, p = 0.44). When added to traditional risk co-variables, NT-proBNP did not significantly improve the C-statistic (0.751 to 0.757; p = 0.09) or net reclassification into <5%, 5 to <7.5%, and ≥7.5% 10-year CVD risk categories (0.014; p = 0.18). In conclusion, in this prospective study of initially healthy women, NT-proBNP concentrations showed statistically significant association with incident CVD that was independent of traditional cardiovascular risk factors but did not substantially improve measures of CVD risk prediction. PMID:26081066

  1. New methods for predicting the magnitude of sunspot maximum

    NASA Technical Reports Server (NTRS)

    Brown, G. M.

    1979-01-01

    Three new and independent methods of predicting the magnitude of a forthcoming sunspot maximum are suggested. The longest lead time is given by the first method, which is based on a terrestrial parameter measured during the declining phase of the preceding cycle. The second method, with only a slightly shorter foreknowledge, is based on an interplanetary parameter derived around the commencement of the cycle in question (sunspot minimum). The third method, giving the shortest prediction lead-time, is based entirely on solar parameters measured during the initial progress of the cycle in question. Application of all three methods to forecast the magnitude of the next maximum (Cycle 21) agree in predicting that it is likely to be very similar to that of Cycle 18.

  2. 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

    2013-02-01

    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. PMID:23301806

  3. 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

  4. 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

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

    PubMed

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

    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

  6. A comparative study of extraction methods reveals preferred solvents for cystine knot peptide isolation from Momordica cochinchinensis seeds.

    PubMed

    Mahatmanto, Tunjung; Poth, Aaron G; Mylne, Joshua S; Craik, David J

    2014-06-01

    MCoTI-I and MCoTI-II (short for Momordica cochinchinensis Trypsin Inhibitor-I and -II, respectively) are attractive candidates for developing novel intracellular-targeting drugs because both are exceptionally stable and can internalize into cells. These seed-derived cystine knot peptides are examples of how natural product discovery efforts can lead to biomedical applications. However, discovery efforts are sometimes hampered by the limited availability of seed materials, highlighting the need for efficient extraction methods. In this study, we assessed five extraction methods using M. cochinchinensis seeds, a source of well-characterized cystine knot peptides. The most efficient extraction of nine known cystine knot peptides was achieved by a method based on acetonitrile/water/formic acid (25:24:1), followed by methods based on sodium acetate (20 mM, pH 5.0), ammonium bicarbonate (5 mM, pH 8.0), and boiling water. On average, the yields obtained by these four methods were more than 250-fold higher than that obtained using dichloromethane/methanol (1:1) extraction, a previously applied standard method. Extraction using acetonitrile/water/formic acid (25:24:1) yielded the highest number of reconstructed masses within the majority of plant-derived cystine knot peptide mass range but only accounted for around 50% of the total number of masses, indicating that any single method may result in under-sampling. Applying acetonitrile/water/formic acid (25:24:1), boiling water, and ammonium bicarbonate (5 mM, pH 8.0) extractions either successively or discretely significantly increased the sampling number. Overall, acetonitrile/water/formic acid (25:24:1) can facilitate efficient extraction of cystine-knot peptides from M. cochinchinensis seeds but for discovery purposes the use of a combination of extraction methods is recommended where practical. PMID:24613804

  7. Comparison of the Utility of Preoperative versus Postoperative B-type Natriuretic Peptide for Predicting Hospital Length of Stay and Mortality after Primary Coronary Artery Bypass Grafting

    PubMed Central

    Fox, Amanda A.; Muehlschlegel, Jochen D.; Body, Simon C.; Shernan, Stanton K.; Liu, Kuang-Yu; Perry, Tjorvi E.; Aranki, Sary F.; Cook, E. Francis; Marcantonio, Edward R.; Collard, Charles D.

    2016-01-01

    Background Preoperative B-type natriuretic peptide (BNP) is known to predict adverse outcomes after cardiac surgery. The value of postoperative BNP for predicting adverse outcomes is less well delineated. The authors hypothesized that peak postoperative plasma BNP (measured postoperative days 1–5) predicts hospital length of stay (HLOS) and mortality in patients undergoing primary coronary artery bypass grafting, even after adjusting for preoperative BNP and perioperative clinical risk factors. Methods This study is a prospective longitudinal study of 1,183 patients undergoing primary coronary artery bypass grafting surgery. Mortality was defined as all-cause death within 5 yr after surgery. Cox proportional hazards analyses were conducted to separately evaluate the associations between peak postoperative BNP and HLOS and mortality. Multivariable adjustments were made for patient demographics, preoperative BNP concentration, and clinical risk factors. BNP measurements were log10 transformed before analysis. Results One hundred fifteen deaths (9.7%) occurred in the cohort (mean follow-up = 4.3 yr, range = 2.38–5.0 yr). After multivariable adjustment for preoperative BNP and clinical covariates, peak postoperative BNP predicted HLOS (hazard ratio [HR] = 1.28, 95% CI = 1.002–1.64, P = 0.049) but not mortality (HR = 1.62, CI = 0.71–3.68, P = 0.25), whereas preoperative BNP independently predicted HLOS (HR = 1.09, CI = 1.01–1.18, P = 0.03) and approached being an independent predictor of mortality (HR = 1.36, CI = 0.96–1.94, P = 0.08). When preoperative and peak postoperative BNP were separately adjusted for within the clinical multivariable models, each independently predicted HLOS (preoperative BNP HR = 1.13, CI = 1.05–1.21, P = 0.0007; peak postoperative BNP HR = 1.44, CI = 1.15–1.81, P = 0.001) and mortality (preoperative BNP HR = 1.50, CI = 1.09–2.07, P = 0.01; peak postoperative BNP HR = 2.29, CI = 1.11–4.73, P = 0.02). Conclusions Preoperative

  8. A protein structural classes prediction method based on predicted secondary structure and PSI-BLAST profile.

    PubMed

    Ding, Shuyan; Li, Yan; Shi, Zhuoxing; Yan, Shoujiang

    2014-02-01

    Knowledge of protein secondary structural classes plays an important role in understanding protein folding patterns. In this paper, 25 features based on position-specific scoring matrices are selected to reflect evolutionary information. In combination with other 11 rational features based on predicted protein secondary structure sequences proposed by the previous researchers, a 36-dimensional representation feature vector is presented to predict protein secondary structural classes for low-similarity sequences. ASTRALtraining dataset is used to train and design our method, other three low-similarity datasets ASTRALtest, 25PDB and 1189 are used to test the proposed method. Comparisons with other methods show that our method is effective to predict protein secondary structural classes. Stand alone version of the proposed method (PSSS-PSSM) is written in MATLAB language and it can be downloaded from http://letsgob.com/bioinfo_PSSS_PSSM/. PMID:24067326

  9. 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.

  10. 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.

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

    PubMed

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

    2012-01-01

    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. PMID:22370884

  12. PREDICTIVE TEST METHODS: PERMEATION OF POLYMERIC MEMBRANES BY ORGANIC SOLVENTS

    EPA Science Inventory

    As the result of screening elastomeric materials that may be suitable for formulating chemical-protective clothing, a simple test method has been developed that allows the prediction of the permeation of an organic solvent through a polymeric membrane. The test method, based on l...

  13. An Integrated Calculation Method to Predict Arc Behavior

    NASA Astrophysics Data System (ADS)

    Li, Xingwen; Chen, Degui

    The precision of magnetic field calculation is crucial to predict the arc behavior using magnetohydrodynamic (MHD) model. A integrated calculation method is proposed to couple the calculation of magnetic field and fluid dynamics based on the commercial software ANSYS and FLUENT, which especially benefits to take into account the existence of the ferromagnetic parts. An example concerning air arc is presented using the method.

  14. Testing prediction methods: Earthquake clustering versus the Poisson model

    USGS Publications Warehouse

    Michael, A.J.

    1997-01-01

    Testing earthquake prediction methods requires statistical techniques that compare observed success to random chance. One technique is to produce simulated earthquake catalogs and measure the relative success of predicting real and simulated earthquakes. The accuracy of these tests depends on the validity of the statistical model used to simulate the earthquakes. This study tests the effect of clustering in the statistical earthquake model on the results. Three simulation models were used to produce significance levels for a VLF earthquake prediction method. As the degree of simulated clustering increases, the statistical significance drops. Hence, the use of a seismicity model with insufficient clustering can lead to overly optimistic results. A successful method must pass the statistical tests with a model that fully replicates the observed clustering. However, a method can be rejected based on tests with a model that contains insufficient clustering. U.S. copyright. Published in 1997 by the American Geophysical Union.

  15. 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

    2016-01-01

    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. PMID:27166969

  16. Effectiveness of CID, HCD, and ETD with FT MS/MS for degradomic-peptidomic analysis: comparison of peptide identification methods

    PubMed Central

    Shen, Yufeng; Tolić, Nikola; Xie, Fang; Zhao, Rui; Purvine, Samuel O.; Schepmoes, Athena A.; Ronald, J. Moore; Anderson, Gordon A.; Smith, Richard D.

    2011-01-01

    We report on the effectiveness of CID, HCD, and ETD for LC-FT MS/MS analysis of peptides using a tandem linear ion trap-Orbitrap mass spectrometer. A range of software tools and analysis parameters were employed to explore the use of CID, HCD, and ETD to identify peptides isolated from human blood plasma without the use of specific “enzyme rules”. In the evaluation of an FDR-controlled SEQUEST scoring method, the use of accurate masses for fragments increased the numbers of identified peptides (by ~50%) compared to the use of conventional low accuracy fragment mass information, and CID provided the largest contribution to the identified peptide datasets compared to HCD and ETD. The FDR-controlled Mascot scoring method provided significantly fewer peptide identifications than with SEQUEST (by 1.3–2.3 fold) at the same confidence levels, and CID, HCD, and ETD provided similar contributions to identified peptides. Evaluation of de novo sequencing and the UStags method for more intense fragment ions revealed that HCD afforded more sequence consecutive residues (e.g., ≥7 amino acids) than either CID or ETD. Both the FDR-controlled SEQUEST and Mascot scoring methods provided peptide datasets that were affected by the decoy database and mass tolerances applied (e.g., the identical peptides between the datasets could be limited to ~70%), while the UStags method provided the most consistent peptide datasets (>90% overlap) with extremely low (near zero) numbers of false positive identifications. The m/z ranges in which CID, HCD, and ETD contributed the largest number of peptide identifications were substantially overlapping. This work suggests that the three peptide ion fragmentation methods are complementary, and that maximizing the number of peptide identifications benefits significantly from a careful match with the informatics tools and methods applied. These results also suggest that the decoy strategy may inaccurately estimate identification FDRs. PMID:21678914

  17. A method for calculating 16O/18O peptide ion ratios for the relative quantification of proteomes.

    PubMed

    Johnson, Kenneth L; Muddiman, David C

    2004-04-01

    A method is described for the identification and relative quantification of proteomes using accurate mass tags (AMT) generated by nLC-dual ESI-FT-ICR-MS on a 7T instrument in conjunction with stable isotope labeling using 16O/18O ratios. AMTs were used for putative peptide identification, followed by confirmation of peptide identity by tandem mass spectrometry. For a combined set of 58 tryptic peptides from bovine serum albumin (BSA) and human transferrin, a mean mass measurement accuracy of 1.9 ppm +/-0.94 ppm (CIM99%) was obtained. This subset of tryptic peptides was used to measure 16O/18O ratios of 0.36 +/- 0.09 (CIM99%) for BSA (micro = 0.33) and 1.48 +/- 0.47 (CIM99%) for transferrin (micro = 1.0) using a method for calculating 16O/18O ratios from overlapping isotopic multiplets arising from mixtures of 16O, 18O1, and 18O2 labeled C-termini. The model amino acid averagine was used to calculate a representative molecular formula for estimating and subtracting the contributions of naturally occurring isotopes solely as a function of peptide molecular weight. The method was tested against simulated composite 16O/18O spectra where peptide molecular weight, 16O/18O ratio, 18O1/18O2 ratios, and number of sulfur atoms were varied. Relative errors of 20% or less were incurred when the 16O/18O ratios were less than three, even for peptides where the number of sulfur atoms was over- or under-estimated. These data demonstrate that for biomarker discovery, it is advantageous to label the proteome representing the disease state with 18O; and the method is not sensitive to variations in 18O1/18O2 ratio. This approach allows a comprehensive differentiation of expression levels and tentative identification via AMTs, followed by targeted analysis of over- and under-expressed peptides using tandem mass spectrometry, for applications such as the discovery of disease biomarkers. PMID:15047049

  18. 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.

  19. Effectiveness of brain natriuretic peptide in predicting postoperative atrial fibrillation in patients undergoing non-cardiac thoracic surgery.

    PubMed

    Toufektzian, Levon; Zisis, Charalambos; Balaka, Christina; Roussakis, Antonios

    2015-05-01

    A best evidence topic was written according to a structured protocol. The question addressed was whether plasma brain natriuretic peptide (BNP) levels could effectively predict the occurrence of postoperative atrial fibrillation (AF) in patients undergoing non-cardiac thoracic surgery. A total of 14 papers were identified using the reported search, of which 5 represented the best evidence to answer the clinical question. The authors, date, journal, country, study type, population, outcomes and key results are tabulated. All studies were prospective observational, and all reported a significant association between BNP and N-terminal (NT)-proBNP plasma levels measured in the immediate preoperative period and the incidence of postoperative AF in patients undergoing either anatomical lung resections or oesophagectomy. One study reported a cut-off value of 30 pg/ml above which significantly more patients suffered from postoperative AF (P < 0.0001), while another one reported that this value could predict postoperative AF with a sensitivity of 77% and a specificity of 93%. Another study reported that patients with NT-proBNP levels of 113 pg/ml or above had an 8-fold increased risk of developing postoperative AF. These findings support that BNP or NT-proBNP levels, especially when determined during the preoperative period, if increased, are able to identify patients at risk for the development of postoperative AF after anatomical major lung resection or oesophagectomy. The same does not seem to be true for lesser lung resections. These high-risk patients might have a particular benefit from the administration of prophylactic antiarrhythmic therapy. PMID:25630332

  20. A structural alphabet for local protein structures: improved prediction methods.

    PubMed

    Etchebest, Catherine; Benros, Cristina; Hazout, Serge; de Brevern, Alexandre G

    2005-06-01

    Three-dimensional protein structures can be described with a library of 3D fragments that define a structural alphabet. We have previously proposed such an alphabet, composed of 16 patterns of five consecutive amino acids, called Protein Blocks (PBs). These PBs have been used to describe protein backbones and to predict local structures from protein sequences. The Q16 prediction rate reaches 40.7% with an optimization procedure. This article examines two aspects of PBs. First, we determine the effect of the enlargement of databanks on their definition. The results show that the geometrical features of the different PBs are preserved (local RMSD value equal to 0.41 A on average) and sequence-structure specificities reinforced when databanks are enlarged. Second, we improve the methods for optimizing PB predictions from sequences, revisiting the optimization procedure and exploring different local prediction strategies. Use of a statistical optimization procedure for the sequence-local structure relation improves prediction accuracy by 8% (Q16 = 48.7%). Better recognition of repetitive structures occurs without losing the prediction efficiency of the other local folds. Adding secondary structure prediction improved the accuracy of Q16 by only 1%. An entropy index (Neq), strongly related to the RMSD value of the difference between predicted PBs and true local structures, is proposed to estimate prediction quality. The Neq is linearly correlated with the Q16 prediction rate distributions, computed for a large set of proteins. An "expected" prediction rate QE16 is deduced with a mean error of 5%. PMID:15822101

  1. Accuracy of Four Tooth Size Prediction Methods on Malay Population

    PubMed Central

    Mahmoud, Belal Khaled; Abu Asab, Saifeddin Hamed I.; Taib, Haslina

    2012-01-01

    Objective. To examine the accuracy of Moyers 50%, Tanaka and Johnston, Ling and Wong and Jaroontham and Godfrey methods in predicting the mesio-distal crown width of the permanent canines and premolars (C + P1 + P2) in Malay population. Materials and Methods. The study models of 240 Malay children (120 males and 120 females) aged 14 to 18 years, all free of any signs of dental pathology or anomalies, were measured using a digital caliper accurate to 0.01 mm. The predicted widths (C + P1 + P2) in both arches derived from the tested prediction equations were compared with the actual measured widths. Results. Moyers and Tanaka and Johnston methods showed significant difference between the actual and predicted widths of (C + P1 + P2) for both sexes. Ling and Wong method also showed statistically significant difference for males, however, there was no significant difference for females. Jaroontham and Godfrey method showed statistical significant difference for females, but the male values did not show any significant difference. Conclusion. For male Malay, the method proposed by Jaroontham and Godfrey for male Thai proved to be highly accurate. For female Malay, the method proposed by Ling and Wong for southern Chinese females proved to be highly accurate. PMID:23209918

  2. Development of peptide-based methods for controlling the structures, compositions, and properties of complex nanoparticle superstructures

    NASA Astrophysics Data System (ADS)

    Song, Chengyi

    This dissertation describes the development of a nanoparticle assembly methodology based on the use of peptide conjugate molecules. The aim of this research was to explore how this methodology could be used to control the structure, metrics, and properties of product nanoparticle superstructures. Specifically, this document describes mechanistic studies aimed at understanding the key factors that govern the nanoparticle synthesis and assembly process. Using what we learned from these studies, we prepared high-quality helical nanoparticle superstructures and studied their chirooptical properties. We coupled theory and experiment to show how tuning the metrics and structure of the helices results in predictable and tailorable circular dichroism (CD) properties. We also describe how the composition of the peptide conjugate can influence both the structure of the nanoparticle assembly and detail how peptide conjugates can be utilized to prepare 'hollow' sub-100nm gold nanoparticle spheres. Finally, to expand the composition scope of the methodology, we present a new cobalt-binding peptide conjugate, which could be used to direct the synthesis and assembly of hollow CoPt nanospherical superstructures exhibiting electrocatalytic activity for methanol oxidation.

  3. 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.

  4. A comparison of four methods of predicting arch length.

    PubMed

    Gardner, R B

    1979-04-01

    1. Four arch length prediction equations (Nance, Johnston-Tanaka, Moyers, and Hixon-Oldfather) were compared by examining pretreatment casts, pretreatment intraoral radiographs, and posttreatment casts of forty-one patients of mixed-dentition age. 2. A comparison of correlation coefficients and slopes of the predicted arch length versus the actual arch lengths revealed that the Hixon-Oldfather method conformed closest to the ideal. 3. No combination of the four methods produced a more accurate equation than the single most accurate method. 4. Neither the sex of the patient nor the type of occlusion affected the prediction accuracy of any of the four equations. 5. All methods tend to overpredict the arch length size by 1 to 3 mm., with the exception of the Hixon-Oldfather equation, which underpredicted by approximately 0.5 mm. 6. An analysis of the intrainvestigator error showed a very low standard error of estimate for individual tooth measurements and for the prediction values. 7. A variance analysis showed that most of the variation was due to arch length (85%), a slight amount was due to the prediction method (8%), and 6% of the variation was due to the rater. 8. A low correlation was found between space available versus actual discrepancy and space available versus actual arch length. 9. High correlation coefficients were found for the predicted arch lengths when compared with the actual arch lengths. As expected, the correlation coefficients for the predicted widths of only the canines and premolars compared with the actual widths were not quite as high. PMID:285614

  5. Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition.

    PubMed

    Chen, Yen-Kuang; Li, Kuo-Bin

    2013-02-01

    The type information of un-annotated membrane proteins provides an important hint for their biological functions. The experimental determination of membrane protein types, despite being more accurate and reliable, is not always feasible due to the costly laboratory procedures, thereby creating a need for the development of bioinformatics methods. This article describes a novel computational classifier for the prediction of membrane protein types using proteins' sequences. The classifier, comprising a collection of one-versus-one support vector machines, makes use of the following sequence attributes: (1) the cationic patch sizes, the orientation, and the topology of transmembrane segments; (2) the amino acid physicochemical properties; (3) the presence of signal peptides or anchors; and (4) the specific protein motifs. A new voting scheme was implemented to cope with the multi-class prediction. Both the training and the testing sequences were collected from SwissProt. Homologous proteins were removed such that there is no pair of sequences left in the datasets with a sequence identity higher than 40%. The performance of the classifier was evaluated by a Jackknife cross-validation and an independent testing experiments. Results show that the proposed classifier outperforms earlier predictors in prediction accuracy in seven of the eight membrane protein types. The overall accuracy was increased from 78.3% to 88.2%. Unlike earlier approaches which largely depend on position-specific substitution matrices and amino acid compositions, most of the sequence attributes implemented in the proposed classifier have supported literature evidences. The classifier has been deployed as a web server and can be accessed at http://bsaltools.ym.edu.tw/predmpt. PMID:23137835

  6. 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.

  7. A Micromechanics-Based Method for Multiscale Fatigue Prediction

    NASA Astrophysics Data System (ADS)

    Moore, John Allan

    An estimated 80% of all structural failures are due to mechanical fatigue, often resulting in catastrophic, dangerous and costly failure events. However, an accurate model to predict fatigue remains an elusive goal. One of the major challenges is that fatigue is intrinsically a multiscale process, which is dependent on a structure's geometric design as well as its material's microscale morphology. The following work begins with a microscale study of fatigue nucleation around non- metallic inclusions. Based on this analysis, a novel multiscale method for fatigue predictions is developed. This method simulates macroscale geometries explicitly while concurrently calculating the simplified response of microscale inclusions. Thus, providing adequate detail on multiple scales for accurate fatigue life predictions. The methods herein provide insight into the multiscale nature of fatigue, while also developing a tool to aid in geometric design and material optimization for fatigue critical devices such as biomedical stents and artificial heart valves.

  8. 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.

  9. Click-Based Libraries of SFTI-1 Peptides: New Methods Using Reversed-Phase Silica.

    PubMed

    Cistrone, Philip A; Dawson, Philip E

    2016-03-14

    Performing sequential reactions for the orthogonal derivatization of peptides in solution often requires intermediate handling and purification steps. To solve these problems, we have exploited the distinct adsorption kinetics of peptides toward particulate reversed-phase (RP) C18 silica material, enabling consecutive reactions to be performed without intermediate elution. To illustrate this approach, sequential CuAAC/click reactions were used to modify an analog of the bicyclic peptide sunflower trypsin inhibitor 1 (SFTI-1), a potent scaffold for trypsin and chymotrypsin-like enzyme inhibitors. The SFTI-1 scaffold was synthesized containing both β-azido alanine and propargyl glycine residues. Despite the mutual reactivity of these groups, site isolation on RP silica enabled consecutive click reactions and associated washing steps to be performed while the peptide remained immobilized. Importantly, this approach eliminated side products that could form between two peptides or within a single peptide. These studies suggest a broad utility for RP silica in solving both peptide handling problems and in improving synthetic workflows. PMID:26914614

  10. 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.

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

    DOEpatents

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

    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.

  12. 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.

  13. 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.

  14. Semiempirical method for predicting vortex-induced rolling moments

    NASA Technical Reports Server (NTRS)

    Allison, D. O.; Bobbitt, P. J.

    1985-01-01

    A method is described for the prediction of rolling moments on a wing penetrating a vortex velocity field generated by a large aircraft. Rolling moments are determined from lifting pressure coefficients computed with an inviscid-flow linear panel method. Two empirical corrections are included to account for the lifting efficiency of an airfoil section and the local stall on the wing. Predicted rolling moments are compared with those from two windtunnel experiments. Results indicate that experimental rolling moments, for which the Reynolds number of the following wing is low, should be interpreted with caution.

  15. 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.

  16. Analysis of CASP8 targets, predictions and assessment methods

    PubMed Central

    Shi, ShuoYong; Pei, Jimin; Sadreyev, Ruslan I.; Kinch, Lisa N.; Majumdar, Indraneel; Tong, Jing; Cheng, Hua; Kim, Bong-Hyun; Grishin, Nick V.

    2009-01-01

    Results of the recent Critical Assessment of Techniques for Protein Structure Prediction, CASP8, present several valuable sources of information. First, CASP targets comprise a realistic sample of currently solved protein structures and exemplify the corresponding challenges for predictors. Second, the plethora of predictions by all possible methods provides an unusually rich material for evolutionary analysis of target proteins. Third, CASP results show the current state of the field and highlight specific problems in both predicting and assessing. Finally, these data can serve as grounds to develop and analyze methods for assessing prediction quality. Here we present results of our analysis in these areas. Our objective is not to duplicate CASP assessment, but to use our unique experience as former CASP5 assessors and CASP8 predictors to (i) offer more insights into CASP targets and predictions based on expert analysis, including invaluable analysis prior to target structure release; and (ii) develop an assessment methodology tailored towards current challenges in the field. Specifically, we discuss preparing target structures for assessment, parsing protein domains, balancing evaluations based on domains and on whole chains, dividing targets into categories and developing new evaluation scores. We also present evolutionary analysis of the most interesting and challenging targets. Database URL: Our results are available as a comprehensive database of targets and predictions at http://prodata.swmed.edu/CASP8. PMID:20157476

  17. Analysis of CASP8 targets, predictions and assessment methods.

    PubMed

    Shi, Shuoyong; Pei, Jimin; Sadreyev, Ruslan I; Kinch, Lisa N; Majumdar, Indraneel; Tong, Jing; Cheng, Hua; Kim, Bong-Hyun; Grishin, Nick V

    2009-01-01

    Results of the recent Critical Assessment of Techniques for Protein Structure Prediction, CASP8, present several valuable sources of information. First, CASP targets comprise a realistic sample of currently solved protein structures and exemplify the corresponding challenges for predictors. Second, the plethora of predictions by all possible methods provides an unusually rich material for evolutionary analysis of target proteins. Third, CASP results show the current state of the field and highlight specific problems in both predicting and assessing. Finally, these data can serve as grounds to develop and analyze methods for assessing prediction quality. Here we present results of our analysis in these areas. Our objective is not to duplicate CASP assessment, but to use our unique experience as former CASP5 assessors and CASP8 predictors to (i) offer more insights into CASP targets and predictions based on expert analysis, including invaluable analysis prior to target structure release; and (ii) develop an assessment methodology tailored towards current challenges in the field. Specifically, we discuss preparing target structures for assessment, parsing protein domains, balancing evaluations based on domains and on whole chains, dividing targets into categories and developing new evaluation scores. We also present evolutionary analysis of the most interesting and challenging targets.Database URL: Our results are available as a comprehensive database of targets and predictions at http://prodata.swmed.edu/CASP8. PMID:20157476

  18. 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

  19. 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.

  20. Application of two direct runoff prediction methods in Puerto Rico

    USGS Publications Warehouse

    Sepulveda, N.

    1997-01-01

    Two methods for predicting direct runoff from rainfall data were applied to several basins and the resulting hydrographs compared to measured values. The first method uses a geomorphology-based unit hydrograph to predict direct runoff through its convolution with the excess rainfall hyetograph. The second method shows how the resulting hydraulic routing flow equation from a kinematic wave approximation is solved using a spectral method based on the matrix representation of the spatial derivative with Chebyshev collocation and a fourth-order Runge-Kutta time discretization scheme. The calibrated Green-Ampt (GA) infiltration parameters are obtained by minimizing the sum, over several rainfall events, of absolute differences between the total excess rainfall volume computed from the GA equations and the total direct runoff volume computed from a hydrograph separation technique. The improvement made in predicting direct runoff using a geomorphology-based unit hydrograph with the ephemeral and perennial stream network instead of the strictly perennial stream network is negligible. The hydraulic routing scheme presented here is highly accurate in predicting the magnitude and time of the hydrograph peak although the much faster unit hydrograph method also yields reasonable results.

  1. 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. PMID:27368017

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

    PubMed Central

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

    2016-01-01

    Abstract 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. PMID:27368017

  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. 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/. PMID:20385580

  5. A Rapid and Simple LC-MS Method Using Collagen Marker Peptides for Identification of the Animal Source of Leather.

    PubMed

    Kumazawa, Yuki; Taga, Yuki; Iwai, Kenji; Koyama, Yoh-Ichi

    2016-08-01

    Identification of the animal source of leather is difficult using traditional methods, including microscopic observation and PCR. In the present study, a LC-MS method was developed for detecting interspecies differences in the amino acid sequence of type I collagen, which is a major component of leather, among six animals (cattle, horse, pig, sheep, goat, and deer). After a dechroming procedure and trypsin digestion, six tryptic peptides of type I collagen were monitored by LC-MS in multiple reaction monitoring mode for the animal source identification using the patterns of the presence or absence of the marker peptides. We analyzed commercial leathers from various production areas using this method, and found some leathers in which the commercial label disagreed with the identified animal source. Our method enabled rapid and simple leather certification and could be applied to other animals whether or not their collagen sequences are available in public databases. PMID:27397145

  6. Flight-Test Evaluation of Flutter-Prediction Methods

    NASA Technical Reports Server (NTRS)

    Lind, RIck; Brenner, Marty

    2003-01-01

    The flight-test community routinely spends considerable time and money to determine a range of flight conditions, called a flight envelope, within which an aircraft is safe to fly. The cost of determining a flight envelope could be greatly reduced if there were a method of safely and accurately predicting the speed associated with the onset of an instability called flutter. Several methods have been developed with the goal of predicting flutter speeds to improve the efficiency of flight testing. These methods include (1) data-based methods, in which one relies entirely on information obtained from the flight tests and (2) model-based approaches, in which one relies on a combination of flight data and theoretical models. The data-driven methods include one based on extrapolation of damping trends, one that involves an envelope function, one that involves the Zimmerman-Weissenburger flutter margin, and one that involves a discrete-time auto-regressive model. An example of a model-based approach is that of the flutterometer. These methods have all been shown to be theoretically valid and have been demonstrated on simple test cases; however, until now, they have not been thoroughly evaluated in flight tests. An experimental apparatus called the Aerostructures Test Wing (ATW) was developed to test these prediction methods.

  7. Simple detection method of amyloid-beta peptide using p-FET with optical filtering layer and magnetic particle

    NASA Astrophysics Data System (ADS)

    Kim, Kwan-Soo; Kim, Chang-Beom; Song, Ki-Bong

    2013-05-01

    This article describes a novel method for detection of amyloid-β (Aβ) peptide that utilizes a photo-sensitive field-effect transistor (p-FET). According to a recent study, Aβ protein is known to play a central role in the pathogenesis of Alzheimer's disease (AD). Accordingly, we investigated the variation of photo current of the p-FET generated by the magnetic beads conjugated with Aβ peptides which are placed on the p-FET sensing areas. Additionally, in order to amplify the output signal, we used the lock-in amplifier (LIA) and confirmed the generating the photo current by a small incident light power under 100 μW. It means that it is possible to simply detect a certain protein using magnetic beads conjugated with Aβ peptide and fluorescent label located on the p-FET device. Therefore, in this paper, we suggest that our method could detect tiny amounts of Aβ peptide for early diagnosis of AD using the p-FET devices.

  8. Sample displacement chromatography as a method for purification of proteins and peptides from complex mixtures

    PubMed Central

    Gajdosik, Martina Srajer; Clifton, James; Josic, Djuro

    2012-01-01

    Sample displacement chromatography (SDC) in reversed-phase and ion-exchange modes was introduced approximately twenty years ago. This method takes advantage of relative binding affinities of components in a sample mixture. During loading, there is a competition among different sample components for the sorption on the surface of the stationary phase. SDC was first used for the preparative purification of proteins. Later, it was demonstrated that this kind of chromatography can also be performed in ion-exchange, affinity and hydrophobic-interaction mode. It has also been shown that SDC can be performed on monoliths and membrane-based supports in both analytical and preparative scale. Recently, SDC in ion-exchange and hydrophobic interaction mode was also employed successfully for the removal of trace proteins from monoclonal antibody preparations and for the enrichment of low abundance proteins from human plasma. In this review, the principals of SDC are introduced, and the potential for separation of proteins and peptides in micro-analytical, analytical and preparative scale is discussed. PMID:22520159

  9. 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…

  10. Prediction of Preclinical Operative Dentistry Performance in Two Instructional Methods.

    ERIC Educational Resources Information Center

    Boyd, Marcia; And Others

    1980-01-01

    The accuracy of manual and academic variables in predicting preclinical operative technique was evaluated. Correlations were low, but significant using the Perceptual Motor Ability Test (PMAT) of the Dental Admission Test. Students with low or average two-dimension scores on the PMAT benefited from an alternate teaching method. (JSR)

  11. Structural Mass Spectrometry: Rapid Methods for Separation and Analysis of Peptide Natural Products

    PubMed Central

    Goodwin, Cody R.; Fenn, Larissa S.; Derewacz, Dagmara K.; Bachmann, Brian O.; McLean, John A.

    2012-01-01

    A significant challenge in natural product discovery is the initial discrimination of discrete secondary metabolites alongside functionally similar primary metabolic cellular components within complex biological samples. A property that has yet to be fully exploited for natural product identification and characterization is the gas phase collision cross section, or, more generally, the mobility-mass correlation. Peptide natural products possess many of the properties that distinguish natural products as they are frequently characterized by a high degree of intramolecular bonding, and possess extended and compact conformations among other structural modifications. This report describes a rapid structural mass spectrometry technique based on ion mobility-mass spectrometry for the comparison of peptide natural products to their primary metabolic congeners using mobility-mass correlation. This property is empirically determined using ion mobility-mass spectrometry, applied to the analysis of linear versus modified peptides, and used to discriminate peptide natural products in a crude microbial extract. Complementary computational approaches are utilized to understand the structural basis for the separation of primary metabolism derived linear peptides from secondary metabolite cyclic and modified cyclic species. These findings provide a platform for enhancing the identification of secondary metabolic peptides with distinct mobility-mass ratios within complex biological samples. PMID:22216918

  12. 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.

  13. Evolutionary computational methods to predict oral bioavailability QSPRs.

    PubMed

    Bains, William; Gilbert, Richard; Sviridenko, Lilya; Gascon, Jose-Miguel; Scoffin, Robert; Birchall, Kris; Harvey, Inman; Caldwell, John

    2002-01-01

    This review discusses evolutionary and adaptive methods for predicting oral bioavailability (OB) from chemical structure. Genetic Programming (GP), a specific form of evolutionary computing, is compared with some other advanced computational methods for OB prediction. The results show that classifying drugs into 'high' and 'low' OB classes on the basis of their structure alone is solvable, and initial models are already producing output that would be useful for pharmaceutical research. The results also suggest that quantitative prediction of OB will be tractable. Critical aspects of the solution will involve the use of techniques that can: (i) handle problems with a very large number of variables (high dimensionality); (ii) cope with 'noisy' data; and (iii) implement binary choices to sub-classify molecules with behavior that are qualitatively different. Detailed quantitative predictions will emerge from more refined models that are hybrids derived from mechanistic models of the biology of oral absorption and the power of advanced computing techniques to predict the behavior of the components of those models in silico. PMID:11865672

  14. Toward the Prediction of FBPase Inhibitory Activity Using Chemoinformatic Methods

    PubMed Central

    Hao, Ming; Zhang, Shuwei; Qiu, Jieshan

    2012-01-01

    Currently, Chemoinformatic methods are used to perform the prediction for FBPase inhibitory activity. A genetic algorithm-random forest coupled method (GA-RF) was proposed to predict fructose 1,6-bisphosphatase (FBPase) inhibitors to treat type 2 diabetes mellitus using the Mold2 molecular descriptors. A data set of 126 oxazole and thiazole analogs was used to derive the GA-RF model, yielding the significant non-cross-validated correlation coefficient r2ncv and cross-validated r2cv values of 0.96 and 0.67 for the training set, respectively. The statistically significant model was validated by a test set of 64 compounds, producing the prediction correlation coefficient r2pred of 0.90. More importantly, the building GA-RF model also passed through various criteria suggested by Tropsha and Roy with r2o and r2m values of 0.90 and 0.83, respectively. In order to compare with the GA-RF model, a pure RF model developed based on the full descriptors was performed as well for the same data set. The resulting GA-RF model with significantly internal and external prediction capacities is beneficial to the prediction of potential oxazole and thiazole series of FBPase inhibitors prior to chemical synthesis in drug discovery programs. PMID:22837677

  15. Effectiveness of CID, HCD, and ETD with FT MS/MS for Degradomic-Peptidomic Analysis: Comparison of Peptide Identification Methods

    SciTech Connect

    Shen, Yufeng; Tolić, Nikola; Xie, Fang; Zhao, Rui; Purvine, Samuel O.; Schepmoes, Athena A.; Moore, Ronald, J.; Anderson, Gordon A.; Smith, Richard D.

    2011-09-02

    We report on use of an Orbitrap Velos mass spectrometer for comparison of fragmentation methods namely CID-, HCD-, and ETD for FT MS/MS analysis of human blood plasma peptidomic peptides. The peptidomic peptides were able to be identified from CID, HCD, and ETD spectra on specific confidence levels (e.g., 1% false discovery rate) with use of conventional SEQUEST database search software, and the number of identified peptides was increased by ~50% using accurate fragments (e.g., with mass tolerance of 0.05Da) in comparison with traditional moderate accuracy fragments (e.g., with 1 Da mass tolerance) for database search. However, the peptide datasets identified with such decoy search strategy were found to be varied by ~25% in the dataset size and ~20% in the dataset content with type of decoy database and precursor mass tolerances used for database search. CID was evaluated as the largest contributor to the identified peptide datasets, and HCD, and ETD provided ~20% and ~22% respectively additional peptides with accurate fragments for peptide identification, in contrast to ~25% and ~13% respectively with use of moderate accuracy fragments. When long (typically ≥7 amino acids) sequences were used for identification of peptides from the previously published UStags and de novo sequencing methods, HCD was evaluated as the largest contributor, and CID and ETD provided ~26% and ~8% respectively additional peptides from the UStags method and ~26% and ~6% respectively additional peptides from the de novo sequencing method. The peptide datasets identified with the UStags method were little influenced by the decoy database and mass tolerance and 98-99% peptide overlaps could be achieved between these datasets. CID, HCD, and ETD contributed their identifications of various charge state peptides in the m/z range highly overlapped and complementary implementation of CID, HCD, and ETD should be applied to maximize the number of peptides identified. Finally, the investigation

  16. 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.

  17. A free wake method for performance prediction of VAWT

    NASA Astrophysics Data System (ADS)

    Ilin, S.; Dumitrescu, H.; Cardos, V.; Dumitrache, A.

    2012-09-01

    Based on the lifting line theory and a free vortex wake model, a method including dynamic stall effects is presented for predicting the performance of a three-dimensional vertical-axis wind turbine (VAWT). A vortex model is used in which the wake is composed of trailing streamwise and shedding spanwise vortices, whose strengths are equal to the change in the bound vortex strength as dictated by Helmholtz and Kelvin's theorems. Performance parameters are calculated by application of the Biot-Savart law along with the Kutta-Joukowski theorem and a semi-empirical dynamic stall model. Predictions are shown to compare favorably with existing experimental data.

  18. Role of Urinary Levels of Endothelin-1, Monocyte Chemotactic Peptide-1, and N-Acetyl Glucosaminidase in Predicting the Severity of Obstruction in Hydronephrotic Neonates

    PubMed Central

    Rafiei, Alireza; Mousavi, Seyed Abdollah; Alaee, Abdulrasool; Yeganeh, Yalda

    2014-01-01

    Purpose Antenatal hydronephrosis (AH) is found in 0.5%-1% of neonates. The aim of the study was to assess the urinary concentrations of 3 biomarkers, endothelin-1 (ET-1), monocyte chemotactic peptide-1 (MCP-1), and N-acetyl-glucosaminidase (NAG) in severely hydronephrotic neonates. Materials and Methods Neonates with a history of prenatal hydronephrosis were enrolled in the prospective study in 2 groups. Group 1 included neonates with severe forms of obstruction requiring surgical intervention and group 2 included neonates with milder forms of obstruction without any functional impairment. Fresh voided urinary levels of ET-1, MCP-1, and NAG were measured and their ratios to urinary Cr were calculated. Results Fourty-two neonates were enrolled into the 2 groups: group 1, 24 patients (21 male, 3 female); group 2, 18 neonates (16 male, 2 female). There were no statistically significant differences between urinary ET-1, NAG, MCP-1 values, and ET-1/Cr and NAG/Cr ratios in groups 1 and 2. The urinary MCP-1/Cr ratio was significantly higher in group 1 than in group 2. For comparison of groups 1 and 2, the cut-off values were measured as 0.5709 ng/mg (sensitivity, 75%; specificity, 67%; positive predictive value [PPV], 71%; negative predictive value [NPV], 71%), 0.927 ng/mg (sensitivity, 77%; specificity, 72%; PPV, 77%; NPV, 72%), and 1.1913 IU/mg (sensitivity, 62%; specificity, 67%; PPV, 68%; NPV, 60%) for ET-1/Cr, MCP-1/Cr, and NAG/Cr ratios, respectively. Conclusions The urinary MCP-1/Cr ratio is significantly elevated in neonates with severe obstruction requiring surgical intervention. Based upon these results, urinary MCP-1/Cr may be useful in identification of severe obstructive hydronephrosis in neonates. PMID:25324951

  19. A protein structural class prediction method based on novel features.

    PubMed

    Zhang, Lichao; Zhao, Xiqiang; Kong, Liang

    2013-09-01

    In this study, a 12-dimensional feature vector is constructed to reflect the general contents and spatial arrangements of the secondary structural elements of a given protein sequence. Among the 12 features, 6 novel features are specially designed to improve the prediction accuracies for α/β and α + β classes based on the distributions of α-helices and β-strands and the characteristics of parallel β-sheets and anti-parallel β-sheets. To evaluate our method, the jackknife cross-validating test is employed on two widely-used datasets, 25PDB and 1189 datasets with sequence similarity lower than 40% and 25%, respectively. The performance of our method outperforms the recently reported methods in most cases, and the 6 newly-designed features have significant positive effect to the prediction accuracies, especially for α/β and α + β classes. PMID:23770446

  20. A Method for Predicting Protein-Protein Interaction Types

    PubMed Central

    Silberberg, Yael

    2014-01-01

    Protein-protein interactions (PPIs) govern basic cellular processes through signal transduction and complex formation. The diversity of those processes gives rise to a remarkable diversity of interactions types, ranging from transient phosphorylation interactions to stable covalent bonding. Despite our increasing knowledge on PPIs in humans and other species, their types remain relatively unexplored and few annotations of types exist in public databases. Here, we propose the first method for systematic prediction of PPI type based solely on the techniques by which the interaction was detected. We show that different detection methods are better suited for detecting specific types. We apply our method to ten interaction types on a large scale human PPI dataset. We evaluate the performance of the method using both internal cross validation and external data sources. In cross validation, we obtain an area under receiver operating characteristic (ROC) curve ranging from 0.65 to 0.97 with an average of 0.84 across the predicted types. Comparing the predicted interaction types to external data sources, we obtained significant agreements for phosphorylation and ubiquitination interactions, with hypergeometric p-value = 2.3e−54 and 5.6e−28 respectively. We examine the biological relevance of our predictions using known signaling pathways and chart the abundance of interaction types in cell processes. Finally, we investigate the cross-relations between different interaction types within the network and characterize the discovered patterns, or motifs. We expect the resulting annotated network to facilitate the reconstruction of process-specific subnetworks and assist in predicting protein function or interaction. PMID:24625764

  1. Paroxysmal atrial fibrillation prediction method with shorter HRV sequences.

    PubMed

    Boon, K H; Khalil-Hani, M; Malarvili, M B; Sia, C W

    2016-10-01

    This paper proposes a method that predicts the onset of paroxysmal atrial fibrillation (PAF), using heart rate variability (HRV) segments that are shorter than those applied in existing methods, while maintaining good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to stabilize (electrically) and prevent the onset of atrial arrhythmias with different pacing techniques. We investigate the effect of HRV features extracted from different lengths of HRV segments prior to PAF onset with the proposed PAF prediction method. The pre-processing stage of the predictor includes QRS detection, HRV quantification and ectopic beat correction. Time-domain, frequency-domain, non-linear and bispectrum features are then extracted from the quantified HRV. In the feature selection, the HRV feature set and classifier parameters are optimized simultaneously using an optimization procedure based on genetic algorithm (GA). Both full feature set and statistically significant feature subset are optimized by GA respectively. For the statistically significant feature subset, Mann-Whitney U test is used to filter non-statistical significance features that cannot pass the statistical test at 20% significant level. The final stage of our predictor is the classifier that is based on support vector machine (SVM). A 10-fold cross-validation is applied in performance evaluation, and the proposed method achieves 79.3% prediction accuracy using 15-minutes HRV segment. This accuracy is comparable to that achieved by existing methods that use 30-minutes HRV segments, most of which achieves accuracy of around 80%. More importantly, our method significantly outperforms those that applied segments shorter than 30 minutes. PMID:27480743

  2. 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.

  3. Benchmarking B cell epitope prediction: underperformance of existing methods.

    PubMed

    Blythe, Martin J; Flower, Darren R

    2005-01-01

    Sequence profiling is used routinely to predict the location of B-cell epitopes. In the postgenomic era, the need for reliable epitope prediction is clear. We assessed 484 amino acid propensity scales in combination with ranges of plotting parameters to examine exhaustively the correlation of peaks and epitope location within 50 proteins mapped for polyclonal responses. After examining more than 10(6) combinations, we found that even the best set of scales and parameters performed only marginally better than random. Our results confirm the null hypothesis: Single-scale amino acid propensity profiles cannot be used to predict epitope location reliably. The implication for studies using such methods is obvious. PMID:15576553

  4. 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. PMID:24630980

  5. 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.

  6. 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.

  7. A computational method to predict carbonylation sites in yeast proteins.

    PubMed

    Lv, H Q; Liu, J; Han, J Q; Zheng, J G; Liu, R L

    2016-01-01

    Several post-translational modifications (PTM) have been discussed in literature. Among a variety of oxidative stress-induced PTM, protein carbonylation is considered a biomarker of oxidative stress. Only certain proteins can be carbonylated because only four amino acid residues, namely lysine (K), arginine (R), threonine (T) and proline (P), are susceptible to carbonylation. The yeast proteome is an excellent model to explore oxidative stress, especially protein carbonylation. Current experimental approaches in identifying carbonylation sites are expensive, time-consuming and limited in their abilities to process proteins. Furthermore, there is no bioinformational method to predict carbonylation sites in yeast proteins. Therefore, we propose a computational method to predict yeast carbonylation sites. This method has total accuracies of 86.32, 85.89, 84.80, and 86.80% in predicting the carbonylation sites of K, R, T, and P, respectively. These results were confirmed by 10-fold cross-validation. The ability to identify carbonylation sites in different kinds of features was analyzed and the position-specific composition of the modification site-flanking residues was discussed. Additionally, a software tool has been developed to help with the calculations in this method. Datasets and the software are available at https://sourceforge.net/projects/hqlstudio/ files/CarSpred.Y/. PMID:27420944

  8. Improved load ratio method for predicting crack length

    SciTech Connect

    Chen, X.; Albrecht, P.; Wright, W.; Joyce, J.A.

    1995-04-01

    The elastic compliance from unloading/reloading sequences in a load-displacement record estimates well crack length in elastic-plastic fracture toughness tests of compact tension [C(T)] and bending type specimens. The need for partial unloading of the specimen makes it difficult to run the test under static loading and impossible under either dynamic loading or very high temperatures. Furthermore, fracture toughness testing in which crack length is determined from elastic compliance requires high precision testing equipment and highly skilled technicians. As a result, such tests are confined usually to research laboratories and seldom used under production settings. To eliminate these problems, an improved load ratio method of predicting crack length is proposed that utilizes only the recorded load versus load-line displacement curve (or load versus crack-mouth-opening displacement curve) without unloading/reloading sequences. As a result, the instrumentation is much simpler than in the elastic compliance or potential drop methods. If only a monotonic load-displacement record is to be measured the fracture toughness test becomes almost as simple to perform as a tension test. The method described here improves in three ways the ``original load ratio method`` proposed by Hu et al. First, a blunting term is added to the crack length before maximum load. Second, a strain hardening correction is included after maximum load. And, third, the initial crack length and the physical (final) crack length measured at the end of the test serve to anchor the predicted crack lengths, forcing agreement between predicted and measured values. The method predicts crack extension with excellent accuracy in specimens fabricated from A302, A508, and A533B piping and pressure vessel steels, A588 and A572 structural steels, and HY-80 ship steel.

  9. Predict octane numbers using a generalized interaction method

    SciTech Connect

    Twu, C.H.; Coon, J.E.

    1996-02-01

    An interaction-based correlation using a new approach can be used to predict research and motor octane numbers of gasoline blends. An ultimately detailed analysis of the gasoline cut is not necessary. This correlation can describe blending behavior over the entire composition range of gasoline cuts. The component-oriented interaction approach is general and will accurately predict, without performing additional blending studies, blending behavior for new gasoline cuts. The proposed correlation fits the data quite closely for blends of many gasoline cuts. The regression gives realistic values for binary interaction parameters between components. A unique set of binary interaction parameters was found for the equation for predicting octane number of any gasoline blend. The binary interaction parameters between components contained in gasoline cuts have been converted to binary interaction parameters between gasoline cuts through a general equation to simplify the calculations. Because of the proposed method`s accuracy, optimum allocation of components among gasoline grades can be obtained and predicted values can be used for quality control of the octane number of marketed gasolines.

  10. 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

  11. Fast prediction unit selection method for HEVC intra prediction based on salient regions

    NASA Astrophysics Data System (ADS)

    Feng, Lei; Dai, Ming; Zhao, Chun-lei; Xiong, Jing-ying

    2016-07-01

    In order to reduce the computational complexity of the high efficiency video coding (HEVC) standard, a new algorithm for HEVC intra prediction, namely, fast prediction unit (PU) size selection method for HEVC based on salient regions is proposed in this paper. We first build a saliency map for each largest coding unit (LCU) to reduce its texture complexity. Secondly, the optimal PU size is determined via a scheme that implements an information entropy comparison among sub-blocks of saliency maps. Finally, we apply the partitioning result of saliency map on the original LCUs, obtaining the optimal partitioning result. Our algorithm can determine the PU size in advance to the angular prediction in intra coding, reducing computational complexity of HEVC. The experimental results show that our algorithm achieves a 37.9% reduction in encoding time, while producing a negligible loss in Bjontegaard delta bit rate ( BDBR) of 0.62%.

  12. Mapping of the SecA signal peptide binding site and dimeric interface by using the substituted cysteine accessibility method.

    PubMed

    Bhanu, Meera K; Zhao, Ping; Kendall, Debra A

    2013-10-01

    SecA is an ATPase nanomotor critical for bacterial secretory protein translocation. Secretory proteins carry an amino-terminal signal peptide that is recognized and bound by SecA followed by its transfer across the SecYEG translocon. While this process is crucial for the onset of translocation, exactly where the signal peptide interacts with SecA is unclear. SecA protomers also interact among themselves to form dimers in solution, yet the oligomeric interface and the residues involved in dimerization are unknown. To address these issues, we utilized the substituted cysteine accessibility method (SCAM); we generated a library of 23 monocysteine SecA mutants and probed for the accessibility of each mutant cysteine to maleimide-(polyethylene glycol)2-biotin (MPB), a sulfhydryl-labeling reagent, both in the presence and absence of a signal peptide. Dramatic differences in MPB labeling were observed, with a select few mutants located at the preprotein cross-linking domain (PPXD), the helical wing domain (HWD), and the helical scaffold domain (HSD), indicating that the signal peptide binds at the groove formed between these three domains. The exposure of this binding site is varied under different conditions and could therefore provide an ideal mechanism for preprotein transfer into the translocon. We also identified residues G793, A795, K797, and D798 located at the two-helix finger of the HSD to be involved in dimerization. Adenosine-5'-(γ-thio)-triphosphate (ATPγS) alone and, more extensively, in conjunction with lipids and signal peptides strongly favored dimer dissociation, while ADP supports dimerization. This study provides key insight into the structure-function relationships of SecA preprotein binding and dimer dissociation. PMID:23935053

  13. 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. PMID:24985454

  14. 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.

  15. Coastal cliff recession: the use of probabilistic prediction methods

    NASA Astrophysics Data System (ADS)

    Lee, E. M.; Hall, J. W.; Meadowcroft, I. C.

    2001-10-01

    A range of probabilistic methods is introduced for predicting coastal cliff recession, which provide a means of demonstrating the potential variability in such predictions. They form the basis for risk-based land-use planning, cliff management and engineering decision-making. Examples of probabilistic models are presented for a number of different cliff settings: the simulation of recession on eroding cliffs; the use of historical records and statistical experiments to model the behaviour of cliffs affected by rare, episodic landslide events; the adaptation of an event tree approach to assess the probability of failure of protected cliffs, taking into account the residual life of the existing defences; and the evaluation of the probability of landslide reactivation in areas of pre-existing landslide systems. These methods are based on a geomorphological assessment of the episodic nature of the recession process, together with historical records.

  16. Predictive Values of N-Terminal Pro-B-Type Natriuretic Peptide and Cardiac Troponin I for Myocardial Fibrosis in Hypertrophic Obstructive Cardiomyopathy

    PubMed Central

    Zhang, Changlin; Liu, Rong; Yuan, Jiansong; Cui, Jingang; Hu, Fenghuan; Yang, Weixian; Zhang, Yan; Chen, Youzhou; Qiao, Shubin

    2016-01-01

    Background Both high-sensitivity cardiac troponin T and B-type natriuretic peptide are useful in detecting myocardial fibrosis, as determined by late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR), in patients with non-obstructive hypertrophic cardiomyopathy. However, their values to predict myocardial fibrosis in hypertrophic obstructive cardiomyopathy (HOCM) remain unclear. We investigated the role of N-Terminal Pro-B-Type Natriuretic Peptide (NT-proBNP) and cardiac troponin I (cTnI) to identify LGE-CMR in patients with HOCM. Methods Peripheral concentrations of NT-proBNP and cTnI were determined in patients with HOCM (n = 163; age = 47.2 ± 10.8 years; 38.7% females). Contrast-enhanced CMR was performed to identify and quantify myocardial fibrosis. Results LGE was detected in 120 of 163 patients (73.6%). Patients with LGE had significantly higher levels of NT-proBNP and cTnI than those without LGE (1386.2 [904.6–2340.8] vs. 866.6 [707.2–1875.2] pmol/L, P = 0.003; 0.024 [0.010–0.049] vs. 0.010 [0.005–0.021] ng/ml, P <0.001, respectively). The extent of LGE was positively correlated with log cTnI (r = 0.371, P <0.001) and log NT-proBNP (r = 0.211, P = 0.007). On multivariable analysis, both log cTnI and maximum wall thickness (MWT) were independent predictors of the presence of LGE (OR = 3.193, P = 0.033; OR = 1.410, P < 0.001, respectively), whereas log NT-proBNP was not. According to the ROC curve analysis, combined measurements of MWT ≥21 mm and/or cTnI ≥0.025ng/ml indicated good diagnostic performance for the presence of LGE, with specificity of 95% or sensitivity of 88%. Conclusions Serum cTnI is an independent predictor useful for identifying myocardial fibrosis, while plasma NT-proBNP is only associated with myocardial fibrosis on univariate analysis. Combined measurements of serum cTnI with MWT further improve its value in detecting myocardial fibrosis in patients with HOCM. PMID:26765106

  17. Epileptic seizure prediction by non-linear methods

    SciTech Connect

    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.

  18. 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.

  19. 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. PMID:24615859

  20. 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

  1. 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.

  2. Turbulent heat transfer prediction method for application to scramjet engines

    NASA Technical Reports Server (NTRS)

    Pinckney, S. Z.

    1974-01-01

    An integral method for predicting boundary layer development in turbulent flow regions on two-dimensional or axisymmetric bodies was developed. The method has the capability of approximating nonequilibrium velocity profiles as well as the local surface friction in the presence of a pressure gradient. An approach was developed for the problem of predicting the heat transfer in a turbulent boundary layer in the presence of a high pressure gradient. The solution was derived with particular emphasis on its applicability to supersonic combustion; thus, the effects of real gas flows were included. The resulting integrodifferential boundary layer method permits the estimation of cooling reguirements for scramjet engines. Theoretical heat transfer results are compared with experimental combustor and noncombustor heat transfer data. The heat transfer method was used in the development of engine design concepts which will produce an engine with reduced cooling requirements. The Langley scramjet engine module was designed by utilizing these design concepts and this engine design is discussed along with its corresponding cooling requirements. The heat transfer method was also used to develop a combustor cooling correlation for a combustor whose local properties are computed one dimensionally by assuming a linear area variation and a given heat release schedule.

  3. B and T Cell Epitope-Based Peptides Predicted from Evolutionarily Conserved and Whole Protein Sequences of Ebola Virus as Vaccine Targets.

    PubMed

    Yasmin, T; Nabi, A H M Nurun

    2016-05-01

    Ebola virus (EBV) has become a serious threat to public health. Different approaches were applied to predict continuous and discontinuous B cell epitopes as well as T cell epitopes from the sequence-based and available three-dimensional structural analyses of each protein of EBV. Peptides '(79) VPSATKRWGFRSGVPP(94) ' from GP1 and '(515) LHYWTTQDEGAAIGLA(530) ' from GP2 of Ebola were found to be the consensus peptidic sequences predicted as linear B cell epitope of which the latter contains a region (519) TTQDEG(524) that fulfilled all the criteria of accessibility, hydrophilicity, flexibility and beta turn region for becoming an ideal B cell epitope. Different nonamers as T cell epitopes were obtained that interacted with different numbers of MHC class I and class II alleles with a binding affinity of <100 nm. Interestingly, these alleles also bound to the MHC class I alleles mostly prevalent in African and South Asian regions. Of these, 'LANETTQAL' and 'FLYDRLAST' nonamers were predicted to be the most potent T cell epitopes and they, respectively, interacted with eight and twelve class I alleles that covered 63.79% and 54.16% of world population, respectively. These nonamers were found to be the core sequences of 15mer peptides that interacted with the most common class II allele, HLA-DRB1*01:01. They were further validated for their binding to specific class I alleles using docking technique. Thus, these predicted epitopes may be used as vaccine targets against EBV and can be validated in model hosts to verify their efficacy as vaccine. PMID:26939891

  4. Species Identification of Bovine, Ovine and Porcine Type 1 Collagen; Comparing Peptide Mass Fingerprinting and LC-Based Proteomics Methods

    PubMed Central

    Buckley, Mike

    2016-01-01

    Collagen is one of the most ubiquitous proteins in the animal kingdom and the dominant protein in extracellular tissues such as bone, skin and other connective tissues in which it acts primarily as a supporting scaffold. It has been widely investigated scientifically, not only as a biomedical material for regenerative medicine, but also for its role as a food source for both humans and livestock. Due to the long-term stability of collagen, as well as its abundance in bone, it has been proposed as a source of biomarkers for species identification not only for heat- and pressure-rendered animal feed but also in ancient archaeological and palaeontological specimens, typically carried out by peptide mass fingerprinting (PMF) as well as in-depth liquid chromatography (LC)-based tandem mass spectrometric methods. Through the analysis of the three most common domesticates species, cow, sheep, and pig, this research investigates the advantages of each approach over the other, investigating sites of sequence variation with known functional properties of the collagen molecule. Results indicate that the previously identified species biomarkers through PMF analysis are not among the most variable type 1 collagen peptides present in these tissues, the latter of which can be detected by LC-based methods. However, it is clear that the highly repetitive sequence motif of collagen throughout the molecule, combined with the variability of the sites and relative abundance levels of hydroxylation, can result in high scoring false positive peptide matches using these LC-based methods. Additionally, the greater alpha 2(I) chain sequence variation, in comparison to the alpha 1(I) chain, did not appear to be specific to any particular functional properties, implying that intra-chain functional constraints on sequence variation are not as great as inter-chain constraints. However, although some of the most variable peptides were only observed in LC-based methods, until the range of

  5. 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.

  6. Exploring Function Prediction in Protein Interaction Networks via Clustering Methods

    PubMed Central

    Trivodaliev, Kire; Bogojeska, Aleksandra; Kocarev, Ljupco

    2014-01-01

    Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach. PMID:24972109

  7. On-resin Diels-Alder reaction with inverse electron demand: an efficient ligation method for complex peptides with a varying spacer to optimize cell adhesion.

    PubMed

    Pagel, Mareen; Meier, René; Braun, Klaus; Wiessler, Manfred; Beck-Sickinger, Annette G

    2016-06-01

    Solid phase peptide synthesis (SPPS) is the method of choice to produce peptides. Several protecting groups enable specific modifications. However, complex peptide conjugates usually require a rather demanding conjugation strategy, which is mostly performed in solution. Herein, an efficient strategy is described using an on-resin Diels-Alder reaction with inverse electron demand (DARinv). This method is compatible with the standard Fmoc/tBu strategy and is easy to monitor. As a proof of concept a titanium binding peptide was modified with a cyclic cell binding peptide (RGD) by DARinv on a solid support applying different tetrazines and alkenes. The generated bulky DARinv linkers were employed to act as the required spacer for RGD mediated cell adhesion on titanium. In vitro studies demonstrated improved cell spreading on DARinv-conjugated peptides and revealed, in combination with molecular dynamics-simulation, new insights into the design of spacers between the RGD peptide and the surface. Performing the DARinv on resin expands the toolbox of SPPS to produce complex peptide conjugates under mild, catalyst free conditions with reduced purification steps. The resulting conjugate can be effectively exploited to promote cell adhesion on biomaterials. PMID:27117044

  8. 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

    2016-01-01

    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. PMID:26527728

  9. SATPdb: a database of structurally annotated therapeutic peptides

    PubMed Central

    Singh, Sandeep; Chaudhary, Kumardeep; Dhanda, Sandeep Kumar; Bhalla, Sherry; Usmani, Salman Sadullah; Gautam, Ankur; Tuknait, Abhishek; Agrawal, Piyush; Mathur, Deepika; Raghava, Gajendra P.S.

    2016-01-01

    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. PMID:26527728

  10. Technetium-99m somatostatin analogues: effect of labelling methods and peptide sequence.

    PubMed

    Decristoforo, C; Mather, S J

    1999-08-01

    In this paper the preclinical evaluation of the somatostatin analogue RC160 labelled with technetium-99m using bifunctional chelators (BFCs) based on the hydrazinonicotinamide (HYNIC) and N(3)S system is described and a comparison made with [Tyr(3)]-octreotide (TOC). Conjugates of both peptides with HYNIC, and of RC160 with benzoyl-MAG(3) and an N(3)S-adipate derivative were prepared and radiolabelling performed at high specific activities using tricine, tricine/nicotinic acid and ethylenediamine-N,N'-diacetic acid (EDDA) as co-ligands for HYNIC conjugates. All conjugates and (99m)Tc-labelled peptides showed preserved binding affinity for the somatostatin receptor (IC50, Kd<5 nM). The biodistribution was markedly dependent on the BFC and co-ligand used, with the amidothiol ligands showing a greater degree of hepatobiliary clearance, the HYNIC/tricine complex higher blood levels and the HYNIC/EDDA complex the highest level of renal excretion and lowest blood levels. All peptide conjugates showed receptor-mediated uptake in tumour xenografts, but tumour uptake was significantly lower for the (99m)Tc-RC160 derivatives compared with (99m)Tc-EDDA/HYNIC-[Tyr(3)]-octreotide (0.2%-3.5%ID/g vs 9.7%ID/g) and correlated well with the reduced internalisation rate for RC160 derivatives. Our results show that the selection of the labelling approach as well as the right choice of the peptide structure are crucial for labelling peptides with (99m)Tc to achieve complexes with favourable biodistribution. Despite the relatively low tumour uptake compared with (99m)Tc-EDDA/HYNIC-[Tyr(3)]-octreotide, (99m)Tc-RC160 could play a role in imaging tumours that do not bind octreotide derivatives. PMID:10436200

  11. [Capillary isotachophoresis--a new method in drug analysis. 2. Analytical capillary isotachophoresis of the synthetic peptide substance P].

    PubMed

    Jannasch, R

    1985-06-01

    For the synthetic undecapeptide substance P being object of actual pharmacological research and a potential peptide-drug experiments were carried out for the assessment of cation analyses by means of capillary isotachophoresis for the purpose of the development of test methods for pharmaceutical judgments of quality. Best conditions exist at a pH range from 6.2 to 6.3 of the leading electrolyte of the discontinuous electrolyte system by using of the counter ion morpholinoethansulfonic acid. The results confirm the high separation efficiency and rate of information of such analyses in the field of peptides. They served the control of purification methods for substance P as well as the determination of the contents in preparations for this peptide including simultaneously in case of need the preservative benzalkonium chloride with sufficient reproducibility (rel. S.D. 0.7-2.7% for substance P and less than or equal to 3% for benzalkonium chloride). It is also possible to estimate impurities and products of decomposition respectively down to the level of 0.1-1% and following that to make statements about the stability. PMID:2412242

  12. Generalized method for probability-based peptide and protein identification from tandem mass spectrometry data and sequence database searching.

    PubMed

    Ramos-Fernández, Antonio; Paradela, Alberto; Navajas, Rosana; Albar, Juan Pablo

    2008-09-01

    Tandem mass spectrometry-based proteomics is currently in great demand of computational methods that facilitate the elimination of likely false positives in peptide and protein identification. In the last few years, a number of new peptide identification programs have been described, but scores or other significance measures reported by these programs cannot always be directly translated into an easy to interpret error rate measurement such as the false discovery rate. In this work we used generalized lambda distributions to model frequency distributions of database search scores computed by MASCOT, X!TANDEM with k-score plug-in, OMSSA, and InsPecT. From these distributions, we could successfully estimate p values and false discovery rates with high accuracy. From the set of peptide assignments reported by any of these engines, we also defined a generic protein scoring scheme that enabled accurate estimation of protein-level p values by simulation of random score distributions that was also found to yield good estimates of protein-level false discovery rate. The performance of these methods was evaluated by searching four freely available data sets ranging from 40,000 to 285,000 MS/MS spectra. PMID:18515861

  13. Calculation of the entropy and free energy of peptides by molecular dynamics simulations using the hypothetical scanning molecular dynamics method.

    PubMed

    Cheluvaraja, Srinath; Meirovitch, Hagai

    2006-07-14

    Hypothetical scanning (HS) is a method for calculating the absolute entropy S and free energy F from a sample generated by any simulation technique. With this approach each sample configuration is reconstructed with the help of transition probabilities (TPs) and their product leads to the configuration's probability, hence to the entropy. Recently a new way for calculating the TPs by Monte Carlo (MC) simulations has been suggested, where all system interactions are taken into account. Therefore, this method--called HSMC--is in principle exact where the only approximation is due to insufficient sampling. HSMC has been applied very successfully to liquid argon, TIP3P water, self-avoiding walks on a lattice, and peptides. Because molecular dynamics (MD) is considered to be significantly more efficient than MC for a compact polymer chain, in this paper HSMC is extended to MD simulations as applied to peptides. Like before, we study decaglycine in vacuum but for the first time also a peptide with side chains, (Val)(2)(Gly)(6)(Val)(2). The transition from MC to MD requires implementing essential changes in the reconstruction process of HSMD. Results are calculated for three microstates, helix, extended, and hairpin. HSMD leads to very stable differences in entropy TDeltaS between these microstates with small errors of 0.1-0.2 kcal/mol (T=100 K) for a wide range of calculation parameters with extremely high efficiency. Various aspects of HSMD and plans for future work are discussed. PMID:16848609

  14. Development of a LC-MS/MS method to monitor palmitoyl peptides content in anti-wrinkle cosmetics.

    PubMed

    Chirita, Raluca-Ioana; Chaimbault, Patrick; Archambault, Jean-Christophe; Robert, Isabelle; Elfakir, Claire

    2009-05-01

    Palmitoyl peptides are anti-aging agents widely used in cosmetics. This article describes the development of a LC-MS/MS analytical procedure that allows, after a liquid-liquid extraction procedure, their unambiguous detection in cosmetic formulation. MS/MS detection is shown to be specific regarding placebo formulations. Limits of quantification, linearity, accuracy and precision of the method were estimated. The results presented show that palmitoyl peptides can be thus reliably assayed. The palmitoylated pentapeptide palmitoyl-lysyl-threonyl-threonyl-lysyl-serine (pal-KTTKS) was assayed in anti-wrinkle creams using palmitoyl-glycyl-histidyl-lysine (pal-GHK) as internal standard. From the results obtained, the influence of the formulation on pal-KTTKS availability is evidenced. PMID:19393372

  15. 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.

  16. Efficient prediction methods for selecting effective siRNA sequences.

    PubMed

    Takasaki, Shigeru

    2010-02-01

    Although short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells, its gene silencing efficacy varies markedly and there are only a few consistencies among the recently reported design rules/guidelines for selecting siRNA sequences effective for mammalian genes. Another shortcoming of the previously reported methods is that they cannot estimate the probability that a candidate sequence will silence the target gene. This paper first reviewed the recently reported siRNA design guidelines and clarified the problems concerning the guidelines. It then proposed two prediction methods-Radial Basis Function (RBF) network and decision tree learning-and their combined method for selecting effective siRNA target sequences from many possible candidate sequences. They are quite different from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. The methods imply high estimation accuracy for selecting candidate siRNA sequences. PMID:20022002

  17. Method for predicting photocatalytic oxidation rates of organic compounds.

    PubMed

    Sattler, Melanie L; Liljestrand, Howard M

    2003-01-01

    In designing a photocatalytic oxidation (PCO) system for a given air pollution source, destruction rates for volatile organic compounds (VOCs) are required. The objective of this research was to develop a systematic method of predicting PCO rate constants by correlating rate constants with physical-chemical characteristics of compounds. Accordingly, reaction rate constants were determined for destruction of volatile organics over a titanium dioxide (TiO2) catalyst in a continuous mixed-batch reactor. It was found that PCO rate constants for alkanes and alkenes vary linearly with gas-phase ionization potential (IP) and with gas-phase hydroxyl radical reaction rate constant. The correlations allow rates of destruction of compounds not tested in this research to be predicted based on physical-chemical characteristics. PMID:12568248

  18. Review of sonic-boom generation theory and prediction methods.

    NASA Technical Reports Server (NTRS)

    Carlson, H. W.; Maglieri, D. J.

    1972-01-01

    The prediction techniques reviedi he present paper permit the calculation of sonic booms produced by rather complex conventional supersonic aircraft designs performing level nonaccelerated flight in a quiet atmosphere. Basic concepts of supersonic flow analysis, for representation of an airplane as a linear distribution of disturbances and for determination of the resultant pressure field complete with shocks, are outlined. Numerical techniques for implementation of the theory are discussed briefly, and examples of the correlation of theory with experimental data from wind tunnel and flight tests are presented. Special attention is given to presentation of a simplified method for rapid 'first-cut' estimation of farfield bow-shock overpressure. Finally, some problems encountered in attempts at applying the prediction techniques for the nearfield at high supersonic Mach numbers are recognized, and the need for further refinement of present techniques or the development of new systems is discussed.

  19. 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

    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.

  20. Prediction of plasma simulation data with the Gaussian process method

    SciTech Connect

    Preuss, R.; Toussaint, U. von

    2014-12-05

    The simulation of plasma-wall interactions of fusion plasmas is extremely costly in computer power and time - the running time for a single parameter setting is easily in the order of weeks or months. We propose to exploit the already gathered results in order to predict the outcome for parametric studies within the high dimensional parameter space. For this we utilize Gaussian processes within the Bayesian framework and perform validation with one and two dimensional test cases from which we learn how to assess the outcome. Finally, the newly implemented method is applied to simulated data from the scrape-off layer of a fusion plasma. Uncertainties of the predictions are provided which point the way to parameter settings of further (expensive) simulations.

  1. A novel fold recognition method using composite predicted secondary structures.

    PubMed

    An, Yuling; Friesner, Richard A

    2002-08-01

    In this work, we introduce a new method for fold recognition using composite secondary structures assembled from different secondary structure prediction servers for a given target sequence. An automatic, complete, and robust way of finding all possible combinations of predicted secondary structure segments (SSS) for the target sequence and clustering them into a few flexible clusters, each containing patterns with the same number of SSS, is developed. This program then takes two steps in choosing plausible homologues: (i) a SSS-based alignment excludes impossible templates whose SSS patterns are very different from any of those of the target; (ii) a residue-based alignment selects good structural templates based on sequence similarity and secondary structure similarity between the target and only those templates left in the first stage. The secondary structure of each residue in the target is selected from one of the predictions to find the best match with the template. Truncation is applied to a target where different predictions vary. In most cases, a target is also divided into N-terminal and C-terminal fragments, each of which is used as a separate subsequence. Our program was tested on the fold recognition targets from CASP3 with known PDB codes and some available targets from CASP4. The results are compared with a structural homologue list for each target produced by the CE program (Shindyalov and Bourne, Protein Eng 1998;11:739-747). The program successfully locates homologues with high Z-score and low root-mean-score deviation within the top 30-50 predictions in the overwhelming majority of cases. PMID:12112702

  2. 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. PMID:25620523

  3. Can the electronegativity equalization method predict spectroscopic properties?

    NASA Astrophysics Data System (ADS)

    Verstraelen, T.; Bultinck, P.

    2015-02-01

    The electronegativity equalization method is classically used as a method allowing the fast generation of atomic charges using a set of calibrated parameters and provided knowledge of the molecular structure. Recently, it has started being used for the calculation of other reactivity descriptors and for the development of polarizable and reactive force fields. For such applications, it is of interest to know whether the method, through the inclusion of the molecular geometry in the Taylor expansion of the energy, would also allow sufficiently accurate predictions of spectroscopic data. In this work, relevant quantities for IR spectroscopy are considered, namely the dipole derivatives and the Cartesian Hessian. Despite careful calibration of parameters for this specific task, it is shown that the current models yield insufficiently accurate results.

  4. 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.

  5. 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.

  6. High-Sensitivity Troponin I and Amino-Terminal Pro–B-Type Natriuretic Peptide Predict Heart Failure and Mortality in the General Population

    PubMed Central

    McKie, Paul M.; AbouEzzeddine, Omar F.; Scott, Christopher G.; Mehta, Ramila; Rodeheffer, Richard J.; Redfield, Margaret M.; Burnett, John C.; Jaffe, Allan S.

    2015-01-01

    INTRODUCTION High-sensitivity cardiac troponin assays have potent prognostic value in stable cardiovascular disease cohorts. Our objective was to assess the prognostic utility of a novel cardiac troponin I (cTnI) high-sensitivity assay, independently and in combination with amino-terminal pro–B-type natriuretic peptide (NT-proBNP), for the future development of heart failure and mortality in the general community. METHODS A well-characterized community-based cohort of 2042 participants underwent clinical assessment and echocardiographic evaluation. Baseline measurements of cTnI with a high-sensitivity assay and NT-proBNP were obtained in 1843 individuals. Participants were followed for new-onset heart failure and mortality with median (25th, 75th percentile) follow-up of 10.7 (7.9, 11.6) and 12.1 (10.4, 13.0) years, respectively. RESULTS When measured with a high-sensitivity assay, cTnI greater than the sex-specific 80th percentile was independently predictive of heart failure [hazard ratio 2.56 (95% confidence interval 1.88 – 3.50), P < 0.001] and mortality [1.91(1.49 – 2.46), P < 0.001] beyond conventional risk factors in this community-based cohort, with significant increases in the net reclassification improvement for heart failure. The prognostic utility of cTnI measured with a high-sensitivity assay goes beyond NT-proBNP, yet our data suggest that these 2 assays are complementary and most beneficial when evaluated together in identifying at-risk individuals in the community. CONCLUSIONS Our findings lay the foundation for prospective studies aimed at identification of individuals at high risk by use of a multimarker approach, followed by aggressive prevention strategies to prevent subsequent heart failure. PMID:24987112

  7. 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. PMID:22707154

  8. 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.

  9. Interim prediction method for externally blown flap noise

    NASA Technical Reports Server (NTRS)

    Dorsch, R. G.; Clark, B. J.; Reshotko, M.

    1975-01-01

    An interim procedure for predicting externally blown flap (EBF) noise spectra anywhere below a powered lift aircraft is presented. Both engine-under-the-wing and engine-over-the-wing EBF systems are included. The method uses data correlations for the overall sound pressure level based on nozzle exit area and exhaust velocity along with OASPL directivity curves and normalized one-third-octave spectra. Aircraft motion effects are included by taking into account the relative motion of the source with respect to the observer and the relative velocity effects on source strength.

  10. Novel Method for Radiolabeling and Dimerizing Thiolated Peptides Using (18)F-Hexafluorobenzene.

    PubMed

    Jacobson, Orit; Yan, Xuefeng; Ma, Ying; Niu, Gang; Kiesewetter, Dale O; Chen, Xiaoyuan

    2015-10-21

    Hexafluorobenzene (HFB) reacts with free thiols to produce a unique and selective perfluoroaromatic linkage between two sulfurs. We modified this chemical reaction to produce dimeric (18)F-RGD-tetrafluorobenzene (TFB)-RGD, an integrin αvβ3 receptor ligand. (18)F-HFB was prepared by a fluorine exchange reaction using K(18)F/K2.2.2 at room temperature. The automated radiofluorination was optimized to minimize the amount of HFB precursor and, thus, maximize the specific activity. (18)F-HFB was isolated by distillation and subsequently reacted with thiolated c(RGDfk) peptide under basic and reducing conditions. The resulting (18)F-RGD-TFB-RGD demonstrated integrin receptor specific binding, cellular uptake, and in vivo tumor accumulation.(18)F-HFB can be efficiently incorporated into thiol-containing peptides at room temperature to provide novel imaging agents. PMID:26086295

  11. A novel method for oral delivery of apolipoprotein mimetic peptides synthesized from all L-amino acids.

    PubMed

    Navab, Mohamad; Ruchala, Piotr; Waring, Alan J; Lehrer, Robert I; Hama, Susan; Hough, Greg; Palgunachari, Mayakonda N; Anantharamaiah, G M; Fogelman, Alan M

    2009-08-01

    Administered subcutaneously, D-4F or L-4F are equally efficacious, but only D-4F is orally efficacious because of digestion of L-4F by gut proteases. Orally administering niclosamide (a chlorinated salicylanilide used as a molluscicide, antihelminthic, and lampricide) in temporal proximity to oral L-4F (but not niclosamide alone) in apoE null mice resulted in significant improvement (P < 0.001) in the HDL-inflammatory index (HII), which measures the ability of HDL to inhibit LDL-induced monocyte chemotactic activity in endothelial cell cultures. Oral administration of L-[113-122]apoJ with niclosamide also resulted in significant improvement (P < 0.001) in HII. Oral administration of niclosamide and L-4F together with pravastatin to female apoE null mice at 9.5 months of age for six months significantly reduced aortic sinus lesion area (P = 0.02), en face lesion area (P = 0.033), and macrophage lesion area (P = 0.02) compared with pretreatment, indicating lesion regression. In contrast, lesions were significantly larger in mice receiving only niclosamide and pravastatin or L-4F and pravastatin (P < 0.001). In vitro niclosamide and L-4F tightly associated rendering the peptide resistant to trypsin digestion. Niclosamide itself did not inhibit trypsin activity. The combination of niclosamide with apolipoprotein mimetic peptides appears to be a promising method for oral delivery of these peptides. PMID:19225094

  12. A novel method for oral delivery of apolipoprotein mimetic peptides synthesized from all L-amino acids

    PubMed Central

    Navab, Mohamad; Ruchala, Piotr; Waring, Alan J.; Lehrer, Robert I.; Hama, Susan; Hough, Greg; Palgunachari, Mayakonda N.; Anantharamaiah, G. M.; Fogelman, Alan M.

    2009-01-01

    Administered subcutaneously, D-4F or L-4F are equally efficacious, but only D-4F is orally efficacious because of digestion of L-4F by gut proteases. Orally administering niclosamide (a chlorinated salicylanilide used as a molluscicide, antihelminthic, and lampricide) in temporal proximity to oral L-4F (but not niclosamide alone) in apoE null mice resulted in significant improvement (P < 0.001) in the HDL-inflammatory index (HII), which measures the ability of HDL to inhibit LDL-induced monocyte chemotactic activity in endothelial cell cultures. Oral administration of L-[113-122]apoJ with niclosamide also resulted in significant improvement (P < 0.001) in HII. Oral administration of niclosamide and L-4F together with pravastatin to female apoE null mice at 9.5 months of age for six months significantly reduced aortic sinus lesion area (P = 0.02), en face lesion area (P = 0.033), and macrophage lesion area (P = 0.02) compared with pretreatment, indicating lesion regression. In contrast, lesions were significantly larger in mice receiving only niclosamide and pravastatin or L-4F and pravastatin (P < 0.001). In vitro niclosamide and L-4F tightly associated rendering the peptide resistant to trypsin digestion. Niclosamide itself did not inhibit trypsin activity. The combination of niclosamide with apolipoprotein mimetic peptides appears to be a promising method for oral delivery of these peptides. PMID:19225094

  13. Method of predicting Splice Sites based on signal interactions

    PubMed Central

    Churbanov, Alexander; Rogozin, Igor B; Deogun, Jitender S; Ali, Hesham

    2006-01-01

    Background Predicting and proper ranking of canonical splice sites (SSs) is a challenging problem in bioinformatics and machine learning communities. Any progress in SSs recognition will lead to better understanding of splicing mechanism. We introduce several new approaches of combining a priori knowledge for improved SS detection. First, we design our new Bayesian SS sensor based on oligonucleotide counting. To further enhance prediction quality, we applied our new de novo motif detection tool MHMMotif to intronic ends and exons. We combine elements found with sensor information using Naive Bayesian Network, as implemented in our new tool SpliceScan. Results According to our tests, the Bayesian sensor outperforms the contemporary Maximum Entropy sensor for 5' SS detection. We report a number of putative Exonic (ESE) and Intronic (ISE) Splicing Enhancers found by MHMMotif tool. T-test statistics on mouse/rat intronic alignments indicates, that detected elements are on average more conserved as compared to other oligos, which supports our assumption of their functional importance. The tool has been shown to outperform the SpliceView, GeneSplicer, NNSplice, Genio and NetUTR tools for the test set of human genes. SpliceScan outperforms all contemporary ab initio gene structural prediction tools on the set of 5' UTR gene fragments. Conclusion Designed methods have many attractive properties, compared to existing approaches. Bayesian sensor, MHMMotif program and SpliceScan tools are freely available on our web site. Reviewers This article was reviewed by Manyuan Long, Arcady Mushegian and Mikhail Gelfand. PMID:16584568

  14. 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.

  15. 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.

  16. 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. PMID:24681218

  17. 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.

  18. Two classifiers based on serum peptide pattern for prediction of HBV-induced liver cirrhosis using MALDI-TOF MS.

    PubMed

    Cao, Yuan; He, Kun; Cheng, Ming; Si, Hai-Yan; Zhang, He-Lin; Song, Wei; Li, Ai-Ling; Hu, Cheng-Jin; Wang, Na

    2013-01-01

    Chronic infection with hepatitis B virus (HBV) is associated with the majority of cases of liver cirrhosis (LC) in China. Although liver biopsy is the reference method for evaluation of cirrhosis, it is an invasive procedure with inherent risk. The aim of this study is to discover novel noninvasive specific serum biomarkers for the diagnosis of HBV-induced LC. We performed bead fractionation/MALDI-TOF MS analysis on sera from patients with LC. Thirteen feature peaks which had optimal discriminatory performance were obtained by using support-vector-machine-(SVM-) based strategy. Based on the previous results, five supervised machine learning methods were employed to construct classifiers that discriminated proteomic spectra of patients with HBV-induced LC from those of controls. Here, we describe two novel methods for prediction of HBV-induced LC, termed LC-NB and LC-MLP, respectively. We obtained a sensitivity of 90.9%, a specificity of 94.9%, and overall accuracy of 93.8% on an independent test set. Comparisons with the existing methods showed that LC-NB and LC-MLP held better accuracy. Our study suggests that potential serum biomarkers can be determined for discriminating LC and non-LC cohorts by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. These two classifiers could be used for clinical practice in HBV-induced LC assessment. PMID:23509784

  19. Predicting Hepatic Steatosis in Living Liver Donors via Noninvasive Methods

    PubMed Central

    Kim, Jong Man; Ha, Sang Yun; Joh, Jae-Won; Sinn, Dong Hyun; Jeong, Woo Kyung; Choi, Gyu-Seong; Gwak, Geum Youn; Kwon, Choon Hyuck David; Kim, Young Kon; Paik, Yong Han; Lee, Joon Hyeok; Lee, Won Jae; Lee, Suk-Koo; Park, Cheol Keun

    2016-01-01

    Abstract Hepatic steatosis assessment is of paramount importance for living liver donor selection because significant hepatic steatosis can affect the postoperative outcome of recipients and the safety of the donor. The validity of various noninvasive imaging methods to assess hepatic steatosis remains controversial. The purpose of our study is to investigate the association between noninvasive imaging methods and pathology to detect steatosis in living liver donors and to propose a prediction model for hepatic steatosis. Liver stiffness measurements (LSMs) and controlled attenuation parameter values in vibration controlled transient elastography, ultrasonography, computed tomography (CT), and magnetic resonance imaging were used as pretransplant screening methods to evaluate living liver donors between 2012 and 2014. Only 1 pathologist assessed tissue sample for hepatic steatosis. The median age of the 79 living donors (53 men and 26 women) was 32 years (16–68 years). The CT liver–spleen attenuation (L–S) difference and the controlled attenuation parameter values were well correlated with the level of hepatic steatosis on liver pathology. Multivariate analysis showed that liver stiffness measurement (LSM) (β = 0.903; 95% CI, 0.105–1.702; P = 0.027) and the CT L to S attenuation difference (β = −3.322; 95% CI, −0.502 to −0.142; P = 0.001) were closely associated with hepatic steatosis. We generated the following equation to predict total hepatic steatosis: Hepatic steatosis = 0.903 × LSM – 0.322 × CT L to S attenuation difference (AUC = 86.6% and P = 0.001). The values predicted by the equation correlated well with the presence of hepatic steatosis (r = 0.509 and P < 0.001). The combination of nonenhanced CT L to S attenuation difference and transient elastography using vibration controlled transient elastography provides sufficient information to predict hepatic steatosis in living liver donor

  20. Predicting Hepatic Steatosis in Living Liver Donors via Noninvasive Methods.

    PubMed

    Kim, Jong Man; Ha, Sang Yun; Joh, Jae-Won; Sinn, Dong Hyun; Jeong, Woo Kyung; Choi, Gyu-Seong; Gwak, Geum Youn; Kwon, Choon Hyuck David; Kim, Young Kon; Paik, Yong Han; Lee, Joon Hyeok; Lee, Won Jae; Lee, Suk-Koo; Park, Cheol Keun

    2016-02-01

    Hepatic steatosis assessment is of paramount importance for living liver donor selection because significant hepatic steatosis can affect the postoperative outcome of recipients and the safety of the donor. The validity of various noninvasive imaging methods to assess hepatic steatosis remains controversial. The purpose of our study is to investigate the association between noninvasive imaging methods and pathology to detect steatosis in living liver donors and to propose a prediction model for hepatic steatosis. Liver stiffness measurements (LSMs) and controlled attenuation parameter values in vibration controlled transient elastography, ultrasonography, computed tomography (CT), and magnetic resonance imaging were used as pretransplant screening methods to evaluate living liver donors between 2012 and 2014. Only 1 pathologist assessed tissue sample for hepatic steatosis. The median age of the 79 living donors (53 men and 26 women) was 32 years (16-68 years). The CT liver-spleen attenuation (L-S) difference and the controlled attenuation parameter values were well correlated with the level of hepatic steatosis on liver pathology. Multivariate analysis showed that liver stiffness measurement (LSM) (β = 0.903; 95% CI, 0.105-1.702; P = 0.027) and the CT L to S attenuation difference (β = -3.322; 95% CI, -0.502 to -0.142; P = 0.001) were closely associated with hepatic steatosis. We generated the following equation to predict total hepatic steatosis: Hepatic steatosis = 0.903 × LSM - 0.322 × CT L to S attenuation difference (AUC = 86.6% and P = 0.001). The values predicted by the equation correlated well with the presence of hepatic steatosis (r = 0.509 and P < 0.001). The combination of nonenhanced CT L to S attenuation difference and transient elastography using vibration controlled transient elastography provides sufficient information to predict hepatic steatosis in living liver donor candidates. PMID:26886612

  1. Decision tree methods: applications for classification and prediction.

    PubMed

    Song, Yan-Yan; Lu, Ying

    2015-04-25

    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

  2. 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.

  3. 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

  4. 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

  5. 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

  6. Vortical Flow Prediction Using an Adaptive Unstructured Grid Method

    NASA Technical Reports Server (NTRS)

    Pirzadeh, Shahyar Z.

    2003-01-01

    A computational fluid dynamics (CFD) method has been employed to compute vortical flows around slender wing/body configurations. The emphasis of the paper is on the effectiveness of an adaptive grid procedure in "capturing" concentrated vortices generated at sharp edges or flow separation lines of lifting surfaces flying at high angles of attack. The method is based on a tetrahedral unstructured grid technology developed at the NASA Langley Research Center. Two steady-state, subsonic, inviscid and Navier-Stokes flow test cases are presented to demonstrate the applicability of the method for solving practical vortical flow problems. The first test case concerns vortex flow over a simple 65 delta wing with different values of leading-edge radius. Although the geometry is quite simple, it poses a challenging problem for computing vortices originating from blunt leading edges. The second case is that of a more complex fighter configuration. The superiority of the adapted solutions in capturing the vortex flow structure over the conventional unadapted results is demonstrated by comparisons with the wind-tunnel experimental data. The study shows that numerical prediction of vortical flows is highly sensitive to the local grid resolution and that the implementation of grid adaptation is essential when applying CFD methods to such complicated flow problems.

  7. Vortical Flow Prediction Using an Adaptive Unstructured Grid Method

    NASA Technical Reports Server (NTRS)

    Pirzadeh, Shahyar Z.

    2001-01-01

    A computational fluid dynamics (CFD) method has been employed to compute vortical flows around slender wing/body configurations. The emphasis of the paper is on the effectiveness of an adaptive grid procedure in "capturing" concentrated vortices generated at sharp edges or flow separation lines of lifting surfaces flying at high angles of attack. The method is based on a tetrahedral unstructured grid technology developed at the NASA Langley Research Center. Two steady-state, subsonic, inviscid and Navier-Stokes flow test cases are presented to demonstrate the applicability of the method for solving practical vortical flow problems. The first test case concerns vortex flow over a simple 65deg delta wing with different values of leading-edge bluntness, and the second case is that of a more complex fighter configuration. The superiority of the adapted solutions in capturing the vortex flow structure over the conventional unadapted results is demonstrated by comparisons with the windtunnel experimental data. The study shows that numerical prediction of vortical flows is highly sensitive to the local grid resolution and that the implementation of grid adaptation is essential when applying CFD methods to such complicated flow problems.

  8. 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

  9. Predicting human height by Victorian and genomic methods.

    PubMed

    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

    2009-08-01

    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

  10. 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

  11. Calculation of the entropy and free energy by the hypothetical scanning Monte Carlo method: application to peptides.

    PubMed

    Cheluvaraja, Srinath; Meirovitch, Hagai

    2005-02-01

    A new approach, the hypothetical scanning Monte Carlo (HSMC), for calculating the absolute entropy, S, and free energy, F, has been introduced recently and applied first to fluids (argon and water) and later to peptides. In this paper the method is further developed for peptide chains in vacuum. S is calculated from a given MC sample by reconstructing each sample conformation i step-by-step, i.e., calculating transition probabilities (TPs) for the dihedral and bond angles and fixing the related atoms at their positions. At step k of the process the chain's coordinates that have already been determined are kept fixed (the "frozen past") and TP(k) is obtained from a MC simulation of the "future" part of the chain whose TPs as yet have not been determined; when the process is completed the contribution of conformation i to the entropy is, S(i) approximately -ln Pi(k) TP(k). In a recent paper we studied polyglycine chains, modeled by the AMBER force field with constant bond lengths and bond angles (the rigid model). Decaglycine [(Gly)(10)] was studied in the helical, extended, and hairpin microstates, while (Gly)(16) was treated only in the first two microstates. In this paper the samples are increased and restudied, (Gly)(16) is also investigated in the hairpin microstate, and for (Gly)(10) approximations are tested where only part of the future is considered for calculating the TPs. We calculate upper and lower bounds for F and demonstrate that like for fluids, F can be obtained from multiple reconstructions of a single conformation. We also test a more realistic model of (Gly)(10) where the bond angles are allowed to move (the flexible model). Very accurate results for S and F are obtained which are compared to results obtained by the quasiharmonic approximation and the local states method. Thus, differences in entropy and free energy between the three microstates are obtained within errors of 0.1-0.3 kcal/mol. The HSMC method can be applied to a macromolecule with

  12. 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.

  13. 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.

  14. Water accessibility in a membrane-inserting peptide comparing Overhauser DNP and pulse EPR methods.

    PubMed

    Segawa, Takuya F; Doppelbauer, Maximilian; Garbuio, Luca; Doll, Andrin; Polyhach, Yevhen O; Jeschke, Gunnar

    2016-05-21

    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 (1)H 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 ϵ, being the relative change of the NMR intensity, by the electron spin concentration, which was determined from a continuous-wave EPR spectrum. PMID:27208942

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. A facile method for expression and purification of (15)N isotope-labeled human Alzheimer's β-amyloid peptides from E. coli for NMR-based structural analysis.

    PubMed

    Sharma, Sudhir C; Armand, Tara; Ball, K Aurelia; Chen, Anna; Pelton, Jeffrey G; Wemmer, David E; Head-Gordon, Teresa

    2015-12-01

    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 (15)N isotope-labeled Aβ42 for structural studies by NMR. The protocol involves utilization of an auto induction system with (15)N isotope labeled medium, for high-level expression of Aβ42 as a fusion with IFABP. After the over-expression of the (15)N isotope-labeled IFABP-Aβ42 fusion protein in the inclusion bodies, pure (15)N 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 (15)N isotope-labeled Aβ42 peptide. Mass spectrometry and (1)H-(15)N 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 (15)N and (13)C in Aβ42 peptide as well as its other variants including any Aβ42 peptide mutants. PMID:26231074

  20. NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ

    PubMed Central

    Karosiene, Edita; Rasmussen, Michael; Blicher, Thomas; Lund, Ole; Buus, Søren; Nielsen, Morten

    2013-01-01

    Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide–MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan-3.0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan-3.0 method is available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0. PMID:23900783

  1. Identification of human adenovirus early region 1 products by using antisera against synthetic peptides corresponding to the predicted carboxy termini.

    PubMed Central

    Yee, S P; Rowe, D T; Tremblay, M L; McDermott, M; Branton, P E

    1983-01-01

    Synthetic peptides were prepared which corresponded to the carboxy termini of the human adenovirus type 5 early region 1B (E1B) 58,000-molecular-weight (58K) protein (Tyr-Ser-Asp-Glu-Asp-Thr-Asp) and of the E1A gene products (Tyr-Gly-Lys-Arg-Pro-Arg-Pro). Antisera raised against these peptides precipitated polypeptides from adenovirus type 5-infected KB cells; serum raised against the 58K carboxy terminus was active against the E1B 58K phosphoprotein, whereas serum raised against the E1A peptide immunoprecipitated four major and at least two minor polypeptides. These latter proteins migrated with apparent molecular weights of 52K, 50K, 48.5K, 45K, 37.5K, and 35K, and all were phosphoproteins. By using tryptic phosphopeptide analysis, the four major species (52K, 50K, 48.5K, and 45K) were found to be related, as would be expected if all were products of the E1A region. The ability of the antipeptide sera to precipitate these viral proteins thus confirmed that the previously proposed sequence of E1 DNA and mRNA and the reading frame of the mRNA are correct. Immunofluorescent-antibody staining with the antipeptide sera indicated that the 58K E1B protein was localized both in the nucleus and in the cytoplasm, especially in the perinuclear region. The E1A-specific serum also stained both discrete patches in the nucleus and diffuse areas of the cytoplasm. These data suggest that both the 58K protein and the E1A proteins may function in or around the nucleus. These highly specific antipeptide sera should allow for a more complete identification and characterization of these important viral proteins. Images PMID:6343626

  2. Information-driven modeling of protein-peptide complexes.

    PubMed

    Trellet, Mikael; Melquiond, Adrien S J; Bonvin, Alexandre M J J

    2015-01-01

    Despite their biological importance in many regulatory processes, protein-peptide recognition mechanisms are difficult to study experimentally at the structural level because of the inherent flexibility of peptides and the often transient interactions on which they rely. Complementary methods like biomolecular docking are therefore required. The prediction of the three-dimensional structure of protein-peptide complexes raises unique challenges for computational algorithms, as exemplified by the recent introduction of protein-peptide targets in the blind international experiment CAPRI (Critical Assessment of PRedicted Interactions). Conventional protein-protein docking approaches are often struggling with the high flexibility of peptides whose short sizes impede protocols and scoring functions developed for larger interfaces. On the other side, protein-small ligand docking methods are unable to cope with the larger number of degrees of freedom in peptides compared to small molecules and the typically reduced available information to define the binding site. In this chapter, we describe a protocol to model protein-peptide complexes using the HADDOCK web server, working through a test case to illustrate every steps. The flexibility challenge that peptides represent is dealt with by combining elements of conformational selection and induced fit molecular recognition theories. PMID:25555727

  3. Fast calculation of molecular total energy with ABEEMσπ/MM method - For some series of organic molecules and peptides

    NASA Astrophysics Data System (ADS)

    Yang, Zhong-Zhi; Lin, Xiao-Ting; Zhao, Dong-Xia

    2016-06-01

    A new ABEEMσπ/MM method for fast calculation of molecular total energy is established by combining ABEEMσπ model with force field representation, where ABEEMσπ is the atom-bond electronegativity equalization model at the σπ level. The calibrated parameters are suitable and transferable. This paper demonstrates that the total molecular energies for series of alcohols, aldehydes, carboxylic acids and peptides calculated by ABEEMσπ/MM method are in fair agreement with those obtained from calculations of ab initio MP2/6-311++G(d, p) method with mean absolute deviation (MAD) being 1.45 kcal/mol and their linear correlation coefficients being 1.0000. Thus it opens good prospects for wide applications to chemical and biological systems.

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. Determination of the gas-phase acidities of cysteine-polyalanine peptides using the extended kinetic method.

    PubMed

    Tan, John P; Ren, Jianhua

    2007-02-01

    We determined the gas-phase acidities of two cysteine-polyalanine peptides, HSCA3 and HSCA4, using a triple-quadrupole mass spectrometer through application of the extended kinetic method with full entropy analysis. Five halogenated carboxylic acids were used as the reference acids. The negatively charged proton-bound dimers of the deprotonated peptides with the conjugate bases of the reference acids were generated by electrospray ionization. Collision-induced dissociation (CID) experiments were carried out at three collision energies. The enthalpies of deprotonation (Delta(acid)H) of the peptides were derived according to the linear relationship between the logarithms of the CID product ion branching ratios and the differences of the gas-phase acidities. The values were determined to be Delta(acid)H(HSCA3) = 317.3 +/- 2.4 kcal/mol and Delta(acid)H (HSCA4) = 316.2 +/- 3.9 kcal/mol. Large entropy effects (Delta(DeltaS) = 13-16 cal/mol K) were observed for these systems. Combining the enthalpies of deprotonation with the entropy term yielded the apparent gas-phase acidities (Delta(acid)G(app)) of 322.1 +/- 2.4 kcal/mol (HSCA3) and 320.1 +/- 3.9 kcal/mol (HSCA4), in agreement with the results obtained from the CID-bracketing experiments. Compared with that in the isolated cysteine residue, the thiol group in HSCA3,4 has a stronger gas-phase acidity by about 20 kcal/mol. This increased acidity is likely due to the stabilization of the negatively charged thiolate group through internal solvation. PMID:17067812

  10. Modeling of hydroxyapatite-peptide interaction based on fragment molecular orbital method

    NASA Astrophysics Data System (ADS)

    Kato, Koichiro; Fukuzawa, Kaori; Mochizuki, Yuji

    2015-06-01

    We have applied the four-body corrected fragment molecular orbital (FMO4) calculations to analyze the interaction between a designed peptide motif (Glu1-Ser2-Gln3-Glu4-Ser5) and the hydroxyapatite (HA) solid mimicked by a cluster model consisting of 1408 atoms. To incorporate statistical fluctuations, a total of 30 configurations were generated through classical molecular dynamics simulation with water molecules and were subjected to FMO4 calculations at the MP2 level. It was found that Ser5 plays a leading role in interacting with the phosphate moieties of HA via charge transfer and also that negatively charged Glu1 and Glu4 provide electrostatic stabilizations with the calcium ions.

  11. 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

  12. 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).

  13. Feasibility of electrodialysis as a fast and selective sample preparation method for the profiling of low-abundant peptides in biofluids.

    PubMed

    Kamphorst, Jurre J; Tjaden, Ubbo R; van der Heijden, Rob; DeGroot, Jeroen; van der Greef, Jan; Hankemeier, Thomas

    2009-07-01

    Considerable interest exists in endogenous peptides as potential biomarkers, since they act as signaling molecules and are formed by degradation of proteins. A crucial step in the profiling of these peptides is the sample preparation, which aims to enrich the low-abundant peptides, while removing interfering matrix compounds. In a feasibility study we examined the suitability of electrodialysis (ED) for this purpose. A custom-made device was developed from the low-binding material Kel-F. It consisted of two compartments separated by a dialysis membrane, over which a voltage was applied. One compartment served as donor (containing the sample), while the smaller acceptor compartment collected the peptides. The procedure was optimized by investigating the effect of the applied voltage, ammonium acetate buffer concentration, and ED duration using model peptides. Optimum conditions were found at 300 V (150 V/cm), 25 mM ammonium acetate buffer (pH 3.8) containing 20% v/v DMSO, and 10 min, respectively. With these optimized parameters, recoveries for the model peptides were found to be 35-85% (average 64%). Additionally, ED was successfully applied to the challenging synovial fluid biological sample (due to its high viscosity). In a synovial fluid sample from a rheumatoid arthritis patient, 27 peptides originating from 12 proteins were identified, of which a considerable fraction was not identified before with other methods. This demonstrates the usefulness and complementary nature of combining ED with nanoLC-MS for biomarker discovery. These results indicate that ED is promising as a fast and selective sample preparation method for the profiling of endogenous peptides. PMID:19569123

  14. 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

  15. 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. PMID:26849676

  16. Developing a fluorescence-coupled capillary electrophoresis based method to probe interactions between QDs and colorectal cancer targeting peptides.

    PubMed

    Liu, Feifei; Wang, Jianhao; Yang, Li; Liu, Li; Ding, Shumin; Fu, Minli; Deng, Linhong; Gao, Li-Qian

    2016-08-01

    As is well known, quantum dots (QDs) have become valuable probes for cancer imaging. In particular, QD-labeled targeting peptides are capable of identifying cancer or tumors cells. A new colorectal cancer targeting peptide, cyclo(1, 9)-CTPSPFSHC, has strong targeting ability and also shows great potential in the identification and treatment of colon cancer. Herein, we synthesized a dual functional polypeptide, cyclo(1, 9)-CTPSPFSHCD2 G2 DP9 G3 H6 (H6 -TCP), to investigate its interaction with QDs inside the capillary. Fluorescence-coupled CE was adopted and applied to characterize the self-assembly of H6 -TCP onto QDs. It was indicated that the formation of the assembly was affected by H6 -TCP/QD molar ratio and sampling time. This novel in-capillary assay greatly reduced the sample consumption and the detection time, which was beneficial for the environment. It is expected that this kind of detection method could find more applications to provide more useful information for cancer diagnosis and detection of harm and hazardous substances/organisms in the environment in the future. PMID:27159348

  17. Development of a novel efficient method to construct an adenovirus library displaying random peptides on the fiber knob.

    PubMed

    Yamamoto, Yuki; Goto, Naoko; Miura, Kazuki; Narumi, Kenta; Ohnami, Shumpei; Uchida, Hiroaki; Miura, Yoshiaki; Yamamoto, Masato; Aoki, Kazunori

    2014-03-01

    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 10(4) 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

  18. 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

  19. Expanded test method for peptides >2 kDa employing immunoaffinity purification and LC-HRMS/MS.

    PubMed

    Thomas, Andreas; Walpurgis, Katja; Tretzel, Laura; Brinkkötter, Paul; Fichant, Eric; Delahaut, Philippe; Schänzer, Wilhelm; Thevis, Mario

    2015-01-01

    Bioactive peptides with an approximate molecular mass of 2-12 kDa are of considerable relevance in sports drug testing. Such peptides have been used to manipulate several potential performance-enhancing processes in the athlete's body and include for example growth hormone releasing hormones (sermorelin, CJC-1293, CJC-1295, tesamorelin), synthetic/animal insulins (lispro, aspart, glulisine, glargine, detemir, degludec, bovine and porcine insulin), synthetic ACTH (synacthen), synthetic IGF-I (longR(3) -IGF-I) and mechano growth factors (human MGF, modified human MGF, 'full-length' MGF). A combined initial test method using one analytical procedure is a desirable tool in doping controls and related disciplines as requests for higher sample throughput with utmost comprehensiveness preferably at reduced costs are constantly issued. An approach modified from an earlier assay proved fit-for-purpose employing pre-concentration of all target analytes by means of ultrafiltration, immunoaffinity purification with coated paramagnetic beads, nano-ultra high performance liquid chromatography (UHPLC) separation, and subsequent detection by means of high resolution tandem mass spectrometry. The method was shown to be applicable to blood and urine samples, which represent the most common doping control specimens. The method was validated considering the parameters specificity, recovery (11-69%), linearity, imprecision (<25%), limit of detection (5-100 pg in urine, 0.1-2 ng in plasma), and ion suppression. The analysis of administration study samples for insulin degludec, detemir, aspart, and synacthen provided the essential data for the proof-of-principle of the method. PMID:26382721

  20. 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. PMID:26290603

  1. 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

  2. N-terminal pro b-type natriuretic peptide (NT-pro-BNP) -based score can predict in-hospital mortality in patients with heart failure.

    PubMed

    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

  3. A Low-Cost Method for Multiple Disease Prediction

    PubMed Central

    Bayati, Mohsen; Bhaskar, Sonia; Montanari, Andrea

    2015-01-01

    Recently, in response to the rising costs of healthcare services, employers that are financially responsible for the healthcare costs of their workforce have been investing in health improvement programs for their employees. A main objective of these so called “wellness programs” is to reduce the incidence of chronic illnesses such as cardiovascular disease, cancer, diabetes, and obesity, with the goal of reducing future medical costs. The majority of these wellness programs include an annual screening to detect individuals with the highest risk of developing chronic disease. Once these individuals are identified, the company can invest in interventions to reduce the risk of those individuals. However, capturing many biomarkers per employee creates a costly screening procedure. We propose a statistical data-driven method to address this challenge by minimizing the number of biomarkers in the screening procedure while maximizing the predictive power over a broad spectrum of diseases. Our solution uses multi-task learning and group dimensionality reduction from machine learning and statistics. We provide empirical validation of the proposed solution using data from two different electronic medical records systems, with comparisons to a statistical benchmark. PMID:26958164

  4. 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. PMID:22158911

  5. Predicting sulphur and nitrogen deposition using a simple statistical method

    NASA Astrophysics Data System (ADS)

    Filip, Oulehle; Jiří, Kopáček; Tomáš, Chuman; Vladimír, Černohous; Iva, Hůnová; Jakub, Hruška; Pavel, Krám; Zora, Lachmanová; Tomáš, Navrátil; Petr, Štěpánek; Miroslav, Tesař; Christopher, Evans D.

    2016-09-01

    Data from 32 long-term (1994-2012) monitoring sites were used to assess temporal development and spatial variability of sulphur (S) and inorganic nitrogen (N) concentrations in bulk precipitation, and S in throughfall, for the Czech Republic. Despite large variance in absolute S and N concentration/deposition among sites, temporal coherence using standardised data (Z score) was demonstrated. Overall significant declines of SO4 concentration in bulk and throughfall precipitation, as well as NO3 and NH4 concentration in bulk precipitation, were observed. Median Z score values of bulk SO4, NO3 and NH4 and throughfall SO4 derived from observations and the respective emission rates of SO2, NOx and NH3 in the Czech Republic and Slovakia showed highly significant (p < 0.001) relationships. Using linear regression models, Z score values were calculated for the whole period 1900-2012 and then back-transformed to give estimates of concentration for the individual sites. Uncertainty associated with the concentration calculations was estimated as 20% for SO4 bulk precipitation, 22% for throughfall SO4, 18% for bulk NO3 and 28% for bulk NH4. The application of the method suggested that it is effective in the long-term reconstruction and prediction of S and N deposition at a variety of sites. Multiple regression modelling was used to extrapolate site characteristics (mean precipitation chemistry and its standard deviation) from monitored to unmonitored sites. Spatially distributed temporal development of S and N depositions were calculated since 1900. The method allows spatio-temporal estimation of the acid deposition in regions with extensive monitoring of precipitation chemistry.

  6. Fluorescence In Situ Hybridization Method Using a Peptide Nucleic Acid Probe for Identification of Salmonella spp. in a Broad Spectrum of Samples ▿

    PubMed Central

    Almeida, C.; Azevedo, N. F.; Fernandes, R. M.; Keevil, C. W.; Vieira, M. J.

    2010-01-01

    A fluorescence in situ hybridization (FISH) method for the rapid detection of Salmonella spp. using a novel peptide nucleic acid (PNA) probe was developed. The probe theoretical specificity and sensitivity were both 100%. The PNA-FISH method was optimized, and laboratory testing on representative strains from the Salmonella genus subspecies and several related bacterial species confirmed the predicted theoretical values of specificity and sensitivity. The PNA-FISH method has been successfully adapted to detect cells in suspension and is hence able to be employed for the detection of this bacterium in blood, feces, water, and powdered infant formula (PIF). The blood and PIF samples were artificially contaminated with decreasing pathogen concentrations. After the use of an enrichment step, the PNA-FISH method was able to detect 1 CFU per 10 ml of blood (5 × 109 ± 5 × 108 CFU/ml after an overnight enrichment step) and also 1 CFU per 10 g of PIF (2 × 107 ± 5 × 106 CFU/ml after an 8-h enrichment step). The feces and water samples were also enriched according to the corresponding International Organization for Standardization methods, and results showed that the PNA-FISH method was able to detect Salmonella immediately after the first enrichment step was conducted. Moreover, the probe was able to discriminate the bacterium in a mixed microbial population in feces and water by counter-staining with 4′,6-diamidino-2-phenylindole (DAPI). This new method is applicable to a broad spectrum of samples and takes less than 20 h to obtain a diagnosis, except for PIF samples, where the analysis takes less than 12 h. This procedure may be used for food processing and municipal water control and also in clinical settings, representing an improved alternative to culture-based techniques and to the existing Salmonella PNA probe, Sal23S10, which presents a lower specificity. PMID:20453122

  7. CancerPPD: a database of anticancer peptides and proteins

    PubMed Central

    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. PMID:25270878

  8. Proof of concept: A bioinformatic and serological screening method for identifying new peptide antigens for Chlamydia trachomatis related sequelae in women☆

    PubMed Central

    Stansfield, Scott H.; Patel, Pooja; Debattista, Joseph; Armitage, Charles W.; Cunningham, Kelly; Timms, Peter; Allan, John; Mittal, Aruna; Huston, Wilhelmina M.

    2013-01-01

    This study aimed to identify new peptide antigens from Chlamydia (C.) trachomatis in a proof of concept approach which could be used to develop an epitope-based serological diagnostic for C. trachomatis related infertility in women. A bioinformatics analysis was conducted examining several immunodominant proteins from C. trachomatis to identify predicted immunoglobulin epitopes unique to C. trachomatis. A peptide array of these epitopes was screened against participant sera. The participants (all female) were categorized into the following cohorts based on their infection and gynecological history; acute (single treated infection with C. trachomatis), multiple (more than one C. trachomatis infection, all treated), sequelae (PID or tubal infertility with a history of C. trachomatis infection), and infertile (no history of C. trachomatis infection and no detected tubal damage). The bioinformatics strategy identified several promising epitopes. Participants who reacted positively in the peptide 11 ELISA were found to have an increased likelihood of being in the sequelae cohort compared to the infertile cohort with an odds ratio of 16.3 (95% c.i. 1.65–160), with 95% specificity and 46% sensitivity (0.19–0.74). The peptide 11 ELISA has the potential to be further developed as a screening tool for use during the early IVF work up and provides proof of concept that there may be further peptide antigens which could be identified using bioinformatics and screening approaches. PMID:24600556

  9. Proof of concept: A bioinformatic and serological screening method for identifying new peptide antigens for Chlamydia trachomatis related sequelae in women.

    PubMed

    Stansfield, Scott H; Patel, Pooja; Debattista, Joseph; Armitage, Charles W; Cunningham, Kelly; Timms, Peter; Allan, John; Mittal, Aruna; Huston, Wilhelmina M

    2013-01-01

    This study aimed to identify new peptide antigens from Chlamydia (C.) trachomatis in a proof of concept approach which could be used to develop an epitope-based serological diagnostic for C. trachomatis related infertility in women. A bioinformatics analysis was conducted examining several immunodominant proteins from C. trachomatis to identify predicted immunoglobulin epitopes unique to C. trachomatis. A peptide array of these epitopes was screened against participant sera. The participants (all female) were categorized into the following cohorts based on their infection and gynecological history; acute (single treated infection with C. trachomatis), multiple (more than one C. trachomatis infection, all treated), sequelae (PID or tubal infertility with a history of C. trachomatis infection), and infertile (no history of C. trachomatis infection and no detected tubal damage). The bioinformatics strategy identified several promising epitopes. Participants who reacted positively in the peptide 11 ELISA were found to have an increased likelihood of being in the sequelae cohort compared to the infertile cohort with an odds ratio of 16.3 (95% c.i. 1.65-160), with 95% specificity and 46% sensitivity (0.19-0.74). The peptide 11 ELISA has the potential to be further developed as a screening tool for use during the early IVF work up and provides proof of concept that there may be further peptide antigens which could be identified using bioinformatics and screening approaches. PMID:24600556

  10. Peptide folding simulations.

    PubMed

    Gnanakaran, S; Nymeyer, Hugh; Portman, John; Sanbonmatsu, Kevin Y; García, Angel E

    2003-04-01

    Developments in the design of small peptides that mimic proteins in complexity, recent advances in nanosecond time-resolved spectroscopy methods to study peptides and the development of modern, highly parallel simulation algorithms have come together to give us a detailed picture of peptide folding dynamics. Two newly implemented simulation techniques, parallel replica dynamics and replica exchange molecular dynamics, can now describe directly from simulations the kinetics and thermodynamics of peptide formation, respectively. Given these developments, the simulation community now has the tools to verify and validate simulation protocols and models (forcefields). PMID:12727509

  11. Modeling for Airframe Noise Prediction Using Vortex Methods

    NASA Astrophysics Data System (ADS)

    Zheng, Z. Charlie

    2002-12-01

    Various components of the airframe are known to be a significant source of noise. With the advent of technology in quieting modern engines, airframe generated noise competes and, in certain instances, surpasses the engine noise. Airframe noise is most pronounced during aircraft approach when the engines are operating at reduced thrust, and airframe components such as high-lift devices and landing gears are in deployed conditions. Recent experimental studies have reaffirmed that the most significant sources of high-lift noise are from the leading-edge slat and the side edges of flaps. Studies of flow field around these structures have consistently shown that there are complicated unsteady vortical flows such as vortex shedding, secondary vortices and vortex breakdown, which are susceptible to far-field radiated sound. The near-field CFD computational data have been used to calculate the far-field acoustics by employing Ffowcs-Williams/Hawkings equation using Lighthill's analogy. However, because of the limit of current computing capacity, it is very time consuming to generate unsteady Navier-Stokes (N-S) computational data for aeroacoustics. Although the N-S simulations are probably necessary to reveal many complex flow phenomena that are unsteady and fully nonlinear, these simulations are not feasible to be used for parametric design. purposes. The objective of this study is thus to develop theoretical models for airframe noise predictions which have quick turn-around computing time. Since it is known that vorticity is a major mechanism responsible for noise generation on high-lift devices, vortex methods have been chosen as modeling tools. Vortex methods are much faster in comparison with other numerical methods, yet they are able to incorporate nonlinear interactions between vortices. Obviously, as with any theoretical model, assumptions have to be made and justified when such models are used in complex flow. The merit and applicability of the models for

  12. Exploring Factors Causing Low Brain Penetration of the Opioid Peptide DAMGO through Experimental Methods and Modeling.

    PubMed

    Lindqvist, Annika; Jönsson, Siv; Hammarlund-Udenaes, Margareta

    2016-04-01

    To advance the development of peptide analogues for improved treatment of pain, we need to learn more about the blood-brain barrier transport of these substances. A low penetration into the brain, with an unbound brain to blood ratio, Kp,uu, of 0.08, is an important reason for the lack of effect of the enkephalin analogue DAMGO (H-Tyr-d-Ala-Gly-MePhe-Gly-ol) according to earlier findings. The aim of this study was to investigate the role of efflux transporters, metabolism in the brain, and/or elimination through interstitial fluid bulk flow for the brain exposure of DAMGO. The in vivo brain distribution of DAMGO was evaluated using microdialysis in the rat. Data were analyzed with population modeling which resulted in a clearance into the brain of 1.1 and an efflux clearance 14 μL/min/g_brain. The efflux clearance was thus much higher than the bulk flow known from the literature. Coadministration with the efflux transporter inhibitors cyclosporin A and elacridar in vivo did not affect Kp,uu. The permeability of DAMGO in the Caco-2 assay was very low, of the same size as mannitol. The efflux ratio was <2 and not influenced by cyclosporin A or elacridar. These results indicate that the well-known efflux transporters Pgp and Bcrp are not responsible for the higher efflux of DAMGO, which opens up for an important role of other transporters at the BBB. PMID:26898546

  13. Honeycomb self-assembled peptide scaffolds by the breath figure method.

    PubMed

    Du, Mingchun; Zhu, Pengli; Yan, Xuehai; Su, Ying; Song, Weixing; Li, Junbai

    2011-04-01

    The self-assembly of molecules into desired architectures is currently a challenging subject for the development of supramolecular chemistry. Here we present a facile "breath figure" assembly process through the use of the self-assembled peptide building block diphenylalanine (L-Phe-L-Phe, FF). Macroporous honeycomb scaffolds were fabricated, and average pore size could be regulated, from (1.00±0.18) μm to (2.12±0.47) μm, through the use of different air speeds. It is indicated that the honeycomb formation is humidity-, solvent-, concentration-, and substrate-dependent. Moreover, water molecules introduced from "breath figure" intervene in the formation of hydrogen bonds during FF molecular self-assembly, which results in a hydrogen bond configuration transition from antiparallel β sheet to parallel β sheet. Meanwhile, as a result of the higher polarity of water molecules, the FF molecular array is transformed from laminar stacking into a hexagonal structure. These findings not only elucidate the FF molecule self-assembly process, but also strongly support the mechanism of breath figure array formation. Finally, human embryo skin fibroblast (ESF) culture experiments suggest that FF honeycomb scaffolds are an attractive biomaterial for growth of adherent cells with great potential applications in tissue engineering. PMID:21387428

  14. Antihypertensive peptides from curd

    PubMed Central

    Dabarera, Melani Chathurika; Athiththan, Lohini V.; Perera, Rasika P.

    2015-01-01

    Introduction: Curd (Dadhi) peptides reduce hypertension by inhibiting angiotensin converting enzyme (ACE) and serum cholesterol. Peptides vary with bacterial species and milk type used during fermentation. Aim: To isolate and assay the antihypertensive peptides, before and after digestion, in two commercially available curd brands in Sri Lanka. Materials and Methods: Whey (Dadhi Mastu) separated by high-speed centrifugation was isolated using reverse-phase-high- performance liquid chromatography (HPLC). Eluted fractions were analyzed for ACE inhibitory activity using modified Cushman and Cheung method. Curd samples were subjected to enzymatic digestion with pepsin, trypsin, and carboxypeptidase-A at their optimum pH and temperature. Peptides isolated using reverse-phase-HPLC was assayed for ACE inhibitory activity. Results: Whey peptides of both brands gave similar patterns (seven major and five minor peaks) in HPLC elution profile. Smaller peptides concentration was higher in brand 1 and penta-octapeptides in brand 2. Pentapeptide had the highest ACE inhibitory activity (brand 2–90% and brand 1–73%). After digestion, di and tri peptides with similar inhibitory patterns were obtained in both which were higher than before digestion. Thirteen fractions were obtained, where nine fractions showed more than 70% inhibition in both brands with 96% ACE inhibition for a di-peptide. Conclusion: Curd has ACE inhibitory peptides and activity increases after digestion. PMID:27011726

  15. 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.

  16. Combination of Urinary Sodium/Creatinine Ratio and Plasma Brain Natriuretic Peptide Level Predicts Successful Tolvaptan Therapy in Patients With Heart Failure and Volume Overload.

    PubMed

    Sato, Yuichi; Dohi, Kaoru; Watanabe, Kiyotaka; Tanimura, Muneyoshi; Takeuchi, Tetsushiro; Sugiura, Emiyo; Sugimoto, Tadafumi; Kumagai, Naoto; Ogura, Toru; Nakamori, Shiro; Fujimoto, Naoki; Yamada, Norikazu; Ito, Masaaki

    2016-01-01

    To evaluate the short-term clinical and hemodynamic effects of tolvaptan therapy and to identify predictors of the therapeutic outcomes, we retrospectively recruited 60 consecutive hospitalized heart failure (HF) patients (70 ± 11 years) with volume overload. The subjects were divided into two groups on the basis of the changes in HF symptom scores and hemodynamic status assessed by right heart catheterization after tolvaptan therapy (median: 7 days). The majority of patients were successfully treated (group 1). However, 22% of patients (group 2) were unsuccessfully treated, in whom 1) the HF symptom score worsened or 2) there was a stationary HF symptom score ≥ 6 points, and mean PCWP > 18 mmHg and mean RAP > 10 mmHg, after tolvaptan therapy. HF symptom scores, hemodynamic parameters, and plasma brain natriuretic peptide (BNP) level improved in group 1, but all of these parameters remained unchanged in group 2. Lower urine sodium/creatinine ratio (UNa/UCr) and higher BNP level at baseline were independently associated with unsuccessful tolvaptan therapy, and UNa/UCr best predicts unsuccessful tolvaptan therapy with a cut-off value of 46.5 mEq/g·Cr (AUC 0.847, 95% CI: 0.718-0.976, sensitivity 77%, specificity 81%, P < 0.01). Double-positive results of UNa/UCr < 46.5 mEq/g·Cr and plasma BNP level > 778 pg/mL predicted unsuccessful tolvaptan therapy with high diagnostic accuracy (sensitivity 54%, specificity 100%, positive predictive value 100%, negative predictive value 89%, and accuracy 90%). In summary, short-term tolvaptan therapy ameliorated HF symptoms and provided hemodynamic improvement in the majority of patients, and UNa/UCr and BNP level strongly predicted the therapeutic outcomes. PMID:26973271

  17. 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.

  18. 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. PMID:25062892

  19. 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

  20. 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.

  1. 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. PMID:12416825

  2. Structural class prediction of protein using novel feature extraction method from chaos game representation of predicted secondary structure.

    PubMed

    Zhang, Lichao; Kong, Liang; Han, Xiaodong; Lv, Jinfeng

    2016-07-01

    Protein structural class prediction plays an important role in protein structure and function analysis, drug design and many other biological applications. Extracting good representation from protein sequence is fundamental for this prediction task. In recent years, although several secondary structure based feature extraction strategies have been specially proposed for low-similarity protein sequences, the prediction accuracy still remains limited. To explore the potential of secondary structure information, this study proposed a novel feature extraction method from the chaos game representation of predicted secondary structure to mainly capture sequence order information and secondary structure segments distribution information in a given protein sequence. Several kinds of prediction accuracies obtained by the jackknife test are reported on three widely used low-similarity benchmark datasets (25PDB, 1189 and 640). Compared with the state-of-the-art prediction methods, the proposed method achieves the highest overall accuracies on all the three datasets. The experimental results confirm that the proposed feature extraction method is effective for accurate prediction of protein structural class. Moreover, it is anticipated that the proposed method could be extended to other graphical representations of protein sequence and be helpful in future research. PMID:27084358

  3. 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...

  4. Comparison of Predictive Control Methods for High Consumption Industrial Furnace

    PubMed Central

    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. PMID:24319354

  5. Preprocedural N‐Terminal Pro‐Brain Natriuretic Peptide (NT‐proBNP) Is Similar to the Mehran Contrast‐Induced Nephropathy (CIN) Score in Predicting CIN Following Elective Coronary Angiography

    PubMed Central

    Liu, Yong; He, Yi‐ting; Tan, Ning; Chen, Ji‐yan; Liu, Yuan‐hui; Yang, Da‐hao; Huang, Shui‐Jin; Ye, Piao; Li, Hua‐long; Ran, Peng; Duan, Chong‐yang; Chen, Shi‐qun; Zhou, Ying‐ling; Chen, Ping‐Yan

    2015-01-01

    Background N‐terminal pro‐brain natriuretic peptide (NT‐proBNP) has been associated with important risk factors for contrast‐induced nephropathy (CIN). However, few studies have investigated the predictive value of NT‐proBNP itself. This study investigated whether levels of preprocedural NT‐proBNP could predict CIN after elective coronary angiography as effectively as the Mehran CIN score. Methods and Results We retrospectively observed 2248 patients who underwent elective coronary angiography. The predictive value of preprocedural NT‐proBNP for CIN was assessed by receiver operating characteristic and multivariable logistic regression analysis. The 50 patients (2.2%) who developed CIN had higher Mehran risk scores (9.5±5.1 versus 4.8±3.8), and higher preprocedural levels of NT‐proBNP (5320±7423 versus 1078±2548 pg/mL, P<0.001). Receiver operating characteristic analysis revealed that NT‐proBNP was not significantly different from the Mehran CIN score in predicting CIN (C=0.7657 versus C=0.7729, P=0.8431). An NT‐proBNP cutoff value of 682 pg/mL predicted CIN with 78% sensitivity and 70% specificity. Multivariable analysis suggested that, after adjustment for other risk factors, NT‐proBNP >682 pg/mL was significantly associated with CIN (odds ratio: 4.007, 95% CI: 1.950 to 8.234; P<0.001) and risk of death (hazard ratio: 2.53; 95% CI: 1.49 to 4.30; P=0.0006). Conclusions Preprocedural NT‐proBNP >682 pg/mL was significantly associated with the risk of CIN and death. NT‐proBNP, like the Mehran CIN score, may be another useful and rapid screening tool for CIN and death risk assessment, identifying subjects who need therapeutic measures to prevent CIN. PMID:25888371

  6. Comparison of several methods for predicting separation in a compressible turbulent boundary layer

    NASA Technical Reports Server (NTRS)

    Gerhart, P. M.; Bober, L. J.

    1974-01-01

    Several methods for predicting the separation point for a compressible turbulent boundary layer were applied to the flow over a bump on a wind-tunnel wall. Measured pressure distributions were used as input. Two integral boundary-layer methods, three finite-difference boundary-layer methods, and three simple methods were applied at five free-stream Mach numbers ranging from 0.354 to 0.7325. Each of the boundary-layer methods failed to explicitly predict separation. However, by relaxing the theoretical separation criteria, several boundary-layer methods were made to yield reasonable separation predictions, but none of the methods accurately predicted the important boundary-layer parameters at separation. Only one of the simple methods consistently predicted separation with reasonable accuracy in a manner consistent with the theory. The other methods either indicated several possible separation locations or only sometimes predicted separation.

  7. Antimicrobial peptides

    PubMed Central

    2014-01-01

    With increasing antibiotics resistance, there is an urgent need for novel infection therapeutics. Since antimicrobial peptides provide opportunities for this, identification and optimization of such peptides have attracted much interest during recent years. Here, a brief overview of antimicrobial peptides is provided, with focus placed on how selected hydrophobic modifications of antimicrobial peptides can be employed to combat also more demanding pathogens, including multi-resistant strains, without conferring unacceptable toxicity. PMID:24758244

  8. 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

  9. 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.

  10. Lassa fever virus peptides predicted by computational analysis induce epitope-specific cytotoxic-T-lymphocyte responses in HLA-A2.1 transgenic mice.

    PubMed

    Boesen, Agnieszka; Sundar, Krishnan; Coico, Richard

    2005-10-01

    Lassa fever is a hemorrhagic disease caused by Lassa fever virus (LV). Although the precise host defense mechanism(s) that affords protection against LV is not completely understood, cellular immunity mediated by cytotoxic T lymphocytes (CTLs) plays a pivotal role in controlling viral replication and LV infection. To date, there have been no reports mapping major histocompatibility complex (MHC) class I-binding CTL epitopes for LV. Using computer-assisted algorithms, we identified five HLA-A2.1-binding peptides of LV glycoprotein (GP) and two peptides from LV nucleoprotein (NP). Synthesized peptides were examined for their ability to bind to MHC class I molecules using a flow cytometric assay that measures peptide stabilization of class I. Three of the LV-GP peptides tested (LLGTFTWTL, SLYKGVYEL, and YLISIFLHL) stabilized HLA-A2. The LV-NP peptides tested failed to stabilize this HLA-A2. We then investigated the ability of the HLA-A2-binding LV-GP peptides to generate peptide-specific CTLs in HLA-A2.1 transgenic mice. Functional assays used to confirm CTL activation included gamma interferon enzyme-linked immunospot (ELISPOT) assays and intracellular cytokine staining of CD8+ T cells from peptide-primed mice. CTL assays were also performed to verify the cytolytic activity of peptide-pulsed target cells. Each of the LV-GP peptides induced CTL responses in HLA-A2-transgenic mice. MHC class I tetramers prepared using one LV-GP peptide that showed the highest cytolytic index (LLGTFTWTL) confirmed that peptide-binding CD8+ T cells were present in pooled lymphocytes harvested from peptide-primed mice. These findings provide direct evidence for the existence of LV-derived GP epitopes that may be useful in the development of protective immunogens for this hemorrhagic virus. PMID:16210487

  11. Lassa Fever Virus Peptides Predicted by Computational Analysis Induce Epitope-Specific Cytotoxic-T-Lymphocyte Responses in HLA-A2.1 Transgenic Mice

    PubMed Central

    Boesen, Agnieszka; Sundar, Krishnan; Coico, Richard

    2005-01-01

    Lassa fever is a hemorrhagic disease caused by Lassa fever virus (LV). Although the precise host defense mechanism(s) that affords protection against LV is not completely understood, cellular immunity mediated by cytotoxic T lymphocytes (CTLs) plays a pivotal role in controlling viral replication and LV infection. To date, there have been no reports mapping major histocompatibility complex (MHC) class I-binding CTL epitopes for LV. Using computer-assisted algorithms, we identified five HLA-A2.1-binding peptides of LV glycoprotein (GP) and two peptides from LV nucleoprotein (NP). Synthesized peptides were examined for their ability to bind to MHC class I molecules using a flow cytometric assay that measures peptide stabilization of class I. Three of the LV-GP peptides tested (LLGTFTWTL, SLYKGVYEL, and YLISIFLHL) stabilized HLA-A2. The LV-NP peptides tested failed to stabilize this HLA-A2. We then investigated the ability of the HLA-A2-binding LV-GP peptides to generate peptide-specific CTLs in HLA-A2.1 transgenic mice. Functional assays used to confirm CTL activation included gamma interferon enzyme-linked immunospot (ELISPOT) assays and intracellular cytokine staining of CD8+ T cells from peptide-primed mice. CTL assays were also performed to verify the cytolytic activity of peptide-pulsed target cells. Each of the LV-GP peptides induced CTL responses in HLA-A2-transgenic mice. MHC class I tetramers prepared using one LV-GP peptide that showed the highest cytolytic index (LLGTFTWTL) confirmed that peptide-binding CD8+ T cells were present in pooled lymphocytes harvested from peptide-primed mice. These findings provide direct evidence for the existence of LV-derived GP epitopes that may be useful in the development of protective immunogens for this hemorrhagic virus. PMID:16210487

  12. Quantitative analysis of single amino acid variant peptides associated with pancreatic cancer in serum by an isobaric labeling quantitative method.

    PubMed

    Nie, Song; Yin, Haidi; Tan, Zhijing; Anderson, Michelle A; Ruffin, Mack T; Simeone, Diane M; Lubman, David M

    2014-12-01

    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

  13. PREDICTING THE EFFECTIVENESS OF CHEMICAL-PROTECTIVE CLOTHING MODEL AND TEST METHOD DEVELOPMENT

    EPA Science Inventory

    A predictive model and test method were developed for determining the chemical resistance of protective polymeric gloves exposed to liquid organic chemicals. The prediction of permeation through protective gloves by solvents was based on theories of the solution thermodynamics of...

  14. EMPIRICAL METHOD TO PREDICT SOLUBILITY IN SUPERCRITICAL FLUIDS

    EPA Science Inventory

    The ability to predict the solubility of analytes in supercritical fluids is important in understanding supercritical fluid extraction (SFE) and supercritical fluid chromatography (SFC). n SFE, an analyte must dissolve in the supercritical solvent before it can be extracted. n SF...

  15. Multiple trait genomic selection methods increase genetic value prediction accuracy

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Genomic selection predicts genetic values with genome-wide markers. It is rapidly emerging in plant breeding and is widely implemented in animal breeding. Genetic correlations between quantitative traits are pervasive in many breeding programs. These correlations indicate that measurements of one tr...

  16. 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…

  17. Predicting Clustered Dental Implant Survival Using Frailty Methods

    PubMed Central

    Chuang, S.-K.; Cai, T.

    2008-01-01

    The purpose of this study was to predict future implant survival using information on risk factors and on the survival status of an individual’s existing implant(s). We considered a retrospective cohort study with 677 individuals having 2349 implants placed. We proposed to predict the survival probabilities using the Cox proportional hazards frailty model, with three important risk factors: smoking status, timing of placement, and implant staging. For a non-smoking individual with 2 implants placed, an immediate implant and in one stage, the marginal probability that 1 implant would survive 12 months was 85.8% (95%CI: 77%, 91.7%), and the predicted joint probability of surviving for 12 months was 75.1% (95%CI: 62.1%, 84.7%). If 1 implant was placed earlier and had survived for 12 months, then the second implant had an 87.5% (95%CI: 80.3%, 92.4%) chance of surviving 12 months. Such conditional and joint predictions can assist in clinical decision-making for individuals. PMID:17122171

  18. Preliminary evaluation of a microwave-assisted metal-labeling strategy for quantification of peptides via RPLC-ICP-MS and the method of standard additions.

    PubMed

    Christopher, Steven J; Kilpatrick, Eric L; Yu, Lee L; Davis, W Clay; Adair, Blakely M

    2012-01-15

    NIST has performed preliminary research on applying a calibration methodology based on the method of standard additions to the quantification of peptides via reverse-phase liquid chromatography coupled to inductively coupled plasma mass spectrometry (RPLC-ICP-MS). A microwave-assisted lanthanide labeling procedure was developed and applied to derivatize peptides using the macrocyclic bifunctional chemical chelator DOTA (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid), which significantly improved the lanthanide labeling yield and reduced reaction times compared to benchtop labeling procedures. Biomolecular MS technologies of matrix-assisted laser desorption ionization (MALDI)-MS and electrospray ionization (ESI)-MS/MS were used in concert with ICP-MS to confirm the results of microwave labeling, sample cleanup and standard additions experiments for several test peptides. The calibration scheme is outlined in detail and contextualized against complementary high accuracy calibration strategies currently employed for ICP-MS detection of biomolecules. Standard additions experiments using native, non-isotopic peptide calibrants confirm the simplicity of the scheme and the potential of applying a blending (recombined sample and spike) procedure, facilitating calibration via co-elution of lanthanide labeled peptides. Ways to improve and fully leverage the analytical methodology are highlighted. PMID:22265570

  19. 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.

  20. 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.

  1. A Simple Method for Predicting Transmembrane Proteins Based on Wavelet Transform

    PubMed Central

    Yu, Bin; Zhang, Yan

    2013-01-01

    The increasing protein sequences from the genome project require theoretical methods to predict transmembrane helical segments (TMHs). So far, several prediction methods have been reported, but there are some deficiencies in prediction accuracy and adaptability in these methods. In this paper, a method based on discrete wavelet transform (DWT) has been developed to predict the number and location of TMHs in membrane proteins. PDB coded as 1KQG is chosen as an example to describe the prediction process by this method. 80 proteins with known 3D structure from Mptopo database are chosen at random as data sets (including 325 TMHs) and 80 sequences are divided into 13 groups according to their function and type. TMHs prediction is carried out for each group of membrane protein sequences and obtain satisfactory result. To verify the feasibility of this method, 80 membrane protein sequences are treated as test sets, 308 TMHs can be predicted and the prediction accuracy is 96.3%. Compared with the main prediction results of seven popular prediction methods, the obtained results indicate that the proposed method in this paper has higher prediction accuracy. PMID:23289014

  2. A sensitive mass spectrometric method for hypothesis-driven detection of peptide post-translational modifications: multiple reaction monitoring-initiated detection and sequencing (MIDAS).

    PubMed

    Unwin, Richard D; Griffiths, John R; Whetton, Anthony D

    2009-01-01

    The application of a targeted mass spectrometric workflow to the sensitive identification of post-translational modifications is described. This protocol employs multiple reaction monitoring (MRM) to search for all putative peptides specifically modified in a target protein. Positive MRMs trigger an MS/MS experiment to confirm the nature and site of the modification. This approach, termed MIDAS (MRM-initiated detection and sequencing), is more sensitive than approaches using neutral loss scanning or precursor ion scanning methodologies, due to a more efficient use of duty cycle along with a decreased background signal associated with MRM. We describe the use of MIDAS for the identification of phosphorylation, with a typical experiment taking just a couple of hours from obtaining a peptide sample. With minor modifications, the MIDAS method can be applied to other protein modifications or unmodified peptides can be used as a MIDAS target. PMID:19444244

  3. Comprehensive peptide marker identification for the detection of multiple nut allergens using a non-targeted LC-HRMS multi-method.

    PubMed

    Korte, Robin; Lepski, Silke; Brockmeyer, Jens

    2016-05-01

    Food allergies have emerged as a global problem over the last few decades; therefore, reliable and sensitive analytical methods to ensure food safety for allergic consumers are required. The application of mass spectrometry is of growing interest in this field and several procedures based on low resolution tandem mass spectrometry using single tryptic peptides as analytical targets have recently been described. However, a comprehensive survey of marker peptides for the development of multi-methods is still missing, as is a consensus guide to marker identification. In this study, we therefore report a consistent approach to the development of liquid chromatography-mass spectrometry (LC-MS) multi-screening methods for the detection of allergens in food matrices. Proteotypic peptides were identified by a shotgun proteomics approach and verified through a thorough investigation of specificity and sensitivity. On the basis of this procedure, we identified 44 suitable tryptic marker peptides from six allergenic nut species and developed the first analytical LC-MS method for the detection of trace nut contaminations in processed foods using high resolution mass spectrometry (HRMS). The analysis of spiked matrix samples gave limits of detection (LODs) below 10 μg/g for several nuts; these LODs are comparable with routinely used methods such as ELISA and PCR. Notably, the HRMS approach can be used in an untargeted fashion to identify multiple allergens also retrospectively. In conclusion, we present here the so far largest consensus set of analytical markers from nut allergens and to the best of our knowledge the first multi-allergen method based on LC-HRMS. Graphical Abstract Identification of allergen peptide marker and LC-HRMS detection. PMID:26894760

  4. 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

  5. A survey of machine learning methods for secondary and supersecondary protein structure prediction.

    PubMed

    Ho, Hui Kian; Zhang, Lei; Ramamohanarao, Kotagiri; Martin, Shawn

    2013-01-01

    In this chapter we provide a survey of protein secondary and supersecondary structure prediction using methods from machine learning. Our focus is on machine learning methods applicable to β-hairpin and β-sheet prediction, but we also discuss methods for more general supersecondary structure prediction. We provide background on the secondary and supersecondary structures that we discuss, the features used to describe them, and the basic theory behind the machine learning methods used. We survey the machine learning methods available for secondary and supersecondary structure prediction and compare them where possible. PMID:22987348

  6. 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...

  7. 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...

  8. 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